I N N O VAT I O N O F T H E R U N WAY S Y S T E M M A I N T E N A N C E
S T R AT E G Y AT A M S T E R D A M A I R P O R T S C H I P H O L U S I N G T H E
VA L U E O P E R AT I O N S M E T H O D O L O G Y
bas bennebroek
— public version —
MSc thesis
Air Transport and Operations
Faculty of Aerospace Engineering
Delft University of Technology
Airfield Maintenance Services
Amsterdam Airport Schiphol
May 2012
Innovation of the runway system maintenance strategy
at Amsterdam Airport Schiphol
using the Value Operations Methodology
MSc thesis
May 2012
Graduation February 10, 2012
Bas Jan Jacob Bennebroek MSc
Student number: 1260634
[email protected]
+31 (0)6 – 18 47 12 99
supervisors:
prof. dr. Ricky Curran (TU Delft)
prof. dr. ir. John Stoop (TU Delft)
dr. ir. Frank van der Zwan (TU Delft)
Frank Kamminga (A/AMS/A&O, Amsterdam Airport Schiphol)
ir. Gijs Emsbroek (A/AMS/A&O, Amsterdam Airport Schiphol)
Cover photo by Dirk-Jan Kraan (http://www.panoramio.com/photo/51436142)
P R E FA C E
About a year ago, amidst heavy snow and canceled flights, I first spoke with
Frank Kamminga about the proposal that would be the starting point for
this research. While not an expert on runway maintenance, I immediately
enthused over the chance of working at Amsterdam Airport Schiphol on
a project that combined technical knowledge with ‘soft’ topics such as a
stakeholder analysis and value trade-offs.
In June 2011 I started with the project, and this thesis is the result of
ten months of research on the runway system maintenance strategy at Amsterdam Airport Schiphol, and on the Value Operations Methodology as
the academic framework. Readers solely interested in the practical findings
for the maintenance strategy are advised to read Chapter 2 and 5–7 first.
Chapter 3 and 4 focus on the theoretical concepts and frameworks used in
this research, and may be of primary interest to academic readers.
In this public version of my thesis, certain confidential information has
been removed. This includes the individual stakeholders’ preferences as
presented in Chapter 5, and the subsequent individual changes in value as
calculated in Chapter 7. Only the average stakeholder’s results are presented
in this version. Please contact Frank Kamminga or Gijs Emsbroek at the
Airfield Maintenance Services department at Amsterdam Airport Schiphol
for permission to use the classified data.
This thesis concludes the Master of Science program at the Faculty of
Aerospace Engineering at Delft University of Technology. After twenty years
of attending school and university, for me it also means the end of being a
student. I thoroughly enjoyed it, and I am looking forward to the next step.
Of course, many people were of great help to me in this research project.
First and foremost, I would like to thank Frank Kamminga and Gijs Emsbroek
for their daily supervision and for giving me the chance of finishing my
studies at Amsterdam Airport Schiphol, and Frank van der Zwan for his
superb guidance and ideas throughout this project. Waking up to an early
email from Frank is a great start of a productive working day. Professor
Ricky Curran and John Stoop, thank you for your time and contributions to
this research. I would also like to thank all the people who made time for
an interview (see Appendix A for the full list); without these interviews the
research would not have been possible. Many thanks also to all the people
at Airfield Maintenance Services, for helping me along with documents,
hints and ideas, and for helping me understand the intricacies of runway
maintenance. To all SIM’ers and other interns at Amsterdam Airport Schiphol:
thanks for the relaxing daily three-o’clock coffee breaks. Thanks to my friends
for helping me enjoy my free time and for six-and-a-half great years in Delft.
Of course, I would like to thank my parents and brothers for being the
best family one could wish for. And finally, Eline, thank you for always
supporting me and for making me happy, even at times when I got stuck.
v
E X E C U T I V E S U M M A RY
Worldwide, the air transport system is becoming increasingly busy and
crowded, and even in the current economic climate the demand for air travel
is expected to grow fast. This increasing demand, together with the highly
punctual schedules of airlines, requires a high availability of all elements
within the air transport system, including aircraft, airspace, terminals and
runways.
In the Netherlands, the growth of ‘mainport’ Amsterdam Airport Schiphol
(AAS) is not only limited by the current (terminal) infrastructure, but also
by strict noise regulations. These regulations ensure that the people living
nearby Schiphol do not experience too much nuisance from air traffic. In an
effort to balance the interests the aviation industry and the local community,
the Dutch government has set up the so-called ‘Alderstafel’. In its final
agreement, the Alderstafel proposed changes in the noise limits, a new
preferential runway use and a limit on the number of air traffic movements
per year.
Both the growth in demand and the Alderstafel agreement affect the
airport’s operations. An important operational aspect is the maintenance of
airside infrastructure, and more specifically of the runway system. Runway
system maintenance is absolutely necessary to guarantee safe operations in
accordance with international regulations. However, due to the large impact
on operations and runway capacity it is often seen as a ‘necessary evil’.
Moreover, a maintenance project requires a large financial investment and
confronts residents with a different runway use resulting in more noise
in some areas. In the near future, the growing demand for air traffic and
the new regulations of the Alderstafel will also result in changing, stricter
requirements for runway maintenance. The Airfield Maintenance Services
(AMS) department at Amsterdam Airport Schiphol is therefore in need of
new solutions that improve the current maintenance strategy.
In previous years several studies have been performed at Amsterdam
Airport Schiphol to generate and analyse alternative maintenance strategies.
These studies generally result in a list of benefits and drawbacks for each
alternative. Unfortunately, these reports lack a trade-off between the pros
and cons, so no conclusion is drawn as to the merits of the alternatives.
Therefore, this research does not only create and analyse a series of alternative
runway system maintenance strategies, but it also strives to build a trade-off
framework to determine whether these alternatives are an improvement over
the current strategy. This framework thus assists the decision making process
regarding the runway system maintenance strategy. In the trade-off not only
the goals of AMS are considered, but the interests of all relevant stakeholders
are taken into account.
The trade-off framework is founded on the concept of ‘value’ and the
philosophy of ‘value-focused thinking’. In this philosophy, value is not
expressed as a monetary unit but defined much broader: value describes
what is important to a stakeholder in the context of the decision situation.
To make this concept and the framework operational, the Value Operations
Methodology (VOM) is chosen as the central methodology in this research.
The VOM calculates the change in value ∆V of an alternative compared to a
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reference situation; if the change in value is positive, the alternative improves
on the reference situation. The main research question then becomes:
How can a quantified change in value for all relevant stakeholders
measure the success of new ideas in improving the runway system
maintenance strategy at Amsterdam Airport Schiphol?
While the general approach of the Value Operations Methodology is
well defined in literature, several steps in the methodology lacked a sound
theoretical foundation. By thoroughly analysing each step, a number of
improvements to the VOM are proposed:
• Based on the Analysis of Complex Neighbourhoods, Savage’s approach
and Mitchell’s approach, a six-step procedure is outlined to identify
and select the relevant stakeholders for inclusion in the value model;
• A systematic approach is suggested for the formulation of the set of
objectives that form the main parameters in the value function. This
approach uses an objective tree to select the right level of abstraction,
and a list of desired properties to improve the formulation of the
objectives;
• A number of improvements are proposed to the Analytic Hierarchy
Process (AHP), which is used to determine the objective weight factors.
It is concluded that:
– Pair-wise comparisons are absolutely necessary when applying
the AHP, as it compels decision makers to make a choice;
– A verbal ‘more/less/equal’ rating scale is best suited to capture
stakeholders’ value trade-offs;
– The simple arithmetic mean of the individual objective weight
factors should be used to determine the average stakeholder’s
preferences.
• A proposal to scrap from the VOM the so-called ‘second tier’ to determine attribute weight factors, and to base these weight factors on the
preferences of decision makers (the ‘first tier’);
• The addition of a systematic approach based on General Morphological
Analysis (GMA), to use the value model to create alternatives. This
approach uses a morphological grid to split up the research object
in separate parameters, which are then used to generate alternative
options;
• The conclusion that global scaling is best used to calculate the attribute
values in alternative scenarios.
With these improvements, the VOM becomes a better structured methodology
for building value models and creating alternatives for real-life problems.
Using this improved Value Operations Methodology, a value model is built
for the context of runway system maintenance at Schiphol. Seven relevant
stakeholders are identified:
• Airport operator (Amsterdam Airport Schiphol);
• Air traffic control (LVNL);
• Airlines;
viii
• National government;
• Regional and local governments;
• Residents and local community groups;
• Passengers.
These seven stakeholders have the following six objectives:
1. Increase capacity (availability and reliability) of airside operations;
2. Reduce maintenance related costs;
3. Increase predictability and transparency regarding maintenance activities;
4. Increase safety, both of airside operations and the maintenance activities;
5. Reduce the environmental impact of maintenance activities;
6. Reduce nuisance to local community.
Based on interviews with most stakeholders, and a literature review for
the others, the objective weight factors are determined for each individual
stakeholder. Also the average stakeholder’s preferences are calculated, in
the assumption that each stakeholder is equally important. It is concluded
that on average, increasing capacity is by far the most important objective,
followed by improving predictability and safety. This is different from the
current practice at the Airfield Maintenance Services department, where oftentimes reducing costs is the most important objective in decisions regarding
maintenance.
Using the value model, a morphological grid is constructed to generate
seven parameters that contain 21 alternatives for the runway system maintenance strategy. From this grid follows the observation that most alternatives
are related to the planning of maintenance. This warrants the conclusion
that at Amsterdam Airport Schiphol, the maintenance strategy is primarily
concerned with scheduling activities.
The 21 alternatives are first individually analysed in a qualitative manner.
The reference situation in these analyses is the large maintenance project
(GOH) on the Kaagbaan in September 2011. From this qualitative analysis it
can be concluded that the current maintenance strategy is quite successful:
most alternatives result in a negative ∆V, indicating that the current strategy
creates more value. It also means that scheduling maintenance during the
winter (one of the alternatives) is not beneficial: due to a longer maintenance
period and a resulting decrease in capacity, value is destroyed compared to
maintenance in the spring and summer.
The most promising alternatives are combined in two scenarios, taking
into account incompatibilities between options from the grid. These two
scenarios are quantitatively analysed. Only the first scenario, in which all
maintenance is done during the night and the runway is operational during
the day, results in a high positive ∆V, indicating that this scenario creates
value for almost all stakeholders. This is primarily due to the major capacity
benefits. However, as the safety risk cannot be quantified, the actual value
creation will be lower than calculated. Still, it is concluded that on the basis
of the value model, the ‘nightly maintenance’ scenario offers significant value
opportunities.
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The value model is checked for its conceptual, logical and operational
validity. The main limitations of the model are the uncertainties regarding:
the level of detail of the analysis of the alternatives; the set of attributes that
measure the fulfillment of the objectives; the representativeness of the interviewees; the choice of the so-called ‘factormax’; and the relative importance
of each stakeholder. Still, the main conclusion regarding the operational
outcome of the value model is that the model works: for an alternative
maintenance strategy it produces a ∆V for each stakeholder. The manager
of Airfield Maintenance Services finds these results useful, not only for
aiding decision making, but also to explain the eventual decisions to other
stakeholders.
In short, based on the value model and its outcomes the three main
conclusions regarding the runway system maintenance strategy are:
1. The value model provides a useful trade-off framework for making
decisions regarding the maintenance strategy. It shows that the value
of the maintenance strategy is only marginally determined by costs.
Instead, capacity, predictability and safety objectives rank much higher
in importance;
2. The current maintenance strategy performs better than most alternatives, including the alternative to schedule all maintenance during the
winter; and
3. The maintenance strategy can be improved by scheduling more maintenance during the night and making the runway available during the
day.
x
CONTENTS
v
preface
executive summary
vii
list of figures
xiii
list of tables
xv
abbreviations
xvii
nomenclature
xviii
1
2
introduction
1.1 Problem statement . . . . . . . . . . . . . .
1.2 Problems, decisions, trade-offs and values
1.3 Research question, scope and contributions
1.4 Research setup and methodologies . . . . .
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current runway system maintenance strategy
2.1 Definition of the runway system . . . . . . . . . . . . . . .
2.2 Organisation of the maintenance process . . . . . . . . . .
2.3 Runway system maintenance activities . . . . . . . . . . .
2.4 New developments affecting runway system maintenance
2.5 Previous studies on the maintenance strategy . . . . . . .
2.6 The current maintenance strategy . . . . . . . . . . . . . .
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the
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value operations methodology
Value-focused thinking . . . . . . . . . . . . . . . . . . . . . . .
The Value Operations Methodology . . . . . . . . . . . . . . .
Creation of alternatives . . . . . . . . . . . . . . . . . . . . . . .
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improvements on the value operations methodology
4.1 Identification and selection of relevant stakeholders . . . . . .
4.2 Formulation of the set of objectives . . . . . . . . . . . . . . . .
4.3 Determination of objective weight factors . . . . . . . . . . . .
4.4 Selection of attributes . . . . . . . . . . . . . . . . . . . . . . . .
4.5 Determination of attribute weight factors . . . . . . . . . . . .
4.6 Combination of all elements in a value model . . . . . . . . .
4.7 Creation of alternatives . . . . . . . . . . . . . . . . . . . . . . .
4.8 Calculation of the value of alternatives . . . . . . . . . . . . .
4.9 An improved Value Operations Methodology . . . . . . . . .
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a value model for the maintenance strategy
5.1 Identification and selection of relevant stakeholders
5.2 Formulation of the set of objectives . . . . . . . . . .
5.3 Determination of objective weight factors . . . . . .
5.4 Selection of attributes . . . . . . . . . . . . . . . . . .
5.5 Determination of attribute weight factors . . . . . .
5.6 Combination of all elements in a value model . . .
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contents
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creation of alternatives
6.1 Parameters and options in the grid . . . . . . . . . . . . . . . .
6.2 Cross-consistency assessment . . . . . . . . . . . . . . . . . . .
6.3 Alternative maintenance strategies . . . . . . . . . . . . . . . .
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calculation of the value of alternatives
7.1 Choice of reference situation . . . . . . . . . . . . . . . . .
7.2 Calculation of the impact of alternatives . . . . . . . . . . .
7.3 Qualitative analysis of all alternative options . . . . . . . .
7.4 Quantitative analysis of two scenarios . . . . . . . . . . . .
7.5 Extrapolation to other runways and maintenance projects
7.6 Conclusions for the overall maintenance strategy . . . . .
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validation and verification of the value
8.1 Validation strategy . . . . . . . . . . . . . . . .
8.2 Conceptual validity . . . . . . . . . . . . . . . .
8.3 Logical validity . . . . . . . . . . . . . . . . . .
8.4 Operational validity . . . . . . . . . . . . . . .
8.5 Creation of alternatives . . . . . . . . . . . . . .
8.6 Calculation of values of alternatives . . . . . .
8.7 Conclusions and the criticality of assumptions
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discussion
9.1 Reflection . . . . . . . . . . . . . . . . . . . . . .
9.2 How to use the value model in practice . . . .
9.3 Limitations of the value model and the results
9.4 Further methodological improvements . . . . .
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10 conclusions and recommendations
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10.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
10.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
10.3 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . 123
bibliography
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a overview of interviews
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b
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maps of amsterdam airport schiphol
c preferential runway use in the alderstafel agreement139
d the improved value operations methodology
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e
determination of stakeholder type
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f
longlist of stakeholders’ objectives
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g comparison matrices
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h impact of alternatives on attributes
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sensitivity analyses
LIST OF FIGURES
Figure 1.1
Figure 1.2
Figure 2.1
Figure 2.2
Figure 2.3
Figure 3.1
Figure 3.2
Figure 4.1
Figure 4.2
Figure 4.3
Figure 4.4
Figure 4.5
Figure 5.1
Figure 5.2
Figure 5.3
Figure 5.4
Figure 5.5
Figure 5.6
Figure 5.7
Figure 5.8
Figure 5.9
Figure 5.10
Figure 5.11
Figure 5.12
Figure 5.13
Work breakdown structure and overview of thesis . .
Overview of the value model for the runway system
maintenance strategy . . . . . . . . . . . . . . . . . . . .
Venn diagram showing the scope of ‘runway system
maintenance’ . . . . . . . . . . . . . . . . . . . . . . . .
Organisational tree for Asset Management . . . . . . .
Timeline for scheduling maintenance activities . . . . .
Difference between alternative-focused and value-focused
thinking . . . . . . . . . . . . . . . . . . . . . . . . . . .
Schematic overview of a value model consisting of
objectives and attributes . . . . . . . . . . . . . . . . . .
The four types of stakeholders identified by Savage’s
approach . . . . . . . . . . . . . . . . . . . . . . . . . . .
Seven types of stakeholders classified by the three attributes power, legitimacy and urgency . . . . . . . . .
Comparison of the fundamental rating scale and the
MLE rating scale, including its numerical translation
using a factormax of 8 . . . . . . . . . . . . . . . . . . .
Linking value-focused thinking with the morphological
grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Schematic overview of the difference between global
and local scaling . . . . . . . . . . . . . . . . . . . . . .
Overview of the value model for the runway system
maintenance strategy . . . . . . . . . . . . . . . . . . . .
Map of stakeholder relationships in committees . . . .
Classification of stakeholder type . . . . . . . . . . . .
Objective tree . . . . . . . . . . . . . . . . . . . . . . . .
Spider graph showing objective preferences for decision makers from AMS, EC, AO and MD . . . . . . . .
Spider graph showing objective preferences for the
airport operator (Amsterdam Airport Schiphol), based
on the average of four interviews . . . . . . . . . . . .
Spider graph showing objective preferences for air traffic control (LVNL), based on the average of two interviews
Spider graph showing objective preferences for the
airlines (KLM) . . . . . . . . . . . . . . . . . . . . . . . .
Spider graph showing objective preferences for the
national government (Ministry of Infrastructure and
the Environment) . . . . . . . . . . . . . . . . . . . . . .
Spider graph showing objective preferences for the
regional and local governments (Haarlemmermeer municipality) . . . . . . . . . . . . . . . . . . . . . . . . . .
Spider graph showing objective preferences for residents and local community groups . . . . . . . . . . .
Spider graph showing objective preferences for passengers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Spider graph showing the average objective preferences
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list of figures
Figure 6.1
Figure 6.2
Figure 6.3
Figure 6.4
Figure 6.5
Figure 7.1
Figure 7.2
Figure 7.3
Figure 7.4
Figure 7.5
Figure 7.6
Figure 7.7
Figure 7.8
Figure 7.9
Figure 7.10
Figure 7.11
Figure 7.12
Figure 7.13
Figure 7.14
Figure 8.1
Figure 8.2
Figure 8.3
Figure 8.4
Figure 8.5
Figure 8.6
Figure 8.7
Figure 10.1
Figure B.1
Figure B.2
Figure I.1
Figure I.2
Grid parameters defining the runway system maintenance strategy . . . . . . . . . . . . . . . . . . . . . . . . 76
Morphological grid with parameters and alternatives
for the runway system maintenance strategy . . . . . . 78
Incompatible options for the alternative ‘night’ . . . . 80
Incompatible options for the alternative ‘split up activities’ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
Incompatible options for the alternative ‘short blocks’
80
Overview of the steps necessary to calculate the value
of alternatives . . . . . . . . . . . . . . . . . . . . . . . . 81
Grid showing the maintenance strategy chosen for the
GOH project on the Kaagbaan in September 2011 . . . 82
Overview of how results are presented graphically . . 83
Qualitative results for parameter ‘month’ . . . . . . . . 86
Qualitative results for parameter ‘timeslot’ . . . . . . . 86
Qualitative results for parameter ‘weekends’ . . . . . . 87
Qualitative results for parameter ‘combination of activities’ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
Qualitative results for parameter ‘quality of assets’ . . 88
Qualitative results for parameter ‘length of maintenance blocks’ . . . . . . . . . . . . . . . . . . . . . . . . 88
Qualitative results for parameter ‘maintenance area’ . 89
Costs vs. ∆V for Amsterdam Airport Schiphol for all
qualitatively analysed alternatives . . . . . . . . . . . . 89
Chosen alternatives for the two quantitative scenarios
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Results for the two quantitatively analysed scenarios . 94
Quantitative results for the ‘nightly maintenance’ scenario broken up per objective . . . . . . . . . . . . . . . 94
Validation strategy . . . . . . . . . . . . . . . . . . . . . 100
Comparison of objective trade-offs by the manager of
AMS and by employees of AMS . . . . . . . . . . . . . 105
Comparison of objective trade-offs by four departments
at Amsterdam Airport Schiphol and by employees of
AMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
Spread in preferences by AMS team members for each
objective . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
An example of ‘flattening’ of the objective weight factors due to a change in factormax: Stakeholder X’s
preferences for a factormax of 8 (base) and 2 . . . . . . 105
Boxplot showing the distribution of the consistency
ratio for different values of factormax . . . . . . . . . . 106
Criticality of relevant assumptions plotted on an importancecertainty graph . . . . . . . . . . . . . . . . . . . . . . . 110
Quantitative results for the ‘nightly maintenance’ scenario broken up per objective. (This figure is a copy of
Figure 7.14.) . . . . . . . . . . . . . . . . . . . . . . . . . 121
Runway layout of Amsterdam Airport Schiphol . . . . 137
Map showing taxiway bottlenecks at Amsterdam Airport Schiphol . . . . . . . . . . . . . . . . . . . . . . . . 138
Comparison of average ∆V including (base) and excluding passengers’ preferences . . . . . . . . . . . . . 166
Comparison of ∆V for different objective weight factors, for the alternative ‘June–September’ . . . . . . . . 167
Figure I.3
Figure I.4
Figure I.5
Figure I.6
Figure I.7
Figure I.8
Figure I.9
Figure I.10
Figure I.11
Figure I.12
Figure I.13
Comparison of ∆V for different objective weight factors, for the alternative ‘night’ . . . . . . . . . . . . . .
Comparison of ∆V for different objective weight factors, for the alternative ‘split up activities’ . . . . . . .
Comparison of ∆V when attributes are weighted according to Table 5.5 and when all attributes are weighted
equally, for the alternative ‘June–September’ . . . . . .
Comparison of ∆V when attributes are weighted according to Table 5.5 and when all attributes are weighted
equally, for the alternative ‘night’ . . . . . . . . . . . .
Comparison of ∆V when attributes are weighted according to Table 5.5 and when all attributes are weighted
equally, for the alternative ‘split up activities’ . . . . .
Comparison of ∆V for a factormax of 8 (base) and 2,
for the alternative ‘June–September’ . . . . . . . . . . .
Comparison of ∆V for a factormax of 8 (base) and 2,
for the alternative ‘night’ . . . . . . . . . . . . . . . . .
Comparison of ∆V for a factormax of 8 (base) and 2,
for the alternative ‘split up activities’ . . . . . . . . . .
Comparison of ∆V when using the arithmetic mean
(base) or geometric mean to combine stakeholders’
assessments, for the alternative ‘June–September’ . . .
Comparison of ∆V when using the arithmetic mean
(base) or geometric mean to combine stakeholders’
assessments, for the alternative ‘night’ . . . . . . . . .
Comparison of ∆V when using the arithmetic mean
(base) or geometric mean to combine stakeholders’
assessments, for the alternative ‘split up activities’ . .
167
168
169
169
170
170
171
171
172
172
172
L I S T O F TA B L E S
Table 1.1
Table 2.1
Table 3.1
Table 4.1
Table 5.1
Table 5.2
Table 5.3
Table 5.4
Table 5.5
Table 7.1
Table 7.2
Table A.1
Table C.1
Table C.2
Overview of this thesis . . . . . . . . . . . . . . . . . . .
8
Overview of maintenance activities . . . . . . . . . . . 14
Random consistency index . . . . . . . . . . . . . . . . 26
Conversion table for the constant, linear and polynomial conversion methods, including xmin and xmax . . 56
Longlist with aviation stakeholders . . . . . . . . . . . 60
Selection of relevant stakeholders . . . . . . . . . . . . 62
Objective weight factors . . . . . . . . . . . . . . . . . . 66
Operational penalty for runway maintenance . . . . . 70
The set of attributes . . . . . . . . . . . . . . . . . . . . 73
Change-impact matrix showing the impact of alternatives on the set of attributes . . . . . . . . . . . . . . . . 85
Attribute values for the ‘nightly maintenance’ and
‘summer maintenance’ scenarios . . . . . . . . . . . . . 92
Table used during objective trade-off interviews . . . . 136
Preferential runway use from Alderstafel agreement . 139
Preferential runway use when runway 18R-36L (Polderbaan) is not available . . . . . . . . . . . . . . . . . . . . 140
xv
xvi
list of tables
Table C.3
Table C.4
Table C.5
Table C.6
Table E.1
Table E.2
Table E.3
Table E.4
Table E.5
Table E.6
Table H.1
Table H.2
Table I.1
Table I.2
Preferential runway use when runway 06-24 (Kaagbaan) is not available . . . . . . . . . . . . . . . . . . . . 140
Preferential runway use when runway 18L-36R (Aalsmeerbaan) is not available . . . . . . . . . . . . . . . . . . . . 140
Preferential runway use when runway 18C-36C (Zwanenburgbaan) is not available . . . . . . . . . . . . . . . 140
Preferential runway use when runway 09-27 (Buitenveldertbaan) is not available . . . . . . . . . . . . . . . 140
List of stakeholder characteristics with their effect on
the stakeholder’s potential for threat and cooperation 146
Stakeholders within scope and their two-letter abbreviation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
Crosscheck of each stakeholder with each characteristic 149
Rankings and potentials for prototype stakeholders . . 149
Distances between stakeholders and prototypes . . . . 150
Triangulation of stakeholder type . . . . . . . . . . . . 150
Change-impact matrix showing the impact of alternatives on the set of attributes. (This table is a copy of
Table 7.1.) . . . . . . . . . . . . . . . . . . . . . . . . . . 156
Attribute values for the ‘nightly maintenance’ and
‘summer maintenance’ scenarios . . . . . . . . . . . . . 160
Objective weight factors for sensitivity analysis . . . . 166
Attribute weight factors in percent for the base value
function, and for the case where all attributes are
weighted equally . . . . . . . . . . . . . . . . . . . . . . 168
A B B R E V I AT I O N S
AAS
Amsterdam Airport Schiphol
AHP
Analytic Hierarchy Process
AMS
Airfield Maintenance Services
AO
Airside Operations
AOM
Airside Operations Manager
ASM
Asset Management
ATM
Air traffic movements
AVM
Airside Value Model
AWV
Airside Work Permits Safety Committee (Airside Workpermits
Veiligheidsoverleg)
BAS
Residents Contact Schiphol (Bewoners Aanspreekpunt Schiphol)
BRS
Administrative Region Schiphol (Bestuurlijke Regio Schiphol)
BWA
Briefing Work Permits Airside
CCA
Cross-consistency assessment
CDM
Collaborative Decision Making
CMC
Construction & Maintenance Control
COBRA Coordination Committee Runways, Taxiways and Aprons
(Coördinatieoverleg Banen, Rijbanen en Aprons)
CROS
Schiphol Regional Consultation Commission (Commissie Regionaal
Overleg luchthaven Schiphol)
CT
Civil engineering maintenance (civiele techniek)
EC
Environmental Capacity
ET
Electrical maintenance (elektrotechniek)
EU
European Union
FOD
Foreign Object Debris
GMA
General Morphological Analysis
GOH
Large maintenance (groot onderhoud)
HWA
Drainage systems maintenance (hemelwaterafvoer)
IBC
Installation Maintenance Concept (Installatie Beheer Concept)
ICAO
International Civil Aviation Organization
ILS
Instrument landing system
KLM
Royal Dutch Airlines (Koninklijke Luchtvaart Maatschappij)
xvii
KNMI
Royal Netherlands Meteorological Institute (Koninklijk Nederlands
Meteorologisch Instituut)
LVNL
Air Traffic Control the Netherlands (Luchtverkeersleiding Nederland)
MD
Market Development
MLE
More/less/equal rating scale
NOH
Normal maintenance (normaal onderhoud)
RGT
Repertory Grid Technique
SLA
Service level agreement
TRE
Terminal Real Estate
TWP
Traffic and Weather Permitting
U/S
Under service
US
Utility Services
VGP
Joint Platforms (Vereniging Gezamenlijke Platforms)
VLI
Aircraft Handling (vliegtuigafhandeling)
VLU
Flight Handling (vluchtafhandeling)
VOM
Value Operations Methodology
N O M E N C L AT U R E
λ
Objective weight factor
Λ
Largest eigenvalue
ω
Attribute weight factor
a
Rating scale base
A
Number of air traffic movements
c
Constant
C
Capacity
CI
Consistency index
CR
Consistency ratio
E
Environment
f
Factormax
F
Extra fuel costs
I
Importance
xviii
nomenclature
k
Scaling constant for utility function
K
Costs
m
Number of elements in a vector
M
Comparison matrix
n
Number of elements in a vector
N
Nuisance
P
Predictability
P
Potential for threat and cooperation matrix
~r
Potential for threat and cooperation vector
R
Revenue
RI
Random consistency index
~s
Stakeholder characteristics vector
S
Safety
∆T
Difference between stakeholder and prototype
u
Utility or value
v
Objective value
∆V
Change in value
w
Weight factor
x
Attribute value
xmax Attribute upper limit
xmin Attribute lower limit
xix
1
INTRODUCTION
Worldwide, the air transport system is becoming increasingly busy and
crowded, and even in the current economic climate the demand for air travel
is expected to grow fast (IATA, 2011). This increasing demand, together
with the highly punctual schedules of airlines, requires a high availability
of all elements within the air transport system, including aircraft, airspace,
terminals and runways.
In the Netherlands, ‘mainport’ Amsterdam Airport Schiphol (AAS)1 is
preparing for the growing demand in its new masterplan, forecasting a
demand for about 60 million passengers and 525, 000 air traffic movements
(ATM) in 2020 (Vlam, 2011). Next to the growth limits imposed by the
current (terminal) infrastructure, the growth of Amsterdam Airport Schiphol
is also restricted by noise regulations. These regulations ensure that the
people living nearby Schiphol in cities such as Hoofddorp, Aalsmeer and
Amsterdam do not experience too much nuisance from aircraft landing or
taking off.
In an effort to balance the required growth of the airport with the interests
of the residents living nearby Schiphol, the Dutch government has set up
the so-called ‘Alderstafel’: a forum in which the Dutch aviation industry,
national and local governments and local community organisations discuss
the future of Schiphol. The forum is chaired by former minister Hans Alders,
hence its name (Tafel van Alders, 2010). In its final agreement, the Alderstafel
proposed changes in the noise limits, a new preferential runway use and a
limit of 510, 000 ATM per year (Alders, 2008, 2010).
1.1
problem statement
This section has been edited and confidential information has been removed.
Both the growing demand and the Alderstafel agreement affect the airport’s
operations. An important operational aspect is the maintenance of airside
infrastructure. Maintenance is absolutely necessary to guarantee safe operations in accordance with international regulations. However, due to the
large impact on operations it is often seen as a ‘necessary evil’. For instance,
recent maintenance of one of Schiphol’s runways2 cost millions of euros
and took one of the airport’s most important runways out of service for
three weeks, affecting the airport’s capacity (Amsterdam Airport Schiphol,
2011b). Moreover, due to diversions of air traffic, the local community was
confronted with a different runway use resulting in more noise for some
residents.
In the near future, the growing demand for air traffic and the new regulations of the Alderstafel will also result in changing, stricter requirements for
1 In the remainder of this thesis, ‘Schiphol’ refers to (the location of) the airport itself, while ‘Amsterdam Airport Schiphol’ refers to the airport organisation and company. To avoid confusion,
the names of the holding corporations ‘Schiphol Group’ and ‘Schiphol Nederland BV’ are not
used in this thesis.
2 In September 2011, the Kaagbaan (runway 06-24) was under maintenance for three weeks; see
Section 7.1 and Appendix H for more details.
1
2
introduction
runway maintenance. The Airfield Maintenance Services (AMS) department
at Amsterdam Airport Schiphol is therefore in need of new solutions that
provide alternatives for the current maintenance strategy.
Of course, only generating alternative strategies is not enough; it is also important to evaluate whether an alternative improves on the current situation.
Amsterdam Airport Schiphol explicitly asked to include all relevant stakeholders in this evaluation, including airlines, air traffic control and residents
(Kamminga, 2010a). While this may seem irrelevant at first, as Amsterdam
Airport Schiphol is the ultimate decision maker regarding the maintenance
strategy, there are at least two good reasons to include the preferences of
other stakeholders:
• Other stakeholders may have the power to hinder or block decisions
by Amsterdam Airport Schiphol if they oppose them. Vice versa, if
stakeholders cooperate and everyone’s interests are incorporated in the
decision making process, the execution of the eventual strategy may be
smoother because all parties have invested in it;
• From an academic point of view it is more interesting to investigate
the overall system including all stakeholders, instead of the (limited)
problem of optimising AMS’s operations.
Thus the problem statement of this research is to improve the current
runway system maintenance strategy at Amsterdam Airport Schiphol for all
relevant stakeholders.
1.2
problems, decisions, trade-offs and values
In order to solve the problem statement described above, it is enlightening
to approach it as a ‘wicked problem’. A wicked problem is a problem that
cannot be described completely and does not have a definitive solution;
instead its solutions can only be ‘better’ or ‘worse’ than the current situation,
and this can even vary for different stakeholders (Ritchey, 2011). Thus, the
problem cannot be solved by a single, exact solution — the challenge is to
make the ‘best’ decision.
Of course, the question is then how, and for whom, to define ‘best’. As
mentioned before, this research project includes all relevant stakeholders and
takes into account their varying preferences regarding different strategies.
This inevitably leads to trade-offs between different stakeholders and between
different goals. These trade-offs should be based on the stakeholders’ values.
In this thesis, ‘value’ is not defined as a monetary unit, but has a much
broader meaning:
“Values are what we care about. As such, values should be the
driving force for our decisionmaking. They should be the basis for
the time and effort we spend thinking about decisions.” (Keeney,
1992, p.3)
Values are thus the key element behind the trade-offs necessary to find a
solution for a decision problem. The philosophy of ‘value-focused thinking’
was developed by Keeney (1992) to aid in decision making. Curran et al.
(2010) used this philosophy, together with elements from value-driven design,
to create the Value Operations Methodology (VOM). This methodology was
then further refined by the theses of Smulders (2010) and Repko (2011). The
VOM and the philosophy of value-focused thinking are explained in detail
1.3 research question, scope and contributions
in Chapter 3. This research can be seen as the next application of the Value
Operations Methodology.
This research will use the Value Operations Methodology to assist decision
making and trade-offs regarding the runway system maintenance strategy,
based on the values of the different stakeholders.
1.3
research question, scope and contributions
These considerations leads to the following research question:
How can a quantified change in value for all relevant stakeholders
measure the success of new ideas in improving the runway system
maintenance strategy at Amsterdam Airport Schiphol?
As is clearly defined in the research question, the scope of this project is
the runway system maintenance strategy. That means the focus is on the strategy. Inevitably, also some tactical and operational issues will be mentioned;
however, current maintenance operations or projects, such as the 2012 maintenance schedule, are out of scope. Also the translation and implementation
of the eventual alternative strategy or strategies to operational maintenance
activities is out of scope. The exact spatial scope, i.e. the definition of ‘runway
system’, is discussed in Section 2.1.
As this thesis project is carried out at the Airfield Maintenance Services
department at Amsterdam Airport Schiphol, the project itself must also make
a trade-off between its contributions to industry on one hand, and to the
academic world on the other. The main contributions to academia are the use
and the strengthening of the Value Operations Methodology. This methodology is quite young, and while the general approach is well described by
Curran et al. (2010) and Smulders (2010), each step in the methodology can
be investigated and explained in more detail. As all these steps influence
the final (change in) value of the alternatives, it is very important that the
methodological framework explicitly explains and supports these steps. This
thesis does precisely that in Chapter 4. Next to these improvements, this
thesis proposes a method to link the value model central to the VOM with
concepts from General Morphological Analysis (GMA) to create a systematic
approach for generating alternatives (see Section 4.7). Moreover, a valuable
contribution to academia is the application of the methodology to a reallife problem in industry. This illustrates the uses and the limitations of the
method. Both the strengthening and the use of the VOM result in a more
robust methodology.
The main contribution to Amsterdam Airport Schiphol is the generation
and evaluation of alternative maintenance strategies, based on the philosophy of value-focused thinking and the Value Operations Methodology. In
this process, a complete overview of the system, its stakeholders and their
preferences is created. This overview can be used by Amsterdam Airport
Schiphol to approach decision problems from a systems perspective and
incorporate value trade-offs, instead of optimising each subsystem on its own
as is currently often the case. Please note that this project does not deliver a
new maintenance schedule or a software tool for planning maintenance.
3
4
introduction
1.4
research setup and methodologies
This section discusses the research setup in Section 1.4.1. The theoretical
frameworks and research methodologies used are listed in Section 1.4.2 and
1.4.3 respectively.
1.4.1
Research setup
The research setup is based on the approaches used by Smulders (2010,
§1.3) and Repko (2011, §1.2), and on the ‘job plan’ by Miles (1972, Chapter
5). This job plan is used in value engineering and consists of four basic
steps: information gathering; alternative generation (creation); evaluation;
and presentation. In this research’s setup, this has been translated into nine
phases, also shown in Figure 1.1:
reference situation The first step is to analyse and describe the current
runway system maintenance strategy, including the organisation of
the maintenance process, new developments affecting maintenance,
and previous studies on the maintenance strategy. This is the focus of
Chapter 2;
theoretical background Chapter 3 consists of a literature review of
value-focused thinking and the Value Operations Methodology;
improvements on the vom Based on the literature review, a number of
additions and improvements on the Value Operations Methodology
are proposed in Chapter 4;
value model The central element in this research is the value model for the
runway system maintenance strategy, built according to the improved
VOM. This value model consists of a set of stakeholders, objectives,
a set of objectives weight factors for each stakeholder, attributes that
measure the fulfillment of the objectives, and finally a set of attribute
weight factors. This is schematically summarised in Figure 1.2. This
value model is built in Chapter 5, which discusses every element in
detail;
creation of alternatives Using the systematic approach for generating alternatives as proposed in Section 4.7, 21 alternative options for
the maintenance strategy are created in Chapter 6;
calculation of value The change in value of the 21 alternative options
is calculated qualitatively and quantitatively in Chapter 7, using a
recent maintenance project on the Kaagbaan as the reference situation;
validation and verification To check the representativeness of the
value model of the system, it is validated in Chapter 8;
discussion Chapter 9 reflects on the merits of the methodology, mentions
the limitations of the value model and its results, lists further improvements to the Value Operations Methodology, and explains how the
value model can be used in practice at Amsterdam Airport Schiphol;
conclusions and recommendations Finally, Chapter 10 discusses
the conclusions of this research, mentions this research’s contributions and lists a number of recommendations for Amsterdam Airport
Schiphol and academia.
1.4 research setup and methodologies
5
How can a quantified change in value for all relevant stakeholders measure the success of
new ideas in improving the runway system maintenance strategy at AAS?
(2) Reference situation
What is the current runway system
maintenance strategy?
(3) Theoretical background
How can the Value Operations
Methodology assist in trade-offs in
decision problems?
(4) Improvements on the VOM
How can the VOM be improved and
extended?
(5) Value model
What is the value function for the
different stakeholders in the case of
runway system maintenance?
(6) Creation of alternatives
Which alternative options might
improve the runway system
maintenance strategy?
(7) Calculation of value
What is the resulting change in value
of the alternative strategies?
(8) Validation and verification
Is the value model a valid
representation of the system?
(9) Discussion
How can the methodology be further
improved and how can the value
model be used in practice?
(10) Conclusions and
recommendations
What are the conclusions and
recommendations regarding the value
model and its results?
Figure 1.1: Work breakdown structure and overview of thesis. The numbers refer to chapter numbers; the grey boxes
focus on academic theory and may therefore be of less value to readers solely interested in the analysis
of the maintenance strategy.
6
introduction
Stakeholders
Objectives
Objective weight factors
Attributes
Value model
Attribute weight factors
Figure 1.2: Overview of the value model for the runway system maintenance strategy
Readers who are primarily interested in the theoretical approach and the
Value Operations Methodology, are advised to focus on Chapter 3 and 4,
and to treat the other chapters as a case study of the methodology. On the
other hand, readers who are solely interested in the analysis of the runway
system maintenance strategy, are advised to focus on Chapter 2 and 5–7, and
only refer to the theoretical chapters for background information.
1.4.2
Theoretical frameworks
This research makes use of a number of theoretical frameworks. These are
listed here in brief together with a reference to their full discussion later
in this thesis. Table 1.1 at the end offers an overview of all phases, the
frameworks and methodologies used in each phase and their results.
value operations methodology The methodological framework that
forms the core of this research is the Value Operations Methodology. Already
briefly mentioned above, it is discussed in detail in Section 3.2. A number of
improvements in the theoretical foundation and practical application of the
methodology is proposed in Chapter 4. These improvements are focused on
the application of the VOM in the Airside Value Model (AVM).
value-focused thinking The philosophical basis of the VOM is the
theory of value-focused thinking, as introduced by Keeney (1992). This theory
is explained in Section 3.1.
stakeholder theories To analyse the stakeholders in the system, a
number of stakeholder theories is used. The Analysis of Complex Neighbourhoods, ATOM Methodology, Savage’s approach and Mitchell’s approach
are all discussed in Section 4.1.
analytic hierarchy process The Value Operations Methodology
uses the Analytic Hierarchy Process (AHP) as the method to determine
stakeholders’ preferences for use as objective weight factors. Section 3.2.1
1.4 research setup and methodologies
explains this method in detail, and Section 4.3 suggests a number of improvements to its implementation in the VOM.
general morphological analysis For the creation of alternative
strategies, use is made of concepts from General Morphological Analysis,
especially the morphological grid and the cross-consistency assessment.
Section 4.7 discusses these methods.
other decision theories A number of other decision theories are
briefly mentioned in this research, including wicked problems, multi-criteria
analysis and cost-benefit analysis. These theories are not discussed in detail
in this thesis because they are not used to create or evaluate alternative
strategies.
validation theories and techniques To validate the value model
and its results, the validation theories and techniques from Landry et al.
(1983), Balci (1994) and Sargent (2007) are used in Chapter 8.
1.4.3
Research methodologies
Apart from these theoretical frameworks, generic research methodologies are
used in this thesis. Their use throughout this thesis is also listed in Table 1.1.
desk research Heavy use is made of studies, reports and other documents from the Amsterdam Airport Schiphol organisation, especially of the
AMS department. These documents are used to analyse the current maintenance strategy, the possibility of alternative options and their impact on
stakeholders’ value. All documents have been included in the bibliography
at the end of this thesis.
literature research For the theoretical foundations of this research,
academic literature on the frameworks listed above is consulted. Especially
the works of Keeney (1992), Curran et al. (2010), Smulders (2010) and Repko
(2011) are frequently cited. All consulted works are listed in the bibliography.
interviews Appendix A lists all interviews conducted with experts and
decision makers at different organisations. In total, almost forty interviews
are used for background information, expert opinions and value trade-offs.
Structured interviews are used in meetings with decision makers from the
relevant stakeholder organisations. Using the questionnaire from Table A.1,
their value trade-offs are captured in pair-wise comparisons. Semi-structured
interviews are held with three experts from different industries to gather
alternative maintenance strategies. Finally, the background interviews with
experts, mostly from Amsterdam Airport Schiphol, were unstructured.
spreadsheet modeling To calculate the change in value as a result of
an alternative maintenance strategy, the value function is programmed as a
series of Microsoft Excel spreadsheets. The results presented in Chapter 7
are all generated using these spreadsheets.
7
8
§
introduction
phase
methodologies
results
2
Reference situation
Desk research, literature
research, interviews
Current runway system
maintenance strategy
3
Theoretical background
Value-focused thinking, VOM,
AHP, other decision theories,
literature research
Overview of the current state
of the VOM
4
Improvements on the VOM
Value-focused thinking, VOM,
stakeholder theories, AHP,
GMA, literature research
Improved VOM
5
Value model
VOM, AHP, desk research,
literature research, interviews,
spreadsheet modeling
Value function for the context
of runway system
maintenance
6
Creation of alternatives
GMA, desk research,
interviews
Morphological grid of seven
parameters and 21 alternatives
for the maintenance strategy
7
Calculation of value
VOM, desk research,
interviews, spreadsheet
modeling
Change in value for all
alternatives
8
Validation and verification
Validation theories and
techniques, desk research,
interviews, spreadsheet
modeling
Conclusions on the validity of
the value model and its
results; importance-certainty
graph of assumptions
9
Discussion
VOM, literature research
Merits of the methodology;
limitations of the value model
and its results; list of possible
further improvements to the
VOM; directions on the
practical use of the value
model at Amsterdam Airport
Schiphol
Conclusions and
recommendations
—
Conclusions; contributions of
this research to academica and
Amsterdam Airport Schiphol;
recommendations for
Amsterdam Airport Schiphol
and for further research
10
Table 1.1: Overview of this thesis, including the methodologies used for each research phase, and the results of each
phase. The first column indicates the chapter number.
2
C U R R E N T R U N WAY S Y S T E M M A I N T E N A N C E
S T R AT E G Y
Any busy international airport’s airside infrastructure, including runways,
taxiways and aprons, is heavily used. Each landing aircraft reduces the
quality of the runway’s touchdown zone, and every hour the remaining life
of the airport lighting is diminished. In aviation, with its stringent safety
regulations, it is unacceptable to let the infrastructure’s quality degrade too
much over time:
“Many of the airport facilities (. . . ) are of such critical importance
to flight safety that every effort has to be made to ensure that
failures do not occur.” (Ashford et al., 1997, p.153)
This explains the need for timely maintenance on the airport’s airside infrastructure: safe flight operations (King, 1986). Therefore, ICAO Annex 14
dedicates a chapter to rules and regulations regarding airport maintenance
(ICAO, 2009, Chapter 10).
However, at an airport as busy as Schiphol, runway maintenance is not a
minor detail. For large maintenance projects the runway may be taken out
of service for weeks, which significantly reduces capacity, hinders airlines
and passengers, changes the noise load for the local community, and costs
millions of euros. A maintenance strategy must choose between these effects.
For instance, regard the trade-off in deciding when to schedule maintenance:
from a maintenance point of view, maintenance in the spring and summer
is preferred, as then the weather conditions are optimal; however, from an
operations point of view, spring and summer are the worst time to perform
maintenance, as then the demand for airside capacity is the highest.
As explained in the previous chapter, this thesis tries to make these tradeoffs explicit in order to improve the runway system maintenance strategy. To
do so, it is important to first analyse the current strategy. This chapter starts
with the definition of the spatial scope of the project in Section 2.1. Section
2.2 discusses the organisation of the maintenance process and Section 2.3
gives an overview of what maintenance is carried out. Section 2.4 examines
new developments that have an effect on the runway system maintenance
strategy, and in Section 2.5 the results of previous studies on the maintenance strategy are discussed. Finally, Section 2.6 summarises the current
maintenance strategy.
2.1
definition of the runway system
The scope of this research is discussed in Section 1.3, except for the spatial
scope. Determining the spatial scope is done by defining what is meant
exactly by ‘runway system’, as this concept features so prominently in the
research question. ICAO (2009, §1.1) does not give a definition for ‘runway
system’, but does define runways, taxiways and aprons, which may be part
of the system:
runway “A defined rectangular area on a land aerodrome prepared for the
landing and take-off of aircraft;”
9
10
current runway system maintenance strategy
taxiway “A defined path on a land aerodrome established for the taxiing
of aircraft and intended to provide a link between one part of the
aerodrome and another (. . . );”
apron “A defined area, on a land aerodrome, intended to accomodate
aircraft for purposes of loading or unloading passengers, mail or cargo,
fuelling, parking or maintenance.”
The definitions for runways and taxiways match those as used by Amsterdam
Airport Schiphol, apart from the fact that those definitions explicitly use the
concept of ‘manoeuvring area’:
manoeuvring area “That part of the airport that is used for the take-off,
landing and taxiing of aircraft including the unpaved sections between
them.” (Bakker and Zeeuw, 2011, p.8)
However, a definition for ‘runway system’ is not given in those documents.
Instinctively, the runway system encompasses more than just the runways.
Nevertheless, it is hard to exactly define what is included: are taxiways part
of the runway system? And what about the aprons?
To determine the spatial scope for this project it is useful to look from the
perspective of maintenance and to keep in mind one of the reasons behind
this research: the inherent tension between maintenance and airport capacity.
Thus the scope of this research is limited to maintenance that may affect
runway availability for incoming or outbound flights. Therefore instead of a
definition for ‘runway system’, a definition for ‘runway system maintenance’
is proposed:
runway system maintenance All maintenance to runways and taxiways that affects the availability of the runway for the take-off and
landing of aircraft.
This clearly includes any runway maintenance, as this means that the affected
runway is not available for take-off or landing. It may also include some
taxiway maintenance, for instance when this results in a reduced availability
of one or more runways1 . Smaller taxiway maintenance, without an effect on
runway availability, is deemed to be outside the scope. Also apron maintenance is considered out of scope. Although apron maintenance may limit the
airport’s airside capacity, it does not have an effect on runway availability.
Figure 2.1 shows the scope schematically.
Of primary concern within this project is the maintenance directed by the
Airfield Maintenance Services department, as they are the initiator of this
research. However, also other organisations carry out maintenance that may
be considered within scope. For instance, Air Traffic Control the Netherlands
(LVNL) maintains the ILS hardware (Van Calck, 2011). The next section
discusses the organisation of the maintenance process in more detail.
2.2
organisation of the maintenance process
This section first explains the organisational structure of the maintenance
department in Section 2.2.1, before discussing the scheduling process for
runway system maintenance in Section 2.2.2.
1 At Amsterdam Airport Schiphol, this includes ‘Punt Pieter’ and the taxiway crossings A8, A12,
A21 and W5 (see also Figure B.2).
2.2 organisation of the maintenance process
Airside maintenance
Runway system maintenance
Taxiway
maintenance
Perimeter
road
maintenance
Runway
maintenance
New
construction
Apron
maintenance
Other
maintenance
Figure 2.1: Venn diagram showing the scope of ‘runway system maintenance’. All
runway maintenance is considered within scope, and some taxiway and
other maintenance. Although ‘new construction’ is strictly speaking not
maintenance, occasionally such works may affect runway availability.
2.2.1
Organisational structure
Amsterdam Airport Schiphol considers its infrastructure such as runways,
aprons and the terminal as assets. Its organisational structure distinguishes
between the user and the owner of the asset. The asset owner is responsible
for making the asset available to the operational user. As part of Asset
Management (ASM), Airfield Maintenance Services is responsible for the
assets on airside and the infrastructure (such as roads and parking areas) on
landside (Van der Vegte and Vallinga, 2011, Chapter 2). AMS signs service
level agreements (SLAs) with the internal users of these assets, in which
the user’s demands and requirements and the responsibility of AMS is
determined (Bakker and Zeeuw, 2011).
AMS has divided its assets over four percelen (parcels). Parcel 1 includes
the runways and taxiways and is therefore the only parcel that is considered
within scope of this project. The parcels are each tendered to contractors; the
Dutch company Heijmans is the main contractor for Parcel 1. Recently, the
choice has been made to outsource more of the maintenance process. This
means that the main contractor is responsible for the maintenance operations
and the actual execution of the maintenance, while AMS is only concerned
with the strategy and the scheduling of the maintenance (Van der Vegte and
Vallinga, 2011, Chapter 2).
11
12
current runway system maintenance strategy
Parcel 1 is managed by the Flight Handling (VLU) department within
AMS. Within VLU, there is a functional division in electrical systems (ET),
civil engineering (CT), and drainage systems (HWA). VLU is also responsible
for some other tasks, such as the mowing of the grass at airside. These small
tasks are however considered out of scope, as they do not affect runway
availability.
To summarise this structure, Figure 2.2 shows the organisational tree for
ASM.
Asset Management
(ASM)
Airfield Maintenance
Services (AMS)
Parcel 1: Flight
Handling (VLU)
Terminal Real
Estate (TRE)
Utility Services
(US)
...
...
Parcels 2-4
Electrical maintenance
(ET)
Aircraft Handling (VLI), ...
Civil engineering
maintenance (CT)
Drainage systems
maintenance (HWA)
Other
Mowing, ...
Figure 2.2: Organisational tree for ASM, showing the division in AMS, TRE and
US; the further division in parcels (VLU and VLI); and the functional
division in ET, CT and HWA. The box ‘Other’ contains small tasks such
as mowing. The grey boxes show departments that fall outside the scope
of this project.
2.2.2
Process for scheduling maintenance
Airfield Maintenance Services employs a planner, who together with the
planners of Construction & Maintenance Control (CMC) is in charge of the
complete maintenance schedule. CMC is the department within Amsterdam
Airport Schiphol that oversees all construction and maintenance activities on
the airport, including works submitted by other parties such as LVNL.
Currently, the planners classify maintenance in one of three categories,
based on the size of the project:
normal maintenance (noh) This maintenance usually takes about
one week, and is done yearly.
2.3 runway system maintenance activities
large maintenance (goh) This maintenance takes longer, up to four
weeks. This kind of maintenance is recurring every couple of years.
traffic and weather permitting (twp) These maintenance activities are planned ahead, but are dependent on a go/no go decision
of the Airside Operations Manager (AOM). If the runway where the
activity is scheduled cannot be missed in the operations of that day, the
AOM will give a no go.
Several consultation committees are formed to discuss maintenance activities with the most important stakeholders. The most important one is
the Coordination Committee Runways, Taxiways and Aprons (COBRA), in
which representatives of AMS, CMC, several other departments, LVNL and
Royal Dutch Airlines (KLM) participate. In the COBRA meetings decisions
are made with regard to maintenance activities. These meetings are held
once every month, or once every two weeks in busy periods (Bosgra, 2011).
Two other maintenance related committees are the Airside Work Permits
Safety Committee (AWV), that is in charge of designing the temporary
situation during maintenance; and the Briefing Work Permits Airside (BWA),
that is in charge of informing the operational services of Amsterdam Airport
Schiphol and LVNL regarding maintenance that is to be carried out. Both
AWV and BWA meet on a weekly basis (Van Calck, 2011).
An analysis of the scheduling process for maintenance results in the
timeline shown in Figure 2.3. This timeline shows that long term planning is
nominally done five years in advance, but the actual scheduling happens in
the year before. It also shows the dependence of TWP maintenance on the
go/no go decision.
2.3
runway system maintenance activities
As explained in the previous section, maintenance is divided in two different
ways: by asset function into ET, CT and HWA; and by impact in TWP, NOH
and GOH. Table 2.1 gives an overview of the maintenance activities for
each combination of these two dimensions. These activities are discussed
in further detail in the following sections, based on interviews with experts
from AMS (see Appendix A for an overview).
2.3.1
Electrical maintenance
Electrical maintenance (ET) is concerned with the maintenance of all electrical
systems related to the runway. This includes the runway lighting, stopbars,
transformers, baanstations (switching stations) and cables. Each runway and
most taxiways have their own switching station. The ILS is also connected to
the runway’s switching station, but it has its own emergency supply and it
is maintained by LVNL, not by AMS.
As can be seen in Table 2.1, there are no specific large maintenance activities
for ET. Instead, when GOH is scheduled for a runway, activities that would
normally be done during TWP or normal maintenance are bundled.
NOH maintenance is focused on the switching station. The station is
inspected and maintained, and tested afterwards. This testing includes a
thermal scan of the station. For this scan, all runway lighting is turned on at
full power. Because the runway is not used during this test, this is confusing
for pilots who see a fully lit runway on which they are not allowed to land.
Also the tuning of stopbars is included in NOH, which takes a lot of time as
13
14
current runway system maintenance strategy
5 years in advance
Long term planning of foreseeable maintenance in the coming five
years, based on recurring maintenance cycles
7 months
ET, CT and HWA supervisors within AMS submit the works they want to
do in the next year to the scheduler, who sends those to the contractor,
who estimates the time and resources needed for these works
5 months
The planners of AMS and CMC combine the works with the highest
impact in main clusters, and present a proposed schedule for these
clusters in COBRA
3 months
The definitive maintenance schedule for the next year is presented in
COBRA
13-6 weeks
For each work in the schedule, the activities are worked out in detail, in
cooperation with the AWV and BWA committees
1 day
For TWP maintenance the go/no go decision is made by the Airside
Operations Manager in consultation with LVNL and KLM
Maintenance
activity
Figure 2.3: Timeline for scheduling maintenance activities
type
et
ct
hwa
TWP
Replacing broken lights; inspections
Inspections
—
NOH
Switching station maintenance and checks, including
thermal scan and tuning of
stopbars; inspections
Repairs based on inspections;
removing rubber; repairing
runway markings
Inspections of pipelines
GOH
Bundling of activities that
would otherwise be TWP or
NOH
Replacing coating; full repairs based on (extrapolated)
inspections
Repairing, reinforcing or replacing pipelines, based on
inspections
Table 2.1: Overview of maintenance activities, based on interviews and Beemsterboer (2010)
2.3 runway system maintenance activities
the tuning of each controller may take up to eight hours and there can be as
much as twelve controllers per runway. Finally, NOH includes the inspection
of the transformer pits.
The main activities done during TWP maintenance are replacing broken lights and inspections. Each month the light output of each runway is
measured and compared to the airport’s lighting norms. These norms are
comparable to the official ICAO norms but with stricter limits. This means
that when the airport’s own norms are unmet, there is still time for repair
before the ICAO norms are breached. Repair of runway lights takes at least
two and a half hours per runway.
2.3.2
Civil engineering maintenance
Civil engineering maintenance (CT) deals mainly with the surfaces and
tarmac of the runways and taxiways.
CT is the main driver for GOH, as the infrastructure that is part of CT
has the shortest lifetime. Based on the extrapolated results of inspections
a long term schedule for the coming five years is made, which is updated
yearly with new inspection results. The exact maintenance activities during
GOH differ, but they include works with a very high impact that take several
weeks, such as replacing the coating of the runway.
During NOH smaller repairs are done, and some recurring works such
as removing rubber deposits (resulting from landing aircraft’s tires) and
repairing runway markings.
Inspections are usually done during TWP maintenance and, as mentioned
above, form the basis for the maintenance activities for NOH and GOH the
coming year(s).
2.3.3
Drainage systems maintenance
Drainage systems maintenance (HWA) is concerned with the underground
infrastructure for the drainage of rain water. As Schiphol is located under sea
level in a country with ample rain, the swift removal of rain water from the
runway system is very important. To this end more than 450 km of pipelines
lay under the entire airport.
There are no TWP maintenance activities possible for the HWA domain
because all the pipelines are underground. Inspections are therefore done
during NOH. During these inspections first the pipelines are cleaned. Then
a camera is guided through the pipelines. Based on the camera’s footage the
pipelines are graded on the quality and the hydraulic capacity. Cracks, leaks,
pollution and root growth lower the quality grade.
Based on the inspection grades the HWA supervisor may decide to repair,
reinforce or replace part of the drainage system. Such activities are done
during GOH, as this usually means that the runway surface must be removed
to work on the pipelines underground. This can take several weeks.
To determine what kind of maintenance concept is used, a so-called
Installation Maintenance Concept (IBC) is made for each asset. In this IBC,
using a ‘Failure Modes & Effects Criticality Analysis’, the possible failures
modes, the risks of occurrence and the effects are analysed. Then, a decision matrix determines whether a storingsafhankelijk (corrective), gebruiksduurafhankelijk (planned) or toestandsafhankelijk (condition-based) maintenance
15
16
current runway system maintenance strategy
concept is used. Combined with a list of mandatory recurring maintenance
tasks, this leads to a list of maintenance activities for each asset (Bakker, 2008;
Van Vugt, 2006).
2.4
new developments affecting runway system maintenance
As explained in Chapter 1, several developments are the cause of additional
strain on the runway system maintenance strategy. These developments can
be roughly divided into three categories:
1. The growth of the airport;
2. The Alderstafel agreement;
3. New security regulations.
The following sections discuss these developments.
2.4.1
The growth of the airport
As mentioned earlier, demand for air travel worldwide is growing fast
again. Also the demand for Amsterdam Airport Schiphol specifically is
expected to grow fast in the coming decade, with an expected annual growth
rate of three percent until 2020. As Schiphol has strict capacity constraints,
due to environmental and operational reasons, there is limited capacity to
accommodate this demand (Vlam, 2011).
As any increase in the number of operations inherently limits the possibilities for maintenance, there is one capacity increasing measure that may have
large consequences for the maintenance strategy. Currently, the available
slots at Schiphol are allocated in peaks, to accommodate the hub function
of the airport and the wave system of the airlines, especially KLM2 . TWP
maintenance may be scheduled in between two of these peaks. However, it
is foreseen that those periods may cease to be available for maintenance, as
the growing demand is accommodated in those off-peak hours (Vlam, 2011).
This removes an important maintenance window.
The nightly capacity of the airport is fixed at the current level, which means
that the night may offer relatively more space for maintenance activities
(Vlam, 2011).
2.4.2
The Alderstafel agreement
The Alderstafel is a forum, set up by the national government, in which the
Dutch aviation industry (Amsterdam Airport Schiphol, KLM and LVNL), national and local governments and local community organisations discuss the
future of Schiphol in relation to the nuisance experienced by the community,
caused by air traffic. This forum is chaired by former minister Hans Alders,
hence its name (Tafel van Alders, 2010).
The Alderstafel published its advice for the medium term in 2008 and a
more elaborate report on the implementation of this advice in 2010 (Alders,
2008, 2010). These agreements try to balance the goals of the aviation industry
on one hand, and the people living near Schiphol on the other. This balance
is exemplified by the fact that Amsterdam Airport Schiphol may grow to
2 For a detailed explanation of the wave system of airlines and the relation with the concept of
hub airports, see for instance Doganis (2002, §9.6).
2.4 new developments affecting runway system maintenance
510, 000 ATM per year3 , but simultaneously nuisance and noise reducing
measures are implemented.
The most important of these measures is the abandonment of the current
system of handhavingspunten (enforcement points) for aircraft noise, and the
implementation of a new preferential runway use. This means that only
external factors (such as weather and wind conditions) should determine the
choice of a combination of runways on a particular day at Schiphol (Alders,
2010; Kamminga, 2010b). Appendix C explains this concept in more detail
and shows the preferential runway use tables.
These preferential runway use tables significantly affect which runways
are used, and thus also the time available for maintenance activities. However, some ambiguity regarding maintenance remains in the Alderstafel
agreements. On one hand, as Table C.2–C.6 in Appendix C show, separate
preferential runway use tables are included in the agreement for the case
when one runway is not available, for instance due to maintenance (Alders,
2010, Appendix 2). This would make the old practice of applying for an
exemption4 of the noise regulations during runway maintenance obsolete
and create more flexibility regarding maintenance scheduling. However, ambiguity arises concerning these tables because of certain passages in the
agreements that state that in the case of runway unavailability divergence of
the rules is allowed (Alders, 2010, p.21 and Appendix 2, p.2). Furthermore,
representatives from Amsterdam Airport Schiphol believe that the upcoming
final regulations will still include some obligation to apply for an exemption
during runway maintenance.
Therefore, this research will take into account the goals of the Alderstafel
agreement (balancing airport growth and reducing nuisance for the surroundings), but will not focus on strictly applying the preferential runway
use tables, as there is still some uncertainty regarding its validity in cases of
runway maintenance.
2.4.3
New security regulations
This section has been edited and confidential information has been removed.
After the terrorist attacks on September 11, 2001, the European Union (EU)
drafted additional legislation for civil aviation security. This legislation creates rules for security checks for persons, goods and vehicles entering airside
(European Parliament and European Council, 2008, Annex 1.3–1.4). Current
practice at Schiphol means that all persons entering airside are screened,
but vehicles and goods are checked randomly. Starting in April 2012, every
vehicle and all goods entering airside must also be security checked. This
means a huge increase in the amount of work for the security agents at the
checkpoints, and will lead to long waiting times. In the context of maintenance, contractors must pass these checkpoints regularly, with vehicles and
goods ranging from simple tools to complete cement trucks.
3 If it is impossible to move 70, 000 ATM to regional airports, Amsterdam Airport Schiphol may
grow to 580, 000 ATM per year.
4 In accordance with article 8.23 of the Wet luchtvaart (Ministerie van Verkeer en Waterstaat,
2011b).
17
18
current runway system maintenance strategy
2.5
previous studies on the maintenance strategy
In the past couple of years, a number of studies have been performed at
Amsterdam Airport Schiphol that (sideways) analyse the runway system
maintenance strategy, and sometimes offer alternative strategies. This section
shortly discusses the conclusions from these studies. These conclusions will
be used in the creation of alternatives in Chapter 6.
While not offering any alternative maintenance strategies, Kamminga
(2010b) explains one reason for this research: the Alderstafel agreement and
its impact on runway use.
Neeteson (2010a,b,c) has developed a simulation tool that predicts the delay
time of a maintenance project, resulting from adverse weather conditions.
This tool shows that, as the experts from AMS know from experience, the
least delay is encountered in April–September. When maintenance is scheduled during the autumn or winter, some activities may be delayed until April
next year.
Brouwer et al. (2011) present seven different timeslots, and five different
periods throughout the year as alternatives for maintenance scheduling. Also
the effects of these alternatives on five criteria, including noise, maintenance
duration, capacity and preferences of the local community, are analysed.
Interestingly, the effect on maintenance costs is not calculated. Furthermore,
from the presented conclusions on the ‘best’ alternative for maintenance
scheduling it is not clear how the trade-off between the criteria is done.
Bus (2008) performed a cost-benefit analysis of different timeslots for
maintenance. However, the scope of this study is very limited as it only
includes ‘non-performance costs’ of KLM as a possible benefit, ignoring
criteria such as noise and safety.
In a study to improve noise calculations, Van Schaik (2010) studies alternative maintenance schedules, differing by the week in which the maintenance
project starts. This study determines the optimal maintenance schedule by
minimising the noise load, thus ignoring other objectives such as capacity
and costs.
Finally, Boezeman (2009), Breumelhof (2008) and Spaargaren (2008) performed a study on an alternative for the GOH of the Zwanenburgbaan in
2008. In this alternative, the runway would be operationally available during
the day, and during the night maintenance would be carried out. This alternative results in 50 percent higher maintenance costs, but also a much lower
reduction in capacity. Unfortunately, this study does not provide a trade-off
between those two criteria.
In conclusion, these studies offer a number of alternatives for the current
maintenance strategy, especially regarding the timeslot and month in which
maintenance is carried out. Moreover, they offer insight in the effects of these
alternatives or tools and methods to calculate these effects. However, none of
the studies offers a comprehensive overview of all relevant stakeholders and
their objectives, and none presents a clear trade-off between those objectives.
This research tries to fill that gap.
2.6
the current maintenance strategy
In short, the current runway system maintenance strategy can be summarised
in five elements:
2.6 the current maintenance strategy
1. There is a differentiation between the asset owner (AMS) and the
asset user (Airside Operations); and between the principal (AMS) and
contractor of maintenance (Heijmans for Parcel 1);
2. There are three different types of maintenance blocks, from short
to long: Traffic and Weather Permitting (TWP), normal maintenance
(NOH), and large maintenance (GOH);
3. Maintenance is split up in three different technical disciplines: civil engineering maintenance (CT), electrical maintenance (ET), and drainage
systems maintenance (HWA);
4. Maintenance is scheduled throughout the year, based on weather conditions and the demand for airside capacity;
5. The three maintenance concepts used (corrective, planned and conditionbased maintenance) result from an analysis of each asset’s specific
failure modes. These are summarised in the Installation Maintenance
Concepts (IBCs).
Primarily because of an increase in demand and changing noise regulations
due to the Alderstafel agreement, there is a need for a study on alternative
maintenance strategies. In the past, studies on this topic have not resulted
in comprehensive conclusions based on a trade-off between all relevant
stakeholders and their objectives. This research tries to fill that gap.
19
3
T H E VA L U E O P E R AT I O N S M E T H O D O L O G Y
As mentioned in the previous chapter, in the last couple of years multiple
studies have been performed at Amsterdam Airport Schiphol on alternative
maintenance strategies, all with their advantages and disadvantages. Unfortunately, these effects are not all comparable, they have consequences for
different stakeholders and varying degrees of certainty. For instance, an alternative strategy may lower costs, which are expressed in euros and benefit
the Airfield Maintenance Services department; but at the same time it could
increase the safety risk, which is highly uncertain and affects passengers
and people living nearby. This benefit and drawback can thus not easily be
subtracted to get a figure of merit for the alternative strategy — instead, the
pros and cons must be traded off against each other. In this so-called ‘decision
context’, the person ultimately making the trade-off and taking the decision
is referred to as the ‘decision maker’. He or she makes these decisions and
trade-offs based on experience, by using expertise opinions from inside and
outside the organisation, and using analyses of the alternatives (Keeney,
1992). Fortunately for the decision maker, tools and methods exist to assist
him or her in the decision making process. Such tools are called ‘decision
support tools’ (Department for Communities and Local Government, 2009).
This chapter discusses one such tool and its theoretical foundation in
detail, namely the Value Operations Methodology. The philosophy behind
this methodology, value-focused thinking, is explained in Section 3.1. Section
3.2 discusses the Value Operations Methodology, including the Analytic
Hierarchy Process and the Airside Value Model. Finally, Section 3.3 mentions
the way in which alternatives are created in the current Value Operations
Methodology.
3.1
value-focused thinking
Next to the VOM, examples of decision support tools are the cost benefit
analysis and the multi-criteria analysis. In a cost benefit analysis, all costs
and benefits are converted to monetary units. The decision is then based
on whether the sum of costs and benefits is positive or negative. However,
not all consequences can be easily converted into money. For instance, what
does an increased safety risk of two percent cost in euros? Or 500 additional
highly annoyed people due to aircraft noise? Because such conversions are
impossible or oversimplify the problem, in a multi-criteria analysis multiple
criteria (thus not only costs) are used to evaluate the options. Each criterion is
weighted according to its importance. All options are then analysed to assess
their performance on these criteria, and finally all options are compared and a
decision can be made (Department for Communities and Local Government,
2009; Pruyt, 2009).
In both methods, often the starting point of the analysis is the creation or
collection of alternatives. Keeney (1992) argues that this ‘alternative-focused
thinking’ is not the best approach for decision making. Decision makers
should rather focus on the reasons they are making the decision in the
first place: their values. By first specifying these values and translating
them into objectives, one is able “to select meaningful decisions to ponder,
21
22
the value operations methodology
Alternative-focused thinking
Value-focused thinking
1) Recognise a decision problem
1) Recognise a decision problem
2) Identify alternatives
2) Specify values
3) Specify values
3) Create alternatives
4) Evaluate alternatives
4) Evaluate alternatives
5) Select an alternative
5) Select an alternative
Figure 3.1: The main difference between alternative-focused and value-focused thinking is the early specification of ‘values’ in value-focused thinking (Keeney,
1992, Table 2.1)
to create better alternatives than those already identified, and to evaluate
more carefully the desirability of alternatives” (Keeney, 1992, p4). Figure 3.1
schematically shows the main difference between this ‘value-focused’ and
the more traditional alternative-focused thinking.
Thanks to the early focus on values, the goals and (subjective) preferences of all stakeholders can be included from the start. And while in the
usual alternative-focused approach the emphasis is on objectives based on
‘hard’ data, the value-focused method allows for more ‘soft’ objectives to be
included and have an effect on the overall value. Both advantages makes
the value-focused approach especially useful for problems with multiple
stakeholders, and thus very applicable to this research.
Value
Objective 1
Attribute 1.1
Objective 2
Attribute 1.2
Attribute 2.1
Objective 3
Attribute 3.1
Attribute 2.2
Attribute 3.2
Attribute 2.3
Figure 3.2: Schematic overview of a value model consisting of objectives and attributes
In an effort to give the value-focused approach a quantitative and mathematical foundation, Keeney (1992) has introduced a ‘value model’1 . This
value model is based on utility theory and consists of value, objectives and
1 In essence, a value model is an example of a multiple criteria decision analysis method (Pruyt,
2009, §6.2.2).
3.2 the value operations methodology
attributes, as schematically shown in Figure 3.2. The attributes (x1 , . . . , xn )
measure the fulfillment of objectives (u1 , . . . , un ). In turn, the fulfillment
of objectives determines the total value (u); as such, the objectives function
as value levers. For example, value may depend on the objective ‘increase
airport capacity’; a related attribute is then the number of peak hour landings
and take-offs. Together, these elements form a utility function2 .
If and only if the attributes are additive independent, the utility function
reduces to the additive utility function3 :
u(x1 , . . . , xn ) =
n
X
ki ui (xi )
(3.1)
i=1
If not, the utility function becomes the multiplicative utility function:
ku(x1 , . . . , xn ) + 1 =
n
Y
(kki ui (xi ) + 1)
(3.2)
i=1
In these functions, ki are scaling constants. These express the trade-off
between objectives and are thus very important in determining what objective
is more important than another.
Although the inclusion of soft or qualitative objectives is possible and
sometimes even encouraged in the value-focused approach, these utility
functions show that ultimately, the model works best when objectives and
attributes are quantified. With quantified attributes and explicit utility functions for the objectives, the value of alternatives can be given as a number.
And while this number alone may not mean much, it can serve as a figure
of merit based on which the alternatives can be compared. It is in such
comparisons that the Value Operations Methodology makes some significant
improvements on the value model, as the next section will show.
3.2
the value operations methodology
Building upon the value-focused approach and the value model, Curran et al.
(2010) introduced the Value Operations Methodology. This methodology
introduces the differential principle and the additive principle. Whereas the
additive principle means that the additive utility function (Equation (3.1)) is
used, the differential principle is the most interesting feature of the VOM.
The differential principle poses that instead of calculating an absolute value
u, it makes more sense when evaluating alternatives to calculate a differential
value ∆V. This ‘change in value’ is a function of the ratio of the value of an
alternative and of the reference situation. In this way, a simple decision rule
for comparing alternatives can be given:
∆V > 1 → alternative creates value
∆V = 1 → alternative just as good as reference
∆V < 1 → alternative destroys value
Moreover, the differential principle enables the use of actual data in the value
function, instead of constructing a utility function for each attribute. Thanks
2 For a comprehensive mathematical overview of utility theory related to value trade-offs, see
Keeney and Raiffa (1993).
3 Keeney (1992, Chapter 5) discusses the derivation of these equations and the definition of
various independence conditions in detail.
23
24
the value operations methodology
to the ∆V approach the actual score of the attributes can be used in the value
function, which are then appropriately weighted using scaling constants.
As summarised by Smulders (2010), combining both principles results in
the following value function4 :
∆V(v1 , . . . , vn ) =
n
X
i=1
λi
(vi )1
(xi , . . . , xim )
(vi )0 1
(3.3)
This equation relates the total change in value ∆V to separate value elements
v1 , . . . , vn for each objective i. The objective values are determined by the
ratio between the alternative ((vi )1 ) and the baseline reference situation
((vi )0 ). λi is essentially the same scaling constant as ki before; it indicates the
importance of objective i and is referred to as the objective’s weight factor.
The ratio of (vi )1 and (vi )0 is a function of the attributes xi1 , . . . , xim :
m
X
(xij )1
(vi )1
ωj
(xi1 , . . . , xim ) =
(vi )0
(xij )0
(3.4)
j=1
In this equation, ωj is the attribute’s weight factor. It can be seen that also
in this function for the objective’s value, the additive principle is used for
summing the attribute ratios.
In practice, a typical value function may look like the following set of
equations:
C
E
K1
+ λC 1 + λE 1
K0
C0
E0
R1
F0
= ωR
+ ωF
R0
F1
A
= ωA 1
A0
N
= ωN 0
N1
∆V = λK
K1
K0
C1
C0
E1
E0
(3.5)
(3.6)
(3.7)
(3.8)
In this example, there are three objectives and four attributes:
1. The first objective, K, is to minimise costs. This objective’s value is
determined by two attributes, as shown in Equation (3.6): additional
revenue R and extra fuel costs F. Because for this last attribute a lower
value is better (the so-called ‘direction of preference’ is downwards),
the ratio has been inverted to the form x0 /x1 , so the reference situation
is divided by the alternative. When the feasible range equations as
explained in Section 3.2.2 and 4.8.1 are used, these inverted ratios are
no longer necessary;
2. Objective C, increase capacity, is a function of only one attribute (Equation (3.7)): the number of air traffic movements A;
3. Finally, the environmental objective E is, as shown in Equation (3.8), a
function of the attribute nuisance N, whose direction of preference is
downwards.
As can be seen in the equations above, the eventual ∆V depends not only on
the change in attribute values, but also on the objective weight factors λ and
the attribute weight factors ω. In the VOM, the Analytic Hierarchy Process
is used to determine the objective weight factors.
4 Alternatively, the value function in the VOM can be written using partial differentials and value
gradients (Curran et al., 2012, p.6). This approach is not used here.
3.2 the value operations methodology
3.2.1
The Analytic Hierarchy Process
Originally the Analytic Hierarchy Process was developed by Saaty (1977) as
a decision support tool on its own. Nonetheless, Curran et al. (2010) chose to
merge the method with the value-focused decision making philosophy of
Keeney (1992) into the Value Operations Methodology. The use of the AHP
in the VOM can be summarised as follows:
1. Compare the objectives in a pair-wise fashion;
2. Collect the results of these comparisons in a reciprocal comparison
matrix;
3. Use the largest eigenvalue from this matrix and the related (normalised)
eigenvector to determine the weight factors for the objectives;
4. Compute the consistency ratio of the eigenvalue to check the consistency of the comparison matrix.
The remainder of this section will discuss these steps, primarily based on the
work by Saaty (1990) and Curran et al. (2010).
Pair-wise comparison of the objectives
The key element in the AHP is the use of pair-wise comparisons. This means
that the objectives are compared in pairs to determine which one is more
important. The comparison is preferably done by the stakeholders themselves,
to capture their subjective preferences regarding the set of objectives. The
scale of these comparisons is between 1–9, meaning that if the decision maker
finds Objective A much more important than Objective B, the relative weight
wA /wB = 9. If A is only moderately more important than B, the relative
weight may be wA /wB = 4. This rating scale is further discussed in Section
4.3.2.
Comparison matrix
When all n objectives are pair-wise compared to each other, the results are
collected in a reciprocal comparison matrix M:

1
w
 B
w
M= A


wn
wA
wA
wB
···
1
···
..
.
wn
wB
···
wA
wn

wB 
wn 




1
The reciprocality of the comparison matrix is a result of the fact that the
relative weight of Objective A compared to B is the reciprocal of the relative
weight of Objective B compared to A:
wA
1
= wB
wB
w
(3.9)
A
And in the comparison matrix this shows as:
Mi,j =
1
Mj,i
(3.10)
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the value operations methodology
Eigenvalue and eigenvector
The objective weight factors can now be determined by calculating the
eigenvalues of the comparison matrix. Because all entries in the matrix are
positive, the largest eigenvalue Λ is real and positive (Saaty, 1977, p.236).
The normalised5 eigenvector related to this largest eigenvalue, contains the
objective weight factors:
~λ = (λA , λB , . . . , λn )
(3.11)
Consistency ratio
A purely rational decision maker will answer the pair-wise comparisons in
a perfectly consistent manner. This means that when Objective A is 6 times
more important than B, and C is 3 times more important than B, the decision
maker also finds A 2 times more important than C:
wA
w
w
1
= A · B = 6· = 2
wC
wB wC
3
(3.12)
If a decision maker answers the pair-wise comparisons perfectly consistent,
in the comparison matrix each row will be a constant multiple of the first
row. In this case, there is only one non-zero eigenvalue.
However, in practice the pair-wise comparisons will not be perfectly consistent. Fortunately, also in the case of small inconsistencies the procedure
as outlined above can be used to determine the vector with objective weight
factors. The consistency ratio CR indicates the degree of inconsistency of
the comparison matrix: the higher CR, the lower the consistency. The consistency ratio can be calculated using the consistency index CI and the random
consistency index RI:
CI
RI
Λ−n
CI =
n−1
CR =
(3.13)
(3.14)
The random consistency index is obtained from Table 3.1.
n
1
2
3
4
5
6
7
8
9
10
RI
0
0
0.58
0.90
1.12
1.24
1.32
1.41
1.45
1.49
Table 3.1: Random consistency index for reciprocal comparison matrices of size n × n
(Curran et al., 2010; Saaty, 1987)
If the consistency ratio is lower than 0.10 (or 10 percent), the inconsistencies
are not too severe, and the resulting vector with weight factors may be used.
If, however, CR > 0.10, the comparison matrix should be inspected, and
inconsistent pair-wise comparisons should be revisited with the decision
maker.
3.2.2
The Airside Value Model
Originally, the VOM was applied to aircraft design according to the valuedriven design philosophy (Collopy and Hollingsworth, 2009; Curran, 2010).
5 See Section 4.3.4 for more details on the normalisation of the eigenvector.
3.3 creation of alternatives
In the past years, two students have graduated on the extension of the
VOM to airport operations. Smulders (2010) extensively documented the
development of an Airside Value Model, which applied the VOM to airside
operations.
Repko (2011) applied this Airside Value Model to an optimisation of
outbound traffic allocation at Amsterdam Airport Schiphol. He also further
improved on the VOM and AVM, mainly by including the concept of a
‘feasible range’ for attributes. This concept recognises the fact that attributes
cannot change infinitely, but are bounded by an upper (xmax ) and lower
(xmin ) limit. The attribute values within the feasible range can be calculated
using the following two equations, for attributes with an upwards and a
downwards direction of preference respectively (Repko, 2011, Figure 11):
(xij )FR
0,1 =
(xij )FR
0,1
(xij )0,1 − xmin
xmax − xmin
(xij )0,1 − xmin
= 1−
xmax − xmin
(3.15)
(3.16)
This concept also improves the VOM’s handling of attributes which have
a downwards direction of preference (for example, Attribute F in Equation
(3.6)). Instead of inverting the ratio for such attributes to x0 /x1 , one can
simply use Equation (3.16) to convert the attribute values to values within
the feasible range, and then the normal ratio x1 /x0 can be used.
An important difference between Smulders (2010) and Repko (2011) and
this research is that the former two focus on processes, while this research’s
topic is a strategy; and while a strategy consists of many processes, it is itself
not a process. Therefore, a lot of process-oriented theory and methods as
used by Smulders (2010) and Repko (2011) are not used in this research.
Excluding the process-oriented material, from both theses a general stepby-step method can be distilled for creating a value function using the VOM:
1. Identify and select the relevant stakeholders;
2. Formulate the set of objectives;
3. Determine the objective weight factors;
4. Formulate the set of attributes;
5. Determine the attribute weight factors;
6. Combine all elements into a value model.
While this general method is clear, the details of each step raise questions
and lack a detailed description and sound theoretical support. Therefore,
this research tries to improve the methodology by investigating each step in
detail. Chapter 4 discusses the issues with the steps and proposes a number
of improvements.
3.3
creation of alternatives
As shown in Figure 3.1, in value-focused thinking “the articulated values are
explicitly used to identify decision opportunities and to create alternatives”
(Keeney, 1992, p.ix). However, the Value Operations Methodology currently
lacks this link between the value model and the creation of alternatives.
Also the Airside Value Model does not provide for such a link: Repko (2011,
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the value operations methodology
Chapter 5) uses techniques from Lean Six Sigma to find alternatives, which
are completely disconnected from the earlier defined value model. This
research will propose such a connection between the value model and the
creation of alternatives, supplementing the VOM with ideas from General
Morphological Analysis. Section 4.7 will discuss this topic in detail.
4
I M P R O V E M E N T S O N T H E VA L U E O P E R AT I O N S
METHODOLOGY
The previous chapter introduced the Value Operations Methodology and
Airside Value Model, based on the philosophy of value-focused thinking.
During the application of the VOM in this research project, it was discovered
that while the general framework is described well by Curran et al. (2010),
Smulders (2010) and Repko (2011), the different steps in the method lack a
detailed description and sound theoretical support. Therefore, this chapter
analyses each step in the methodology in detail, tries to improve on the
description and theoretical foundation, and proposes several changes to the
overall methodology. This is done in the sequence of steps as used in the
VOM framework:
1. Identify and select the relevant stakeholders: Section 4.1;
2. Formulate the set of objectives: Section 4.2;
3. Determine the objective weight factors: Section 4.3;
4. Formulate the set of attributes: Section 4.4;
5. Determine the attribute weight factors: Section 4.5;
6. Combine all elements into a value model: Section 4.6.
Next, Section 4.7 discusses the creation of alternatives, and Section 4.8 treats
the calculation of these alternatives’ value. Finally, Section 4.9 discusses the
conclusions of this chapter, including the most important additions and
proposals to the VOM.
4.1
identification and selection of relevant stakeholders
The first step in creating the value model is a stakeholder analysis. This
analysis has a large influence on the ultimate value function because of two
reasons:
• Only the objectives of selected stakeholders are incorporated in the
value model, and thus only their objectives influence the value of
alternatives;
• Only the selected stakeholders’ preferences are weighted in order to
determine the objective weight factors in the value model.
It is therefore very important to use a well-defined and substantiated approach to determine who are the relevant stakeholders in the decision problem at hand. Numerous different stakeholder analysis methods exist to assist
in these steps. Unfortunately, such methods are not yet clearly integrated
into the Value Operations Methodology. This is demonstrated by the fact
that Smulders (2010, §3.2) and Repko (2011, §2.2) both use different methods
for their stakeholder analyses. This section discusses those methods, along
with several others, and proposes a combination of the best elements from
those methods as the stakeholder analysis method for the VOM.
Freeman (1984, p.46), as cited by Mitchell et al. (1997, p.856), defines a
stakeholder as such:
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improvements on the value operations methodology
“A stakeholder in an organization is (by definition) any group or
individual who can affect or is affected by the achievement of the
organization’s objectives.”
This definition refers to the organisation as the centre of the analysis, which
implies that the organisation itself is not considered as a stakeholder. Mitchell
et al. (1997, p.859) call this the ‘firm centred’ view, in which the organisation
wants to profit in some way (e.g. by influencing public policy or seizing
opportunities) from the knowledge it gains from a stakeholder analysis.
In contrast, in the ‘system centred’ view the analysis focuses more on the
complete ecosystem of all the stakeholders and the organisation’s place
therein. As in the VOM the goal of the stakeholder analysis and the value
model is to help solve a decision problem, the firm centred view is most
applicable: the organisation central to the decision problem wants to solve
its decision problem and thus profit in some way. Therefore, the stakeholder
analysis method for the VOM will use the firm centred view. To be clear, also
the goals and preferences of the organisation itself are analysed in this view.
Of course, when the aforementioned definition is taken literally with the
organisation central to the decision problem as the focal point, this results
in a scope of the stakeholder analysis that is too broad. The decision maker
is not interested in all stakeholders of the complete organisation, but only
in those stakeholders that are relevant for the decision problem. Therefore,
although the complete organisation is the centre of the stakeholder analysis,
only the stakeholders relevant for the decision context are taken into account
in the resulting value model. Essentially, this calls for a two-step approach
for the stakeholder analysis:
1. Identify all stakeholders for the organisation;
2. Select those stakeholders relevant for the decision context.
The remainder of this section will study the different stakeholder analysis
methods to determine how they perform on these two steps.
4.1.1
Analysis of Complex Neighbourhoods
Repko (2011, §2.2) applies the Analysis of Complex Neighbourhoods as
introduced by Enserink et al. (2003). This methods uses a total of eleven steps
to identify and select the relevant stakeholders, as summarised by Repko
(2011, p.13):
1. “Start with the formulation of the problem;
2. Provide the overview of the involved stakeholders:
a) Which stakeholders are actively involved?
b) Which stakeholders have powers that have a role in creating or
solving the problem?
c) Which stakeholders possess resources that can be useful for the
problem?
d) Which stakeholders can be assumed to require involvement at
some point?
e) Which stakeholders are not actively involved but are part of the
problem?
4.1 identification and selection of relevant stakeholders
3. Provide a chart that presents the formal relations between the stakeholders;
4. Determine the interests, objectives and problem perception of each
stakeholder;
5. Provide an overview that presents the dependencies between each
stakeholder;
6. Determine the consequences of the findings to the formulation of the
problem.”
Although this results in a rather long and highly detailed analysis process,
the general flow of these steps seems to be useful, especially in determining
a longlist of stakeholders. However, this method has four more serious
drawbacks.
primary usage in problem analysis Enserink et al. (2003, p.107)
describe the Analysis of Complex Neighbourhoods as a method primarily
used for problem analysis. Moreover, the eventual goal of the analysis is to
come up with strategies for dealing with the different stakeholders.
This is fundamentally different from the primary goal of the stakeholder
analysis in the Value Operations Methodology. In the VOM this primary
goal is to identify the relevant stakeholders and their values and objectives,
so they can be included in the value model, and alternatives can be evaluated
on basis of these objectives.
While Enserink et al. (2003, p.107) mention that the Analysis of Complex
Neighbourhoods may also be applied to testing the feasibility of alternatives,
they do not explain how. Therefore, in this respect this method is not entirely
suitable for the VOM.
no selection method As mentioned earlier, not all identified stakeholders will be relevant in the context of runway system maintenance, and
thus not all stakeholders will be included in the value model. Unfortunately,
the Analysis of Complex Neighbourhoods does not provide an explicit
method for selecting which stakeholders are relevant. Thus its use in the
VOM is diminished.
focus on public administration As a method developed in the context of policy analysis and management, it is unsurprising that the Analysis
of Complex Neighbourhoods is primarily focused on issues in the public
policy field. This reduces its use in the current project, as the focus here is on
technological solutions for a private company, albeit in a public environment.
division in cooperative or non-cooperative The Analysis of
Complex Neighbourhoods ultimately classifies stakeholders as either ‘cooperative’ or ‘non-cooperative’. However, this division is too simplistic as
it neglects different positions on different issues. For example, an airline
may be cooperative to Amsterdam Airport Schiphol in lobbying to the government, but non-cooperative in shutting down runways for maintenance.
Furthermore, it ignores the possibility of the middle road, where a stakeholder may not have made up its mind or does not have a clear opinion.
Enserink et al. (2003, p.119) acknowledge these shortcomings, and add that
this division may also work polarising and as a self-fulfilling prophecy. They
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improvements on the value operations methodology
do not, however, provide solutions for these flaws. This makes the method
less suited for the VOM.
In conclusion, the Analysis of Complex Neighbourhoods is useful in some
regards, especially in determining a longlist of stakeholders, but lacks in
other important ways. Therefore this method is not used step-by-step in the
stakeholder analysis of this project.
A variant of the Analysis of Complex Neighbourhoods is the ATOM
Methodology, in which the stakeholders are classified as either positive
or negative on three dimensions: power; attitude; and interest (Verbraeck,
2008). This results in eight (23 ) possible types of stakeholders. However, the
ATOM Methodology has some of the same issues as the Analysis of Complex
Neighbourhoods: the division is too strict; there is no clear way to select the
relevant stakeholders; and the method is too focused on providing strategies
for dealing with the stakeholders. Therefore also the ATOM Methodology is
deemed unsuitable for the Value Operations Methodology.
4.1.2
Savage’s stakeholder approach
The stakeholder analysis method used by Smulders (2010, §3.2) was developed by Savage et al. (1991) and is referred to as ‘Savage’s stakeholder
approach’. Although this approach is conceptually not unlike the Analysis
of Complex Neighbourhoods, some important details are different.
Most importantly, Savage’s approach does not distinguish between two
extremes, such as ‘cooperative’ and ‘non-cooperative’. Instead, it ranks stakeholders on basis of the potential for threat, and the potential for cooperation.
Because of the focus on potentials instead of fixed positions, the approach
incorporates the fact that positions of stakeholders can shift, e.g. resulting
from goodwill shown by the organisation.
Savage’s approach uses a list of characteristics to rank these potentials for
threat and cooperation for each identified stakeholder (Savage et al., 1991,
Exhibit 1). Based on this ranking, Savage et al. (1991) classify the stakeholders
as one of four possible types (see also Figure 4.1):
supportive This stakeholder “supports the organization’s goals and actions” (Savage et al., 1991, p.65). This results in a low potential for
threat and a high potential for cooperation. The recommended strategy
is to involve the supportive stakeholder.
mixed blessings This stakeholder is very important to the organisation,
as both its potential for threat and cooperation is high. This means
the stakeholder has a lot of power. The recommended strategy is to
collaborate with the mixed blessing stakeholder.
non-supportive With a high potential for threat and a low potential
for cooperation, this stakeholder is unlikely to help the organisation
reach its goals. The recommended strategy is to defend against the
non-supportive stakeholder.
marginal Having a low potential for threat and cooperation, this stakeholder does not have much power to influence the organisation. The
recommended strategy is to monitor the marginal stakeholder.
Savage et al. (1991, p.61) also mention explicitly that ultimately, the organisation must try:
HIGH
Supportive
Mixed blessings
LOW
Potential for cooperation
4.1 identification and selection of relevant stakeholders
Marginal
Non-supportive
LOW
HIGH
Potential for threat
Figure 4.1: The four types of stakeholders identified by Savage’s approach (Savage
et al., 1991, Exhibit 2). It is proposed that only marginal stakeholders may
be ignored in the value model.
“(. . . ) to change relationships with stakeholders from less favorable categories (e.g., nonsupportive) to more favorable ones (e.g.,
mixed blessing).”
This fits with the goals of the Value Operations Methodology, as stakeholders’
values and objectives are taken into account in the value model. Another
advantage of Savage’s approach compared to the Analysis of Complex Neighbourhoods is the focus of the former on business and managers, instead of
on public policy. As explained before, this better matches the focus of this
project.
However, Savage’s approach is not perfect for inclusion in the VOM. Just
like the Analysis of Complex Neighbourhoods the eventual goal of the
approach is to come up with strategies for dealing with the different stakeholders. In the VOM, however, the goal is to include relevant stakeholders’
objectives in the value function.
Furthermore, just like the Analysis of Complex Neighbourhoods, Savage’s
approach lacks a clear decision rule for determining which identified stakeholder must be considered relevant. Smulders (2010) implicitly only includes
the stakeholders classified as mixed blessings and non-supportive. However,
this does not match with the aforementioned strategies for the different
types of stakeholders as formulated by Savage et al. (1991), which can be
considered quite ‘active’ (using the verbs ‘involve’, ‘collaborate’ and ‘defend’)
not only towards mixed blessings and non-supportive stakeholders, but also
towards supportive ones. Only the strategy towards marginal stakeholders is
more passive (using the verb ‘monitor’) and explicitly states that resources
should not be wasted in involving these stakeholders (Savage et al., 1991,
p.66). Thus, this leads to the decision rule that only the marginal stakeholders
may be considered as irrelevant.
In conclusion, the following elements from Savage’s approach are considered useful for the stakeholder analysis in the VOM: the ranking of
stakeholders according to the potential for threat and the potential for cooperation; the classification in one of the four types shown in Figure 4.1; and
the decision rule stating that only marginal stakeholders can be considered
irrelevant.
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improvements on the value operations methodology
4.1.3
Mitchell’s approach
To check the conclusions made on Savage’s approach in the previous paragraph, the work of Mitchell et al. (1997) is considered. Mitchell et al. (1997)
have made an effort to integrate years of research on stakeholder theory into
a single encompassing model, hereby referred to as ‘Mitchell’s approach’.
Very importantly, Mitchell’s approach clearly distinguishes between the
identification of stakeholders and the selection of relevant stakeholders
(‘stakeholder salience’):
“(. . . ) the question of stakeholder salience—the degree to which
managers give priority to competing stakeholder claims—goes
beyond the question of stakeholder identification (. . . )” (Mitchell
et al., 1997, p.854)
This distinction matches with the two-step approach of identification and
selection as introduced in the beginning of this section.
Power
1. Dormant
Legitimacy
4. Dominant
5. Dangerous
7. Definitive
2. Discretionary
6. Dependent
3. Demanding
Urgency
Figure 4.2: Seven types of stakeholders classified by the three attributes power, legitimacy and urgency (Mitchell et al., 1997, Figure 2)
As in the ATOM Methodology and Savage’s approach, also in Mitchell’s approach stakeholders are ranked on the basis of several attributes. In Mitchell’s
approach these are power, legitimacy and urgency. Because of the subjectivism that is part of the firm centred view, this ranking cannot be measured
objectively, but is always “a matter of multiple perceptions and is a constructed reality rather than an ‘objective’ one” (Mitchell et al., 1997, p.868).
The combinations of these attributes result in a total of seven possible stakeholder classifications (see Figure 4.2). Stakeholder types 1, 2 and 3 only
possess one attribute and are labeled ‘latent’ stakeholder as a group. According to Mitchell et al. (1997, p.874), stakeholder salience will be low for latent
stakeholders, and not too much resources should be spent on them. This
makes the latent stakeholders comparable to the marginal stakeholders in
Savage’s approach. Also the determined strategy to handle these stakeholders
4.1 identification and selection of relevant stakeholders
is comparable: do not spend much effort and resources on the marginal/latent stakeholders, but keep monitoring them. Or in the words of Mitchell
et al. (1997, p.875): remain cognisant.
The other four stakeholder types in Mitchell’s approach are not easily
mapped to a stakeholder category from Savage’s approach. Regardless,
it is clear in both approaches that the remaining stakeholder types 4–7
are important to the organisation and must thus be selected as a relevant
stakeholder.
In conclusion, the proposed stakeholder approach does not use the seven
types of stakeholders of Mitchell’s approach. Instead it uses the four types
from Savage’s approach, as it includes a list of characteristics to determine the
stakeholder type. Mitchell’s approach is used, though, to determine which
stakeholder types may be ignored in the value model: the latent stakeholders,
which correspond to the marginal stakeholders in Savage’s approach.
4.1.4
Stakeholder approach for the Airside Value Model
Using the conclusions on the different stakeholder analysis methods as discussed above, now a general stakeholder approach for the Value Operations
Methodology can be defined. Again, this method is based on a two-step
approach: first, the identification of all stakeholders; then the selection of the
stakeholders relevant for inclusion in the value model. Combined with the
best steps from the other methods, this results in the following approach:
1. Identify all stakeholders:
a) Create a longlist of stakeholders, e.g. by using the subquestions
from Step 2 from the Analysis of Complex Neighbourhoods;
b) Select the stakeholders that fall within the scope of the value
model;
c) Map the relationships between the stakeholders (Step 3 in the
Analysis of Complex Neighbourhoods);
2. Select the relevant stakeholders:
a) Rank the identified stakeholders on their potential for threat and
cooperation, using Savage’s approach (Savage et al., 1991, Exhibit
1);
b) Based on their rankings, classify the stakeholders in one of the
four categories from Savage’s approach;
c) Select the stakeholders that are classified as supportive, mixed
blessings and non-supportive as the relevant stakeholders.
Step 1b is important to filter out those identified stakeholders that fall
outside the scope of the issue at hand. This is useful because then generic
longlists with (aviation) stakeholders can be used in Step 1a. Step 1c can be
used to identify possibly missing stakeholders and gives insight into how
stakeholders can exert their influence.
Step 2 basically uses Savage’s approach to rank, classify and select the stakeholders. Unfortunately, how to perform the classification of the stakeholders
(Step 2b) is not self-evident. Appendix E, performing this classification for
the runway system maintenance strategy value model, uses a numerical
method. This method is however not fully validated, and a full validation
is considered outside the scope of this research project. It is recommended
that future research focuses on this Step 2b. For this project’s purposes, the
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improvements on the value operations methodology
numerical outcome of Step 2b is triangulated with experts’ assessments to
determine the definitive stakeholder classification.
4.2
formulation of the set of objectives
The fundamental definition of ‘value’ follows from the set of objectives in the
value model. These objectives define what is important — or ‘valuable’ — for
the relevant stakeholders. Therefore, the selection and formulation of this set
of objectives have a large impact on the resulting value function. Obviously,
the selected objectives become the parameters in the value function. But
also the formulation of the objectives is important, as the wording of the
objectives will influence how the stakeholders will rank their importance in
the next step.
Unfortunately, both Smulders (2010) and Repko (2011) do not provide an
explicit method or approach to determine the set of objectives. Based on
their implicit methodologies, the relevant literature on stakeholder theory as
used in the previous section and the work of Keeney (1992, Chapter 3), an
approach for the formulation of the set of objectives is proposed:
1. Create a longlist of objectives for each relevant stakeholder;
2. Create an objective tree consisting of the relevant objectives from the
longlist;
3. Select from this objective tree the level of objectives that has the right
level of abstraction for the research scope;
4. Finalise the formulation of the objectives using the list with desired
properties of the set of objectives (Keeney, 1992, Table 3.2).
In the following sections each step is explained in further detail.
4.2.1
Create a longlist of objectives
The first step is to focus on each stakeholder separately and to compile
a longlist with objectives for each stakeholder (Enserink et al., 2003, §5.5).
This can be done using interviews or desk research, or a combination of
both. Keeney (1992, Table 3.1) offers a list of techniques that may aid the
interviewer, such as the use of a wish list, a set of alternatives, or focussing
on problems and shortcomings. Interesting documents for desk research may
include strategy or position papers, annual reports, official legislation and
policy documents.
When the longlist is finished, objectives that are clearly unrelated to the
research scope can be omitted — though one must be careful not to omit
strategic objectives, as these serve as the higher objectives in the objective
tree that will be created in the next step.
4.2.2
Create an objective tree
A first look on any objective longlist will reveal that the objectives are of a
varying abstraction level or strategic importance. For instance, the objective
‘Strengthening the national economy’ is of a higher level of abstraction
than the objective ‘Reduce maintenance costs’. It is therefore important to
distinguish between the so-called fundamental objectives and the means
objectives. As clearly explained by Keeney (1992, p.34):
4.2 formulation of the set of objectives
“A fundamental objective characterizes an essential reason for
interest in the decision situation. A means objective is of interest
(. . . ) because of its implication for the degree to which another
(more fundamental) objective can be achieved.”
An objective tree can aid in structuring the objectives and distinguishing between fundamental and means objectives (Keeney, 1992, §3.3–3.4).
Moreover, the objective tree helps in finding missing objectives.
The objective tree is simply made by linking the objectives to each other in
a hierarchy, in which the most fundamental objectives are at the top; these
are the so-called strategic objectives. The other objectives then follow in steps,
as means objectives to the higher level and fundamental objectives to the
lower level.
Keeney (1992, §3.3) and Enserink et al. (2003, Chapter 4) differentiate
between a ‘fundamental objectives hierarchy’ and ‘means-ends objectives
network’. The key difference between those two structures is the direction of
the hierarchy. In the approach proposed here, this difference is neglected, as
it is argued that the core goal of this step is the structuring of the objectives
found in the longlist of objectives. Whether this structure is created top-down
or bottom-up, makes little difference.
4.2.3
Select the right level of abstraction
As not all objectives are suitable for inclusion in the value model, the real
challenge is the selection of the set of (fundamental) objectives from the
objective tree. It requires a fine balancing act to select the right level of
abstraction for the objectives. As mentioned by Keeney (1992, p.67):
“Strategic objectives are too allencompassing for most decision
situations.”
But a very low-level set of (means) objectives is also not suitable, as these
are too narrowly focused to have a real impact on the ultimate goals. Based
on Keeney (1992, Table 3.2), Smulders (2010, Table 2.3) offers a useful list of
desired properties of the set of fundamental objectives:
essential “All objectives indicate the consequences in terms of the fundamental reasons for interest in the decision situation.” This ensures that
the set of objectives is fundamental enough;
controllable “The consequences (of the decision) are only influenced by
the choice of alternatives, as opposed to some other mechanism that is
not included.” This ensures that the set of objectives is not too strategic;
complete “All fundamental aspects of the consequences of the alternatives
have been included;
non-redundant The set of fundamental objectives should not contain
similar items, in order to avoid double-counting of the consequences of
the alternatives;
measurable The objectives are defined precisely and specify the degrees
to which objectives may be achieved;
operational The requirements the objectives pose on information gathering for the analysis are reasonable. This includes considering time and
effort available;
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decomposable This allows objectives to be treated separately in the analysis;
concise The set of objectives should not contain more items than necessary
to the analysis;
understandable All fundamental objectives should be easily interpreted
by those involved in the decision making process. This facilitates communication and enhances insights.”
The objective tree can help with selecting a set of objectives adhering to
this list of desired properties, as it shows comparable objectives on the same
level. This relates to the first four properties (essential, controllable, complete
and non-redundant). However, the other properties are not automatically
fulfilled by using the objective tree. Therefore the set of objectives selected
may need to be reformulated to also adhere to these properties (measurable,
operational, decomposable, concise and understandable).
4.2.4
Finalise the formulation
The selected set of fundamental objectives for the value model is finalised
by fine-tuning the formulation of the objectives. This is an important step:
the objectives will be compared with each other in the next step, and to be
comparable the formulation of the objectives should be similar.
Checking the set of objectives with the list of desired properties is a first
step in fine-tuning the formulation. Next, attention is given to Keeney (1992,
p.34):
“Objectives are characterized by three features: decision context,
object, direction of preference.”
Thus, every objective of the set is checked for these three features. For
instance, the objective ‘Reduce maintenance related costs’ is characterised
by its decision context ‘maintenance’ (of the runway system), the object is
‘costs’, and the direction of preference is downwards (‘reduce’).
As was discovered during this research, the wording of the direction
of preference is quite important. Two modes can be distinguished: using
‘increase/reduce’, or ‘maximise/minimise’. While perhaps not fundamentally
changing the objective, the difference between these two modes may affect
the decision maker who must compare the objectives in pairs in the next
step. As was found out, using ‘maximise/minimise’ can give the decision
maker the impression of a quantifiable optimisation. For wicked problems
(see Section 1.2), this is misleading for three reasons:
1. The actual effect of alternatives on the attributes that measure the
objectives may be uncertain;
2. The attributes, and thus the objectives, may be affected by other factors
that are outside the research scope and thus outside the scope of the
decision context;
3. The actual solution space for alternatives may be very limited.
Therefore, it is unlikely that the methodology will find a maximum or
minimum for any objective, let alone for the complete set of objectives. It is
thus proposed that the objectives will be formulated using ‘increase/reduce’,
and not ‘maximise/minimise’. Furthermore, it is recommended that this
4.3 determination of objective weight factors
issue, and the topic of the formulation of objectives and their effect on the
decision maker, is researched further.
In conclusion, the steps above will lead to a set of objectives that encompasses the decision problem and that will form the main parameters in the
value function. The next step is now to determine which objectives are the
most important, and to compute the weight factors.
4.3
determination of objective weight factors
Not every objective determined in the previous step is shared by all stakeholders, and not every stakeholder finds each objective equally important.
To incorporate the stakeholders’ different preferences with regard to the
objectives, each objective is given a weight factor in the value function. The
higher the weight factor, the more important this objective is to the relevant
stakeholders, and the more influential this objective is in determining the
value of alternatives. This section discusses in detail the methodology used
to determine the objective weight factors.
As explained in Section 3.2.1, the Value Operations Methodology uses
parts of the Analytic Hierarchy Process to determine the weight factors
for the objectives. Originally the AHP, developed by Saaty (1977), has a
much broader application and is essentially a decision making tool on its
own. Interestingly, Curran et al. (2010) chose to merge this decision making
tool with the value-focused decision making philosophy of Keeney (1992).
The resulting VOM combines many sound elements from these theories;
but at the ‘crossings’ of the different methodologies this combination also
creates uncertainty about which method to use or how exactly to apply a
method. This section focuses on the use of the AHP in the Value Operations
Methodology to determine the weight factors for the objectives in the value
model. It tries to clarify the procedure for this determination, thus removing
some of the aforementioned uncertainty and thereby making the VOM a
more robust methodology.
For reference, the use of the Analytic Hierarchy Process in the VOM can
be summarised as follows (see also Section 3.2.1):
1. Compare the objectives in a pair-wise fashion;
2. Collect the results of these comparisons in a reciprocal comparison
matrix;
3. Use the largest eigenvalue from this matrix and the related (normalised)
eigenvector to determine the weight factors for the objectives;
4. Compute the consistency ratio of the eigenvalue to check the consistency of the comparison matrix.
As mentioned, this procedure per se is a robust method for determining the
weight factors and will as such be used in Chapter 5 to build a value model
for the runway system maintenance strategy. Instead, this section focuses on
the current shortcomings in some details of this procedure:
• The necessity of pair-wise comparisons (Section 4.3.1);
• Different rating scales for the comparisons (Section 4.3.2);
• How to combine assessments from multiple stakeholders (Section 4.3.3);
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improvements on the value operations methodology
• Normalising the eigenvector (Section 4.3.4);
Finally Section 4.3.5 discusses some criticism on the Analytic Hierarchy
Process.
4.3.1
Pair-wise comparisons
Even before he coined the term ‘Analytic Hierarchy Process’, Saaty (1977)
introduced the concept of pair-wise comparisons. He also explained that
without pair-wise comparisons, using the eigenvector to determine the objectives’ weights is pointless:
“(. . . ) the imposition of perfect consistency by the experimenter
produces an uninteresting result of exact scalability (. . . )" (Saaty,
1977, p.278)
If, as Repko (2011, Appendix I) did, perfect consistency is imposed by not
using pair-wise comparisons, Saaty (1977, p.278) argues that one can just
take normal, single-factor comparisons instead.
On a more fundamental level, it is interesting to ask oneself whether the
AHP in general and the pair-wise comparison of objectives in particular
are compatible with the philosophy behind value-focused thinking. After
all, the AHP and its core element of pair-wise comparisons are based on a
binary choice between two ‘alternatives’ (in this case, objectives). This sounds
eerily like alternative-focused thinking, to which Keeney (1992) diametrically
positions his value-focused thinking (see Section 3.1). However, also Keeney
(1992, p.133, 145) acknowledges that to determine the weight factors for the
objectives a trade-off is necessary. Furthermore, the binary choices are not
used to select only one of the objectives; rather it is used to create a ranking
of all objectives. It can thus be concluded that using pair-wise comparisons
according to the AHP is compatible with the philosophy behind valuefocused thinking, as long as this use is limited to determining the weight
factors for objectives.
4.3.2
Rating scales
To rate the pair-wise comparisons, Saaty (1977, 1990, 2001) introduced a
‘fundamental scale’ based on psychological stimulus-response theories. This
scale uses the integers 1 through 9 and their reciprocals. It does not include
0. In the pair-wise comparison of Objective A and B, a 1 indicates that both
objectives are equally important. A 9 indicates that Objective A has ‘absolute
importance’ over Objective B (Saaty, 1977, Table 1). Symbolically, this can be
written as wA /wB = 9. By the scale’s definition, this means that the inverse
is wB /wA = 1/9. This is the source of the reciprocals. The intermediate
values between 1 and 9 denote increasing importance of A over B. In short,
the original AHP thus requires the decision maker to rate his preference of
A over B on this fundamental 1–9 scale.
However, in the interviews conducted for this research it quickly became
clear that this requirement considerably complicates the assessments. The
decision maker must choose between nine different possibilities with vague
distinctions. For instance, the difference between ratings 5, 7 and 9 is ‘strong’,
‘very strong’ and ‘extreme importance’ respectively (Saaty, 1987, Table 1). For
many decision makers this difference has little meaning. Moreover, because
of the need to put numerical values on their preferences they get the feeling
4.3 determination of objective weight factors
41
that their answers must be precise and substantiated, while the method is
meant to capture their subjective preferences as a decision maker. Contrary to
Forman and Gass (2001, p.483) who have not encountered this problem with
decision makers, Lootsma (1989, p.110) recognises both issues and agrees
that a numerical pair-wise comparison by decision makers is very difficult.
Instead, he proposes an interview in which the decision maker is asked to
verbally express his preference. Also Holder (1990, p.1074) criticises the use
of the 1–9 scale, and Dyer (1990, p.249) states that:
“(. . . ) the elicitation questions posed in classical utility theory are
well defined, and depend on a choice among alternatives by the
subject rather than on a subjective response on a ratio scale.”
In other words, he argues that instead of asking decision makers to numerically grade their preferences, the decision maker should rather be given the
choice between two alternatives. Therefore, it is proposed that during the
interviews, for each pair-wise comparison of Objective A and B, the decision
maker must not choose a rating from the fundamental scale but only between
three choices: Objective A is either more, less or equally important relative
to Objective B. The difference between this rating scale and the fundamental
scale is shown in Figure 4.3. This figure also shows the numerical translation
of the scale using factormax, as explained below.
increasing importance of objective B
Fundamental scale
wA
=
wB
9
8
7
6
5
4
3
2
1
1/2
1/3
1/4
1/5
1/6
1/7
1/8
1/9
More/less/equal rating scale
wA
=
wB
more
equal
less
MLE rating scale with factormax 8
wA
=
wB
8
4
1
1/4
1/8
Figure 4.3: Comparison of the fundamental rating scale and the MLE rating scale, including its numerical translation
using a factormax of 8
This ‘more/less/equal’ (MLE) rating scale does have a serious disadvantage. Using this scale will result in a ranking of the objectives; this scale
cannot, though, express how much more important one objectives is over
another. Thus, the rating is according to an ordinal, not a cardinal scale. However, in the eventual value function not only the ranking of the objectives
but also the difference in importance (cardinality) between two objectives
is required. While this is also argued by Keeney (1992, §5.4), he also warns
for asking decision makers out of context how much more important one
objective is over another. He proposes instead a very detailed interview in
which objectives are compared to each other at various points (Keeney, 1992,
§5.5).
In conclusion though, this research will use the ‘more/less/equal’ rating
scale as explained above. The greater simplicity and understandability of this
method for the interviewed decision makers outweigh the lack of cardinality.
Moreover, neither Smulders (2010) nor Repko (2011) use the fundamental
42
improvements on the value operations methodology
scale in their interviews with decision makers1 . This means that the VOM
has implicitly ignored the fundamental scale for use in interviews so far.
In essence, this research only makes this choice explicitly and offers the
alternative of the ‘more/less/equal’ rating scale.
The factormax problem
As mentioned, only a ranking of the objectives is not enough; cardinality will
have to be brought back to this ranking to determine the objective weight
factors. The use of the MLE scale does thus not obviate the need to compile
a numerical comparison matrix for each decision maker’s assessment, as
this matrix is the basis of the eigenvalue and eigenvector calculations that
ultimately result in the objective weight factors. This means that the verbal
ratings ‘more’, ‘equally’ and ‘less important’ have to be translated to a
number. Just as in the fundamental scale, ‘equally important’ translates to 1.
‘More important’ is coded as f and ‘less important’ is the reciprocal 1f . In the
fundamental scale, f = 9. However, f could also be 2, 5, or any other number.
Determining what number f should be is thus not obvious and referred to as
‘the factormax2 problem’. This section tries to solve this problem.
According to Saaty (2001, p.397), the maximum in the rating scale should
be in the same order of magnitude as the number of items in the pair-wise
comparisons. For this research3 , this would mean f = 6. On the other hand,
Saaty (1977, p.245) argues for differences of one unit in the rating scale,
resulting in f = 2 for the MLE scale. Finally, both Lootsma (1989, p.112) and
Holder (1990, p.1074) argue that the rating scale should be expressed as a
power series:
. . . , a−3 , a−2 , a−1 , a0 , a1 , a2 , a3 , . . .
(4.1)
Here, a0 = 1 expresses the ‘equally important’ answer; the negative exponentials express the reciprocals.
In the interviews conducted for this research, it was discovered that sometimes, just the three answers in the MLE scale are not enough: at times, an
interviewee stated his preference in between ‘equally’ and ‘more important’.
To incorporate these preferences, factormax cannot be 2, as then there is no
possibility to convert the in-between answer to an integer.
Ultimately, the choice is made to assign f = 8 because of three reasons:
1. A factormax of 8 keeps close to the original fundamental scale, where
f = 9;
2. This factormax is in the same order of magnitude as the number of
items in the pair-wise comparisons (the number of objectives), which is
6 in this research;
3. A factormax of 8 fits in the power series scale, with a base of 2 and the
possibility of in-between answers: 22 = 4 for the in-between answer,
and 23 = 8 for factormax.
1 Repko (2011, Appendix I) does use the fundamental scale to translate interviews to the comparison matrix.
2 In the MATLAB code used for this research, f is called factormax, hence the title of the problem.
3 Section 5.2 discusses the selection of the set of objectives for this research. The procedure results
in a set of six objectives.
4.3 determination of objective weight factors
In summary and as shown in Figure 4.3, this results in the following rating
scale (with I(A) indicating the importance of Objective A):
I(A) I(B) →
I(A) > I(B) →
I(A) = I(B) →
I(A) < I(B) →
I(A) I(B) →
wA
wB
wA
wB
wA
wB
wA
wB
wA
wB
=f=8
=
f
=4
2
=1
1
4
1
=
8
=
As mentioned in Section 3.2.1, in the original AHP using the fundamental
scale, a consistency ratio of at most 10 percent is allowed. However, Saaty
(1977, p.248) shows that different scales and different values for f result in
different average consistency ratios. Moreover, also the random consistency
values may need to be adapted as this table is based on the fundamental
scale (Saaty, 1987, p.171). It is uncertain what the exact effect is of using a
different rating scale or power series on these consistency checks. Further
research into this topic is therefore recommended. Ergu et al. (2011, p.246)
list some recent studies into the consistency ratio of the Analytic Hierarchy
Process. This may be a good starting point for further research.
4.3.3
Combining assessments from multiple stakeholders
The set of objective weight factors for an organisation becomes more representative of the stakeholder if more people within the organisation are
interviewed. Additionally, it may be insightful to determine the average
stakeholder’s preferences (Keeney and Raiffa, 1993, §1.5.6). In both cases,
multiple assessments from multiple interviews must be combined into one
set of objective weight factors. Both Curran et al. (2010) and Smulders (2010,
p.53) do not combine the assessments of multiple stakeholders to determine
objective weight factors. Repko (2011, Appendix I) simply sums the scores
of the different stakeholders before putting them (in a perfectly consistent
manner) in the overall comparison matrix, unfortunately without an explanation of his method. It must therefore be concluded that the VOM lacks
a clear method for the combination of multiple stakeholders’ assessments.
This section discusses four of such methods:
1. Average of the comparison matrices;
2. Manual score assignment;
3. Multiplication of scores;
4. Average of the weight factors.
The first three methods work with the individual comparison matrices before
the eigenvalues are computed. The last method is used after the largest
eigenvalue and corresponding eigenvector have been calculated.
Average of the comparison matrices
The simplest method to combine multiple assessments may be to take the
average score for each cell of the comparison matrix. However, this destroys
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improvements on the value operations methodology
the reciprocality of the comparison matrix. Take for instance the set of rating
scores for the pair-wise comparison of Objective A and B (8, 8, 1/8). The
average score of this set is 5.375. The comparison of B and A should then
be the reciprocal: 1/5.375. However, using the reciprocality of the individual
departments’ comparison matrices, the rating set for B and A is (1/8, 1/8, 8).
This set has an average of 2.75 6= 1/5.375. It can thus be concluded that taking
the average score destroys the reciprocality of the comparison matrix. This is
one of the most important features of the comparison matrix, as it ensures
several important characteristics of the matrix’s eigenvalues and eigenvectors
(Saaty, 1977, §2). The average score method is thus rejected.
Manual score assignment
Another approach is to manually assign the combination scores. For instance,
when the individual assessments of Objective A and B are more, less, and
equally important, one would expect that the overall assessment should be
‘equally important’. So, the set (8, 1/8, 1) (in any order) should be combined
to the overall score 1. Also, the set (8, 8, 8) should translate to the overall
assessment of 8, (1/8, 1/8, 1/8) to 1/8 and (1, 1, 1) to 1.
However, there are a number of combinations that do not have an obvious
overall score. For instance, should the set (1, 8, 8) be translated to 8? But
then there is no difference with the set (8, 8, 8). And should the set (1, 1, 1/8)
translate to 1 or 1/8 or some value in between? Because these combinations do
not have an obvious answer, and because this method becomes increasingly
complex when combining more assessments, also this approach is rejected.
Multiplication of scores
Using the power series introduced by Lootsma (1989, p.112), the individual
comparison matrices can also be multiplied entry-by-entry. In this way, the
reciprocality of the comparison matrices is maintained:
a·b·c =
1
a−1 · b−1 · c−1
(4.2)
Moreover, the method easily scales up and it is easily programmed in software such as Microsoft Excel or MATLAB.
However, the big disadvantage of this method is the enormous growth
in the entries’ values. For instance, when combining three assessments,
the largest value in the comparison matrix increases from 8 to 8 · 8 · 8 =
512, and the smallest values becomes 1/512 — and these extreme values
increase exponentially when more assessments are combined. This results in
a comparison matrix and a resulting set of objective weight factors which are
only focused on the extremes. Therefore also this method is rejected.
Average of the weight factors
The three methods mentioned above all work with the individual comparison
matrices. Another method is to combine the assessments after the eigenvector
containing the objective weight factors has been calculated, by taking the
average of the weight factors for each objective. Thus, when combining
the assessments of interviewees I, II and III, the average weight factor for
Objective A becomes:
λA =
(λA )I + (λA )II + (λA )III
3
(4.3)
4.3 determination of objective weight factors
Advantages of this method are its simplicity and the easy scalability
for more assessments. Moreover, because this method is used after the
eigenvector is computed, reciprocality is no longer an issue.
In the averaging above the arithmetic mean method was used. Alternatively,
Barzilai et al. (1987) suggests the use of the geometric mean:
p
λA = 3 (λA )I · (λA )II · (λA )III
(4.4)
However, there is no consensus about the chosen method, as Ramanathan
and Ganesh (1994) discourages the use of the geometric mean. Therefore, the
arithmetic mean method is chosen, and the sensitivity of the value model for
this choice is tested in Section 8.3.2.
In conclusion, the only non-rejected method to combine assessments is to
take the average of the weight factors after they have been calculated using
the individual comparison matrices. This method is also the most simple
method, avoiding complex operations and a sense of false security. This
research uses the arithmetic mean to calculate this average. In Section 8.3.2
the differences in ∆V with the geometric mean method are analysed.
On a final note: when combining assessments to create the average stakeholder’s preferences, it is necessary to determine the relative importance of
each stakeholder. Are all stakeholders equally important or not? This essentially means that another weight factor should be added to the value function
to account for these differences in importance. This research assumes for
the average stakeholder that these weight factors are all 1, and thus that
all stakeholders are equally important in the average stakeholder. This assumption should be researched further, as discussed in Section 9.4.3. Perhaps
the stakeholder classification as explained in Section 4.1 may be helpful in
determining the relative importance (and subsequent weight factor) of each
stakeholder.
4.3.4
Normalising the eigenvector
After the comparison matrix is filled in, the largest eigenvalue and the
corresponding eigenvector can be computed. Then, the final step in the
Analytic Hierarchy Process is to normalise this eigenvector. The normalised
eigenvector then gives the objective weight factors.
The definition of ‘normalisation’ can cause some confusion though. Usually,
normalising a vector means that the vector is divided by its length (the norm
of the vector), to create a unit vector of length 1:
~
~λnormalised = λ
k~λk
(4.5)
However, in the AHP the goal is not to create a vector of length 1, but to
create a vector of which the sum of the elements is 1 (Saaty, 1977, 2001). This
is not a trivial difference, as in this definition of normalisation the vector is
not divided by its norm, but by the sum of the n vector elements:
~
~λnormalised = Pnλ
i=1 λi
(4.6)
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improvements on the value operations methodology
4.3.5
Criticism on the Analytic Hierarchy Process
After its rise in the 1980s, the Analytic Hierarchy Process attracted some
criticism. This criticism is primarily focused on a phenomenon called rank
reversal, and on the subjectivity inherent to the AHP. These subjects are
discussed in this section. Also some alternatives to the AHP are mentioned.
Rank reversal
In their critique on the Analytic Hierarchy Process, Dyer (1990) and Holder
(1990) mention ‘rank reversal’. This means that in some cases there may
be arbitrary reversals in the ranking of alternatives: at first, alternative A
is better than alternative B; but when alternative C is added, alternative B
ranks higher than alternative A (regardless of the ranking of alternative C).
Dyer (1990) and Holder (1990) see this as a symptom of arbitrary ranking
and an important flaw of the AHP and its hierarchy principle. On the other
hand, Forman and Gass (2001, §8.1) argue that this rank reversal is not a
problem at all.
Whether or not this criticism is valid, it concerns a part of the Analytic Hierarchy Process that is not used in the Value Operations Methodology. In the
VOM, only the AHP’s ranking and weighting of objectives (not alternatives)
is of concern. This part of the AHP is not under discussion, as Dyer (1990,
p.256) mentions:
“The weights on the criteria can be obtained in the traditional
AHP manner.”
Furthermore, as a solution of the problems regarding rank reversal Dyer
(1990, p.257) suggests a synthesis of the Analytic Hierarchy Process with
conventional multi-attribute utility theory. This is exactly what the VOM
aims to do, by using certain parts from the AHP in a multi-attribute value
model based on the work of Keeney (1992).
Subjective vs. objective assessments
Some criticise the importance given to subjective preferences in the AHP and
argue that objective assessments should form the core of value trade-offs
(Bevilacqua and Braglia, 2000; Forman and Gass, 2001). However, in this
author’s opinion, decision making must always be subjective. It is impossible
to completely make a decision on objective terms, as a decision always
appeals to personal or organisational preferences. These preferences are
subjective and may well be different for other persons or organisations. Thus,
the decision making process is inherently subjective. This argument is also
supported by Curran et al. (2012, §2.2).
This does not mean that objective analysis should not play a role in decision making. Naturally, the decision maker should carefully and objectively
analyse or compute the impact of alternative solutions on the defined goals —
although this evaluation process may be partly subjective, for instance when
determining which alternative is ‘more elegant’.
Nonetheless, decision making cannot exist without subjectivity. Most importantly, subjectivity (in the form of personal or organisational preferences)
plays a role in determining which goals are the most important. In the Value
Operations Methodology, the comparison matrices from the Analytic Hierarchy Process are used for determining the objective weight factors. Therefore,
the critique that the AHP is too subjective is fundamentally disconnected
4.4 selection of attributes
from the use of the AHP in the VOM: determining the subjective weight
factors.
Alternatives to the Analytic Hierarchy Process
Not only the Analytic Hierarchy Process can be used to determine the
objective weight factors; there are also other methods. As mentioned before,
Repko (2011, Appendix I) essentially uses single-factor comparisons by not
using pair-wise comparisons. Also in the literature on the multi-criteria
analysis other methods to quantify subjective preferences are mentioned,
such as the outranking method (Department for Communities and Local
Government, 2009, p.27).
Alternatively, in the Repertory Grid Technique (RGT) factor analysis and
hierarchical cluster analysis are used to determine objective weight factors
(Schneider, 2011). Two interesting similarities exist between these methods
and the AHP: factor analysis also uses eigenvalues, but more as an indication
for variance than as a measure for the objective weight factors via its corresponding eigenvector; and, like the fundamental scale discussed in Section
4.3.2, in the RGT rating scales in the order or 5–7 are used (Garson, 2011;
Schneider, 2011).
It is recommended to further research these and other alternatives to the
AHP for the determination of objective weight factors. Certain aspects of
other methods may improve the current approach in the VOM.
In conclusion, the Analytic Hierarchy Process is a useful method to determine the objective weight factors in the value model, especially because
of its focus on pair-wise comparisons that force decision makers to make
their preferences explicit. This section has outlined in detail how the different steps in the AHP must be carried out and has proposed a number
of refinements and additions, such as the use of the MLE rating scale. It is
recommended that the effects of these refinements on the consistency ratio
is researched further, as well as the possible improvements that methods
other than the AHP may offer in capturing stakeholders’ preferences and
determining objective weight factors.
4.4
selection of attributes
As the selection of attributes heavily depends on the decision problem, it is
not possible to offer a generic approach to the formulation of the attributes.
This section will therefore only address some general comments regarding
the selection of attributes. In the end, the goal is to find a set of attributes
that measure the achievement of the objectives as good as possible.
The philosophy of Keeney (2007, p.118) is to assign only one attribute
to each objective. This obviates the need for attribute weight factors and
thus removes the difficulties related to this weighting, as described in the
next section. However, the effect of this philosophy is that the number of
objectives and sub-objectives increases, to almost thirty objectives (and related
attributes) in his example. This means that the decision makers must assess
their preferences for a far larger number of objectives. Essentially, this only
moves the issue of weighting attributes to weighting a larger number of more
detailed objectives. On the other hand, Saaty (2001, p.397) argues that the
number of items (objectives) that is compared using the Analytic Hierarchy
Process must be small. This is an argument for a limited number of objectives,
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improvements on the value operations methodology
and consequently more than one attribute per objective if necessary. Because
of the importance of the AHP in determining objective weight factors, this
argument is followed, and thus more than one attribute per objective is
allowed.
Keeney (1992, §4.2) distinguishes between three types of attributes:
natural attributes are logically and obviously connected with their
objective. For instance, for the objective ‘minimise cost’, the natural
attribute is ‘costs in euro’;
constructed attributes When a natural attribute is not available, an
attribute may be constructed. For instance, for the objective ‘reduce
nuisance to the local community’, an attribute may be constructed that
can have the integer values 0–5, with 0 expressing no nuisance, and 5
expressing the maximum amount of nuisance;
proxy attributes Finally, a proxy attribute measures the achievement
of the objective only indirectly. For instance, when the objective is to
reduce fatalities, one can measure the number of vehicle incidents.
While this proxy attribute does not directly measure the number of
fatalities, it is certainly related (Keeney, 2007, p.119). Proxy attributes
are used when it is difficult or impossible to measure the achievement
of the objective more directly.
Thus, in general natural attributes are preferred to constructed attributes,
and constructed attributes are preferred to proxy attributes (Keeney, 1992,
§4.2).
Analogous to the list of desired properties for objectives as described
in Section 4.2.3, Keeney (2007, p.121) offers a list of desired properties for
attributes:
unambiguous “A clear relationship exists between consequences and descriptions of consequences using the attribute;
comprehensive The attribute levels cover the range of possible consequences for the corresponding objective, and value judgments implicit
in the attribute are reasonable;
direct The attribute levels directly describe the consequences of interest;
operational The information necessary to describe consequences can be
obtained and value tradeoffs can reasonably be made;
understandable Consequences and value tradeoffs made using the attribute can readily be understood and clearly communicated.”
One should put effort into making the set of attributes adhere to these
properties.
4.5
determination of attribute weight factors
Analogous to the weighting of the objectives, also each attribute should be
given a weight factor indicating their importance compared to the other
attributes belonging to the same objective. The sum of the weight factors for
all attributes associated with the same objective should be 1 (see also Section
4.3.4 on the normalisation of eigenvectors). This section discusses methods
to determine these attribute weight factors.
4.5 determination of attribute weight factors
Smulders (2010, §3.6) came up with two different ‘tiers’ to determine
attribute weight factors. In the first tier, decision makers are asked to rate the
importance of each attribute using pair-wise comparisons and uses the AHP
to process the results and determine the weight factors. This is the same
approach as used to determine the objective weight factors.
The second tier, as described by Smulders (2010, §3.6) and applied by
Repko (2011, Appendix I), is based on a change-impact matrix of (airside)
processes: for each process, the impact of doing this process faster, cheaper,
or better on the defined attributes is measured in a qualitative way. These
impacts, regardless whether these are positive or negative, are then summed
and result in a weight factor for each attribute. This second tier approach
essentially accentuates the attributes that are highly impacted by changes in
processes. For instance, consider Attribute A and B. Attribute A is highly
impacted by a change in process X, while the impact on Attribute B is small.
Using the second tier to determine attribute weight factors, this results in a
high weight factor for A, and a low one for B. Also, the value differential in
the eventual value function will be higher for A than for B. When the weight
factor is multiplied with the value differential, this amplifies the difference
in value between A and B. Symbolically:
∆VA
ωA > ωB
A1
B
> 1
A0
B0
A
B
= ωA 1 ∆VB = ωB 1
A0
B0
However, one may ask if this behaviour is desirable. In fact, there are three
issues with this amplifying effect:
1. The fact that an attribute is highly impacted by a change in a process
does not inherently mean that this attribute is also very important
to the fulfillment of the objective (expressed by a high weight factor).
These two characteristics are not necessarily linked;
2. Another problem with the second tier is the dependence on process
changes to determine the change-impact matrix. Not all decision problems can easily be separated into a flow of processes that can be
performed faster, cheaper or better; the second tier does not, however,
offer an alternative to determine the change-impact matrix;
3. More importantly, the process changes that form the basis for the
attribute weight factors in the value function, are in fact alternatives
that should be evaluated using that same value function. In other words,
the process alternatives help to establish the evaluation tool that is then
used to evaluate those alternatives. Instinctively, this seems either to
result in a self-fulfilling prophecy or a catch-22; in any case it mixes
two concepts from the Value Operations Methodology — alternatives
on one hand and the development of the value function on the other —
that should not be mixed.
Because of these issues with the second tier, it is proposed that this method
for determining attribute weight factors is scrapped from the VOM. Instead,
only the (subjective) first tier approach remains that bases attribute weight
factors on the trade-offs of decision makers. This method is therefore also
more in line with the philosophy of Keeney (2007, p.118), who consequently
assigns only one attribute to each objective, and who lets decision makers
49
50
improvements on the value operations methodology
make the value trade-offs between those objectives. Janssen (2011, §7.4.2.1)
(implicitly) uses the first tier in his application of the VOM when he asks the
relevant decision maker to assess the importance of each attribute compared
to the other attributes in one objective.
Basically, the method to determine the attribute weight factors is thus the
same method as used to calculate the objective weight factors. The details
of the AHP as explained in Section 4.3 for the objective weight factors, can
thus also be applied to the first tier approach to determine attribute weight
factors.
4.6
combination of all elements in a value model
The final step in creating the value model, is putting all elements together in
a value function in the form of:
K1
C
E
+ λC 1 + λE 1 − 1
K0
C0
E0
R
F
= ωR 1 + ωF 1
R0
F0
A1
= ωA
A0
N
= ωN 1
N0
∆V = λK
K1
K0
C1
C0
E1
E0
(4.7)
(4.8)
(4.9)
(4.10)
Here, K, C and E are the objectives (Section 4.2); λi expresses the weight
factor for objective i (Section 4.3); R, F, A and N are the attributes (Section
4.4); and ωj expresses the weight factor for attribute j (Section 4.5). As the
feasible range equations (Equation (3.15) and (3.16)) are used, all ratios are of
the form x1 /x0 , in which the alternative is divided by the reference situation.
The only improvement to the VOM in this step, is the addition of the term
−1 at the end of Equation (4.7). This changes the decision rule as explained
at the start of Section 3.2 to:
∆V > 0 → alternative creates value
∆V = 0 → alternative just as good as reference
∆V < 0 → alternative destroys value
This means that any alternative that has a lower value than the reference
situation, will result in a negative ∆V; vice versa, any alternative with a
higher value than the reference situation, will result in a positive ∆V. The use
of 0 instead of 1 as the reference level makes the comparison of alternatives
somewhat easier, especially when bar charts are used (see for example
Chapter 7).
4.7
creation of alternatives
As mentioned in Section 3.3, the Value Operations Methodology lacks a
systematic approach for creating alternatives. This section proposes that
ideas from General Morphological Analysis are used to fill this gap. Janssen
(2011, §6.1.1) first used a so-called morphological grid to create alternatives
for use in the Value Operations Methodology.
The morphological grid is the main tool in the General Morphological
Analysis (GMA) — a theory developed by Fritz Zwicky that is about “multidimensional, non-quantifiable, problem complexes” (Ritchey, 1998, p.1).
4.7 creation of alternatives
GMA uses the morphological grid to identify all possible solutions for a
certain problem. In this approach, first the main parameters in the solution
space are identified and listed. Then, for each parameter every theoretically
possible solution is collected and listed. The final step is the selection of one
solution for each parameter; the combination of these elements is then a
solution for the complete problem4 (Roozenburg and Eekels, 2003, §7.5.3).
Unfortunately, GMA and especially the morphological grid are primarily
used in (industrial) design, not in strategic decision making. Moreover, as
Roozenburg and Eekels (2003, p.220) mention, it is difficult to choose the
right parameters.
At first sight the VOM is not compatible with the morphological grid, as
the value model’s objectives and attributes cannot function as the parameters
in the morphological grid. This is because the objectives and attributes are
related to output (the impact of implementing an alternative), whereas the
grid’s parameters are related to input (the alternatives’ characteristics and
basic features).
Nonetheless, during the course of this research a method was found
to connect the value model and the morphological grid, using a five-step
approach. The main driver in this approach is a technique mentioned by
Keeney (1992, §7.2) that uses the value model’s set of objectives to generate
alternatives. The proposed approach works as follows:
1. List the objectives from the value model as the rows of the grid;
2. Create, per objective, alternatives that improve on that objective, according to the procedure explained by Keeney (1992, §7.2). In this way
the grid is filled in horizontally;
3. In the thus created grid, find the similar and opposite alternatives and
group these in columns;
4. Define the common element in each column: this is the morphological
grid’s parameter;
5. Find missing alternatives per column.
Figure 4.4 shows this approach graphically.
4
2
1
5
3
Figure 4.4: Linking value-focused thinking with the morphological grid in five steps:
(1) list the set of objectives from the value model as rows in the grid; (2)
create alternatives that improve the objectives; (3) find similar and opposite
alternatives and group them column-wise; (4) define the common element
in each column; (5) find missing alternatives per column.
4 Interestingly, Keeney (1992, §7.7) seems to suggest a likewise approach to finding ‘coordinated’
alternatives.
51
52
improvements on the value operations methodology
As an example, consider the objective to increase capacity5 . Two possible
alternatives that fulfill this objective, are (A) to plan maintenance only during
the winter (when the schedule is less busy), and (B) to plan maintenance
only during the night. Considering another objective, to reduce nuisance6 , an
alternative would be (C) to plan maintenance only during the day, and (D) to
plan maintenance during the winter. Now, alternatives A and D are grouped
in the same column because they are similar, and B and C are grouped because they are the opposite of each other. The column containing alternatives
A and D is now called ‘Season’, while the column with alternatives B and
C in it is called ‘Day or night’. These column names are the parameters
in the morphological grid. Finally, missing alternatives are generated per
column. In the ‘Season’ column, this would be ‘maintenance during summer’,
‘maintenance during all seasons’ etcetera, while in the ‘Day or night’ column
the missing alternative is ‘maintenance during day and night (continuous)’.
It is not guaranteed that all possible parameters and options are found
in this way. Therefore, it is recommended that more sources are used to
find the parameters for the decision context. In this research, next to the
approach outlined above, previous studies, expert interviews, the reference
situation and the ‘5W+H’ technique are used to find all parameters for the
morphological grid (see Chapter 6).
Now the morphological grid is created, some additional steps are needed
to translate it to a series of alternatives:
6. Consolidate the grid by collecting the unique options per parameter
(i.e. remove the double entries per column);
7. Remove all unacceptable options;
8. Assess cross-consistency to remove incompatibilities in certain combinations of options.
In Step 7 all unacceptable options are removed, for example an option to
reduce safety levels below regulations. Step 8 refers to the cross-consistency
assessment (CCA) that aims to reduce the set of all combinations of the
options to “a smaller set of internally consistent configurations representing
a ‘solution space’ ” (Ritchey, 1998, p.6). For instance, the combination of
the options ‘long blocks’ and ‘split up activities’ in the context of runway
maintenance are incompatible as the goal of splitting up activities is to create
short blocks. Removing such inconsistent combinations reduces the total
number of possible combinations.
The final step of the GMA is to list all remaining combinations of the
options as alternatives. However, as these possible combinations can easily
result in thousands of alternatives, it would take a lot of time to assess the
impact of all these alternatives on the overall value. Therefore, it is proposed
to analyse the individual options first.
An alternative to the morphological grid is the ‘design option tree’. The
design option tree only looks at viable alternatives and may therefore prevent
the generation of thousand of alternatives (Hamann and Van Tooren, 2006,
§8.1.3.1). It relies on ‘OR-trees’ in which the alternatives are split up at
each level in the tree. However, because of this reliance on OR-trees, in the
case of finding alternative strategies there would be a lot of double entries.
For example, the first split in the design option tree would be between the
5 Chapter 5 will establish this as Objective 1 in the value model for the runway system maintenance
strategy.
6 ‘Reduce nuisance’ is Objective 6 in the value model for the maintenance strategy.
4.8 calculation of the value of alternatives
alternatives ‘day’ and ‘night’. The next level would consist of the alternatives
‘summer’, ‘winter’ etc. But because of the strict split up, these options would
be present in both the ‘day’ and the ‘night’ tree. Therefore, the design option
tree is not seen as a good replacement for the morphological grid.
4.8
calculation of the value of alternatives
After the alternatives have been created, they can be compared to each other
and the reference situation using the value model. The input for the value
model are the attribute values (xi1 , . . . , xim in Equation (3.4)). These attribute
values can be put in directly in their own units; for instance, for the attribute
‘costs’ the values then become x1 = e50 in the alternative and x0 = e150 for
the reference. As usually lower costs are better, in the original VOM the ∆V
for this attribute is calculated by:
∆V =
x0
150
=
=3
x1
50
(4.11)
This ∆V is higher than 1, so the alternative creates value. However, this
method has two problems: it does not use consistent ratios; and it lacks
scaling. These two issues are discussed in Section 4.8.1. Section 4.8.2 discusses
how to adapt the value model for qualitative analysis.
4.8.1
Consistent ratios and feasible range
As mentioned above, the consistency in the fractions used in the VOM, and
the feasible range equations as introduced in Section 3.2.2, can be somewhat
improved.
Consistent ratios
In the original VOM, based on the direction of preference of the attribute,
either x1 /x0 or x0 /x1 is used as the attribute ratio. This means that for
the attributes with an upward direction of preference ∆V changes linearly;
and for the attributes with a downward direction of preference it changes
hyperbolically:
x1
= c1 x1 → linear
x0
1
x
direction of preference downwards: ∆V = ω 0 = c2
→ non-linear
x1
x1
direction of preference upwards: ∆V = ω
To remove this (unwanted) distinction, it is better to use consistent ratios.
Thus, the ratio always divides the alternative by the reference (x1 /x0 ) for
all attributes, regardless whether the direction of preference is upwards or
downwards.
Global vs. local scaling of attribute values
It is conventional to scale the attribute values to a number between 0 and
1. This can be done in two ways: using ‘global’ or ‘local’ scaling. In global
scaling, 0 represents the worst level, and 1 the best level the attribute can get,
regardless whether any of the alternatives actually meets those levels. In local
scaling, 0 represents the worst (and 1 the best) level in the set of alternatives.
(Department for Communities and Local Government, 2009, §5.6). Figure 4.5
shows the difference schematically.
53
54
improvements on the value operations methodology
global scaling
1: best level (€0)
local scaling
0.87
Alternative C (€20)
1
0.83
Alternative B (€25)
0.94
0.67
Reference (€50)
0.63
0.33
Alternative A (€100)
0
1: best level (€20)
0: worst level (€100)
0: worst level (€150)
Figure 4.5: Schematic overview of the difference between global and local scaling,
including an example with three alternatives, the reference and their
values on both scales. In the global scale, the best and worst levels (1
and 0 on the scale) are independent of the actual attribute values. In the
local scale, these levels are directly determined by the highest and lowest
attribute values.
The choice between global and local scaling methods is not obvious. While
Kuo et al. (2008, §2.1) argues that local scaling is better, Monat (2009) thinks
that one should use global scaling. An important downside of local scaling
however, is that if the reference performs the worst, it will have a value of
0. And because in the VOM the attribute value is divided by the reference,
this results in a ‘division by zero’ error. In global scaling this can be avoided
by taking a worse level than the reference for the 0 value. Therefore, in the
VOM one should use global scaling.
Improved feasible range equations
The feasible range equations (Equation (3.15) and (3.16)), as presented in
Section 3.2.2, already use consistent ratios and (implicitly) apply global
scaling. The equations can be somewhat simplified by rewriting them (Kuo
et al., 2008, p.82):
(xij )FR
0,1 =
(xij )FR
0,1
(xij )0,1 − xmin
xmax − xmin
xmax − (xij )0,1
(xij )0,1 − xmin
= 1−
=
xmax − xmin
xmax − xmin
(4.12)
(4.13)
4.8 calculation of the value of alternatives
Equation (4.12) is used when the direction of preference is upwards; Equation
(4.13) is used when the direction of preference is downwards. The attribute
values for the reference situation ((xij )0 ) and for the alternative ((xij )1 ) are
scaled separately.
4.8.2
Qualitative analysis
The equations used in the Value Operations Methodology all depend on
numerical inputs. This means that the VOM can in principle only handle
quantitative analysis. However, in some cases it may be more suitable, due to
a lack of data, resources or time, to perform a qualitative analysis of the generated alternatives. In Chapter 7, this research first analyses all options from
the morphological grid qualitatively; subsequently, a quantitative analysis is
performed on the most promising alternatives.
The method for scoring the qualitative assessments and for the conversion
to numerical values was introduced by Janssen (2011, §6.3). In the qualitative
analysis the impact of each alternative on all attributes, compared to the
reference situation, is scored with one of seven options (Janssen, 2011, Table
3.1):
− − − for a high negative change to the attribute
−− for a medium negative change
− for a low negative change
0 for no change
+ for a low positive change
++ for a medium positive change
+ + + for a high positive change
It is important to note that a positive change in the attribute does not
necessarily imply a positive change in value. This depends on the direction
of preference of the attribute. Essentially, this process creates a so-called
‘change-impact matrix’, and was also used by Smulders (2010, Appendix I)
and Repko (2011, Appendix I–J). The main difference is that these two studies
used the change-impact matrix to determine the attribute weight factors as
well as calculate ∆V. As mentioned in Section 4.5, this research does not use
the change-impact matrix to determine attribute weight factors. Instead, the
matrix is only used to qualitatively determine the ∆V of the alternatives.
Of course, the essence of the qualitative analysis is how to convert these
seven options to numerical values. Following Janssen (2011, §6.3.3), three
different score conversion methods are used (in all three conversions, the
qualitative score of 0 maps to the numerical value 0):
constant All three positive changes (low, medium and high) translate to
1, and all negative changes translate to −1;
linear A low positive change (+) translates to 1, medium to 2 and high to
3; the low, medium and high negative changes translate to −1, −2 and
−3 respectively;
polynomial Low, medium and high positive changes translate to 1, 3
and 9; low, medium and high negative changes to −1, −3 and −9
respectively.
Table 4.1 shows the complete conversions including the feasible range values.
55
56
improvements on the value operations methodology
conversion
+++
++
+
0
−
−−
−−−
xmin
xmax
Constant
1
1
1
0
−1
−1
−1
−1
1
Linear
3
2
1
0
−1
−2
−3
−3
3
Polynomial
9
3
1
0
−1
−3
−9
−9
9
Table 4.1: Conversion table for the constant, linear and polynomial conversion methods, including xmin and xmax
The results from all three conversions give the range of ∆V for each
qualitatively analysed alternative. Essentially, the use of these three different
score conversions is a sensitivity analysis on its own.
4.9
an improved value operations methodology
In this chapter, every step in the Value Operations Methodology has been
thoroughly and critically analysed. This has resulted in a number of additions
and changes to the methodology. Appendix D summarises the improved
VOM step-by-step.
stakeholder analysis It was found that the VOM lacks a clear method
to identify and select the relevant stakeholders who should be included in the
value model. Therefore, several approaches have been compared in Section
4.1. This has resulted in a detailed six-step approach to identify and select
the relevant stakeholders, as formulated in Section 4.1.4. It is proposed that
this approach forms the basis for stakeholder selection in the VOM.
formulation of objectives Also for the formulation of the set of
objectives, an explicit approach was lacking in the VOM. Section 4.2 proposes
a detailed approach for this step, based on literature on stakeholder theory
and value-focused thinking. This approach uses a longlist of objectives and
an objective tree to select the right level of abstraction for the set of objecties.
Besides, it is proposed that the final formulation of these objectives all
similarly use the terms ‘increase/reduce’ instead of ‘maximise/minimise’. It
is recommended though that this proposal, and the effect of the formulation
of objectives on decision makers in general, is researched further.
objective weight factors Section 4.3 scrutinises the use of the Analytic Hierarchy Process to determine objective weight factors. It is concluded
that the use of pair-wise comparisons is absolutely necessary when the AHP
is used. It is proposed that these comparisons use the ‘more/less/equal’
rating scale instead of a numerical one. Nonetheless, these ratings have to
be translated to numerical values for use in the value model. It is proposed
that this translation is done according to a power series, with a so-called
factormax of 8. However, it is also recommended that the effect of the MLE
rating scale and the choice of factormax on the consistency ratio is further
researched.
Furthermore, this section offers a method for combining the assessments of
multiple stakeholders, using the arithmetic mean of the individual objective
weight factors. Also this method’s effects on the consistency ratio should be
further researched.
4.9 an improved value operations methodology
An essential realisation is that in the AHP, the normalisation of the eigenvector means that the sum of the elements in the vector is 1, not that its norm
is 1.
Finally, this section discussed some criticism on the AHP and recommended that other methods to determine weight factors should be researched
to improve this step of the VOM.
selection of attributes The selection of attributes is discussed in
Section 4.4. This step is already adequately defined in the VOM, so no
additions have been made.
attribute weight factors The Airside Value Model included two
‘tiers’ to determine the attribute weight factors. As explained in Section 4.5,
it is proposed that the second tier is scrapped from the methodology. Thus,
only the first tier approach, which determines attribute weight factors on the
basis of decision makers’ preferences, should be used.
combination of elements in the value model Finally, all aforementioned elements are combined in a value model, as shortly discussed in
Section 4.6. An important improvement to the VOM is the addition of the
term −1 at the end of the value function (see Equation (4.7)). This ensures
that alternatives that perform worse than the reference situation result in a
negative ∆V, while value-creating alternatives result in a positive ∆V.
creation of alternatives Section 4.7 discusses how General Morphological Analysis can be used to link the value model with the creation
of alternatives. In short, a morphological grid is created by generating alternatives that improve on the value model’s objectives. Because it is not
guaranteed that using this method all alternatives are found, it is recommended that also other sources than the value model are used to complete
the grid.
calculation of alternatives’ value In Section 4.8 the need for
consistent ratios and the use of global scaling in calculating attribute values are discussed, resulting in slightly improved feasible range equations
(Equation (4.12) and (4.13)). Furthermore, a method to analyse alternatives
qualitatively using the VOM is explained.
With the theory explained in this and the previous chapter, and the newly
improved Value Operations Methodology, now the value model for the
runway system maintenance strategy at Amsterdam Airport Schiphol can be
built in the next chapter. Thereafter, in Chapter 6 alternative strategies are
created, and their value is computed in Chapter 7.
57
A VA L U E M O D E L F O R T H E M A I N T E N A N C E S T R AT E G Y
Using the Value Operations Methodology as presented in Chapter 3 and
4, this chapter builds a value model for the runway system maintenance
strategy of Amsterdam Airport Schiphol. This will be done according to
these, now well-known, steps (see also Figure 5.1):
1. Identify and select the relevant stakeholders: Section 5.1;
2. Formulate the set of objectives: Section 5.2;
3. Determine the objective weight factors: Section 5.3;
4. Formulate the set of attributes: Section 5.4;
5. Determine the attribute weight factors: Section 5.5;
6. Combine all elements into a value model: Section 5.6.
Stakeholders (§5.1)
Objectives (§5.2)
Objective weight factors (§5.3)
Attributes (§5.4)
Value model
(§5.6)
Attribute weight factors (§5.5)
Figure 5.1: Overview of the value model for the runway system maintenance strategy
5.1
identification and selection of relevant stakeholders
The first step in creating the value model is a stakeholder analysis. It is
important to identify the relevant stakeholders because Amsterdam Airport
Schiphol is — literally and figuratively — situated in the middle of a network
of other players, such as customers (both airlines and passengers), governments that impose regulation and residents nearby who complain about
noise. Therefore, it is explicitly expressed in the research question to include
the viewpoints of all relevant stakeholders. Using the stakeholder analysis
approach detailed in Section 4.1, now the stakeholders in the case of runway
system maintenance can be identified and selected.
59
5
60
a value model for the maintenance strategy
5.1.1
Identification of all stakeholders
The first step in the stakeholder approach is to identify all stakeholders.
Based on generic longlists with aviation stakeholders the stakeholder longlist
in Table 5.1 is generated.
Airport operator
Air traffic control
Airlines
Main contractor
National government
Regional and local
governments
Residents and local
community groups
Passengers
Military
Ground handlers
Suppliers
Utilities
Emergency services
Security
Meteorological service
Fuel suppliers
Aircraft engineering
Catering and sanitary
services
Visitors
Chambers of
commerce
Environmental
activists
Table 5.1: Longlist with aviation stakeholders (Ashford et al., 1997; Repko, 2011;
Smulders, 2010)
Next, the stakeholders that are considered within the scope of this research
are selected. Only the stakeholders that are directly related to the maintenance process or have a major stake in the outcome of the maintenance
strategy (such as residents and passengers) are considered within scope.
Ground handlers, (fuel) suppliers, security, aircraft engineering, catering,
sanitary services and visitors are considered out of scope because their main
interests lay in the terminal or apron, not in the runway system. The military
and emergency services do sometimes operate in the runway system, but are
not of primary concern in day-to-day operations or their objectives are part
of (safety) regulations. Utilities are considered out of scope because the Utility Services department within Amsterdam Airport Schiphol defends their
interests. Finally, the chambers of commerce and environmental activists are
considered out of scope because their advocacy for economic and environmental interests respectively is already handled by the primary stakeholders
themselves, such as the airlines, the different levels of government and the
local community groups.
Step 1c of the stakeholder analysis approach is to map the relationships
between the stakeholders. Figure 5.2 shows these relationships by grouping
the stakeholders by the committees they take part in. This map shows
that there are quite a lot of overlapping discussion forums between the
same stakeholders. It also shows that the passengers, Royal Netherlands
Meteorological Institute (KNMI) and Heijmans do not take part in any formal,
organised committee. This decreases their power and their possibilities to
engage in coalitions, and thus their salience.
5.1.2
Selection of relevant stakeholders
Figure 5.3 classifies each stakeholder as one type from Savage’s approach.
Appendix E explains the determination of each stakeholder’s type in detail.
It is important to note that Amsterdam Airport Schiphol has not been
5.1 identification and selection of relevant stakeholders
Alderstafel
CROS
COBRA
AAS
KLM
BRS
LVNL
3 provinces
VGP
43 municipalities
28 local community
groups
National gov't
(Ministry of I&M)
Meteo
(KNMI)
Passengers
Contractor
(Heijmans)
HIGH
Supportive
Air traffic control
Airlines
Mixed blessings
National gov't
Local gov't
Passengers
LOW
Potential for cooperation
Figure 5.2: Map of stakeholder relationships in committees, including the Schiphol
Regional Consultation Commission (CROS), the Joint Platforms (VGP)
and the Administrative Region Schiphol (BRS) (Bosgra, 2011; CROS, 2010;
Kuipers and Vonk, 2011; Tafel van Alders, 2010; VGP, 2011)
Marginal
Main contractor
Meteo service
Non-supportive
Residents
LOW
HIGH
Potential for threat
Figure 5.3: Classification of stakeholder type, as explained in Appendix E
61
62
a value model for the maintenance strategy
classified. This is because the airport operator is the focal point of the
research, conforming with the firm centred view as discussed in Section 4.1.
As mentioned at the end of Section 4.1.4, the numerical method used in
Appendix E is not fully validated. Therefore, the outcome of this numerical
method is compared with the assessments of experts from Airfield Maintenance Services, resulting in a triangulated definitive classification of each
stakeholder. It is this triangulated classification that is shown in Figure 5.3
and the fourth column of Table 5.2.
stakeholder
organisation
scope?
type
relevant?
Airport operator
AAS
X
—
X
Air traffic control
LVNL
X
Supportive
X
Airlines
KLM
X
Supportive
X
National government
Ministry of I&M
X
Mixed blessing
X
Regional and local
governments
BRS
X
Mixed blessing
X
Residents and local
community groups
CROS, VGP, BAS
X
Non-supportive
X
X
Mixed blessing
X
Passengers
Main contractor
Heijmans
X
Marginal
Meteorological service
KNMI
X
Marginal
Table 5.2: Selection of relevant stakeholders in the case of runway system maintenance. The national government is
represented by the Ministry of Infrastructure and the Environment (I&M). Regional and local governments
are represented by the BRS; residents and local community groups are organised in the CROS and the
VGP, and further represented by Residents Contact Schiphol (BAS). The determination of the stakeholder
type is explained in detail in Appendix E.
The stakeholder classification results in two supportive stakeholders (LVNL
and the airlines); three mixed blessings (national, regional and local governments and passengers); one non-supportive stakeholder (residents and local
community groups); and two marginal stakeholders (the main contractor
and KNMI). According to the stakeholder approach from Section 4.1.4, this
means that these last two stakeholders may be ignored in the value model.
To be clear, this does not imply that their knowledge and expertise are not
used in the generation of alternatives. It means that their potential for threat
and cooperation is deemed so low, that their objectives will not be included
in the value model.
Summarising, the stakeholder analysis results in seven relevant stakeholders for this research, as shown in the last column of Table 5.2:
• Airport operator (Amsterdam Airport Schiphol);
• Air traffic control (LVNL);
• Airlines;
• National government;
• Regional and local governments;
• Residents and local community groups;
• Passengers.
5.2 formulation of the set of objectives
5.2
formulation of the set of objectives
The second step in the VOM is the formulation of the set of objectives. As
explained in Section 4.2, the selected objectives are the main parameters in
the value function. Moreover, their formulation will influence how decision
makers will rate their relative importance in the next step.
This section will follow the approach as defined in Section 4.2:
1. Create a longlist of objectives for each relevant stakeholder;
2. Create an objective tree consisting of the relevant objectives from the
longlist;
3. Select from this objective tree the level of objectives that has the right
level of abstraction for the research scope;
4. Finalise the formulation of the objectives using the list with desired
properties of the set of objectives.
5.2.1
Longlist of objectives
The first step is to create a longlist of objectives for each relevant stakeholder.
Appendix F contains this longlist, based on a desk research of policy documents, strategy and position papers, annual reports and official legislation,
and on interviews with experts.
5.2.2
Objective tree and selection of objective level
Collecting similar objectives from the longlist and creating a hierarchy based
on a means-ends objectives network, results in the objective tree shown in
Figure 5.4.
This figure shows how the different objectives are related to each other.
Clearly visible is the strategic objective of Amsterdam Airport Schiphol to
become ‘Europe’s preferred airport.’ Four objectives are classified as means
objectives for this strategic objective. The (national) government’s strategic
objectives are shown at the top of the objective tree. Two objectives are related
to the objective ‘Ensure sustainable airport growth.’
The safety-related objective needs some explanation for its position in the
tree: ‘safety’ can be seen as a fundamental strategic objective on its own right,
however it is listed as a means objective to ‘Europe’s preferred airport’ in the
objective tree. This is because in its current formulation, the safety objective
is clearly related to airport operations and maintenance activities, and these
activities fall under the strategy of Amsterdam Airport Schiphol.
Clearly, the upper four are too strategic and too broadly defined to be the
right selection for this research project. Therefore the lowest level in Figure
5.4 is selected as the set of fundamental objectives for the current decision
problem. This leads to the following set of objectives:
1. Increase capacity (availability and reliability) of airside operations of
Amsterdam Airport Schiphol;
2. Reduce maintenance related costs;
3. Increase predictability and transparency regarding maintenance activities;
63
64
a value model for the maintenance strategy
Grow regional and
national economy
Strengthen mainport
function of Schiphol
Ensure sustainable
airport growth
'Europe's preferred airport'
Increase capacity
(availability and
reliability) of airside
operations of AAS
Increase predictability
and transparency
regarding maintenance
activities
Reduce maintenance
related costs
Reduce the
environmental impact
of maintenance
activities
Increase safety, both of
airside operations and
the maintenance
activities
Reduce nuisance to
local community
Figure 5.4: Objective tree, with the set of fundamental objectives shown in grey boxes
4. Increase safety, both of airside operations and the maintenance activities;
5. Reduce the environmental impact of maintenance activities;
6. Reduce nuisance to local community.
5.2.3
Final formulation of the set of objectives
During the creation of the objective tree, attention was paid to the formulation
of the set of objectives, so that they adhere to the guidelines from Section 4.2.4.
All objectives contain a decision context, object and direction of preference.
Furthermore, the terms ‘increase/reduce’ are used consistently.
Finally, the set of objectives is checked with the list of desired properties.
The objectives mostly adhere to these properties, except for a number of
issues. These cases are shortly discussed below.
operational issues Objective 1, 3, 4 and 5 have operational issues: for
these objectives, information may not be readily available. Therefore, extra
attention must be given to the attribute selection for these objectives.
measurability issues Objective 4 may be hard to measure. It is likely
that ‘safety’ cannot be measured as such, and that proxy attributes like the
number of incidents must be selected.
5.3 determination of objective weight factors
understandability issues Objective 3, 5 and 6 may not be easily
understood. Objective 3 (‘Increase predictability and transparency’) refers
to the predictability of airside operations, which is helpful both for aviation
parties such as LVNL and KLM, but also for residents. ‘Predictability’ may
seem similar to the terms ‘availability’ and ‘reliability’ from Objective 1, but
there is a clear difference: certain measures can be thought of that increase
the availability and reliability of the airport, but are completely unpredictable
beforehand. This must be explained well to decision makers before they make
their trade-offs regarding to Objective 3.
Objective 5 and 6 are ambiguous, as the terms ‘environmental impact’ and
‘nuisance’ are somewhat vague. Therefore, also this objective must be clearly
explained to decision makers, especially that any noise effects are measured
in Objective 6, not 5.
Overall, the set of objectives is considered complete, without redundancies
and as concise as possible. Therefore, it is concluded that the aforementioned
list of six objectives is the right set to use in the value model for this research
project.
5.3
determination of objective weight factors
In the eventual model, value preferences are expressed using objective weight
factors. The higher the weight factor, the more important the objective is.
The weight factors for the six objectives formulated in the previous section
are determined by the relevant stakeholders as identified in Section 5.1.
Representatives of these stakeholders participated in structured interviews in
which they were asked to state their preference in pair-wise combinations of
the objectives. For instance, each stakeholder was asked whether they find the
objective ‘Reduce maintenance related costs’ more, less, or equally important
compared to the objective ‘Reduce nuisance to local community’. The full
form of questions and the list of people interviewed can both be found in
Appendix A. The results of these pair-wise comparisons are processed using
the Analytic Hierarchy Process, as explained in detail in Section 3.2.1 and 4.3.
All comparison matrices can be found in Appendix G. This section discusses
the results, first per stakeholder, and for the average stakeholder in Section
5.3.2.
5.3.1
Objective weight factors per stakeholder
As explained in Section 4.3.3, the objective weight factors become more
representative of the organisation if multiple people are interviewed. In
the end, it was possible to interview four decision makers from Amsterdam
Airport Schiphol and two from LVNL. The other stakeholders are represented
by one interviewed decision maker. Unfortunately, it was not possible to
interview a representative for the national government and for the passengers.
Therefore their objective weight factors are based on a literature study and
desk research.
The resulting objective weight factors of the individual stakeholders, and
the average weight factors, are listed in Table 5.3. These weight factors
can be graphically shown in a spider graph with six axes, with each axis
representing one objective. These spider graphs are shown in Figure 5.5–5.12.
The results are discussed for each stakeholder below.
65
66
a value model for the maintenance strategy
The individual stakeholders’ objective weight factors are classified and therefore removed from the public version of this thesis. Only the average weight
factors are public. Also the passengers’ objective weight factors are public, as
these are based on literature.
#
objective
aas
lvnl
airlines
im
nh
residents
pax
average
40
28
5
4
40
21
1
Capacity
2
Costs
3
Predictability
4
Safety
5
20
5
Environment
5
8
6
Nuisance
5
19
Table 5.3: Objective weight factors for the individual stakeholders and for the average stakeholder, in percent. These
values represent the λi in Equation (3.3). Not all columns add up to 100 percent because of rounding
errors. ‘IM’ and ‘NH’ are the stakeholders ‘national government’ and ‘regional and local governments’
respectively.
Passengers
Finally, the passengers’ preferences must be analysed. Unfortunately, the
internal market research of Amsterdam Airport Schiphol does not include
passengers’ priorities regarding the set of objectives as used in this project.
Therefore, literature is consulted to gather passengers’ overall objectives, and
these are translated to choices in the pair-wise comparisons.
Geilenkirchen et al. (2010, Table S.3) state that the price elasticity for air
travel is −0.8 (ranging from −0.6 to −1.1), implying that air travel is inelastic.
Moreover, as ticket prices are related to maintenance costs (Objective 2)
only indirectly, via airport charges that depend on many other factors, it
is assumed that passengers are indifferent towards reducing maintenance
related costs.
Steer Davies Gleave (2006, p.2) shows that the most important reasons for
passengers to choose a particular mode of transportation are, in order:
1. Shorter travel times;
2. Reliability and punctuality of the time schedule;
3. Accessibility of the terminal;
4. Ticket price.
From this list, it is concluded that Objective 1 and 4, relating to the reliability
and predictability of the airport’s operation, are important. The passenger is
assumed to be indifferent towards the other objectives.
These conclusions are translated to the passengers’ comparison matrix
by choosing consequently for Objective 1 or 4 as ‘more important’. In a
comparison of two other objectives, the option ‘equally important’ is chosen.
In the comparison of Objective 1 and 4, also the option ‘equally important’ is
chosen. This comparison matrix results in the graph shown in Figure 5.12.
Logically, the capacity and predictability objectives are the most important.
5.3 determination of objective weight factors
CONFIDENTIAL
This image has been removed from the public version of this thesis
Figure 5.5: Spider graph showing objective preferences for decision makers from
Airfield Maintenance Services, Environmental Capacity, Airside Operations and Market Development, four departments at Amsterdam Airport
Schiphol
CONFIDENTIAL
This image has been removed from the public version of this thesis
Figure 5.6: Spider graph showing objective preferences for the airport operator (Amsterdam Airport Schiphol), based on the average of four interviews
CONFIDENTIAL
This image has been removed from the public version of this thesis
Figure 5.7: Spider graph showing objective preferences for air traffic control (LVNL),
based on the average of two interviews
CONFIDENTIAL
This image has been removed from the public version of this thesis
Figure 5.8: Spider graph showing objective preferences for the airlines (KLM)
CONFIDENTIAL
This image has been removed from the public version of this thesis
Figure 5.9: Spider graph showing objective preferences for the national government
(Ministry of Infrastructure and the Environment)
CONFIDENTIAL
This image has been removed from the public version of this thesis
Figure 5.10: Spider graph showing objective preferences for the regional and local
governments (Haarlemmermeer municipality)
CONFIDENTIAL
This image has been removed from the public version of this thesis
Figure 5.11: Spider graph showing objective preferences for residents and local community groups
67
68
a value model for the maintenance strategy
*+,+-./0#
)!"#
(!"#
'!"#
[email protected]+<-5#
&!"#
*12/2#
%!"#
$!"#
!"#
;<=.41<>5</#
3456.-/+7.8./0#
9+:5/0#
Figure 5.12: Spider graph showing objective preferences for passengers
*+,+-./0#
)!"#
(!"#
'!"#
[email protected]+<-5#
&!"#
*12/2#
%!"#
$!"#
!"#
;<=.41<>5</#
3456.-/+7.8./0#
9+:5/0#
Figure 5.13: Spider graph showing the average objective preferences
5.4 selection of attributes
Section 8.2.2 will perform a sensitivity analysis to assess the impact of
these assumptions of passengers’ preferences on the outcomes of the value
model.
5.3.2
Average objective weight factors
As explained in Section 4.3.3, the arithmetic mean is used to compute how
the average stakeholder ranks the objectives. Figure 5.13 shows these results
graphically.
It can be concluded that on average, the capacity objective is the most
important, with the predictability, safety and nuisance objectives following
closely. The average stakeholder cares little for the environmental impact or
the maintenance costs. This can be explained by the fact that only Amsterdam
Airport Schiphol, and within Amsterdam Airport Schiphol only the Airfield
Maintenance Services department, ‘feels’ the costs, as they are the ones
paying directly for maintenance. On the other hand, almost all stakeholders
profit directly from a higher capacity on the airport.
Using the average objective weight factors, the value function for the
runway system maintenance strategy becomes:
∆V = 0.28
K
P
S
E
N
C1
+ 0.04 1 + 0.21 1 + 0.20 1 + 0.08 1 + 0.19 1 − 1 (5.1)
C0
K0
P0
S0
E0
N0
Here, C refers to Objective 1 (capacity), K to Objective 2 (costs), P to Objective
3 (predictability), S to Objective 4 (safety), E to Objective 5 (environment)
and N to Objective 6 (nuisance).
However, this average dilutes the differences between the stakeholders.
Furthermore, in the calculation of the average weight factors, it is assumed
that all stakeholders are equally important. This assumption may be too
simplistic for the real world1 . Therefore, the change in value is not only
calculated for the average stakeholder, but for each individual stakeholder.
Chapter 7 will also show the ∆V for each alternative for all stakeholders.
The value functions for each individual stakeholder can be easily assembled
using the weight factors given in Table 5.3.
5.4
selection of attributes
Now the first level of the value function is determined, the next step is
to select the attributes that are used to measure the achievement of the
objectives as discussed in Section 4.4. This section discusses this selection for
each objective. The selection is based on the attributes used in the existing
Airside Value Models by Smulders (2010, §3.4) and Repko (2011, §3.3.2)
and on discussions with experts at Amsterdam Airport Schiphol. In the
formulation of the attributes, care is taken to adhere to the list of desired
properties as mentioned in Section 4.4. Table 5.5 at the end of this section
summarises the final set of attributes, including the weight factors that are
determined in Section 5.5.
Objective 1 — Capacity
Asset owner Airfield Maintenance Services expresses capacity using the two
terms beschikbaarheid (availability) and betrouwbaarheid (reliability) (Van der
1 This topic is further discussed in Section 8.2.1.
69
70
a value model for the maintenance strategy
Vegte and Vallinga, 2011, §1.1). These two terms are therefore the most
obvious candidates for the attributes.
Availability can be expressed as the ‘technical capacity’, which is the
percentage of time that the runway is available for airport operations. This
percentage is calculated by subtracting the number of days the runway
is under service (U/S) from the total number of days in a year. A more
direct way to measure availability is the number of days a runway is under
service due to maintenance. The direction of preference of this attribute is
downwards.
The term ‘reliability’ has many different meanings and is thus not unambiguous, also within Amsterdam Airport Schiphol. The asset owner (Airfield
Maintenance Services) defines reliability as the number of days the runway
is actually available, as a percentage of the number of days it was planned to
have the runway available. So when the planned technical capacity was 350
days, but the actual availability was 300 days, the reliability is then 300
350 = 86
percent. However, the asset user (Airside Operations) is only interested in
capacity at times they need it for the operation, so when demand is high.
This reliability is expressed using the concept of ‘sustainability’2 . However,
the sustainability itself is only indirectly influenced by the maintenance strategy, and therefore not a good attribute to measure the value of alternative
strategies. Furthermore, reliability is not independent of other attributes.
Objective 3 regarding predictability uses attributes that measure the delay of
maintenance activities. This delay directly influences the reliability. Because
of this dependence, and because of the lack of a clear definition, ‘reliability’
is not used as an attribute.
Instead, it is recognised that the value of capacity lies in accommodating demand of airlines to fly to Schiphol. Thus, removing capacity for maintenance
activities destroys value when the demand is high. This is incorporated in
the, more comprehensive, constructed attribute ‘operational penalty’, which
expresses the severity of the operational impact when a runway is taken
under service. This severity is higher during the day than during the night,
and higher during peak months in the summer. Table 5.4 shows the different
attribute levels. The direction of preference of the attribute is downwards.
jan
feb
mar
apr
may
jun
jul
aug
sep
oct
nov
dec
Day (06–23h)
2
2
4
4
8
6
8
8
6
6
4
2
Night (23–06h)
1
1
2
2
4
3
4
4
3
3
2
1
Table 5.4: Operational penalty for runway maintenance. During the peak months May, July and August the penalty
is highest (Amsterdam Airport Schiphol, 2009, 2010, 2011c). During the day the penalty is assumed to be
twice as high as during the night.
Finally, also the number of storingen (failures) was considered as an attribute. However, as an asset failure causes a lower predictability (Objective
3) and more days under service, this is not taken into account as a separate
attribute.
In conclusion, the metrics ‘availability’, ‘reliability’, ‘sustainability’ and
‘number of failures’ are not used as attributes. Instead, the capacity objective
has two attributes: ‘days under service’ and ‘operational penalty’.
2 See Brouwer et al. (2010, §4.1) for a short introduction of how to calculate sustainability, or
Mooyaart (2011) for a detailed research of the concept of reliability at Schiphol.
5.4 selection of attributes
Objective 2 — Costs
The most natural attribute for this objective is maintenance costs in euro.
This attribute clearly adheres to the list of desired properties.
Another attribute considered for this objective was stable cashflow, meaning that each year roughly the same amount of money is spent on maintenance. This attribute was rejected, however, as it became clear that such an
attribute might destroy value (when assets are replaced or maintained too
early, before it is necessary) and that a stable cashflow strategy only works
on a companywide level, not on a departmental level.
Also an attribute related to extra costs due to waiting time at security gates
was considered, but rejected as this waiting time is part of the fixed price
contracts with contractors, and the risk is therefore not carried by AMS.
The resulting attribute is thus maintenance costs in euro. The direction of
preference is downwards.
Objective 3 — Predictability
Predictability of maintenance is lowered by a delay of the start of the maintenance activity, by a delay of the duration of the activity, and by more
non-scheduled activities.
The two delay components are combined into one attribute, because in
the evaluation of the alternatives these components cannot be determined
separately. Thus, to make the attribute operational, the first attribute is
maintenance delay in hours. The direction of preference is downwards.
When the number of non-scheduled activities increases, the predictability
of maintenance for the operation decreases. This attribute is expressed as
a percentage of the the total number of activities. Again, the direction of
preference is downwards.
Objective 4 — Safety
The safety objective relates both to the safety of airside operations and
to the safety of maintenance activities. They are however combined into
one attribute, ‘safety risk resulting from maintenance’, as they cannot be
discerned in the evaluation of the alternatives. The direction of preference of
the attribute is downwards.
Unfortunately, a lack of data causes this attribute to be insufficiently
operational, as predicted in Section 5.2.3. Therefore, this attribute is only
assessed qualitatively. In a quantitative analysis the attribute is ignored.
Objective 5 — Environment
The environmental impact of maintenance activities is determined by two
major components: the change in environmental impact by changed airside
operations; and the environmental impact of the maintenance activity itself.
The first is expressed as ‘extra fuel costs’ in euros. Brouwer et al. (2011)
has calculated this attribute for maintenance periods of one week, for each
runway. These costs are essentially a proxy attribute for the emissions of
greenhouse gases and the consumption of fossil fuels due to longer flight and
taxi times caused by unavailable runways due to maintenance. The direction
of preference is downwards.
The environmental impact of maintenance itself relates to construction
waste, use of energy and water, and emissions of greenhouse gases during
maintenance. It is expressed as a constructed attribute with three levels,
71
72
a value model for the maintenance strategy
where 3 represents the highest environmental impact. The direction of preference is therefore downwards.
It is important to note that noise attributes are not included in this objective,
but in Objective 6.
Objective 6 — Nuisance
Nuisance to the local community is primarily caused by aircraft noise. Runway maintenance may lead to a different runway usage, which may affect
the distribution of noise over the surrounding community.
The legal noise limits are the number of houses within 58 dB(A) Lden ,
and the number of highly annoyed people within 48 dB(A) Lden . These
norms refer to noise in 24-hour periods. There are also norms for nightly
noise (Lnight ), but because their impact is similar to the day-and-night norms
they are not included as separate attributes (Repko, 2011, p.21). All noise
attributes’ direction of preference is downwards.
Next to noise, more elements of the maintenance strategy have affect nuisance. Unfortunately, no natural attribute exists for these elements. Therefore,
a constructed attribute is created based on a study by Residents Contact
Schiphol (BAS). This study shows that residents have the following preferences regarding runway maintenance (Wever, 2010):
• Maintenance during the winter is preferred to maintenance during the
summer;
• Maintenance during the day is preferred to nightly maintenance;
• Maintenance during the weekend is not preferred;
• Residents prefer the combination of as much activities as possible;
• Residents prefer early and widespread communication regarding maintenance activities;
• Residents prefer as little maintenance as possible;
• Residents prefer maintenance as short as possible.
These seven preferences together form the constructed attribute ‘Residents’
preferences fulfilled’. This attribute has the value 1 when no preferences are
fulfilled, and the value 8 when all preferences are fulfilled. The direction of
preference is upwards.
The resulting attributes are thus the constructed attribute of residents’
preferences fulfilled, the number of houses within 58 dB(A) Lden , and the
number of highly annoyed people within 48 dB(A) Lden .
5.5
determination of attribute weight factors
Now the attributes have been selected, the attribute weight factors can be
determined according to the procedure explained in Section 4.5. Per objective
the sum of the attribute weight factors should be 100 percent. Table 5.5 shows
all attributes and their weight factors.
objective 1 — capacity In an interview with an expert of Airside
Operations, it was concluded that both the number of days U/S and the
operational penalty are equally important. Therefore they both get a weight
factor of 50 percent.
5.5 determination of attribute weight factors
attribute
unit
dop
wf
objective 1 — capacity
Days under service
dus
days
↓
50
Operational penalty
opp
—
↓
50
cos
e
↓
100
Maintenance delay
del
hours
↓
67
Non-scheduled vs. scheduled works
nss
%
↓
33
saf
—
↓
100
Extra fuel costs
efc
e
↓
11
Environmental impact of maintenance
env
—
↓
89
Residents’ preferences fulfilled
res
—
↑
6
Number of houses within 58 dB(A) Lden
hou
#
↓
47
Number of highly annoyed people within 48
dB(A) Lden
hap
#
↓
47
objective 2 — costs
Maintenance costs
objective 3 — predictability
objective 4 — safety
Safety risk resulting from maintenance
objective 5 — environment
objective 6 — nuisance
Table 5.5: The set of attributes, including abbreviation, unit, the direction of preference
(dop), and weight factor in percent (wf). The weight factors are the ωj in
Equation (3.4).
objective 2 — costs This objective has only one attribute, which automatically gets a weight factor of 100 percent.
objective 3 — predictability Experts of Airfield Maintenance Services and Airside Operations indicated that the delay attribute is twice as
important as the amount of non-scheduled activities. Therefore the first gets
a weight factor of 67 percent, and the latter a weight factor of 33 percent.
objective 4 — safety This objective has only one attribute, which
automatically gets a weight factor of 100 percent.
objective 5 — environment Because the extra fuel costs are only a
small part of the total environmental impact of maintenance, it is seen as less
important as the constructed attribute environmental impact of maintenance.
Thus the following comparison matrix is used to calculate the attribute
weight factors for this objective:
#
"
1 18
8
1
Using the same method to calculate the objective weight factors from a comparison matrix (see Section 3.2.1 and 4.3), this results in a weight factor of 11
73
74
a value model for the maintenance strategy
percent for the attribute ‘extra fuel costs’, and 89 percent for ‘environmental
impact of maintenance’.
objective 6 — nuisance Because the number of houses and highly
annoyed people are both official norms that are defined in the law, they are
both considered more important than residents’ preferences (Ministerie van
Verkeer en Waterstaat, 2011b, Article 8a.45). Alders (2010, p.12) does not
distinguish between the two noise attributes; therefore they are considered
equally important. The following comparison matrix is therefore used to
calculate the attribute weight factors for this objective:


1 18 18


8 1 1 


8 1 1
Processing this comparison matrix results in a weight factor of 6 percent
for the attribute ‘resident’s preferences fulfilled’, and a weight factor of 47
percent for each of the two noise attributes.
5.6
combination of all elements in a value model
The final step in creating the value model is the combination of all elements
from the previous sections into one value function. Equation (5.1) gave the
first level of this value function for the average stakeholder:
∆V = 0.28
C1
K
P
S
E
N
+ 0.04 1 + 0.21 1 + 0.20 1 + 0.08 1 + 0.19 1 − 1
C0
K0
P0
S0
E0
N0
This function can be constructed for each individual stakeholder using the
weight factors presented in Table 5.3.
The equations for the individual objectives are the same for all stakeholders.
Incorporating the previously defined attributes and their weight factors, these
functions become:
C1
C0
K1
K0
P1
P0
S1
S0
E1
E0
N1
N0
= 0.50
dus1
opp1
+ 0.50
dus0
opp0
cos1
cos0
del1
nss1
= 0.67
+ 0.33
del0
nss0
saf1
=
saf0
efc1
env1
= 0.11
+ 0.89
efc0
env0
res1
hou1
hap1
= 0.06
+ 0.47
+ 0.47
res0
hou0
hap0
=
(5.2)
(5.3)
(5.4)
(5.5)
(5.6)
(5.7)
In these equations, all attribute values are first converted to a value between 0
and 1, so within its feasible range, using Equation (4.12) or (4.13) depending
on the attribute’s direction of preference.
All together, Equation (5.1) through (5.7) form the value function for the
runway system maintenance strategy at Amsterdam Airport Schiphol.
6
C R E AT I O N O F A LT E R N AT I V E S
Next to building a value model for the runway system maintenance strategy,
the main goal of this research project is to generate alternatives to the
current strategy. These alternatives should ideally improve on the objectives
formulated in Chapter 5, thereby creating value for all stakeholders.
Section 4.7 explained how concepts from General Morphological Analysis
can help to create alternatives. In short, a morphological grid is created for
the maintenance strategy by splitting it up into discrete parameters. Then,
all possible options for each individual parameter are found. Finally, an
alternative strategy is created by choosing one option from each parameter.
Section 4.7 also proposed a five-step approach to connect the morphological
grid with the value model, by using the set of objectives to find the parameters
for the grid. To improve the grid’s exhaustiveness, not only this approach is
used. The following sources are used to create the morphological grid:
1. The six objectives from the value model (see Section 5.2): following
Keeney (1992, §7.2), options are generated that improve on the objectives. As explained in Section 4.7, grouping these alternatives result in
a set of parameters;
2. The reference situation as described in Chapter 2: analysis of the current
strategy yields a number of parameters;
3. Previous studies on alternative maintenance strategies (see Section 2.5);
4. Interviews with experts from other industries (see Appendix A for an
overview); and
5. The ‘5W+H’ technique: as used by Janssen (2011, p.15), this technique
asks six questions (who, what, where, when, why and how) to look at
a problem from all angles.
The resulting parameters and options are presented in Section 6.1.
To assess the mutual compatibilities of these options, a cross-consistency
assessment (CCA) is performed in Section 6.2. Finally, Section 6.3 presents
the resulting morphological grid with all alternatives.
6.1
parameters and options in the grid
As mentioned above, the first step in building the morphological grid is to
find all parameters. According to General Morphological Analysis theory,
an object can be completely broken up into discrete parameters. Then, all
parameters together define the complete object. For a relatively vague concept
such as a strategy, this may not be the case; i.e. splitting a strategy in
parameters may cause overlap of some parameters, or white spots that are
not covered by a parameter. However, defining the parameters and using
them to generate alternatives is a systematic and repeatable approach in
coming up with alternative solutions. Moreover, the variety of sources as
listed above increases the exhaustiveness of the grid.
75
76
creation of alternatives
Weekends
Combination of
activities
Change in airside
operations
Timeslot
Runway system
maintenance strategy
Month
Quality of assets
Maintenance area
Maintenance concept
Categories of
maintenance
Runway
Length of
maintenance blocks
Flexibility in planning
Figure 6.1: Grid parameters defining the runway system maintenance strategy
Using these five sources, twelve parameters are generated. Figure 6.1
shows these parameters that together define the runway system maintenance
strategy.
Now the challenge is to find all possible options (or ‘alternatives’) for
each parameter. These alternatives are found by incorporating the current
situation as an alternative; by discussing the grid and the parameters with
experts at the Airfield Maintenance Services department; and filling in the
missing alternatives using logical thinking. Below the argumentation for each
parameter and its alternatives is given:
month In principle, each month or combination of months is a theoretically
possible alternative in this parameter. However, from previous studies and current practice, six options remain: maintenance regardless
of the month (yearlong); maintenance in spring and summer (April–
September); in autumn and winter (October–March); after the summer
peak (September); before the summer peak (June); and during the
summer peak (June–September).
timeslot Theoretically, every hour and combination of hours can be a
timeslot. In practice, the division is between maintenance during the
night (23:00–06:00) or during the day (06:00–23:00); and the alternative
to work continuously (24h). Variants such as ‘after the first peak’ or
‘night extended’ have been discarded because they differ little from
these three alternatives.
weekends The logical possibilities are to work all week; to not work during
weekends; and to only work during weekends.
combination of maintenance activities The current strategy is to
combine as much maintenance activities as possible in NOH and GOH
projects. It is also possible to split up activities and schedule and execute
them separately. A more detailed investigation of this parameter may
yield more alternatives than only these two.
quality of assets Currently, Airfield Maintenance Services uses its own
norms to determine when maintenance must be carried out. These
norms are stricter than the minimum ICAO norms. It is of course also
6.2 cross-consistency assessment
possible to use these minimum norms. The other way around, another
alternative is to maximise the quality of the asset and thus its lifetime.
Other alternatives included the use of CAT I, II and III as the norms,
but as there are also other standards to include, these alternatives are
discarded.
length of maintenance blocks As a first division, maintenance blocks
may be ‘long’ (at least one day) or ‘short’ (a couple of hours). Further
research may specify these options in more detail.
maintenance area The runway that is under service may be turned into
landside, or keep its airside status, including higher security demands.
Not all parameters contain viable alternatives. For other parameters, there
is no free choice for an alternative. Therefore, the following parameters are
discarded:
flexibility in planning Flexibility in planning refers to how far maintenance activities are planned in advance. This parameter is discarded
because its alternatives (‘high’ and ‘low flexibility’) and their impacts
on attribute values cannot be formulated specifically.
maintenance concept The alternatives in this parameter are the corrective, planned and condition-based maintenance concepts as used
at Amsterdam Airport Schiphol. However, because the Installation
Maintenance Concepts specify which concept is to be used, one is not
free to choose in this parameter. Therefore it is discarded.
change in airside operations Consisting of the alternatives ‘change
operations’ and ‘no change in operations’, this parameter is discarded
because the parameter ‘length of maintenance blocks’ has essentially
the same alternatives: short blocks can be done in between operations,
and for long blocks the operations have to be changed.
categories of maintenance This parameter included several alternatives that group the different types of maintenance, e.g. ‘NOH, GOH,
TWP’ or ‘small/big’. As these are not really strategic alternatives,
but more a result of the chosen strategy, the parameter is discarded.
Moreover, in the next chapter a case study is performed for a large
maintenance project (GOH).
runway The alternatives in this parameter are Schiphol’s six runways. As
the operational translation of the maintenance strategy to specific plans
for each runway is out of this project’s scope, the parameter is discarded.
Instead, in the next chapter the alternatives’ ∆V will be calculated for a
maintenance project on the Kaagbaan.
Figure 6.2 shows the resulting morphological grid, consisting of the seven
remaining parameters and their options in rows. With this morphological
grid, new strategies can be generated by picking one alternative per parameter. The combined alternatives then form the maintenance strategy.
6.2
cross-consistency assessment
In theory, using the grid from Figure 6.2 one can form 6 · 3 · 3 · 2 · 3 · 2 · 2 = 1296
possible strategies. However, not all combinations of alternatives are possible.
A cross-consistency assessment helps to find these impossible combinations.
77
78
creation of alternatives
Month
Yearlong
April-September
October-March
Timeslot
24h
Night (23-06h)
Day (06-23h)
Weekends
All week
Not during
weekends
Only during
weekends
Combination of
activities
Combine as much as
possible
Split up activities
Quality of assets
Schiphol norms
(stricter than ICAO)
ICAO minimum
Length of
maintenance blocks
Short blocks
Long blocks
Maintenance area
Landside
Airside
parameter
alternatives
September
June
June-September
Maximise asset
lifetime
Figure 6.2: Morphological grid with parameters and alternatives for the runway system maintenance strategy
In a CCA all alternatives are compared to each other in a pair-wise fashion
to check for incompatibilities. The incompatibilities found through the CCA
are:
• The alternatives ‘June’ and ‘night’, from the two parameters ‘month’
and ‘timeslot’, are incompatible because one month is too short to
completely carry out a large maintenance project, when it is only
allowed to work during the night;
• Also the combination ‘September’ and ‘night’ is incompatible, because
again one month is too short to perform all maintenance when it is
only allowed to work during the night;
• The alternative ‘night’ is incompatible with the alternative ‘long blocks’,
because the latter assumes that work is done continuously for a long
time. When the runway is used during the day, the blocks cannot be
long;
• ‘Night’ is also incompatible with ‘maximise asset lifetime’, as the splitting of the maintenance over multiple nights (in a large maintenance
project) means that the quality of the work decreases and the maximum
quality cannot be reached;
• ‘Split up activities’ and ‘maximise asset lifetime’ are incompatible, as
the quality of the asset can only be increased when the whole runway
is maintained in one go;
• For the same reason, the alternatives ‘maximise asset lifetime’ and
‘short blocks’ are incompatible;
• ‘Split up activities’ and ‘long blocks’ are incompatible because they
imply the opposite: short blocks for distinct maintenance activities, or
long blocks in which multiple activities are combined, respectively; and
• The alternative ‘landside’ is incompatible with ‘night’, ‘split up activities’ and ‘short blocks’: it is not worth the time and effort for small
maintenance blocks to switch a runway to landside and back.
6.3 alternative maintenance strategies
Figure 6.3–6.5 show these incompatibilities. But even without these combinations, 408 alternative strategies remain possible.
6.3
alternative maintenance strategies
In conclusion, Figure 6.2 shows the morphological grid with all alternative
options for the runway system maintenance strategy. In combination with the
results from the CCA (Figure 6.3–6.5), 408 alternative maintenance strategies
can be generated.
A closer look at the grid in Figure 6.2 reveals that most parameters for
the maintenance strategy are concerned with the scheduling of maintenance
activities: the parameters ‘month’, ‘timeslot’ and ‘weekends’ all refer to when
maintenance is carried out. In a broader sense, also the parameter ‘quality of
assets’ deals with planning, as this parameter determines how often maintenance must be carried out. This warrants the observation that apparently, at
Amsterdam Airport Schiphol the maintenance strategy is primarily about the
scheduling of activities. This means that other decisions in the maintenance
process depend on the tactical and operational translation of the chosen
strategy. For example, the choice for specific materials used during runway
maintenance depends on the strategic choice for the parameter ‘quality of
assets’. Furthermore, whether there is a need for technical solutions for working during the night or in winter conditions, depends on the strategic choice
for the parameter ‘month’ or ‘timeslot’. This observation is consistent with
the choice, mentioned in Section 2.2.1, to outsource more of the maintenance
process to contractors.
Now the question remains how much value the alternative strategies in
the morphological grid create, compared to the current situation. This is the
subject of the next chapter.
79
80
creation of alternatives
Month
Yearlong
April-September
October-March
Timeslot
24h
Night (23-06h)
Day (06-23h)
Weekends
All week
Not during
weekends
Only during
weekends
Combination of
activities
Combine as much as
possible
Split up activities
Quality of assets
Schiphol norms
(stricter than ICAO)
ICAO minimum
Length of
maintenance blocks
Short blocks
Long blocks
Maintenance area
Landside
Airside
September
June
June-September
Maximise asset
lifetime
Figure 6.3: Incompatible options for the alternative ‘night’
Month
Yearlong
April-September
October-March
Timeslot
24h
Night (23-06h)
Day (06-23h)
Weekends
All week
Not during
weekends
Only during
weekends
Combination of
activities
Combine as much as
possible
Split up
activities
Quality of assets
Schiphol norms
(stricter than ICAO)
ICAO minimum
Length of
maintenance blocks
Short blocks
Long blocks
Maintenance area
Landside
Airside
September
June
June-September
Maximise asset
lifetime
Figure 6.4: Incompatible options for the alternative ‘split up activities’
Month
Yearlong
April-September
October-March
Timeslot
24h
Night (23-06h)
Day (06-23h)
Weekends
All week
Not during
weekends
Only during
weekends
Combination of
activities
Combine as much as
possible
Split up activities
Quality of assets
Schiphol norms
(stricter than ICAO)
ICAO minimum
Length of
maintenance blocks
Short blocks
Long blocks
Maintenance area
Landside
Airside
September
June
Maximise asset
lifetime
Figure 6.5: Incompatible options for the alternative ‘short blocks’
June-September
7
C A L C U L AT I O N O F T H E VA L U E O F A LT E R N AT I V E S
This research aims to improve the runway system maintenance strategy
at Amsterdam Airport Schiphol by taking into account the interests of all
relevant stakeholders, using the Value Operations Methodology. The key
concept in this methodology is the change in value (∆V). As explained in
Chapter 3 and 4, ∆V represents an alternative’s change in value with respect
to a reference situation. This change in value is determined by calculating the
alternative’s impact on the attributes defined in the value model. The value
model for the maintenance strategy is built in Chapter 5. In this value model,
all stakeholders, their objectives, preferences and value trade-offs have been
taken into account. Using a morphological grid, 408 different alternative
strategies have been created in Chapter 6. This chapter uses the value model
to calculate the change in value of these alternatives.
Firstly, Section 7.1 treats the reference situation: the large maintenance
project (GOH) on the Kaagbaan in September 2011. Next, Section 7.2 explains
how ∆V is calculated and presented: first using a qualitative analysis of
all individual options; then using a quantitative computation of the most
promising combinations of options. Section 7.3 and 7.4 show the results of
the qualitative and quantitative analysis, respectively. Afterwards, Section
7.5 extrapolates these results to the other runways and maintenance projects.
Finally, Section 7.6 discusses the conclusions for Amsterdam Airport Schiphol.
Figure 7.1 shows the structure of this chapter schematically.
Qualitative assessment of
attribute impact
(Appendix H)
Alternatives
(Chapter 6)
∆V for all
stakeholders
(§7.3)
combination of
promising alternatives
Quantitative calculation of
attribute impact
(Appendix H)
Reference situation
(§7.1)
∆V for all
stakeholders
(§7.4)
Extrapolation to other
runways and maintenance
(§7.5)
Conclusions for the overall
maintenance strategy
(§7.6)
Figure 7.1: Overview of the steps necessary to calculate the value of alternatives
7.1
choice of reference situation
As this research focuses on finding alternative runway system maintenance
strategies, the most logical reference situation is the current strategy, as
described in Section 2.6. However, not the complete strategy, but a concrete
maintenance project was chosen as the reference. The reason for this is
81
82
calculation of the value of alternatives
that for a single project the impact of an alternative is relatively easily
determined, and the effect of the alternatives can be clearly shown. For the
complete strategy, encompassing hundreds of activities, this would be much
more complex and less enlightening. At the end of this chapter, Section 7.6
extrapolates the results to the overall maintenance strategy.
The maintenance project chosen is the large maintenance project (GOH)
on the Kaagbaan in September 2011. This project is representative for most
GOH projects on runways. Furthermore, as the Kaagbaan is one of the most
important runways for Amsterdam Airport Schiphol, improvements in the
maintenance strategy are much welcomed. Finally, because this project was
done recently, information and data is readily available.
Month
Yearlong
April-September
October-March
Timeslot
24h
Night (23-06h)
Day (06-23h)
Weekends
All week
Not during
weekends
Only during
weekends
Combination of
activities
Combine as much
as possible
Split up activities
Quality of assets
Schiphol norms
(stricter than ICAO)
ICAO minimum
Length of
maintenance blocks
Short blocks
Long blocks
Maintenance area
Landside
Airside
September
June
June-September
Maximise asset
lifetime
Figure 7.2: Grid showing the maintenance strategy chosen for the GOH project on the Kaagbaan in September 2011
Figure 7.2 shows the maintenance strategy for this reference case in the
morphological grid. The project was carried out in September, during day
and night, all week long, essentially in one long block of 21 days (BAS,
2011b,c; Heijmans, 2011a). In line with the current strategy, activities were
combined as much as possible and performed according to the Schiphol
norms (Meijerhof, 2011). Finally, the maintenance area was turned into
landside, with the exception of an aircraft crossing in the middle of the
runway (Huang, 2011).
7.2
calculation of the impact of alternatives
As seen in the previous chapter, the morphological grid results in a total of
408 possible maintenance strategies (excluding incompatible combinations).
These combined strategies are not all evaluated; instead at first each option
from the grid is considered individually. In this way, only 6 + 3 + 3 + 2 + 3 +
2 + 2 = 21 alternatives must be evaluated. Essentially this means that for each
evaluated alternative, everything stays the same as in the reference situation,
except for one parameter. For example, when evaluating the alternative
option ‘not during weekends’, the actual evaluated strategy is the same as in
the reference situation (see Figure 7.2) except for the parameter ‘weekend’.
This also means that incompatibilities found in Section 6.2 are not taken into
account in this analysis.
7.2 calculation of the impact of alternatives
83
The first analysis of the 21 options is done qualitatively. Here, the impact
of an alternative on an attribute is scored according to the seven-option scale,
ranging from + + + to − − −, as explained in Section 4.8.2. This results in
a change-impact matrix. Then, these qualitative scores are converted using
three different methods to numerical values, that are used in the value
function to calculate ∆V. In the results in Section 7.3, these three score
conversions result eventually in three different ∆V. The resulting charts show
the median value, and plot the range in ∆V using error bars (see Figure 7.3).
This gives the range of ∆V for the alternative.
+,-./01-23.#%
+,-./01-23.#(
!"#$%&%&%' !"#$%&(&)' !"#$%&)&*'
)(456
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)456
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"#$%
Maximum
Median
Minimum
&#$%
'#$%
(#$%
)#$%
*#$%
+#$%
!"#
#$%
!+#$%
!*#$%
!)#$%
!(#$%
Figure 7.3: Overview
of how results are presented graphically
!'#$%
After all options are qualitatively
analysed, the most promising ones
!&#$%
are combined into two scenarios. These scenarios take into account the
incompatibilities from the !"#$%
cross-consistency assessment. The ∆V of these two
,,-%presented ./0.%
0789473%:9;<=%
.9>73%:9;<=%
scenarios is calculated quantitatively and
in Section 7.4.,1231456%
For both the qualitative and quantitative analysis, the important result is
not the exact value of ∆V, as this depends on the exact attribute values and .74@61@5% ,1261@5%
the conversion method for the qualitative analysis. Rather, it is important
whether the (range of) ∆V is positive or negative, according to the decision
rule presented in Section 4.6: if ∆V is positive, the alternative creates value;
when ∆V is negative, the alternative destroys value compared to the reference
situation.
Before the results are presented, two remarks have to be made concerning
the parameter ‘quality of assets’ and the noise attributes.
?561
84
calculation of the value of alternatives
Parameter ‘quality of assets’
The first remark concerns the alternatives in the parameter ‘quality of assets’.
The reference situation uses the ‘Schiphol norms’. The other two alternatives,
however, have a different impact on the attributes when looking from two
different perspectives:
1. The perspective of a single (GOH) maintenance project, such as the
reference Kaagbaan project;
2. The perspective that looks at the complete lifetime of the asset.
From the first perspective, the alternative ‘ICAO minimum’ with lower
asset requirements, will result in less work and thus result in a decrease in the
attribute ‘maintenance costs’. On the other hand, the alternative ‘maximise
asset lifetime’ will increase costs.
However, the results are different from the second perspective. Over the
long timespan of the asset’s lifetime, the alternative ‘maximise asset lifetime’
may well reduce the total maintenance costs, while the alternative ‘ICAO
minimum’ may increase costs. As these counter effects from the different
perspectives cannot be evaluated in one alternative, both alternatives are
evaluated twice: once from the perspective of one project, and once from the
perspective of the asset’s lifetime.
Noise attributes
The second remark regards the two noise attributes. Brouwer et al. (2011,
Slide 12) show that maintenance that takes the Kaagbaan under service has
no effect on the number of houses or highly annoyed people within the noise
contours. This means that these two attributes will show a value change
of 0 for all alternatives. However, this does not hold for the other runways.
For instance, the same study shows that shutting down the Polderbaan for
maintenance causes a significant increase in houses and people within the
noise contours. Here, alternatives that shorten the maintenance may result in
a higher ∆V due to this effect. And maintenance on the Buitenveldertbaan
even results in a lower noise load for the surroundings. Generalisation of the
Kaagbaan results must take these effects into account.
7.3
qualitative analysis of all alternative options
As mentioned in the previous section, the qualitative analysis looks at each
option from the morphological grid individually. This means that the incompatibilities from the cross-consistency assessment are not taken into
account.
Table 7.1 shows the change-impact matrix used for the qualitative analysis.
Appendix H explains each entry in the matrix. This section presents the
results per parameter, by showing the ∆V for each alternative for each stakeholder, and for the average stakeholder. This is done so that the differences
between stakeholders are not hidden in only one, average value; instead they
are presented explicitly.
Because of the confidentiality of the individual stakeholders’ objective weight
factors, in the public version of this thesis the ∆V are not shown for the
individual stakeholders.
7.3 qualitative analysis of all alternative options
alternative
dus
85
opp
cos
del
nss
saf
efc
env
res
hou
hap
parameter ‘month’
Yearlong
+
+
+
+
0
0
0
0
0
0
0
April–September
+
++
+
0
0
0
0
0
0
0
0
+++
−
+++
+++
0
0
+
+
+
0
0
September
0
0
0
0
0
0
0
0
0
0
0
June
0
0
0
−
0
0
0
0
0
0
0
June–September
0
+++
0
−
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
−−−
−−−
+++
0
0
+
−−
+
−
0
0
++
0
−
0
0
0
+
−
0
0
0
All week
0
0
0
0
0
0
0
0
0
0
0
Not during weekends
+
0
−−
0
0
0
0
0
+
0
0
Only during weekends
+
0
+++
0
0
0
0
0
−
0
0
October–March
parameter ‘timeslot’
24h
Night (23–06h)
Day (06–23h)
parameter ‘weekends’
parameter ‘combination of activities’
0
0
0
0
0
0
0
0
0
0
0
++
0
++
−−
0
+
0
0
−
0
0
0
0
0
0
0
0
0
0
0
0
0
ICAO minimum
(project)
−−
0
−−
−−
0
0
−
0
0
0
0
Maximise asset lifetime
(project)
++
0
++
++
0
0
+
0
−
0
0
ICAO minimum
(lifetime)
0
0
0
−−
+
+
0
0
−
0
0
Maximise asset lifetime
(lifetime)
0
0
0
++
−
−
0
0
0
0
0
Combine as much as
possible
Split up activities
parameter ‘quality of assets’
Schiphol norms (stricter
than ICAO)
parameter ‘length of maintenance blocks’
Short blocks
−−−
0
+++
++
+++
+
−−
0
−−
0
0
Long blocks
0
0
0
0
0
0
0
0
0
0
0
parameter ‘maintenance area’
Landside
0
0
−
0
0
−
+
0
0
0
0
Airside
0
0
++
0
0
++
−
0
0
0
0
Table 7.1: Change-impact matrix showing the impact of alternatives on the set of attributes. The entries are explained
in Appendix H.
86
calculation of the value of alternatives
40%%
30%%
20%%
10%%
0%%
!10%%
∆V# !20%%
!30%%
!40%%
!50%%
!60%%
!70%%
!80%%
AAS%
LVNL%
Yearlong%
Airlines%
April!September%
Na9onal%gov't%
October!March%
Local%gov't%
September%
Residents%
June%
Passengers%
Average%
June!September%
Figure 7.4: Qualitative results for parameter ‘month’. The alternative ‘September’ is used in the reference situation,
so it has a ∆V of 0.
60%%
50%%
40%%
30%%
20%%
10%%
0%%
∆V#
!10%%
!20%%
!30%%
!40%%
!50%%
!60%%
!70%%
AAS%
LVNL%
Airlines%
Na8onal%gov't%
24h%
Night%(23!06h)%
Local%gov't%
Residents%
Passengers%
Average%
Day%(06!23h)%
Figure 7.5: Qualitative results for parameter ‘timeslot’. The alternative ‘24h’ is used in the reference situation, so it
has a ∆V of 0.
7.3 qualitative analysis of all alternative options
87
10%%
0%%
!10%%
∆V#
!20%%
!30%%
!40%%
AAS%
LVNL%
Airlines%
All%week%
Na5onal%gov't%
Not%during%weekends%
Local%gov't%
Residents%
Passengers%
Average%
Only%during%weekends%
Figure 7.6: Qualitative results for parameter ‘weekends’. The alternative ‘all week’ is used in the reference situation,
so it has a ∆V of 0.
10%%
0%%
!10%%
!20%%
∆V# !30%%
!40%%
!50%%
!60%%
!70%%
AAS%
LVNL%
Airlines%
Na8onal%gov't%
Combine%as%much%as%possible%
Local%gov't%
Residents%
Passengers%
Average%
Split%up%ac8vi8es%
Figure 7.7: Qualitative results for parameter ‘combination of activities’. The alternative ‘combine as much as possible’
is used in the reference situation, so it has a ∆V of 0.
88
calculation of the value of alternatives
60%%
50%%
40%%
30%%
20%%
10%%
∆V#
0%%
!10%%
!20%%
!30%%
!40%%
!50%%
!60%%
!70%%
AAS%
LVNL%
Airlines%
Na8onal%gov't%
Local%gov't%
Schiphol%norms%(stricter%than%ICAO)% ICAO%minimum%(project)%
ICAO%minimum%(life8me)%
Residents%
Passengers%
Average%
Maximise%asset%life8me%(project)%
Maximise%asset%life8me%(life8me)%
Figure 7.8: Qualitative results for parameter ‘quality of assets’, showing ∆V for both a project and lifetime perspective.
The alternative ‘Schiphol norms’ is used in the reference situation, so it has a ∆V of 0.
20%%
10%%
0%%
!10%%
∆V# !20%%
!30%%
!40%%
!50%%
!60%%
AAS%
LVNL%
Airlines%
Na7onal%gov't%
Short%blocks%
Local%gov't%
Residents%
Passengers%
Average%
Long%blocks%
Figure 7.9: Qualitative results for parameter ‘length of maintenance blocks’. The alternative ‘long blocks’ is used in
the reference situation, so it has a ∆V of 0.
7.3 qualitative analysis of all alternative options
89
70%%
60%%
50%%
40%%
30%%
20%%
10%%
∆V#
0%%
!10%%
!20%%
!30%%
!40%%
!50%%
!60%%
!70%%
AAS%
LVNL%
Airlines%
Na8onal%gov't%
Landside%
Local%gov't%
Residents%
Passengers%
Airside%
Figure 7.10: Qualitative results for parameter ‘maintenance area’
CONFIDENTIAL
This image has been removed from the public version of this thesis
Figure 7.11: Costs vs. ∆V for Amsterdam Airport Schiphol for all qualitatively analysed alternatives. Only median values are displayed. The horizontal
axis represents maintenance costs (negative values indicate lower costs
compared to the reference situation). The vertical axis expresses ∆V for
Amsterdam Airport Schiphol. Here, ∆V is recalculated to exclude the
costs objective.
Average%
90
calculation of the value of alternatives
Parameter ‘month’
Figure 7.4 shows the results for the parameter ‘month’. As the alternative
‘September’ is used in the reference situation, this alternative results in a
∆V of 0. Clearly, this option performs quite well, as most other alternatives
have a negative value for all stakeholders. This is because, due to worse
weather conditions, maintenance would take longer in the other alternatives,
resulting in a decrease in value for the capacity and costs objectives.
Only the alternative ‘June’ outperforms the current situation, with a positive ∆V for all stakeholders. However, the difference is low, averaging only a
5 percent increase in value.
Parameter ‘timeslot’
Figure 7.5 shows the results for the parameter ‘timeslot’. As the alternative
‘24h’ is used in the reference situation, it results in a ∆V of 0. On average,
the alternative ‘night’ creates value. This is the result of the large increase in
capacity due to the availability of the runway during the day. The alternative
‘day’ results in a negative ∆V due to the longer maintenance period and the
resulting decrease in capacity, even though costs would be lower.
Parameter ‘weekends’
Figure 7.6 shows the results for the parameter ‘weekends’. As the alternative
‘all week’ is used in the reference situation, it results in a ∆V of 0. The
alternative ‘not during weekends’ results in a negative value. This is due to
the lower costs (maintenance during the weekend is more expensive) and the
small decrease in capacity. Vice versa, scheduling all maintenance during the
weekend results in higher costs, which explains the high negative change in
value.
Parameter ‘combination of activities’
Figure 7.7 shows the results for the parameter ‘combination of activities’.
As the alternative ‘combine as much as possible’ is used in the reference
situation, its ∆V is 0. The other alternative in which activities are split up,
performs much worse. This is due to an increase in maintenance duration,
costs and safety risks.
Parameter ‘quality of assets’
Figure 7.8 shows the results for the parameter ‘quality of assets’. As the
alternative ‘Schiphol norms’ is used in the reference situation, its ∆V is 0.
The aforementioned difference between the two perspectives on quality
can be clearly seen in the chart. From a project perspective, the alternative
‘ICAO minimum’ results in a positive ∆V, mainly due to a shorter timeframe
and lower costs. Vice versa, in this perspective the alternative ‘maximise asset
lifetime’ results in a negative value change. From a lifetime perspective, however, the results are much more mixed. For the alternative ‘ICAO minimum’
at least part of the ∆V range is negative; and the alternative ‘maximise asset
lifetime’ ends up much more positively. Overall, these results show that the
perspective one takes to analyse an alternative can make quite a difference in
the ultimate ∆V. Therefore it is recommended that this alternative is further
researched to combine both perspectives.
7.4 quantitative analysis of two scenarios
Parameter ‘length of maintenance blocks’
Figure 7.9 shows the results for the parameter ‘length of maintenance blocks’.
As the alternative ‘long blocks’ is used in the reference situation, its ∆V is 0.
The other alternative ‘short blocks’ has a negative ∆V. This is mainly due to
higher maintenance costs and the increase in delay, resulting in a decrease in
predictability.
Parameter ‘maintenance area’
Figure 7.10 shows the results for the parameter ‘maintenance area’. As
mentioned in Section 7.1, in the reference situation most of the maintenance
area was made landside, except for one aircraft crossing. Therefore, the
alternative ‘landside’ gives a positive ∆V, thanks to lower costs and lower
safety risks. On the other hand, the alternative ‘airside’ costs more and
increases safety risks, and results therefore in a negative ∆V.
Overall, the qualitative analysis shows that only a few alternatives result in
a positive ∆V, meaning that only a few alternatives improve on the current
situation. This indicates that the current maintenance strategy of Amsterdam
Airport Schiphol is already quite good.
Because in practice the maintenance costs are an important factor in
choosing the maintenance strategy, the costs for all alternatives are shown
separately on the horizontal axis in Figure 7.11. On the vertical axis an
adapted ∆V for Amsterdam Airport Schiphol is shown: ∆V is recalculated
to exclude the costs objective, because it is already shown separately.
Of course, the results as presented above only reflect the qualitative analysis of a single maintenance project on one runway. Further research is
necessary to translate these results to the whole airport. The most promising
alternatives are combined into two scenarios and analysed quantitatively in
the next section.
7.4
quantitative analysis of two scenarios
The results from the previous section give a first impression of the value of
the alternatives, but have two important limitations:
• ∆V is based only on a qualitative assessment; and
• The incompatibilities from the cross-consistency assessment as explained in Section 6.2 are not taken into account.
Therefore this section combines the most promising options from the qualitative analysis into two scenarios that are assessed quantitatively, taking into
account the results from the cross-consistency assessment:
1. The first scenario is called ‘nightly maintenance’. This scenario starts
from the alternative ‘night’, which shows a large positive ∆V for the
aviation stakeholders (see Figure 7.5). The selected options from the
other parameters are:
• From the parameter ‘month’ the best alternatives are ‘June’ and
‘September’. Unfortunately, according to the CCA these are both
incompatible with ‘night’. Therefore the alternative ‘June–September’
is chosen;
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calculation of the value of alternatives
• Furthermore, the alternatives ‘not during weekends’, ‘combine as
much as possible’ and ‘Schiphol norms’ are selected;
• As the best alternatives ‘long blocks’ and ‘landside’ are incompatible with maintenance during the night, the alternatives ‘short
blocks’ and ‘airside’ are chosen.
Essentially, this scenario means that all maintenance is done during the
night. At the end of the night, all maintenance equipment and materials
are removed and the runway is made operational for the day. Then, at
the beginning of the night, maintenance picks up again. A strategy like
this was used for runway maintenance in Frankfurt and Eindhoven,
indicating that it is technically possible;
2. The second scenario is called ‘summer maintenance’. It starts with the
alternative ‘June’, as this option showed a positive ∆V in Figure 7.4.
The selected options from the other parameters are:
• ‘Combine as much as possible’, ‘Schiphol norms’, ‘long blocks’
and ‘landside’;
• As these alternatives are incompatible with ‘night’, the alternative
‘24h’ is chosen in the ‘timeslot’ parameter;
• Although the alternative ‘not during weekends’ is only slightly
positive for Amsterdam Airport Schiphol, this alternative is included because it represents a noted change to the current strategy.
Figure 7.12 shows the chosen alternatives for each scenario graphically. The
resulting attribute values are shown in Table 7.2 and explained in Appendix
H. Also shown in this table, the reference situation is equal to the reference
situation in the qualitative analysis: the GOH project on the Kaagbaan in
September 2011. Figure 7.13 shows the resulting ∆V for these two scenarios.
Because of the confidentiality of the individual stakeholders’ objective weight
factors, in the public version of this thesis the ∆V are not shown for the
individual stakeholders. Moreover, due to the confidentiality of the total
maintenance costs, this information is removed from Table 7.2.
scenario
dus
opp
21
6
eXX, 000, 000
6
0%
—
xmin
0
1
e0
0
0%
xmax
30
8
eXX, 000, 000
24
Nightly
0
4
eXX, 500, 000
Summer
25
6
eXX, 000, 000
Reference
cos
del
nss
saf
efc
env
res
hou
hap
e500, 000
2
4
0
0
—
e−1, 000, 000
1
1
0
0
100%
—
e1, 000, 000
3
8
12, 300
239, 500
20
0%
—
e50, 000
3
4
0
0
3
0%
—
e550, 000
2
4
0
0
Table 7.2: Attribute values for the ‘nightly maintenance’ and ‘summer maintenance’ scenarios. Also the reference
values and the feasible range values xmin and xmax are listed. The entries are explained in Appendix H.
7.4.1 Results for the ‘nightly maintenance’ scenario
Figure 7.13 shows that the ‘nightly maintenance’ scenario creates value. To
analyse the reasons for this, Figure 7.14 shows each objective’s contribution
7.4 quantitative analysis of two scenarios
Month
Yearlong
April-September
October-March
Timeslot
24h
Night (23-06h)
Day (06-23h)
Weekends
All week
Not during
weekends
Only during
weekends
Combination of
activities
Combine as much as
possible
Split up activities
Quality of assets
Schiphol norms
(stricter than ICAO)
ICAO minimum
Length of
maintenance blocks
Short blocks
Long blocks
Maintenance area
Landside
Airside
September
Maximise asset
lifetime
93
June
June-September
legend
Nightly
maintenance
Summer
maintenance
Figure 7.12: Chosen alternatives for the two quantitative scenarios. Grey boxes indicate the alternatives in the ‘nightly
maintenance’ scenario; alternatives with a thick border are selected for the ‘summer maintenance’
scenario.
to ∆V for this scenario. This figure shows that only the capacity objective
creates value; all other objectives perform worse than the reference situation.
However, the gain in capacity is so large that the overall ∆V is positive.
Safety considerations for ‘nightly maintenance’
As explained in Section 5.4, the safety attribute cannot be calculated quantitatively. Therefore the effect of the ‘nightly maintenance’ scenario on safety
has not been included in Table 7.2 and Figure 7.13.
However, this scenario would definitely increase safety risks. In the scenario, every morning the runway switches from a maintenance area to an
active runway, and the reverse happens every evening. Every switch creates
new safety risks, for instance a runway incursion resulting from leftover
materials, equipment or debris on the runway. Moreover, LVNL and pilots
have to deal with a situation that changes twice per day. This also increases
the risk of runway confusion, possibly resulting in an aircraft using a runway under service. This can have serious consequences: in 2000, a Boeing
747-400 accidentally took off from a runway that was under construction at
Taipei-Chiang Kai Shek Airport, resulting in an accident with 83 fatalities
(Aviation Safety Network, 2009).
Unfortunately, these risks cannot be determined quantitatively, partly
because there is little to no data available on maintenance related risks.
Nonetheless, Figure 7.14 gives an indication of the relative effect of the
safety objective on ∆V, based on stakeholders’ weight factor for this objective.
This shows that the increased safety risk will negatively impact ∆V, but on
average the effect is small.
Moreover, safety risks can be mitigated by taking appropriate measures,
for instance more rigorous Foreign Object Debris (FOD) checks each morning
or additional briefings to increase awareness for air traffic controllers and
pilots. Such mitigation measures may decrease the safety risk, but at the
same time increase costs, resulting in a zero net change to ∆V.
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calculation of the value of alternatives
90%%
80%%
70%%
60%%
50%%
40%%
∆V#
30%%
20%%
10%%
0%%
!10%%
!20%%
AAS%
LVNL%
Airlines%
Na:onal%gov't%
Nightly%maintenance%
Local%gov't%
Residents%
Passengers%
Average%
Summer%maintenance%
Figure 7.13: Results for the two quantitatively analysed scenarios
100%%
90%%
80%%
70%%
60%%
50%%
40%%
∆V#
30%%
20%%
10%%
0%%
!10%%
!20%%
!30%%
!40%%
!50%%
AAS%
LVNL%
Capacity%
Airlines%
Costs%
Na:onal%gov't%
Predictability%
Local%gov't%
Environment%
Residents%
Nuisance%
Passengers%
Average%
Safety%
Figure 7.14: Quantitative results for the ‘nightly maintenance’ scenario broken up per objective. The values for the
safety objective are only an indication as these cannot be computed quantitatively.
7.5 extrapolation to other runways and maintenance projects
It is recommended that Amsterdam Airport Schiphol studies the additional
safety risks in this scenario to get more insight in the risks and possible
mitigation measures.
7.4.2
Results for the ‘summer maintenance’ scenario
Figure 7.13 clearly shows that the ‘summer maintenance’ scenario does not
create value. It results in a negative ∆V. This is due to the decrease in capacity
because no maintenance is done during the weekend. The resulting lower
maintenance costs cannot make up for this difference. It can therefore be
concluded that this scenario will not improve the runway system maintenance
strategy at Amsterdam Airport Schiphol.
7.5
extrapolation to other runways and maintenance projects
The previous sections calculated the value of the alternatives compared to
the GOH project on the Kaagbaan. This section discusses the extrapolation of
these results to Schiphol’s other runways, and to other types of maintenance.
7.5.1
Extrapolation to other runways
As mentioned, the analysis performed in this chapter compares the alternatives only to maintenance on the Kaagbaan. Schiphol has five more runways
(see Appendix B), each with their own characteristics that may influence the
∆V of alternative maintenance strategies. The choice of a specific runway
especially affects the safety, extra fuel and noise attributes.
Safety risks
As explained in the previous section, part of the safety risk is the result of
a possible runway confusion by pilots or air traffic controllers. This risk is
higher for often-used runways and runways nearby the terminal (such as the
Kaagbaan and the Aalsmeerbaan), than for runways that are less often used
and further away (such as the Oostbaan and the Polderbaan, respectively).
This means that when extrapolating the results from the Kaagbaan, the
safety risk will likely stay the same or decrease, resulting in a higher ∆V.
Extra fuel costs
The environmental objective is partly measured by extra fuel costs due
to maintenance. In the case of maintenance on the Kaagbaan, these costs
are relatively high compared to other runways. This is because when the
Kaagbaan is under service, aircraft must taxi to further-away runways. Vice
versa, when the Polderbaan, Zwanenburgbaan or Aalsmeerbaan is under
service, these extra fuel costs will be negative because aircraft have to taxi
less (Brouwer et al., 2011, Slide 16).
Table 7.2 shows that in the ‘nightly maintenance’ scenario the extra fuel
costs are lower than in the reference, because the runway is operational
during the day. This results in an increase in ∆V. However, lowering the
number of taxi detours for maintenance on the Polderbaan, Zwanenburgbaan
or Aalsmeerbaan will (slightly) decrease ∆V.
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calculation of the value of alternatives
Noise load
As mentioned in Section 7.2, maintenance on the Kaagbaan has no effect on
the two noise attributes. However, this does not hold for the other runways.
For instance, when the Polderbaan is under service the number of houses and
highly annoyed people within the noise contours increase up to 15 percent for
a four-week maintenance period (Brouwer et al., 2011, Slide 12). This means
that the ‘nightly maintenance’ scenario, which reduces the number of days
the runway is under service, will result in a higher ∆V. Vice versa, shutting
down the Buitenveldertbaan will result in a lower noise load for the local
community (Brouwer et al., 2011, Slide 12). The ∆V of an alternative strategy
that reduces the maintenance duration will thus be lower for this runway.
However, because this runway is needed during certain wind conditions,
alternatives that lengthen the maintenance duration may not be acceptable.
In the value model, this may be expressed through the operational penalty
attribute.
7.5.2
Extrapolation to other maintenance projects
The reference situation for the analyses in this chapter was a large maintenance project on the Kaagbaan. Smaller maintenance projects have not been
analysed, and the effects of alternative strategies on these smaller projects
are uncertain.
Still, a general trend can be inferred from these analyses: increasing capacity creates a lot of value and easily compensates higher costs. So, also for
smaller activities an alternative strategy that schedules maintenance outside
peak hours (e.g. during the night) will likely create value.
Another limitation with translating the results of the Kaagbaan to the
complete maintenance strategy are the total costs. As mentioned above
and explained in Appendix H, the ‘nightly maintenance’ scenario is 50
percent more expensive. While this expense results in a positive ∆V, the
financial position of Amsterdam Airport Schiphol may bar the extension of
this strategy to the complete maintenance strategy for all runways. In other
words, Amsterdam Airport Schiphol may simply not be able to afford a 50
percent increase in its total runway system maintenance budget, without
diverting resources from other investments. This limit on the overall possible
increase in costs is not incorporated in the value model. On the other hand,
increasing the capacity may increase revenue from airport fees and sales, so
that higher maintenance costs may be affordable.
7.6
conclusions for the overall maintenance strategy
The analysis of the alternatives results in three main conclusions for Amsterdam Airport Schiphol:
1. The value model provides a trade-off framework for making decisions
regarding the maintenance strategy;
2. The current maintenance strategy performs better than most alternatives; and
3. The maintenance strategy can be improved by scheduling more maintenance during the night and making the runway available during the
day.
7.6 conclusions for the overall maintenance strategy
Regarding the first conclusion, this research noted in Section 2.5 that although the maintenance strategy has been studied many times in the past,
these studies lack a clear trade-off mechanism for alternative strategies. As
shown in this chapter, the value model built using the Value Operations
Methodology is very useful in providing such a trade-off framework: all alternatives that result in a negative ∆V are discarded, and only the alternative
strategies with a positive ∆V are considered an improvement to the current
maintenance strategy.
The results in Section 7.3 show that the current maintenance strategy
performs quite well: most alternatives result in a negative ∆V, indicating that
the current strategy is better. This means that Airfield Maintenance Services
has already greatly optimised the strategy. It also means that scheduling
maintenance during the spring and summer is advantageous as it shortens
the maintenance duration; maintenance during the winter months resulted
in value destruction.
Still, the results from this chapter point to one area in which significant
improvements may be found: scheduling maintenance during the night. The
analysis of the ‘nightly maintenance’ scenario shows that the increase in
capacity that can be achieved by making the runway operational during the
day, and performing maintenance during the night, greatly outnumbers (in
terms of ∆V) the increase in costs and other downsides. Amsterdam Airport
Schiphol is therefore recommended to focus on this scenario, and study its
implementation.
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8
VA L I D AT I O N A N D V E R I F I C AT I O N O F T H E VA L U E
MODEL
After the value model was built in Chapter 5, after the alternatives were
created in Chapter 6 and after the value of these alternatives was determined
in Chapter 7, the question remains whether these outcomes are correct and
useful. To answer this question, this chapter will discuss the validation of the
value model, including the creation of alternatives and the calculation of ∆V.
Section 8.1 explains the validation strategy based on some validation
theory. In Section 8.2–8.6 this strategy is executed, and in Section 8.7 the
conclusions on the validity of the value model are presented.
8.1
validation strategy
The value model as built in this thesis is a representation of the real world.
It tries to describe how different stakeholders in the context of runway
system maintenance define ‘value’, and how these stakeholders make tradeoffs. Furthermore, the model is used to create and evaluate alternative
maintenance strategies. To determine the correctness and usefulness of this
value model, it needs to be validated.
There is a difference between ‘validation’ and ‘verification’ of a model. As
explained by Balci (1994, p.121,123), “model validation deals with building
the right model”, while “model verification deals with building the model
right.” In other words, verification is more related to the inner (mathematical)
workings of the model, while validation encompasses the translation of the
real world to a model, and of the model’s outcomes to the real world.
Figure 8.1 shows the validation strategy and the steps that are necessary
to draw conclusions on the validity of the value model. On a first level, three
main elements have to be validated: the value function as defined in Chapter
5; the morphological grid with alternatives as defined in Chapter 6; and the
calculation of these alternatives’ impact on the attributes, as presented in
Chapter 7 and Appendix H. The latter two are discussed in Section 8.5 and
Section 8.6, respectively.
The validation of the value function is again split up in three parts:
• Validation of the ‘building blocks’ of the value function, or the main input items: are the right stakeholders, objectives and attributes selected?
Are the right objective and attribute weight factors used in the value
function?
• Validation of the inner workings of the value function ‘black box’, e.g.
the choice of factormax and the concepts of linearity and independence;
• Validation of the outcome of the value function: is the outcome ‘correct’?
And is the output in the form of ∆V actually useful to decision makers?
Each part matches one of the theoretical types of validity. The first part on
the validity of the building blocks is essentially about the question whether
the conceptual model is a good representation of the decision problem in
the real world. Landry et al. (1983, §3.1) and Sargent (2007, p.126) call this
‘conceptual validation’. Section 8.2 discusses this type of validity.
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validation and verification of the value model
Validation and
verification
Value function
Creation of
alternatives (§8.5)
Conceptual validity
(§8.2)
Logical validity
(§8.3)
Operational validity
(§8.4)
Input:
building blocks
Black box
Output:
correct and useful?
Calculation of value
of alternatives (§8.6)
Figure 8.1: The validation strategy looks into the value model itself, the creation of alternatives and the calculation
of the value of alternatives. The value model is validated by looking into conceptual, logical and
operational validity. These three concepts can be compared to the input or building blocks of the model;
the functioning of the ‘black box’ itself; and the correctness and usefulness of the output, respectively.
Next, logical validity is discussed in Section 8.3. This type of validity concerns the translation of the conceptual model in a right way to a mathematical
function and a computer program, including the verification of the computer
code (Landry et al., 1983, §3.2).
Finally, Landry et al. (1983, §3.4) and Sargent (2007, p.127) call the validation of the quality of the output ‘operational validation’. This type of validity,
in which both the correctness and the quality of the output is studied, is
discussed in Section 8.4.
Using these three different validity concepts, the value model is validated
from three different perspectives. Based on this ‘triangulation’ conclusions
on the validity of the value model can be drawn.
8.2
conceptual validity
The conceptual validity concerns the model’s conceptual representation of the
decision problem in the real world. For the value function, this representation
results in the sets of stakeholder, objectives and attributes, and the associated
weight factors. These elements are discussed in this section.
8.2.1
Selection of stakeholders, objectives and attributes
The selection and formulation of stakeholders, objectives and attributes is
validated using ‘desk checking’. Desk checking “is the process of thoroughly
examining one’s work to ensure correctness, completeness, consistency, and
unambiguity” (Balci, 1994, p.130). Chapter 4 and 5 explained in detail how
stakeholders, objectives and attributes are selected. Moreover, they have been
discussed with experts within Amsterdam Airport Schiphol to correct any
mistakes. Finally, the correctness, completeness, consistency and unambi-
8.2 conceptual validity
guity of the objectives and attributes are checked using the lists of desired
properties, as discussed in Section 4.2.3, 4.4, 5.2.3 and 5.4.
These checks result in the following conclusions:
• All relevant stakeholders have been identified and are included in the
value model. Although, as mentioned in Section 4.1.4, the method to
classify each stakeholder’s type is not fully validated, the results from
Appendix E are triangulated and thus believed to be valid for use in
the value model;
• The relative weight of each stakeholder (e.g. is Amsterdam Airport
Schiphol as important in the decision making process as the passengers?) is uncertain. This is primarily a theoretical gap in the methodology. Therefore, the ∆V results in Chapter 7 are presented for each
stakeholder separately;
• The set of objectives is complete and valid, apart from some issues
with the understandability, especially regarding the ‘predictability’ and
‘nuisance’ objectives. The meaning of these objectives must be explained
clearly when the value function’s outcomes are presented;
• The set of attributes can be improved. Currently, concepts such as
‘reliability’ and ‘availability’ lack a clear definition, which means these
metrics cannot be used as attributes. The current safety and environmental impact attributes cannot be determined quantitatively.
8.2.2
Objective weight factors
The main uncertainty regarding the conceptual validity of the objective
and attribute weight factors, is whether the interviewed decision maker is
representative for the organisation’s trade-offs. To thoroughly check this,
multiple decision makers from each organisation should be interviewed. Unfortunately, this was not possible for each stakeholder within the timeframe
of this research. Only the objective weight factors of Amsterdam Airport
Schiphol and LVNL are based on multiple interviews. Figure 5.5 shows that
indeed different decision makers from different departments can have widely
varying preferences.
The results of the pair-wise comparisons (Figure 5.5–5.13) have been subjected to ‘face validation’, in which users of the model and experts assess
how reasonable the outcomes are, based on their experience and intuition
(Balci, 1994, §3.1.3). The outcomes were deemed reasonable, and as such the
objective weight factors seem to be valid.
Different viewpoints in the same organisation
The spread in answers by the different departments at Amsterdam Airport
Schiphol was the most discussed in the face validation. Also, there is the question of spread within a single department. Therefore, to give an impression
how different people in the same organisation can have different viewpoints,
six team members of the Airfield Maintenance Services department were
also asked to trade-off the objectives in a pair-wise comparison, using the
same questionnaire as shown in Table A.1.
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validation and verification of the value model
Because of the confidentiality of the individual stakeholders’ objective weight
factors, in the public version of this thesis the comparison between the
manager’s and the team’s trade-offs is removed.
Figure 8.2 and 8.3 also show the range of answers by the six team members.
Figure 8.4 graphs the spread in their answers in more detail. Large spreads
may have a number of reasons:
• The questionnaire was sent by email, which limits the possibility of the
interviewer to make sure that the interviewee understands the question
and answers the questions in a consistent manner;
• The team members may have answered from different perspectives:
their personal beliefs; their professional preferences; or the (perceived)
preferences of Airfield Maintenance Services or even Amsterdam Airport Schiphol as a whole;
• The fact that the six weight factors must always add up to 100 percent
means that an increase in one objective’s weight factor automatically
decreases another weight factor. This may cause a large spread between
two team members, while in fact they only have answered one question
differently.
In conclusion, these figures show that even within the same department,
people may hold widely varying views and make different trade-offs. The
question remains whose preferences are more representative of the actual
trade-offs made in decisions at Amsterdam Airport Schiphol. As managers
have the final say in decisions, their trade-offs as shown in Figure 5.6 are
seen as the most representative.
Sensitivity of passengers’ preferences
As the passengers’ preferences are based on a limited literature study (see
Section 5.3), the results for this stakeholder are uncertain. Therefore a sensitivity analysis has been performed to study the effect of passengers’ preferences
on the average ∆V.
Figure I.1 in Appendix I shows the results. It can be seen that the conclusion
on alternatives, based on a positive or negative ∆V, does not change when
passengers’ preferences are not included. This is because passengers find the
capacity and predictability objectives the most important, which is shared by
other stakeholders. Moreover, their preferences only represent one-seventh
of the average objective weight factors. This means that the uncertainty in
the passengers’ objective weight factors has little impact on the validity of
the average value function.
Sensitivity of objective weight factors
Finally, a sensitivity analysis has been performed of the effect of the objective
weight factors on ∆V. As these weight factors represent the trade-offs made
by the decision makers, it is expected that the change in value is very sensitive
to these weight factors. In other words, if a decision maker makes a different
trade-off, this should be visible in the ∆V.
Figure I.2–I.4 in Appendix I show the results for this sensitivity analysis.
In these charts it can be clearly seen that the objective weight factors indeed
8.3 logical validity
have this large effect on ∆V: the results differ greatly for the different weight
factors. This strengthens the conclusion that the objective weight factors are a
very important element of the value model. When building the value model,
one should thus pay considerable attention to the determination of these
weight factors. Moreover, any uncertainty in these weight factors will be
reflected in uncertain ∆V values.
8.2.3
Attribute weight factors
To assess the validity of the attribute weight factors, the effect of these weight
factors on ∆V is analysed using a sensitivity analysis, shown in Figure I.5–I.7.
Here, the attribute weight factors as explained in Section 5.5 are compared
to a value function in which each attribute is weighted equally (see Table
I.2 for the different weight factors). As can be seen, there are only small
differences between the two cases. This is because some objectives have
only one attribute, always resulting in a weight factor of 100 percent. Other
attribute weight factors are already quite close to each other in the base
case. This means that the uncertainty in the attribute weight factors has little
influence on the overall ∆V. The objective weight factors are much more
important.
Overall it can be concluded that, with some limitations regarding mainly
the measurability of some attributes, the model’s conceptual translation is
valid. And while the passengers’ preferences and the attribute weight factors
are difficult to validate, they have little effect on the eventual ∆V. On the other
hand, the objective weight factors are one of the most important building
blocks of the value model. The representativeness of these weight factors
can be improved by interviewing more decision makers in the stakeholder
organisations.
8.3
logical validity
Now the validity of the conceptual model has been discussed, the next
step in the validation strategy is to assess the logical validity. This concerns
the translation of the conceptual model into a formal, mathematical model
and computer code. This section discusses the impact of a different value
for factormax; different methods for combining stakeholders’ preferences;
linearity and independence concepts; and the verification of the computer
model.
8.3.1
Impact of factormax
In Section 4.3.2 it is argued that the trade-offs by the interviewed decision
makers should be rated on a scale with a maximum value (factormax) of
8. While this choice is supported by a number of arguments, one could
also argue that another value for factormax better captures decision makers’
preferences. Therefore, a sensitivity analysis has been performed to see what
the effect is on ∆V when this factormax value changes. Figure I.8–I.10 show
the results of this analysis. From these three charts, it is clear that the choice
of factormax has a large influence on the eventual ∆V. Especially for the alternative ‘night’, the conclusion on whether the alternative results in a positive
or negative change of value, depends on the value of factormax. This is due
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validation and verification of the value model
to the ‘flattening’ of the objective weight factors: the ranking of preferences
remains the same when the factormax changes, but the magnitude of the
weight factor changes. Figure 8.5 shows this flattening clearly for Stakeholder
X’s preferences.
The value of factormax also affects the consistency ratio, discussed in
Section 3.2.1. Figure 8.6 shows a boxplot of the range of consistency ratios
resulting from the pair-wise comparisons made by the interviewed decision
makers, for different factormax values. This plot clearly shows that the
consistency ratio is highly dependent on factormax. This is because in the
original Analytic Hierarchy Process all values from 1–9 are used in the pairwise comparisons, while in this research only the values 1, factormax, and
sometimes an in-between value are used. For factormax 8, this increases
the consistency ratio. For the original AHP, the maximum allowed ratio is
10 percent. As can be seen from Figure 8.6, for a factormax of 8 all ratios
are above this threshold. However, the consistency of each decision maker’s
pair-wise comparisons was checked during the interviews. Therefore, it is
believed that, even though the consistency ratio is above the threshold, the
comparison matrices used for determining the objective weight factors are
consistent enough. Therefore, the results for factormax 8 are still used to
determine the objective weight factors.
Overall, it must be concluded that the choice of factormax heavily influences the values and the quality of the objective weight factors. The choice
for one particular value of factormax thus increases the uncertainty of the
objective weight factors. It is therefore recommended to further research this
topic. However, as the resulting objective weight factors have been subject to
conceptual validation in Section 8.2.2, these weight factors are still considered
useful for determining ∆V.
8.3.2
Combining stakeholders
Section 4.3.3 discusses different methods to combine assessments from multiple decision makers into one set of objective weight factors. Ultimately, the
arithmetic mean was selected for use in this research. To test the effects of a
different method, Figure I.11–I.13 show the results of a sensitivity analysis of
the use of the geometric mean. It can be seen that the differences between the
two methods are small. Therefore this part of building the model is deemed
sufficiently valid.
8.3.3
Linearity and independence
One of the most important assumptions made in building the value model
was that it can be expressed as an additive utility function (see Section
3.2). According to Keeney (1992), this assumption is only correct if the
objectives and attributes are additive independent. While this is difficult to
prove mathematically, when one has “an appropriate set of fundamental
objectives for the context of a decision, additive independence is probably a
very reasonable assumption” (Keeney, 1992, p.167). This is also supported
by Department for Communities and Local Government (2009, p.25). The
set of fundamental objectives is discussed in detail in Section 5.2, and its
validity in Section 8.2.1. Moreover, the use of an additive utility function is
engrained in the Value Operations Methodology since its inception (Curran
et al., 2010; Repko, 2011; Smulders, 2010). Therefore it is concluded that using
an additive utility function as the basis for the value function is valid.
8.3 logical validity
CONFIDENTIAL
This image has been removed from the public version of this thesis
Figure 8.2: Comparison of objective trade-offs by the manager of AMS and by employees of AMS. Also the range of trade-offs by the team members is
shown using the dashed maximum and minimum lines.
CONFIDENTIAL
This image has been removed from the public version of this thesis
Figure 8.3: Comparison of objective trade-offs by four departments at Amsterdam
Airport Schiphol and by employees of AMS. Also the range of trade-offs
by the team members is shown using the dashed maximum and minimum
lines.
CONFIDENTIAL
This image has been removed from the public version of this thesis
Figure 8.4: Spread in preferences by AMS team members for each objective
Capacity#
60%#
50%#
40%#
Nuisance#
30%#
Costs#
20%#
10%#
0%#
Environment#
Predictability#
Safety#
Stakeholder#X#(base)#
Stakeholder#X#(factormax#2)#
Figure 8.5: An example of ‘flattening’ of the objective weight factors due to a change
in factormax: Stakeholder X’s preferences for a factormax of 8 (base) and
2
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validation and verification of the value model
*!"#
)!"#
(!"#
'!"#
!"#
&!"#
%!"#
$!"#
!"#
+,-./01,2#%#
+,-./01,2#'#
+,-./01,2#3#
Figure 8.6: Boxplot showing the distribution of the consistency ratio for different values of factormax, based on the
pair-wise comparisons made by the interviewed decision makers, excluding the AMS team members
8.4 operational validity
8.3.4
Verification of the computer model
To calculate the change in value several spreadsheets are used. MATLAB
was used to compute the eigenvectors from the comparison matrices. These
computer tools are verified by checking the code after writing it; by following
variables throughout the process; and by incorporating checks into the code
(e.g. verifying that the objective weight factors for each stakeholder add up
to 100 percent). While these actions cannot guarantee that the tools are error
free, the chance that one remains has been minimised.
In conclusion, the logical validity of the value model hinges on the factormax. The choice for a factormax of 2 or 8 determines whether an alternative
results in a positive or negative value change. This research has argued why
a factormax of 8 seems the best choice. Moreover, the resulting objective
weight factors have been conceptually validated.
8.4
operational validity
The operational validity concerns the quality of the outcome of the value
model. The quality is determined both by the correctness and the usefulness
of the outcome. Both elements are discussed in this section.
The use of the value function in Chapter 7 to calculate the change in value
resulting from alternative strategies, is already an operational validation of
the value model: it has been proven that the model can be used to calculate
∆V from a change in attributes.
8.4.1
Correctness of outcome
The correctness of the outcome cannot be validated objectively or proven
mathematically, due to the subjectivity of how ‘value’ is interpreted in the
decision context. This is also mentioned by Collopy (2009, p.15):
“Value models cannot be tested against the real world the way a
physical performance model, like a mass model, can be. Therefore,
the only way to validate a value model is to study its form and
be convinced that the model itself is reasonable.”
Thus, the correctness of the outcome depends on the building blocks and the
methodological concepts on which the value model is created. The validation
of these elements and concepts has been discussed in the previous sections.
Also the correctness of the calculation of the impact of alternatives on the
attributes determines the correctness of the outcome of the value model. This
is discussed in Section 8.6.
In an attempt to assess the correctness of the outcome, the conclusions
on the different maintenance strategies can be compared with those from a
previous study by Brouwer et al. (2011). This is called ‘convergent validation’
(Landry et al., 1983, Table 2). For the Kaagbaan, this study concludes that
normal maintenance should be scheduled in the night, and large maintenance
should be executed in the period May–September, as short as possible. This
does not completely match the conclusions of the value model: schedule
GOH in the nightly hours in the period June–September, breaking up the
maintenance so the total duration is longer. The difference can be explained
by the fact that the cited study does not include all objectives from the value
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validation and verification of the value model
model as built in this research. Moreover, the trade-off mechanism in the
study is not clear, while this research provides a clear trade-off framework
using objective weight factors based on stakeholders’ preferences. Because of
these differences, no conclusions can be made regarding the correctness of
the outcome of the value model based on this convergent validation.
8.4.2
Usefulness of outcome
The convergent validation as mentioned above does, however, help in assessing the usefulness of the outcome of the value model. Previous studies that
analyse alternative maintenance strategies do not present a trade-off that
results in a conclusive advice; instead they usually only offer the changes
on a set of criteria or objectives (see Section 2.5). The value model as presented in this research does offer a final advice, based on a transparent
trade-off process. This is a clear advantage of the value model and proves its
usefulness.
To further assess the usefulness of the outcome of the value model, this
has been discussed with the manager of Airfield Maintenance Services (in
what could be called ‘face validation’). Five topics were discussed:
1. Is the concept and the goal of the value model clear?
2. Are all relevant issues (stakeholders and objectives) included in the
model?
3. Is the outcome, in the form of ∆V, understandable?
4. Is the value model useful and applicable in aiding decision making?
5. What could be improved?
The main conclusions are that the concept and goal of the value model are
clear, and that the idea of ‘value’ is useful in decision making. Furthermore,
the value model and its outcomes cannot only be used in aiding decision
making, but also in explaining and justifying these decisions to other stakeholders. The usefulness of the model can be improved when the costs are not
‘hidden’ in the overall ∆V, but are made explicit; and when ∆V can be split
up per objective, to see where the gains and losses are. These suggestions
have since been incorporated in the visualisation of the outcomes, especially
in Figure 7.11 (showing costs vs. ∆V) and in Figure 7.14 (showing the ∆V for
the ‘nightly maintenance’ scenario broken up per objective).
In conclusion, the value model has been operationally validated as far
as the usefulness is concerned: the primary decision maker for whom the
model has been built has acknowledged the usefulness. The correctness of
the outcome depends on the other types of validity, as discussed in the
previous sections.
8.5
creation of alternatives
The validation of the creation of the alternatives using the morphological
grid, basically concerns two questions: is the grid exhaustive; and are the
resulting alternatives feasible?
8.6 calculation of values of alternatives
8.5.1
Exhaustiveness of the morphological grid
There is no formal way to ensure that the morphological grid is exhaustive,
and that all possible alternatives are found. This is a known issue with General Morphological Analysis (Roozenburg and Eekels, 2003, §7.5.3). However,
through the extensive use of a wide array of sources to find the parameters
and alternatives in the grid, as described in Section 6.1, effort has been
made to make the grid as exhaustive as possible — while at the same time
considering the feasibility of the alternatives.
8.5.2
Feasibility of alternatives
The feasibility of the alternatives has been validated using the cross-consistency
assessment as described in Section 6.2. This CCA ensures that incompatible
combinations of alternatives, based on the feasibility of these combinations,
are excluded.
As mentioned before, the implementation details of the alternatives, and
any impracticalities herein, have not been included in the feasibility check
for the alternatives. These details must be analysed before the conclusions of
the value model are followed.
In conclusion, an inherent characteristic of General Morphological Analysis
and the morphological grid is that they cannot be objectively validated. Still,
effort has been made to validate the exhaustiveness and feasibility of the
alternatives in the morphological grid.
8.6
calculation of values of alternatives
The final part that must be validated is the calculation of the changes in
attribute values, as done in Chapter 7 and Appendix H. This calculation has
been done in two steps: a qualitative analysis of all alternatives separately;
and a quantitative analysis of two combined scenarios. The calculations are
supported by expert opinions and desk research. Still, the outcomes are
partly uncertain due to a lack of data or calculation tools. For instance, the
attributes concerning safety risks and environmental impact could not be
determined quantitatively.
The conversion of the qualitative scoring to numerical inputs for the
value model has been done in three different ways, in what is essentially a
sensitivity analysis (see Section 4.8.2). The indications of the resulting range
in ∆V for each alternative is shown in the qualitative results in Section 7.3.
And, as concluded in that section, while the magnitude of the value change
differs per method, the conclusion whether ∆V is positive or negative is the
same.
In conclusion, this part of the research is less solid than the value model
itself. If one wants to improve on this research, it is recommended that this
part is focused on first.
8.7
conclusions and the criticality of assumptions
The previous sections discussed in detail the different types of validity of the
value model. The conclusion based on this validation is not that the value
model is ‘100 percent valid’. Instead, these validations result in conclusions
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validation and verification of the value model
most
certain
legend
Use of additive
utility function
Methodological
Maintenance
strategy related
Selection of
stakeholders
Correctness of
computer tools
Usefulness of
outcome
Set of
objectives
Objective
weight factors
Method for combining
stakeholders' trade-offs
Feasibility of
alternatives
Translation method of
qualitative analysis
least
important
most
important
Quantitative
assessment
Exhaustiveness of
morphological grid
Set of
attributes
Passengers'
preferences
Choice of
factormax
Attribute weight
factors
least
certain
Change-impact
matrix
Correctness of
outcome
Representativeness of
interviewed decision
makers
Relative weights of
stakeholders
Figure 8.7: Criticality of relevant assumptions plotted on an importance-certainty graph. The graph distinguishes
between methodological and case-specific (maintenance strategy related) assumptions. The arguments
for the location of each box can be found in the previous sections.
8.7 conclusions and the criticality of assumptions
on the certainty and the importance of variables and assumptions in the
model. For instance, it was concluded that the selection of the stakeholders
is quite important and quite certain, while the correctness of the passengers’
value trade-offs are much less certain, but also not that important. All these
conclusions are plotted in Figure 8.7 in a so-called ‘importance-certainty
graph’ (Landry et al., 1983, §4.3). This graph is not so much about the exact
location of each box, but more about in which quadrant each assumption
is: more attention or care should be paid to assumptions in the lower-right
‘most important/least certain’ quadrant than to those in the upper-left ‘least
important/most certain’ one. In this way, the graph gives the user of the
value model a quick overview of its limitations and its strengths. Moreover,
it provides the next researcher with a starting point of where to put effort to
improve the methodology and the value model.
As a final note on the validity of the value model as presented in this
research, it can be concluded that the model works: feeding it alternative
maintenance strategies, it presents the resulting change in value for all
relevant stakeholders. Nonetheless, some important uncertainties remain,
both in the methodology and in the case-specific assumptions (shown in the
lower-right quadrant of Figure 8.7). Therefore, more research should be done
on those elements before the value model’s conclusions are implemented.
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9
DISCUSSION
At the end of this research, this chapter contemplates the strengths and limitations of the methodology, and its value for Amsterdam Airport Schiphol.
Section 9.1 reflects on the merits of the value model and the scope definition.
Next, Section 9.2 discusses how the value model may be used in practice at
Amsterdam Airport Schiphol.
In this research, an attempt has been made to refine the Value Operations
Methodology. This has resulted in a number of improvements to the methodology. A summary of the improved VOM can be found in appendix D. Still,
the methodology has some limitations, as explained in Section 9.3. Section
9.4 lists several further possible improvements to the VOM.
9.1
reflection
The origin of this research is the proposal by Kamminga (2010a). He proposes
a study to the innovation of the runway system maintenance strategy at
Amsterdam Airport Schiphol, while taking into account the interests of all
important stakeholders. This proposal explains that the need for this study
follows from the Alderstafel agreement (explained in Section 2.4.2), which
affects runway use and thus limits the possibilities for maintenance.
Two key goals were formulated in the translation of this proposal to a
research project:
1. Create several alternative maintenance strategies; and
2. Evaluate whether these alternatives improve the current strategy, by
taking into account all relevant stakeholders.
A review of previous studies on the maintenance strategy (see Section 2.5)
showed that these studies propose and analyse a number of alternative
strategies, resulting in several benefits and drawbacks for each alternative.
However, in none of these studies a clear trade-off framework is offered
to decide whether the benefits outweigh the drawbacks; and as such, they
do not offer a conclusion whether the alternative improves on the current
situation.
Therefore, for this research the choice was made to use a framework
founded on decision making based on trade-offs: the Value Operations
Methodology. In this methodology, a value model is built that uses stakeholders’ preferences to create a set of weighted objectives. Using this value
model, for each alternative the change in value compared to a reference
situation is calculated. When this ∆V is positive, the alternative creates value
for the stakeholder and thus the benefits outweigh the drawbacks. If ∆V is
negative, this is not the case. In this way, the value model helps to decide
which alternatives improve on the current strategy.
Merits of the value model
Above, the theoretical benefits of the VOM are mentioned. At the end of the
research, the methodology can be evaluated for its actual merits.
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discussion
As mentioned above, the previous studies lack a clear trade-off framework.
Also, they do not give a complete overview of the relevant stakeholders
and their interests. The value model built in this research addresses both
issues: the model incorporates all relevant stakeholders, their objectives
and preferences; and the model and the resulting ∆V offer a clear trade-off
mechanism and decision rule for alternatives, as shown in Chapter 7. In
this way, the (improved) VOM proves its merits as a decision support tool.
Nevertheless, the previous studies are heavily used to calculate the impact
of alternatives (see Appendix H).
Moreover, this research proposes a method to use the value model to
generate alternatives, based on General Morphological Analysis. This method
is used to fulfill the first goal of this research: creating alternative maintenance
strategies.
Still, the main value of the methodology for Amsterdam Airport Schiphol is
not the exact outcome of the value model, but more the philosophy behind it:
that not all benefits and drawbacks can and should be converted to monetary
values. This is what the analysis of stakeholders’ preferences shows: on
average, costs are only marginally important, while capacity, predictability
and safety are deemed much more important. Because in a cost-benefit
analysis the focus is always on expenses, this makes the costs objective
more important than it is. Therefore, analysing and evaluating each objective
on its own terms instead of in monetary values results in more balanced
conclusions.
Finally, the insights the value model gives into the preferences and interests
of other stakeholders (as shown in the spider charts in Section 5.3) are of
great value to Amsterdam Airport Schiphol: it clearly shows where interests
overlap and where they differ. This knowledge may help in future discussions
and negotiations with these stakeholders regarding runway maintenance.
Scope definition
In Section 1.3 and 2.1, the research scope has been necessarily limited to the
strategy and to the runway system. This means that tactical and operational
issues, and other parcels such as apron maintenance are excluded from the
research.
This limited scope has the effect that the alternatives cannot be implemented without further research. Also, as mentioned at the end of Section
6.3, it is likely that the exclusion of tactical and operational issues limits the
solution space of the alternatives to mostly planning related options. Still,
the focus on strategy results in the contours of an improved maintenance
strategy.
The exclusion of other parcels limits the direct applicability of the conclusions to, for instance, apron maintenance. Nevertheless, the philosophy
behind the value model, the methodology and the general direction of the
conclusions are applicable to other maintenance areas than the runway system: also for apron maintenance a trade-off must be made between capacity
and costs, and between different stakeholders. For the runway system, maintenance during the night offers an increase in capacity that outweigh the
higher costs; this scenario is therefore also worth investigating for other
parcels.
9.2 how to use the value model in practice
9.2
how to use the value model in practice
The final results as presented in Chapter 7 can be used to determine what alternative strategies are viable for implementation. It is strongly recommended
though that Airfield Maintenance Services researches these alternatives more
thoroughly, as the operational implementation of these strategies was not
included in this research.
However, the results of this thesis can be used in more ways at Amsterdam
Airport Schiphol. First of all, as mentioned in Section 8.4.2, the results of the
analysis, the value model and the way in which the value model was built,
can all be used to explain the eventual decisions regarding the maintenance
strategy, not only to people within Amsterdam Airport Schiphol, but also to
other stakeholders outside the airport organisation.
Furthermore, the value model and especially each stakeholder’s preferences (as shown in Figure 5.5–5.12) can be used in other projects and
decisions regarding maintenance at Schiphol. To this end, it is possible to
use the value model as the basis of a computer decision support tool, built
for instance in Microsoft Excel.
Finally, the approach and methodology as used to build the value model in
the context of maintenance, can also be used to create value models for other
topics or projects within Amsterdam Airport Schiphol. Especially projects
or decisions that involve multiple stakeholders with varying objectives and
preferences may benefit from this approach.
9.3
limitations of the value model and the results
During the validation in Chapter 8, most assumptions underlying the value
model have been tested. The results of these tests are plotted in a so-called
importance-certainty graph (Figure 8.7). In this graph, the assumptions
that are the most important, but have the lowest certainty, form the main
limitations of the value model and its results. This section discusses these
limitations.
The results of the qualitative and quantitative analysis of the alternatives
are uncertain. This is not so much a limitation of the value model, as it is
a result of the focus of this research. With more effort, the attribute values
for the alternatives can be calculated with more certainty. These attribute
values can then serve as inputs in the value model as created in this research.
Nonetheless, the correctness of the results as presented in this research is to
some extent debatable due to these uncertainties.
A limitation of the value model itself is the set of attributes. For instance,
measuring the capacity objective is done in the value model using the attributes ‘days under service’ and ‘operational penalty’, which may both not
entirely capture the essence of what stakeholders understand as ‘capacity’.
This is the case for other objectives as well. Therefore, to improve the value
model, better attributes with a higher measurability should be found.
The objective weight factors, on which the eventual ∆V is highly dependent,
are based on interviews with representatives of the stakeholder organisations.
However, the representativeness of these interviewees for their entire organisation can be doubted. Therefore, four interviews were conducted within
Amsterdam Airport Schiphol, and two within LVNL. Unfortunately, at most
one person was interviewed for the other stakeholders. This part of the value
model can be easily improved by interviewing more decision makers in the
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discussion
stakeholder organisations, and processing their pair-wise comparisons using
the Analytic Hierarchy Process as outlined in this thesis.
Within the AHP, however, the main limitation is the choice of factormax,
which determines the conversion of the interviewed decision makers’ qualitative pair-wise comparisons, to a numerical value. In this research factormax
is chosen as 8. Because of its large influence on ∆V though, the uncertainty
of this choice is reflected as a limitation of the value model.
In determining the average stakeholder’s objective weight factors, each
stakeholder is assumed to be equally important. In reality, this is probably
not a realistic assumption. Further research should be done on this topic,
as it forms a gap in the Value Operations Methodology. In order to deal
with this uncertainty, the choice is made in this research to not only show
the change in value for the average stakeholder, but also for each individual
stakeholder. In this way, it leaves the trade-off between different stakeholders
for the decision maker to decide.
Another limitation is that the safety risk cannot be quantified in the analysis
of the two alternative scenarios. Qualitatively, it can be said that maintenance
during the night would increase these risks, which would have to be reduced
with mitigation measures.
The qualitative and quantitative analysis have only been performed for
a large maintenance project on the Kaagbaan. Especially the noise and
environmental attributes are influenced by the choice of runway. Section 7.5
discusses in brief the extrapolation of these results to the other runways and
types of maintenance.
Finally, this research has not analysed the operational implementation of
the alternative strategies. Also the question whether higher maintenance
costs, even when it results in a positive ∆V, can be financed by Amsterdam
Airport Schiphol, is outside the scope of this research.
9.4
further methodological improvements
This section shortly discusses some additional ideas for improving the Value
Operations Methodology. These ideas should be seen as a starting point for
further research.
9.4.1
Non-linear value models
As explained in Section 3.2 and 8.3.3, the use of a linear, additive value
function is at the core of the Value Operations Methodology. Nonetheless, it
seems plausible that in some cases, non-linear value models better capture
the change in value of alternatives.
For instance, near the boundary values or feasible ranges of attributes
(xmax and xmin in Equation (4.12) and (4.13)) perhaps in the decision maker’s
opinion, value no longer increases linearly; instead, it is no longer considered
worth the effort to make only a little improvement. This would signal that
the value function approaches the boundary values asymptotically, and as
such the value function would be more like an S-curve or logistic function.
This would mean the value function could for instance look like:
u(x) = c
1
1 + e−x
(9.1)
Keeney (1992, p.189,191) discusses the existence of such value functions, and
may be a good starting point for further research.
9.4 further methodological improvements
The shape of the value function also depends on the decision maker’s
risk attitude and whether uncertainty is included in the value model, as
discussed next.
9.4.2
Uncertainty, probability and risk attitude
The value functions as currently produced by the VOM do not take into
account uncertainty. However, Keeney (1992, §4.7) explains in some detail
how utility functions can be built incorporating probabilistic concepts. In
some cases, explicitly including uncertainty in the value model may improve
the outcomes. For example, in the value model built for the runway system
maintenance strategy, an important attribute is the duration of maintenance,
and the delay of maintenance. Both attributes are partly determined by uncertain weather conditions, which can be modeled by explicitly incorporating
probability in the value function.
When uncertainty is added to the value model, then also the concept of
‘risk attitude’ of the decision maker plays a role. Keeney (1992, p.142–143)
distinguishes between three risk attitudes, that also influence the shape of
the value function:
risk aversion If a decision maker is risk averse, he prefers a certain
outcome over an uncertain event, even if the uncertain event may result
in a higher-value outcome. In this case, the value function is concave;
risk neutrality The decision maker has no preference for either the
certain outcome, or the uncertain event. In this case, the value function
is linear;
risk proneness If the decision maker prefers the uncertain event, he is
risk prone. This results in a convex value function.
Decision making under risk is also the area of interest of prospect theory
(Experimental Economics Center, 2006; Kahneman and Tversky, 1979). This
may be a good starting point for further research.
9.4.3
Relative importance of stakeholders
As discussed earlier in this thesis, the calculation of the average stakeholder
assumes that all stakeholders are equally important. This assumption may
not be the best reflection of reality. The VOM could therefore be significantly
improved with further research on this issue. An idea might be to assign
importance weight factors based on the classification of each stakeholder in
one of the four types, as explained in Section 4.1.2.
In this research the choice is made to not only rely on the average stakeholder to determine whether an alternative creates or destroys value, but
to also show ∆V for all stakeholders separately, as done for the results in
Chapter 7. While this approach has the advantage of clearly showing who
profits from an alternative and who pays, it does not offer help in the ensuing
trade-off between the different stakeholders.
In other words: before the creation of the value model the decision maker
had to make his own trade-off between the different pros and cons of alternative strategies. The value model as presented in this thesis has made this
trade-off explicit and functional. However, the value model provides little assistance in the trade-off between stakeholders. Therefore, it is recommended
that further research is done on this point.
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discussion
9.4.4
Synergies in the morphological grid
This thesis has proposed a method to link the Value Operations Methodology
with theories from General Morphological Analysis, to also use the value
model to create alternatives. This method uses a morphological grid, in which
the research object is split up in parameters for which alternative options
are generated. These options are checked for incompatibilities using a crossconsistency assessment. Section 4.7 and Chapter 6 explain this method in
detail.
However, the CCA may not only be used to find incompatibilities, but
also positive and negative synergies between the options. A positive synergy
means that two options reinforce each other’s advantages, while a negative
synergy means that the combined ∆V of two options is lower than their separate ∆V values. In any case, it means that ∆V resulting from one parameter
is not independent of the choice of an option for another parameter:
∆V(night, September) − ∆V(day, September)
6= ∆V(night, June) − ∆V(day, June)
(9.2)
This equation expresses that the difference in value for the alternatives ‘night’
and ‘day’ depends on the choice for the alternative ‘September’ or ‘June’. It is
recommended that the possibilities of such synergies are further researched,
as they may offer new insights into the value of alternatives and the use of
the morphological grid.
9.4.5
Combination of qualitative and quantitative attributes
In Chapter 7, first a qualitative and then a quantitative analysis was performed to determine the ∆V of the alternative maintenance strategies. Unfortunately, two attributes (safety risks and environmental impact) could not be
quantified due to a lack of data. They were therefore ignored in the quantitative analysis. However, it was possible to give a qualitative assessment
of these two attributes. It is therefore technically possible to incorporate the
qualitative assessments for these two attributes in the quantitative analysis.
This has not been done in this research, as it raises new questions about
how to convert qualitative assessments in the form of − or ++ to numerical
values. Section 7.2 discusses three of such conversion methods, but none
of these methods were considered reliable enough to mix its results with
quantitative data. However, as the technical implementation of this mix of
qualitative and quantitative data is quite simple, it is recommended that the
theoretical implications are researched further.
10
C O N C L U S I O N S A N D R E C O M M E N D AT I O N S
At the end of this thesis, the main findings are discussed in this chapter.
Section 10.1 treats the conclusions; Section 10.2 makes this research’s contributions explicit; and Section 10.3 lists a number of recommendations, both
for the academic community and for Amsterdam Airport Schiphol.
10.1
conclusions
The main research question for this research is:
How can a quantified change in value for all relevant stakeholders
measure the success of new ideas in improving the runway system
maintenance strategy at Amsterdam Airport Schiphol?
Due to an increasing demand for airside capacity at Schiphol and changing
noise regulations due to the Alderstafel agreement, there is a need for
alternative maintenance strategies. Previous studies on this topic have not
resulted in comprehensive conclusions based on a trade-off between all
relevant stakeholders and their objectives.
To fill this gap, this research chose to approach the problem statement
from the direction of value-focused thinking, more specifically using the
Value Operations Methodology. The VOM uses a value model to calculate
the change in value ∆V with respect to a certain reference situation.
10.1.1
Conclusions on the Value Operations Methodology
While the general approach of the VOM is well defined, several steps in the
methodology lacked a sound theoretical foundation. By thoroughly analysing
each step, it is concluded that the VOM can be improved in the following
ways:
• Based on the Analysis of Complex Neighbourhoods, Savage’s approach
and Mitchell’s approach, a six-step procedure is outlined to identify
and select the relevant stakeholders for inclusion in the value model;
• A systematic approach is suggested for the formulation of the set of
objectives that form the main parameters in the value function;
• A number of improvements are proposed to the Analytic Hierarchy
Process, which is used to determine the objective weight factors. It is
concluded that:
– Pair-wise comparisons are absolutely necessary when applying
the AHP, as it compels decision makers to make a choice;
– A verbal ‘more/less/equal’ rating scale is best suited to capture
stakeholders’ value trade-offs;
– The simple arithmetic mean of the individual objective weight
factors should be used to determine the average stakeholder’s
preferences.
119
120
conclusions and recommendations
• A proposal to scrap from the Value Operations Methodology the socalled ‘second tier’ to determine attribute weight factors, and to base
these weight factors on the preferences of decision makers (the ‘first
tier’);
• The addition of a systematic approach based on General Morphological
Analysis, to use the value model to create alternatives. This approach
uses a morphological grid to split up the research object in separate
parameters, which are then used to generate alternative options;
• The conclusion that global scaling is best used to calculate the attribute
values in alternative scenarios.
With these improvements, the VOM becomes a better structured methodology
for building value models and creating alternatives for real-life problems.
10.1.2
Conclusions on the maintenance strategy
Using this improved Value Operations Methodology, a value model is built
for the context of runway system maintenance at Schiphol. Seven relevant
stakeholders are identified:
• Airport operator (Amsterdam Airport Schiphol);
• Air traffic control (LVNL);
• Airlines;
• National government;
• Regional and local governments;
• Residents and local community groups;
• Passengers.
These seven stakeholders have the following six objectives:
1. Increase capacity (availability and reliability) of airside operations;
2. Reduce maintenance related costs;
3. Increase predictability and transparency regarding maintenance activities;
4. Increase safety, both of airside operations and the maintenance activities;
5. Reduce the environmental impact of maintenance activities;
6. Reduce nuisance to local community.
Based on interviews with most stakeholders, and a literature review for
the others, the objective weight factors are determined for each individual
stakeholder. Also the average stakeholder’s preferences are calculated, in
the assumption that each stakeholder is equally important. It is concluded
that on average, increasing capacity is by far the most important objective,
followed by improving predictability and safety. This is different from the
current practice at the Airfield Maintenance Services department, where
10.1 conclusions
121
oftentimes reducing costs is the most important objective in decisions regarding maintenance. This research concludes that this is not in line with the
views of most stakeholders.
The value model is finalised by selecting and weighting one or more
attributes per objective, based on desk research and interviews with experts.
Using the value model, a morphological grid is constructed to generate
seven parameters that contain 21 alternatives for the runway system maintenance strategy. From this grid follows the observation that most alternatives
are related to the planning of maintenance. This warrants the conclusion
that at Amsterdam Airport Schiphol, the maintenance strategy is primarily
concerned with scheduling activities.
The 21 alternatives are first individually analysed in a qualitative manner.
The reference situation in these analyses is the large maintenance project
(GOH) on the Kaagbaan in September 2011. From this qualitative analysis it
can be concluded that the current maintenance strategy is quite successful:
most alternatives result in a negative ∆V, indicating that the current strategy
creates more value. It also means that scheduling maintenance during the
winter (one of the alternatives) is not beneficial: due to a longer maintenance
period and a resulting decrease in capacity, value is destroyed compared to
maintenance in the spring and summer.
100%%
90%%
80%%
70%%
60%%
50%%
40%%
∆V#
30%%
20%%
10%%
0%%
!10%%
!20%%
!30%%
!40%%
!50%%
AAS%
LVNL%
Capacity%
Airlines%
Costs%
Na:onal%gov't%
Predictability%
Local%gov't%
Environment%
Residents%
Nuisance%
Passengers%
Average%
Safety%
Figure 10.1: Quantitative results for the ‘nightly maintenance’ scenario broken up per objective. The values for the
safety objective are only an indication as these cannot be computed quantitatively. (This figure is a copy
of Figure 7.14.)
The most promising alternatives are combined in two scenarios, taking
into account incompatibilities between options from the grid. These two
scenarios are quantitatively analysed. Only the first scenario, in which all
maintenance is done during the night and the runway is operational during
the day, results in a high positive ∆V, indicating that this scenario creates
122
conclusions and recommendations
value for almost all stakeholders. As Figure 10.1 shows, this is primarily due
to the major capacity benefits. However, as the safety risk is not quantified,
the actual value creation will be lower than calculated. Still, it is concluded
that on the basis of the value model, the ‘nightly maintenance’ scenario offers
significant value opportunities.
The value model is checked for its conceptual, logical and operational
validity. The main conclusion regarding the operational outcome of the value
model is that the model works: for an alternative maintenance strategy it
produces a ∆V for each stakeholder. The manager of Airfield Maintenance
Services finds these results useful, not only for aiding decision making, but
also to explain the eventual decisions to other stakeholders. Moreover, the
concept of ‘value’ and the value model are useful in a decision context. A
review showed that previous studies on alternative maintenance strategies
lacked a clear trade-off framework; this research has provided such a trade-off
framework in the form of the value model.
In summary, based on the value model and its outcomes, the three main
conclusions regarding the runway system maintenance strategy are:
1. The value model provides a useful trade-off framework for making
decisions regarding the maintenance strategy. It shows that the ‘value’
of the maintenance strategy is only marginally determined by costs.
Instead, capacity, predictability and safety objectives rank much higher
in importance;
2. The current maintenance strategy performs better than most alternatives, including the alternative to schedule all maintenance during the
winter; and
3. The maintenance strategy can be improved by scheduling more maintenance during the night and making the runway available during the
day.
10.2
contributions
This research has made a number of contributions, both to academia and to
Amsterdam Airport Schiphol. These are discussed separately in this section.
10.2.1
Contributions to academia
The most important academic contribution of this research is the series of
improvements to the Value Operations Methodology. In this research, each
step in the VOM is thoroughly analysed. This led to a wide range of improvements, as listed above in Section 10.1.1. With these improvements, the VOM
becomes a more robust methodology. Moreover, the methodology becomes
easier to understand and to apply to new problems, now each step is described in detail. For instance, this research introduces a systematic approach
for identifying and selecting relevant stakeholders and for formulating the set
of objectives, and solves some issues with the use of the Analytic Hierarchy
Process in the VOM.
Previously, finding alternatives was detached from the value model and
the VOM, while the philosophy of value-focused thinking clearly describes a
relation between specifying values and creating alternatives (see Figure 3.1).
Therefore, an important academic contribution of this research is the link
10.3 recommendations
created between the VOM and GMA, enabling the creation of alternatives
based on the value model using a morphological grid.
A final academic contribution is the application of the Value Operations
Methodology to a real-life problem in the aviation industry. The theoretical
foundations of frameworks are important, but it is in solving problems where
the methodology shows its value. Furthermore, this research applied the
VOM to strategic decision making instead of a process-oriented problem,
proving that the methodology can be used for both types of problems.
10.2.2
Contributions to Amsterdam Airport Schiphol
This research’s most important contribution to Amsterdam Airport Schiphol
is the creation and analysis of alternative maintenance strategies. This analysis shows that the current maintenance strategy is quite successful; most
alternatives perform worse than the current strategy. Still, there is one area
in which substantial improvements can be found: scheduling maintenance
during the night as to increase capacity during the day.
Moreover, this research contributes to Amsterdam Airport Schiphol’s
knowledge of its stakeholders and their preferences. In the process of building
the value model, the relevant stakeholders are identified, their main objectives
in the context of maintenance are established, and their preferences regarding
these objectives are determined. Amsterdam Airport Schiphol can use this
knowledge to take better decisions for itself and for its stakeholders, and to
better explain these decisions.
Finally, this research contributed to Amsterdam Airport Schiphol by
demonstrating the use of the concept of ‘value’ in decision making. Previously, reducing costs was usually the most important goal; this research
shows that other objectives are considered much more important. Moreover,
not converting other benefits and drawbacks to monetary value but analysing
them in their own respect, results in more balanced conclusions.
10.3
recommendations
This thesis neither concludes the study to better maintenance strategies at
Amsterdam Airport Schiphol, nor the research into decision theory, valuefocused thinking and the Value Operations Methodology. Therefore some
recommendations are made, for the academic world in Section 10.3.1 and for
Amsterdam Airport Schiphol in Section 10.3.2.
10.3.1
Recommendations for further research
In Section 9.4, a number of areas are discussed in which the Value Operations
Methodology can be improved by further research. Also in Chapter 4 and 8
such areas were mentioned. In short, these topics are:
• Non-linear value models, such as S-curves or logistic functions;
• The risk attitude of decision makers;
• Uncertainty and probability concepts, for instance by incorporating
elements from prospect theory;
• How to determine a stakeholder’s type using the classification scheme
from Savage’s approach;
123
124
conclusions and recommendations
• The effect of the formulation of objectives (e.g. using ‘reduce’ instead
of ‘minimise’) on a stakeholder’s pair-wise comparisons;
• The representativeness of an interviewed decision maker in expressing
the organisation’s preferences;
• The value of factormax and the effect of its value and different rating
scales on the consistency ratio;
• The relative importance of stakeholders in determining the preferences
of the average stakeholder;
• Alternatives to the Analytic Hierarchy Process to determine the objective weight factors in the value function;
• The use of the cross-consistency assessment to identify positive and
negative synergies in the morphological grid;
• The combination of qualitatively and quantitatively analysed attributes
in the same value function.
10.3.2
Recommendations for Amsterdam Airport Schiphol
Regarding the value model for the runway system maintenance strategy and
its results, the following recommendations are made:
• The foremost recommendation for Amsterdam Airport Schiphol is the
further study of the ‘nightly maintenance’ scenario, as this alternative
results in a high positive change in value. Still, the operational implementation of this scenario must be analysed, for instance by using
best practices from other airports. Moreover, it is recommended that
academic insights from other fields of research are used in the implementation phase. For example, Kroon (1990) has applied principles
from operations research to improve the aircraft maintenance process
at KLM;
• To further improve the value model, it is recommended that the set of
attributes is refined. As mentioned in this thesis, there is a mismatch
between the asset owner and the asset user regarding the meaning of
concepts such as ‘reliability’ and ‘capacity’. If these misunderstandings
are resolved, the set of attributes can be improved. Also, the costs
attribute could be analysed in a higher level of detail by taking into account the differences between labour, capital and material maintenance
costs;
• It is recommended that the Airfield Maintenance Services department
uses the morphological grid and its parameters to generate more alternatives; and to use the cross-consistency assessment to identify positive
or negative synergies between options;
• It is also recommended that AMS further studies the alternatives for
the parameter ‘quality of assets’, as the options for this parameter are
now evaluated from two different perspectives;
• It was found out that maintenance projects, such as the GOH Kaagbaan,
are not evaluated regularly. It is recommended that these evaluations
are always done, so it can be measured whether maintenance took
10.3 recommendations
longer than expected or cost more. This data can then be used for the
reference situation in the value model. This might be integrated in the
COCKPIT project, which strives to collect measurable data regarding
maintenance at Schiphol.
Apart from the value model, the following recommendations are made:
• In an operational context, Amsterdam Airport Schiphol should analyse
the effect of a possible ‘2+2’ runway use and of the more frequent
use of the Oostbaan on the maintenance strategy. Furthermore, it is
recommended that the effect of changing average weather conditions is
studied;
• AMS can be more transparent about its maintenance strategy by communicating the maintenance schedule in a early phase to all stakeholders, including the regional and local authorities;
• AMS should consider the possibility of ‘locking’ maintenance projects
several weeks or even months before the starting date, and thus refusing
to add any last-minute activities. In this way, maintenance schedules
may be followed more strictly and there are less changes at the last
moment;
• Finally, it is recommended to study the possible links between the
research done on alternative maintenance strategies and stakeholders’
value trade-offs in this context, and the work done in this area in the
Collaborative Decision Making (CDM) project.
125
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137.
Savage, G. T., Nix, T. W., Whitehead, C. J. and Blair, J. D. (1991), “Strategies
for Assessing and Managing Organizational Stakeholders”, Academy of
Management Executive , Vol. 5, pp. 61–75.
Schiphol
Group
(2011),
‘Strategie’,
SchipholGroup1/Onderneming/Strategie.htm
http://www.schiphol.nl/
Visited September 23,
2011.
Schneider, D. K. (2011), ‘Repertory grid technique’, http://edutechwiki.
unige.ch/en/Repertory_grid_technique Visited September 8, 2011.
Smulders, F. (2010), The Airside Value Model, Master’s thesis, Delft University of Technology, Faculty of Aerospace Engineering, graduation August
2011.
Spaargaren, T. (2008), Vergelijkingstudie Groot Onderhoud Zwanenburgbaan
18C-36C, CDVI.
Steer Davies Gleave (2006), Air and Rail Competition and Complementarity,
European Commission DG TREN.
Tafel van Alders (2010), ‘Wat is de Tafel van Alders?’, http://www.
alderstafel.nl/schiphol/start/ Visited July 4, 2011.
Van Calck, E. (2011), Operationeel PvE, Amsterdam Airport Schiphol.
Van der Vegte, R. F. and Vallinga, R. (2011), Meerjarenonderhoudsplan AMS
2012-2016, Amsterdam Airport Schiphol.
Van Schaik, R. (2010), Noise Minimization in the Vicinity of Schiphol, Master’s thesis, Tilburg University, Faculty of Economics and Business Administration, graduation November 25, 2010.
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Amsterdam Airport Schiphol.
Verbraeck, A. (2008), ‘Stakeholders’, Slides from spm6300 - Project Risk
Management.
VGP (2011), ‘Leden van de VGP’, http://www.vgpplatforms.nl/algemeen/
14/sub/65/Leden-van-de-VGP.html Visited August 2, 2011.
Vlam, M. (2011), Sheets prognoses, Amsterdam Airport Schiphol.
Wever, R. (2010), Voorkeuren m.b.t. uitvoering onderhoud aan banen vanuit
perspectief bewoners, Amsterdam Airport Schiphol.
Wubbels, A. (2011), ‘Schiphol Amsterdam Airport Basiskaart’, Amsterdam
Airport Schiphol.
A
O V E RV I E W O F I N T E RV I E W S
This appendix gives an overview of the interviews conducted for this research
project.
background interviews
The following unstructured interviews were conducted to gather background
information on various topics:
• Neeteson, C., graduate intern at Amsterdam Airport Schiphol (A/CAP/EC); June 7, 2011 on the Lead Time Maintenance Tool
• Keet, M., process developer at Amsterdam Airport Schiphol (A/OPS/AO/PM); June 9, 2011 on the runway availability strategy; November
9, 2011 on the difference between sustainability and reliability
• Van Grootel, H. and Brouwer, M., junior advisor and coordinator at
Amsterdam Airport Schiphol (A/CAP/EC); June 9, 2011 on the runway
availability strategy
• Van Schaik, R., graduate at Amsterdam Airport Schiphol and Frontier;
June 14, 2011 on his thesis results
• Meijerhof, F., works coordinator at Amsterdam Airport Schiphol (A/AMS/VLU); July 4, 2011 on maintenance scheduling; November 8, 2011 on
the GOH Kaagbaan in September 2011
• Ponsen, M., technical maintenance supervisor at Amsterdam Airport
Schiphol (A/AMS/VLU); July 6, 2011 on ET maintenance; October 7,
2011 on the morphological analysis
• Van Calck, E., advisor at Amsterdam Airport Schiphol (A/OPS/AO/C);
July 7, 2011 on CMC, maintenance scheduling and bottlenecks
• Vlam, M., senior advisor at Amsterdam Airport Schiphol (D/AD/AV);
July 21, 2011 on the masterplan 2025 and growth prospects of Amsterdam Airport Schiphol
• Duiveman, R., technical maintenance supervisor at Amsterdam Airport
Schiphol (A/AMS/VLU); July 21, 2011 on HWA maintenance
• Kamminga, F., advisor at Amsterdam Airport Schiphol (A/AMS/A&O);
July 22, 2011 on stakeholder classification (via email); September 1, 2011
on key performance indicators and attributes; January 9, 2012 on the
conclusions
• Bakker, R., aviation security manager at Amsterdam Airport Schiphol
(A/SSE/SP); July 25, 2011 on 100% security checks
• Bakker, M., technical maintenance supervisor at Amsterdam Airport
Schiphol (A/AMS/VLU); July 25, 2011 on CT maintenance
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134
overview of interviews
• Emsbroek, G., manager at Amsterdam Airport Schiphol (A/AMS/A&O);
August 16, 2011 on stakeholders and objectives; October 18, 2011 on
attributes
• Woud, D., controller at Amsterdam Airport Schiphol (A/ASM/CON);
October 19, 2011 on cashflow; November 24, 2011 on cost differentials
• Van Zuijlen, E., project manager CDM at Amsterdam Airport Schiphol
(A/OPS/AO/P); November 1, 2011 on stakeholder preferences
• Van der Meer, M., manager process management airside at Amsterdam
Airport Schiphol (A/OPS/AO/PM); November 3, 2011 on attribute
trade-offs
• Kapteijn, M., supervisor at Amsterdam Airport Schiphol (PLUS); November 30, 2011 on the impact of alternatives
• Nijdam, J., manager at Amsterdam Airport Schiphol (A/AMS/VLI);
December 1, 2011 on cost overruns and planning
• Ho, G., manager at Amsterdam Airport Schiphol (A/AMS); December
8, 2011 on the validation of the value model
• Zeeuw, P., manager at Amsterdam Airport Schiphol (A/AMS/VLU);
December 14, 2011 on the results for the ‘nightly maintenance’ scenario
• Stoop, J., professor at Delft University of Technology (Faculty of Aerospace
Engineering); December 21, 2011 and January 11, 2012 on quantifying
safety risks
objective trade-off interviews
Structured interviews were conducted with stakeholders to understand
decision makers’ preferences and trade-offs regarding the set of objectives in
the value model. For this, the list of pair-wise comparisons of objectives in
Table A.1 was used.
The following interviews were conducted:
• Ho, G., manager at Amsterdam Airport Schiphol (A/AMS); August 17,
2011
• Van Vroonhoven, D., manager at Amsterdam Airport Schiphol (A/CAP/EC); August 24, 2011
• Van den Bergh, R., manager at Amsterdam Airport Schiphol (A/OPS/AO); August 31, 2011
• De Jong, B., strategic advisor at Haarlemmermeer municipality (Economic and Airport Affairs); September 1, 2011
• Van der Hagen, R., operational expert at LVNL; September 5, 2011
• Plaisier, G., flight support manager at KLM; September 12, 2011
• Wever, R., manager at BAS; September 13, 2011
• Lekkerkerk, M., marketing manager at Amsterdam Airport Schiphol
(A/MD/AVM); November 2, 2011
• Dijkgraaf, F., manager at LVNL; November 10, 2011
overview of interviews
Furthermore, the following people working at AMS filled in the trade-off
table via email:
• De Graaf, T., technical maintenance supervisor; November 8, 2011
• De Bruijn, M., advisor; November 8, 2011
• Ponsen, M., technical maintenance supervisor; November 11, 2011
• Terpstra, A., advisor; November 14, 2011
• Bakker, M., technical maintenance supervisor; November 14, 2011
• Gans, P., technical maintenance supervisor; November 18, 2011
alternative maintenance strategies interviews
Semi-structured interviews were conducted with various professionals in
the maintenance industry to gather alternative maintenance strategies. The
following elements of a maintenance strategy were the topics discussed:
1. Distinction between asset owner, maintenance scheduler and maintenance conductor;
2. Distinction between ‘blocks’ of maintenance;
3. Different technical disciplines;
4. Scheduling maintenance in certain periods of the year;
5. Maintenance concepts (preventive, corrective, etc.).
The following interviews were conducted:
• Coonen, R., commercial manager at BAM Rail; September 15, 2011;
maintenance of railway infrastructure
• Tomassen, E., advisor runways and taxiways at Ministry of Defence;
September 16, 2011
• Aleven, P., engineering unit manager at KLM Engineering and Maintenance; September 26, 2011
135
136
overview of interviews
objective a
A>B
A=B
A<B
objective b
Increase capacity (availability and
reliability) of airside operations of
AAS
Reduce maintenance related costs
Increase predictability and
transparency regarding maintenance
activities
Reduce the environmental impact of
maintenance activities
Reduce maintenance related costs
Increase predictability and
transparency regarding maintenance
activities
Reduce the environmental impact of
maintenance activities
Reduce maintenance related costs
Increase predictability and
transparency regarding maintenance
activities
Increase capacity (availability and
reliability) of airside operations of
AAS
Reduce nuisance to local community
Increase safety, both of airside
operations and the maintenance
activities
Increase predictability and
transparency regarding maintenance
activities
Increase safety, both of airside
operations and the maintenance
activities
Reduce nuisance to local community
Increase predictability and
transparency regarding maintenance
activities
Increase capacity (availability and
reliability) of airside operations of
AAS
Increase safety, both of airside
operations and the maintenance
activities
Reduce the environmental impact of
maintenance activities
Reduce maintenance related costs
Increase safety, both of airside
operations and the maintenance
activities
Reduce the environmental impact of
maintenance activities
Increase safety, both of airside
operations and the maintenance
activities
Reduce maintenance related costs
Reduce the environmental impact of
maintenance activities
Increase capacity (availability and
reliability) of airside operations of
AAS
Reduce maintenance related costs
Reduce nuisance to local community
Reduce nuisance to local community
Increase capacity (availability and
reliability) of airside operations of
AAS
Table A.1: Table used during objective trade-off interviews. Interviewees had to express their preference for objective
A or B, or declare them equally important. In the interviews, a Dutch translation of this table was used.
B
M A P S O F A M S T E R D A M A I R P O RT S C H I P H O L
Figure B.1: The runway layout of Amsterdam Airport Schiphol (original image by NielsB (2007), edited by author)
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maps of amsterdam airport schiphol
Figure B.2: Map showing taxiway bottlenecks at Amsterdam Airport Schiphol (original image by Wubbels (2011), edited by author)
C
P R E F E R E N T I A L R U N WAY U S E I N T H E A L D E R S TA F E L
AGREEMENT
As explained in Section 2.4.2, the Alderstafel agreements from 2008 and 2010
try to balance the objectives of the Dutch aviation industry and the people
living near Schiphol, by allowing air traffic growth only while simultaneously
implementing nuisance reducing measures. One of these measures is a
new preferential runway use. This means that only external factors (such
as weather and wind conditions) should determine which combination
of runways is used on a particular day at Schiphol (Alders, 2010). Table
C.1 shows this preferential runway use table. In January 2011 a two-year
experiment with these new preferential tables started. After this period, this
system can be implemented in the official regulations. This will mean that
the old noise limitation system of handhavingspunten (enforcement points)
will be abandoned (Kamminga, 2010b).
The Alderstafel agreement from 2010 also includes preferential runway use
tables for situations when one runway is not available, for instance due to
maintenance. As explained in Section 2.4.2, the validity of these tables during
runway maintenance is still uncertain. For the sake of completeness, these
tables are shown in Table C.2–C.6. The main effect of these preferential tables
is a more frequent use of the Polderbaan and the Kaagbaan (Kamminga,
2010b, Slide 9).
preference
l1
l2
t1
t2
day (06–23h); visibility > 5000 m; cloud base > 1000 ft; daylight
(36R)
36L
(36C)
18R
(18C)
24
(18L)
06
(36R)
09
(36L)
27
(18R)
24
(18L)
1
06
2
3
4
day (06–23h); visibility > 1500 m; cloud base > 300 ft
5
36R
(36C)
36L
(36C/09)
6
18R
(18C)
18L
(18C/24)
night (23–06h)
1
06
36L
2
18R
24
3
36C
36L
4
18R
18C
Table C.1: Preferential runway use from Alderstafel agreement (Alders, 2010, Appendix 2). Based on the weather
conditions, LVNL selects the runway combination that has the highest preference. During the day a 2+1
runway use is allowed, meaning two runways for landing (L1 and L2) and one for takeoff (T1) or vice
versa, when two runways are used for take-off (T1 and T2). During the night only 1+1 runway use is
allowed.
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preferential runway use in the alderstafel agreement
preference
l1
l2
t1
t2
1
06
(36R)
36C
(09)
2
18C
(27)
24
(18L)
3
06
(36R)
09
(36C)
4
27
(18C)
24
(18L)
Table C.2: Preferential runway use when runway 18R-36L (Polderbaan) is not available
(Alders, 2010, Appendix 2)
preference
l1
l2
t1
t2
1
36R
(36C)
36L
(09)
2
18R
(27)
18C
(18L)
3
36R
(36C)
36L
(36C)
4
18R
(18C)
18L
(18C)
Table C.3: Preferential runway use when runway 06-24 (Kaagbaan) is not available
(Alders, 2010, Appendix 2)
preference
l1
l2
t1
t2
1
06
(27)
36L
(36C)
2
18R
(18C)
24
(09)
Table C.4: Preferential runway use when runway 18L-36R (Aalsmeerbaan) is not
available (Alders, 2010, Appendix 2)
preference
l1
l2
t1
t2
1
06
(36R)
36L
(09)
2
06
(36R)
09
(36L)
3
18R
(27)
24
(18L)
4
27
(18R)
24
(18L)
Table C.5: Preferential runway use when runway 18C-36C (Zwanenburgbaan) is not
available (Alders, 2010, Appendix 2)
preference
l1
l2
t1
t2
1
06
(36R)
36L
(36C)
2
18R
(18C)
24
(18L)
3
36R
(36C)
36L
(36C)
4
18R
(18C)
18L
(18C)
Table C.6: Preferential runway use when runway 09-27 (Buitenveldertbaan) is not
available (Alders, 2010, Appendix 2)
T H E I M P R O V E D VA L U E O P E R AT I O N S M E T H O D O L O G Y
In Chapter 4, a number of additions and improvements to the Value Operations Methodology are proposed. This appendix is a step-by-step guide to
the improved VOM including all these proposals. The theory behind these
steps is explained in Chapter 3 and 4; Chapter 5–7 apply the steps to the
runway system maintenance strategy.
Starting with a current situation that has to be improved, three main
steps lead to a series of alternatives and conclusions on whether or not they
improve the current situation:
1. Build a value model;
2. Create a set of alternatives;
3. Calculate each alternative’s change in value compared to the reference
situation.
At the end, each alternative with a positive ∆V creates value and as such
improves on the current situation. The three steps are discussed in detail in
the next sections.
1. build a value model
Six steps are necessary to build a value model:
1. Identify and select the relevant stakeholders;
2. Formulate the set of objectives;
3. Determine the objective weight factors;
4. Select attributes to measure the fulfillment of the objectives;
5. Determine the attribute weight factors;
6. Combine all elements in a value model.
Each step is discussed below.
1.1 Identify and select the relevant stakeholders
In a decision situation where the interests of multiple stakeholders have to be
taken into account, a stakeholder analysis is necessary. The approach listed
below first identifies all possible stakeholders, and subsequently selects those
stakeholders that are relevant for the decision situation:
1. Identify all stakeholders:
a) Create a longlist of all possible stakeholders;
b) Select the stakeholders that fall within the scope of the decision
situation and discard the others;
c) Map the relationships between the stakeholders, for instance in
formal committees.
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D
142
the improved value operations methodology
2. Select the relevant stakeholders:
a) Rank the identified stakeholders on their potential for threat and
cooperation, using the list of characteristics from Savage’s approach (see for an example Appendix E);
b) Based on their rankings, classify the stakeholders in one of the
four categories from Savage’s approach;
c) Select the stakeholders that are classified as ‘supportive’, ‘mixed
blessings’ and ‘non-supportive’ as the relevant stakeholders; discard the ‘marginal’ stakeholders.
The stakeholders that are selected in the final step are included in the rest of
the approach, by including their objectives and preferences.
1.2 Formulate the set of objectives
The next step is to formulate a set of objectives. These objectives define the
meaning of ‘value’ in the decision context. The following steps assist in
formulating the objectives:
1. Create a longlist of objectives for each relevant stakeholder;
2. Create an objective tree consisting of the relevant objectives from the
longlist. In this tree, strategic objectives are on top, while means objectives are near the bottom of the tree;
3. Select from this objective tree the level of objectives that has the right
level of abstraction for the research scope;
4. Finalise the formulation of the objectives:
• Ensure that the objectives are essential, controllable, complete,
non-redundant, measurable, operational, decomposable, concise
and understandable. In this way, the set of objectives adheres to
the list of desired properties (Keeney, 1992, Table 3.2);
• Ensure that each objective consists of a decision context, an object
and a direction of preference;
• Ensure that the set of objectives employs a consistent use of ‘increase/reduce’.
1.3 Determine the objective weight factors
Each stakeholder makes a different trade-off between the objectives as formulated in the previous step. Therefore, through an interview with a representative of each stakeholder, the objective weight factors for each stakeholder
are determined:
1. Let each representative compare the objectives in a pair-wise fashion.
For each pair, the representative must choose whether Objective A is
more or less important than Objective B. The third possibility is that
Objective A and B are equally important;
2. Collect the results of these comparisons in a reciprocal comparison matrix. The verbal answer ‘more important’ is converted to the factormax
value 8 in the comparison matrix;
the improved value operations methodology
3. The normalised eigenvector corresponding to the largest eigenvalue
from this matrix is the vector with the weight factors for the objectives.
Here, normalised means that the sum of the weight factors is 1;
4. Compute the consistency ratio of the eigenvalue using Equation (3.13)
and (3.14) to check the consistency of the comparison matrix.
When the objective weight factors for all individual stakeholders have been
determined, the average stakeholder’s weight factors can be easily computed:
for each objective, the mean is taken of the individual weight factors for that
objective.
1.4 Select attributes
To measure the fulfillment of the objectives, a set of attributes is selected for
each objective. Preferably, these should be natural attributes; if no natural
attribute is available, constructed or proxy attributes may be used.
Analogous to the objectives, the attributes should also adhere to a list of
desired properties: the attributes should be unambiguous, comprehensive,
direct, operational and understandable (Keeney, 2007, p.121).
Finally, the attributes may be bounded by physical, legal, financial or
practical limits. Therefore, the boundary values xmin and xmax for each
attribute should be determined.
1.5 Determine the attribute weight factors
Not all attributes are equally important in determining the fulfillment of
an objective. Therefore, also the attributes are weighted. For an individual
objective, the sum of its attributes must be 1.
If an objective has more than one attribute, the attribute weight factors
are determined in the same way as the objective weight factors: using pairwise comparisons by the appropriate stakeholders. These comparisons are
collected in a comparison matrix, and the (normalised) eigenvector corresponding to the largest eigenvector contains the attribute weight factors.
1.6 Combine all elements in a value model
Finally, all elements are combined in a value function. The change in value
∆V is equal to the sum of the weighted ratios of objective values, minus one:
∆V =
n
X
λi
i=1
(vi )1
−1
(vi )0
(D.1)
The objective ratios are equal to the sum of all weighted attribute ratios:
m
(xij )1
(vi )1 X
=
ωj
(vi )0
(xij )0
(D.2)
j=1
Using these equations, the change in value of each alternative can be calculated compared to the reference situation.
2. create a set of alternatives
In principle, the value function can handle any alternative. However, in
value-focused thinking it is recommended to use the value model also for
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the improved value operations methodology
creating alternatives. This can be done in a systematic way by building a
morphological grid. Such a grid first splits the decision context into discrete
parameters; then an attempt is made to find all possible alternative options
per parameter.
It is recommended to build the grid using a wide variety of sources to
ensure exhaustiveness. One source is the value model itself, as the following
steps show:
1. List the objectives from the value model as the rows of the grid;
2. Create, per objective, alternatives that improve on that objective, according to the procedure explained by Keeney (1992, §7.2). In this way
the grid is filled in horizontally;
3. In the thus created grid, find the similar and opposite alternatives and
group these in columns;
4. Define the common element in each column: this is a parameter in the
grid;
5. Find missing alternatives per column.
When all parameters and options are found using the value model and other
sources, the following steps are used to finalise the grid:
6. Consolidate the grid by collecting the unique options per parameter
(i.e. remove the double entries per column);
7. Remove all unacceptable options;
8. Assess cross-consistency to remove incompatibilities in certain combinations of options.
Alternatives can now be generated by choosing one option from each parameter, while taking into account the incompatibilities between options.
3. calculate the change in value
The following steps assist in calculating the ∆V of an alternative:
1. Choose a reference situation and calculate the attribute values for this
situation;
2. Assess the impact of the alternative on each attribute. The impact can
be expressed in a qualitative or a quantitative manner. For a qualitative
analysis, score the impact between + + + and − − −, and use the three
conversion schemes from Table 4.1 to convert these scores to numerical
values;
3. Scale the attribute values for the reference situation and the alternative
separately using Equation (4.12) or (4.13), depending on the direction
of preference of the attribute;
4. Use the value function (in the form of Equation (D.1) and (D.2)) to
calculate the alternative’s ∆V, for each stakeholder separately or for
the average stakeholder.
If ∆V is positive, the alternative creates value for the corresponding stakeholder, and as such is an improvement over the reference situation.
E
D E T E R M I N AT I O N O F S TA K E H O L D E R T Y P E
This appendix treats Step 2a through 2c from the stakeholder approach
for the Value Operations Methodology, as explained in Section 4.1.4. It
thus discusses the ranking of the stakeholders on their potential for threat
and cooperation and the subsequent determination of their stakeholder
type. As explained in Section 4.1.2 and visualised in Figure 4.1, there are
four stakeholder types: supportive; mixed blessings; non-supportive; and
marginal.
Using Savage’s approach (as explained in Section 4.1.2) the ranking of
the stakeholders on both potentials is done using a list of characteristics,
reproduced in Table E.1.
Only the stakeholders that are considered to be within the scope of the
project are ranked. For reference, these stakeholders are repeated in Table E.2.
To save space in the upcoming tables, each stakeholder also gets a two-letter
abbreviation.
Next, each stakeholder is crosschecked with each characteristic. If the
characteristic applies to the stakeholder, the combination is graded 1. If not,
the combination gets a 0. For each stakeholder, this ranking is explained
below. The complete results can be found in Table E.3.
air traffic control controls key resources without which the airport
cannot function, namely the control and allocation of airspace. They
can be seen as equally powerful as the airport operator because they
depend on each other. They are as likely to take supportive action as
they are to take non-supportive action, because their interests do not
always overlap with the airport’s goals. For example, LVNL may refuse
to accept a certain maintenance activity because this would result in too
high a workload for them. Still, they are likely to form a coalition with
the airport operator because they both represent the aviation sector, as
proven by their participation in the COBRA and the aviation cluster in
the Alderstafel.
airlines control key resources, namely the supply of flights without which
the airport cannot exist. Especially KLM is powerful in this respect
because of their large share of Schiphol’s traffic. However, because
they also depend on the airport operator, they can be seen as equally
powerful. Airlines are likely to take supportive actions to the outside
world and are likely to form a coalition with the airport operator, as
proven by their participation in the COBRA and the aviation cluster in
the Alderstafel.
main contractor does not control key resources, as they are hired by
the airport organisation but can be replaced by another, competing
contractor. Therefore the main contractor is less powerful than the
airport operator, and they are unlikely to take any action outside the
activities agreed to in the tender. As proven by their exclusion of forums
(see Figure 5.2) they are unlikely to form any coalition.
national government controls key resources, namely the legislation
and regulations the airport has to comply with. Therefore they are
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146
determination of stakeholder type
∇ threat
∇ cooperation
Stakeholder controls key resources (needed by organisation)
Increases (1)
Increases (1)
Stakeholder does not control key resources
Decreases (−1)
None (0)
Stakeholder more powerful than organisation
Increases (1)
None (0)
Stakeholder as powerful as organisation
None (0)
None (0)
Stakeholder less powerful than organisation
Decreases (−1)
Increases (1)
Stakeholder likely to take action (supportive of the
organisation)
Decreases (−1)
Increases (1)
Stakeholder likely to take non-supportive action
Increases (1)
Decreases (−1)
Stakeholder unlikely to take any action
Decreases (−1)
Decreases (−1)
Stakeholder likely to form coalition with other stakeholders
Increases (1)
None (0)
Stakeholder likely to form coalition with organisation
Decreases (−1)
Increases (1)
Stakeholder unlikely to form any coalition
Decreases (−1)
Decreases (−1)
Table E.1: List of stakeholder characteristics with their effect on the stakeholder’s potential (∇) for threat and
cooperation (Savage et al., 1991, Exhibit 1)
stakeholder
organisation
abbreviation
Air traffic control
LVNL
LV
Airlines
KLM
KL
Main contractor
Heijmans
HY
National government
Ministry of I&M
IM
Regional and local governments
BRS
NH
Residents and local community groups
CROS, VGP
RE
Passengers
PX
Meteorological service
KNMI
Table E.2: Stakeholders within scope and their two-letter abbreviation
KN
determination of stakeholder type
more powerful than the airport operator, because it can change the
laws or revoke the airport’s permissions. From the airport’s point of
view, the national government can take both supportive (e.g. allowing
more air traffic movements) and non-supportive actions (e.g. imposing
stricter noise limits). As the national government is supposed to be
above all parties, they are unlikely to join a coalition.
regional and local governments have much less power than the
national government and do not control key resources. They can take
both supportive and non-supportive action, as they represent the interests both of the citizens and the firms in the region (including the
airport). As an impartial government, they are unlikely to form any
coalition, other than with other layers of government (e.g. the BRS).
residents and local community groups do not control key resources
and are less powerful than the airport operator; their only power is
their voice and vote, which the different levels of government have to
take into account. They are likely to take non-supportive actions, such
as filing noise complaints. As seen in Figure 5.2, they are likely to form
a coalition with others to amplify their voice.
passengers control key resources, namely their wallet and their choice for
a particular mode of transport, airline and airport. However, because
they all act as individuals, they are less powerful than the airport
operator. They are therefore, as a group, unlikely to take any action or
to form any coalition.
meteorological service controls key resources, namely the supply of
meteorological data to the airport. However, they are less powerful as
their reason for existence is this provision of data. As a solitary player,
they are unlikely to take any action or take part in any coalition, as
proven by Figure 5.2.
Based on this crosschecking, the cumulative potential for threat and cooperation for each stakeholder can be calculated using a simple equation:
~r = ~sT P
(E.1)
Here, ~s is a vector containing the stakeholder characteristics; i.e. the ones
and zeros from each stakeholder’s column in Table E.3. For example, the
vector for LVNL would be ~sLV = (1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0)T . The matrix P
contains the effects of the different characteristics on the potential for threat
and cooperation (and thus contains the ones and zeros from Table E.1), and
is defined as:
"
#T
1 −1 1 0 −1 −1 1 −1 1 −1 −1
P=
(E.2)
1 0 0 0 1
1 −1 −1 0 1 −1
Finally, ~r is the resulting vector containing the total potential for threat and
cooperation.
147
148
determination of stakeholder type
For LVNL, this results in:

~rLV
1

−1


1

0


−1

T
= ~sLV P = (1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0) 
−1

1


−1

1


−1
−1
1


0


0

0


1

1
 = (0, 2)

−1


−1

0


1
(E.3)
−1
So, LVNL has a potential for threat of 0 and a potential for cooperation of 2.
For the other stakeholders the results are:
~rKL = (−1, 3)
~rHY = (−4, −1)
~rIM = (1, 0)
~rNH = (−3, 0)
~rRE = (0, 0)
~rPX = (−2, 0)
~rKN = (−2, 0)
However, these numbers alone do not mean anything. A frame of reference is needed to determine the stakeholder type. To do so, a prototype
stakeholder is created for each type, defining e.g. the ‘ultimate’ supportive
stakeholder. For example, the prototype for the non-supportive stakeholder is
created by ranking the characteristics in the most non-supportive way thinkable: controlling key resources; more powerful; likely to take non-supportive
action and to form a coalition with others. The rankings for all prototype
stakeholders and their resulting potentials for threat and cooperation can be
found in Table E.4.
Now, the difference between each stakeholder’s potentials and the prototypes can be easily calculated by computing the norm of the difference
vector:
∆T = k~r −~rprototype k
(E.4)
For the distance between stakeholder LVNL and the supportive prototype
stakeholder, this results in:
√
∆TLV,SUP = k~rLV −~rSUP k = k(0, 2) − (2, 4)k = 8 ≈ 2.8
(E.5)
Table E.5 lists the distances for all combinations of stakeholders and prototypes. The final step is to select for each stakeholder the minimal distance
to a prototype stakeholder. The associated prototype is then the stakeholder
type.
However, this is not the end of the story. The mathematics behind this
numerical analysis should not hide the fact that the determination of the
stakeholder type is not objective. The ranking of stakeholder characteristics in
Table E.3 is by definition subjective, because it is done from the viewpoint of
the organisation (in this case, the airport operator). Moreover, the numerical
analysis based on multiplications with the matrix P has not been thoroughly
tested; the list of characteristics from Savage’s approach (Table E.1) may not
be exhaustive and mutually exclusive; and the determination of the prototype
determination of stakeholder type
149
lv
kl
hy
im
nh
re
px
kn
Controls key resources
1
1
0
1
0
0
1
1
Does not control key resources
0
0
1
0
1
1
0
0
More powerful
0
0
0
1
0
0
0
0
Equally powerful
1
1
0
0
0
0
0
0
Less powerful
0
0
1
0
1
1
1
1
Likely to take supportive action
1
1
0
1
1
0
0
0
Likely to take non-supportive action
1
0
0
1
1
1
0
0
Unlikely to take any action
0
0
1
0
0
0
1
1
Likely to form coalition with others
0
0
0
0
0
1
0
0
Likely to form coalition with organisation
1
1
0
0
0
0
0
0
Unlikely to form any coalition
0
0
1
1
1
0
1
1
Table E.3: Crosscheck of each stakeholder with each characteristic: if the characteristic applies, the combination is
graded 1; if not, 0
sup
mix
non
mar
Controls key resources
1
1
1
0
Does not control key resources
0
0
0
1
More powerful
0
0
1
0
Equally powerful
0
1
0
0
Less powerful
1
0
0
1
Likely to take supportive action
1
1
0
0
Likely to take non-supportive action
0
1
1
0
Unlikely to take any action
0
0
0
1
Likely to form coalition with others
0
0
1
0
Likely to form coalition with organisation
1
0
0
0
Unlikely to form any coalition
0
1
0
1
(−2, 4)
(0, 0)
(4, 0)
(−4, −1)
Total potential for threat and cooperation
Table E.4: Rankings and potentials for prototype stakeholders of the supportive (SUP), mixed blessings (MIX), nonsupportive (NON) and marginal (MAR) types; ranking analogues to Table E.3; total potential calculated
using Equation (E.1)
150
determination of stakeholder type
stakeholder
∆TSUP
∆TMIX
∆TNON
∆TMAR
2.8
2.0
Air traffic control
1.4
Airlines
type
4.5
5.0
Mixed blessings
3.2
5.8
5.0
Supportive
4.1
8.1
Main contractor
5.4
National government
5.0
Regional and local
governments
4.1
Residents and local
community groups
4.5
0.0
4.0
4.1
Mixed blessings
Passengers
4.0
2.0
6.0
2.2
Mixed blessings
Meteorological service
4.0
2.0
6.0
2.2
Mixed blessings
1.0
3.0
3.0
7.0
0.0
5.1
1.4
Marginal
Mixed blessings
Marginal
Table E.5: Distances between stakeholders and prototypes, calculated using Equation (E.4). The minimum distance
for each stakeholder is highlighted; the corresponding type is listes in the last column.
stakeholders cannot be checked. To improve on all these points would be
a research project on its own, and will thus not be done here. The method
used can therefore only give an indication of the stakeholder type, and other
methods are needed to validate its outcomes.
Therefore, an advisor at AMS and the author of this thesis have each
(apart from each other) classified the stakeholders as one of the four types.
The results from this triangulation are shown in Table E.6. The stakeholder
type that has been selected by two or three out of the three methods (the
numerical analysis and two assessments) is the final selected stakeholder
type.
stakeholder
sup
mix
Air traffic control
FB
N
Supportive
Airlines
NB
F
Supportive
Main contractor
F
National government
F
non
mar
NB
NB
overall type
Marginal
Mixed blessings
Regional and local governments
FB
N
Residents and local community groups
N
Passengers
NF
B
Mixed blessings
Meteorological service
N
FB
Marginal
FB
Mixed blessings
Non-supportive
Table E.6: Triangulation of stakeholder type by numerical analysis (N), assessment of an advisor at AMS (F) and
the author’s assessment (B). The type that has been selected by two out of the three methods is the final
selected stakeholder type, and is listed in the last column.
F
L O N G L I S T O F S TA K E H O L D E R S ’ O B J E C T I V E S
This appendix contains the longlist of the relevant stakeholders’ objectives,
as explained in Section 5.2. Note that the formulation of the objectives is not
yet refined, as this will be done in a next step.
Next to the quoted documents, also interviews with the different stakeholders are used to determine their objectives. A list of interviews is given in
Appendix A.
airport operator The airport operator is obviously represented by
Amsterdam Airport Schiphol, so its objectives are used (Amsterdam Airport Schiphol, 2011a; Groot and Hen, 2011; Kamminga, 2010a; Repko, 2011;
Smulders, 2010; Van Calck, 2011; Van der Vegte and Vallinga, 2011):
• Maximise the capacity of the airport;
• Maximise efficiency and minimise costs;
• Increase predictability and reliability of operations;
• Increase transparency to other stakeholders;
• Increase safety;
• Improve business climate and spur national and regional economic
development;
• Reduce impact on surroundings;
• Grow sustainably.
The fundamental objective of Amsterdam Airport Schiphol is the core of its
strategy: to be ‘Europe’s preferred airport’ (Schiphol Group, 2011).
air traffic control
2011):
Air traffic control is represented by LVNL (Repko,
• Increase predictability of air traffic and airport conditions;
• Decrease workload for air traffic controllers;
• Increase safety.
airlines The airlines are represented by the largest airline operating at
Schiphol, KLM (Kamminga, 2010a; KLM, 2010; Repko, 2011; Smulders, 2010):
• Grow profitably and sustainably;
• Increase network quality;
• Strengthen mainport;
• Find a balance between the airlines’ goals and the local community’s
concerns regarding noise and the environment.
151
152
longlist of stakeholders’ objectives
national government The national government is represented by the
Ministry of Infrastructure and the Environment (Alders, 2010; Ministerie van
Verkeer en Waterstaat, 2009; Smulders, 2010):
• Grow regional and national economy;
• Improve business climate;
• Improve mainport function of Schiphol;
• Protect residents;
• Ensure sustainable growth.
regional and local governments The regional and local governments are represented by the BRS (Kuipers and Vonk, 2011; Repko, 2011):
• Ensure a balance between development and growth of the airport, and
protection of residents;
• Enact annoyance restricting measures.
residents and local community groups The residents and local
community groups are represented by the VGP, the CROS and BAS (BAS,
2011a; Kamminga, 2010a; Repko, 2011; Wever, 2010):
• Reduce nuisance;
• Minimise noise annoyance;
• Increase transparency and predictability of airport operations.
passengers There is no single representative organisation for the passengers, however their objectives as used in this thesis are (Kamminga, 2010a;
Smulders, 2010; Steer Davies Gleave, 2006):
• Ensure punctual arrival and departure times;
• Maximise safety;
• Reduce travel times;
• Increase accessibility of the terminal;
• Reduce ticket price.
G
C O M PA R I S O N M AT R I C E S
This appendix contains the comparison matrices for each individual stakeholder, and the overall comparison matrix, as used in Section 5.3. The matrices
are based on the pair-wise comparisons of the representatives of the relevant
stakeholders, mentioned in Appendix A.
The individual stakeholders’ objective weight factors are classified. Therefore
the underlying comparison matrices are removed from the public version of
this thesis. Only the passengers’ comparison matrix is public, as its entries
are based on literature.
The comparison matrix for the passengers is:


1
8
1
8
8
8
1

8

1
1

8
1
8
1
1
8
1
1
8
1
8
8
1
1
8
1
8
1
8
1
1
1
1

1


8

1


1
1
1
1
1
8
1
1
153
H
I M PA C T O F A LT E R N AT I V E S O N AT T R I B U T E S
This appendix presents the impact of the alternatives (created in Chapter
6) on the attributes. For the qualitative analysis the change-impact matrix
as presented below is used; afterwards the quantitative impact is explained.
The results of this analysis are discussed in Chapter 7.
change-impact matrix
Table H.1 repeats the change-impact matrix for all alternatives. This matrix
is the basis for the qualitative results discussed in Section 7.3. Here, for each
parameter the entries in the matrix are explained, based on a desk analysis
of the GOH project on the Kaagbaan in September 2011 and interviews with
experts.
Parameter ‘month’
The reference situation is equal to the alternative ‘September’.
days u/s (dus) will increase for the alternatives ‘yearlong’ and ‘April–
September’, and significantly for ‘October–March’, as the maintenance
will take longer due to worse weather conditions;
operational penalty (opp) will increase for the alternatives ‘yearlong’,
‘April–September’ and ‘June–September’, and decrease for ‘October–
March’, based on the penalties described in Table 5.4;
maintenance costs (cos) will increase for the three alternatives that
experience an increase in maintenance duration, as project length is an
important driver behind costs;
maintenance delay (del) will increase slightly in the ‘yearlong’ alternative, quite a lot for the ‘October–March’ alternative, and decrease
slightly for the summer alternatives (Neeteson, 2010a,b,c);
non-scheduled vs. scheduled works (nss) will not change based
on the month;
safety risk resulting from maintenance (saf) will also not change;
extra fuel costs (efc) will increase slightly for the ‘October–March’
alternative, as the huge increase in maintenance duration will also have
its effect on taxiroutes;
environmental impact of maintenance (env) will increase slightly
for the alternative ‘October–March’ as then more heating is needed;
residents’ preferences fulfilled (res) will increase slightly for the
alternative ‘October–March’, because only then maintenance is carried
out in the winter, as preferred by residents;
number of houses within 58 db(a) l den (hou) will not change for
any alternative, as explained in Section 7.2;
155
156
alternative
impact of alternatives on attributes
dus
opp
cos
del
nss
saf
efc
env
res
hou
hap
parameter ‘month’
Yearlong
+
+
+
+
0
0
0
0
0
0
0
April–September
+
++
+
0
0
0
0
0
0
0
0
+++
−
+++
+++
0
0
+
+
+
0
0
September
0
0
0
0
0
0
0
0
0
0
0
June
0
0
0
−
0
0
0
0
0
0
0
June–September
0
+++
0
−
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
−−−
−−−
+++
0
0
+
−−
+
−
0
0
++
0
−
0
0
0
+
−
0
0
0
All week
0
0
0
0
0
0
0
0
0
0
0
Not during weekends
+
0
−−
0
0
0
0
0
+
0
0
Only during weekends
+
0
+++
0
0
0
0
0
−
0
0
October–March
parameter ‘timeslot’
24h
Night (23–06h)
Day (06–23h)
parameter ‘weekends’
parameter ‘combination of activities’
0
0
0
0
0
0
0
0
0
0
0
++
0
++
−−
0
+
0
0
−
0
0
0
0
0
0
0
0
0
0
0
0
0
ICAO minimum
(project)
−−
0
−−
−−
0
0
−
0
0
0
0
Maximise asset lifetime
(project)
++
0
++
++
0
0
+
0
−
0
0
ICAO minimum
(lifetime)
0
0
0
−−
+
+
0
0
−
0
0
Maximise asset lifetime
(lifetime)
0
0
0
++
−
−
0
0
0
0
0
Combine as much as
possible
Split up activities
parameter ‘quality of assets’
Schiphol norms (stricter
than ICAO)
parameter ‘length of maintenance blocks’
Short blocks
−−−
0
+++
++
+++
+
−−
0
−−
0
0
Long blocks
0
0
0
0
0
0
0
0
0
0
0
parameter ‘maintenance area’
Landside
0
0
−
0
0
−
+
0
0
0
0
Airside
0
0
++
0
0
++
−
0
0
0
0
Table H.1: Change-impact matrix showing the impact of alternatives on the set of attributes. (This table is a copy of
Table 7.1.)
impact of alternatives on attributes
number of highly annoyed people within 48 db(a) l den (hap) will
also not change for any alternative, as explained in Section 7.2.
Parameter ‘timeslot’
The reference situation is equal to the alternative ‘24h’.
dus will increase to 0 for the ‘night’ alternative, as then the runway can be
used during the day. When only work is done during the day, the total
number of days U/S will increase;
opp will decrease for the ‘night’ alternative and stay the same for ‘day’ as
can be seen in Table 5.4;
cos will increase significantly for ‘night’, as those working hours are much
more expensive (Boezeman, 2009; Heijmans, 2011b); vice versa costs
will decrease for the ‘day’ alternative, but as still some hours in this
alternative are outside the normal hours for contractors (07:00–16:00)
the decrease is only low;
del will not change based on the timeslot;
nss will also not change;
saf will increase for the ‘night’ alternative, as working during the night is
less safe because of the lower visibility;
efc will increase slightly for the ‘day’ alternative, as then the maintenance
project will take longer; it will decrease for the ‘night’ alternative,
because the runway is operational during the day which removes the
need for flight and taxi detours;
env will increase slightly for the ‘night’ alternative, as then more lighting is
needed which increases energy use; vice versa, for the alternative ‘day’
this attribute will decrease;
res will stay the same for the alternative ‘day’: the preference ‘maintenance
during the day instead of during the night’ is fulfilled, but not the
preference ‘maintenance as short as possible’, so overall these changes
cancel each other out; for the alternative ‘night’ this last preference is
also not fulfilled, decreasing the attribute.
Parameter ‘weekends’
The reference situation is equal to the alternative ‘all week’.
dus will increase for both alternatives: in the alternative ‘not during weekends’ the runways are not made operational during the weekend,
resulting in an increase of the total number of days the runway is under
service; in the alternative ‘only during weekends’ the runway is made
available during the week, but this switching back and forth increase
the total maintenance duration;
opp will not change according to Table 5.4;
cos are much higher in the weekend, and thus will decrease for the alternative ‘not during weekends’ and increase for the other (Heijmans,
2011b);
157
158
impact of alternatives on attributes
del will not change;
nss will also not change based on this parameter;
saf will not change as a result of whether maintenance is done during the
weekend or not;
efc will also not change;
env is not dependent on whether maintenance is done during the weekend
or not and will thus not change;
res will increase for the alternative ‘not during weekends’ as then the preference ‘no maintenance during weekends’ is fulfilled; it will decrease
for the other alternative because then the preference ‘maintenance as
short as possible’ is no longer fulfilled.
Parameter ‘combination of activities’
The reference situation is equal to the alternative ‘combine as much as
possible’. For the alternative ‘split up activities’:
dus will increase as more time is needed to prepare the runway for maintenance before each activity, and make it available for operations afterwards;
opp will not change based on this parameter alone;
cos will increase because security and preparation costs are spread over
less activities;
del will decrease because of the higher flexibility in waiting for the right
weather or the right time for maintenance;
nss will not change, as split up activities can still be scheduled in advance;
saf will decrease as it requires more adjusting from all actors to numerous
little maintenance activities, and it increases the number of — inherently
risky — switches to a runway under service;
efc will not change based on this parameter;
env will not change;
res will decrease, as the preference ‘combine activities as much as possible’
is no longer fulfilled.
Parameter ‘quality of assets’
The reference situation is equal to the alternative ‘Schiphol norms’. As explained in Section 7.2, this parameter can be analysed from two perspectives:
a project or a lifetime perspective. From a project perspective:
dus will decrease for the alternative ‘ICAO minimum’ as less maintenance
is needed; vice versa it will increase for the alternative ‘maximise asset
lifetime’;
opp will not change based on this parameter;
cos will be lower for the minimum quality alternative, as less maintenance
is done or lower quality materials are used; vice versa it will increase
for the alternative ‘maximise asset lifetime’;
impact of alternatives on attributes
del will increase for the alternative ‘maximise asset lifetime’, as then the
acceptable weather conditions are more limited; vice versa, the delay
will decrease for the alternative ‘ICAO minimum’;
nss will not change from this perspective;
saf will not change from a project perspective;
efc will, due to the change in maintenance duration, decrease slightly for
the alternative ‘ICAO minimum’ and increase for the other;
env will not change based on this parameter;
res will decrease slightly for the alternative ‘maximise asset lifetime’ because
it does not fulfill the preference ‘as little maintenance as possible’.
And from a lifetime perspective:
dus will probably be equal for both alternatives;
cos will depend on the net present value of the initial investment in the
asset’s quality and future expenses on maintenance. This cannot be
easily determined, therefore here the difference between the alternatives
is set to 0;
del will have the same effect as from the project perspective;
nss will increase for the alternative ‘ICAO minimum’ as the asset will
experience more failures due to its lower quality; vice versa it will
decrease for the alternative ‘maximise asset lifetime’;
saf will decrease for the alternative ‘maximise asset lifetime’ as it will have
a higher quality for a longer period of time; vice versa the risk will
increase for the alternative ‘ICAO minimum’;
efc will not change from a lifetime perspective;
env will not change based on this parameter;
res will, from this perspective, decrease slightly for the alternative ‘ICAO
minimum’ because it does not fulfill the preference ‘as little maintenance as possible’.
Parameter ‘length of maintenance blocks’
The reference situation is equal to the alternative ‘long blocks’. For the
alternative ‘short blocks’:
dus will decrease because maintenance is done in short blocks in between
operations;
opp will not change based on this parameter alone;
cos will increase because now for each activity separate switching and
security costs have to be included;
del will increase, as there is less flexibility in waiting for the right weather
conditions;
nss will increase if it is assumed that short blocks imply a high flexibility in
scheduling;
159
160
impact of alternatives on attributes
saf will increase as there are more risk-increasing switching situations;
efc will decrease as there are no extended periods in which aircraft must
take a detour;
env will not change based on this parameter;
res will decrease because the preferences ‘combine activities as much as
possible’ and ‘announce activities early’ are not fulfilled.
Parameter ‘maintenance area’
In the reference situation, the maintenance area was mostly landside, apart
from an aircraft crossing. Therefore the reference situation does not completely match one of the alternatives.
dus will not change for either alternative;
opp will not change based on this parameter alone;
cos will increase for the alternative ‘airside’ because more (expensive) security measures are needed; it will decrease a little for the alternative in
which the complete maintenance area is landside;
del will not change based on this parameter alone;
nss will not change;
saf will increase for the alternative ‘airside’, as the risk of a confrontation with airside operations is higher; therefore, the risk for ‘landside’
decreases a little;
efc will decrease for the alternative ‘airside’, as aircraft may cross the
maintenance area; for the alternative ‘landside’ detours will increase
and thus the extra fuel costs;
env will not change;
res will not change.
quantitative analysis
Table H.2 shows the attribute values for the two quantitatively analysed
scenarios, as described and discussed in Section 7.4. Also the reference values
are included in this table. In this section each attribute value is explained.
scenario
dus
opp
cos
del
nss
saf
efc
env
res
hou
hap
21
6
eXX, 000, 000
6
0%
—
e500, 000
2
4
0
0
xmin
0
1
e0
0
0%
—
e−1, 000, 000
1
1
0
0
xmax
30
8
eXX, 000, 000
24
100%
—
e1, 000, 000
3
8
12, 300
239, 500
Reference
Nightly
0
4
eXX, 500, 000
20
0%
—
e50, 000
3
4
0
0
Summer
25
6
eXX, 000, 000
3
0%
—
e550, 000
2
4
0
0
Table H.2: Attribute values for the ‘nightly maintenance’ and ‘summer maintenance’ scenarios. Also the feasible
range values xmin and xmax are listed. (This table is a copy of Table 7.2.)
impact of alternatives on attributes
Reference situation and feasible range
As the total maintenance costs are confidential, the following sections and
Table H.2 have been edited to remove this information.
dus The Kaagbaan was under service for maintenance, and not available for
operations, for 21 days (BAS, 2011b,c; Heijmans, 2011a). The minimum
number of days is obviously 0, while the maximum is set to 30 days
for one continuous maintenance project;
opp The maintenance took place day and night in September 2011. Using
Table 5.4, this results in an operational penalty of 6. This constructed
attribute has a minimum value of 1 and a maximum value of 8;
cos The total maintenance costs were approximately eXX, 000, 000 (Amsterdam Airport Schiphol, 2011b; Kooper, 2010). Logically, the absolute
minimum maintenance costs are e0, while the maximum is set to
double the current costs: eXX, 000, 000;
del The project started on time, but ended 6 hours late. Minimum delay is
0 hours, and has a maximum of 24 hours: after this delay the operation
needs the runway back and schedule a new maintenance period;
nss As everything was scheduled for and the runway was under service
for three weeks, the amount of non-scheduled activities was 0. As a
percentage of the total number of activities, this attribute’s feasible
range is 0–100 percent;
saf Although no incidents or accidents occurred during this maintenance
project, the attribute is not quantified;
efc According to Brouwer et al. (2011, Slide 16) one week of maintenance on
the Kaagbaan costs e166, 700 in extra fuel costs; so for three weeks of
maintenance this is approximately e500, 000. The minimum value for
this attribute is e−1, 000, 000, so a profit. This occurs when for example
the Polderbaan is under service (Brouwer et al., 2011, Slide 16). The
maximum value is twice the current value: e1, 000, 000;
env For the reference situation, the environmental impact is set as 2. This
gives the alternatives room for improvement, but also for an increase
in impact. This constructed attribute has a minimum value of 1 and a
maximum value of 3;
res Of the seven preferences, four are fulfilled in the reference situation:
activities are combined; maintenance is communicated early; as little
maintenance as possible is done; and the project was as short as possible
(continuous work). This constructed attribute has a minimum value of
1 and a maximum value of 8;
hou, hap As mentioned in Section 7.2, the noise attributes do not change
for maintenance on the Kaagbaan. Therefore these attributes are set to
0. Their minimum values are logically 0, while the maximum values
are set in the legislation: 12, 300 and 239, 500 respectively (Alders, 2010,
p.12).
161
162
impact of alternatives on attributes
Nightly maintenance
dus Performing all maintenance during the night means that the runway is
not under service during operational hours, so the attribute is set to 0
days (Boezeman, 2009);
opp The maintenance is performed in June–September, but only during the
night. According to Table 5.4 this means an operational penalty of 4;
cos According to Boezeman (2009), performing all maintenance during the
night costs 50 percent more. This is mostly due to an increase of 30
percent in labour costs (Heijmans, 2011b). Also equipment and security
costs increase due to a longer maintenance period and more switching
moments from maintenance area to operational runway. The reduction
in labour costs due to the lack of (more expensive) weekend hours is
negligible. Thus, total maintenance costs become eXX, 500, 000;
del The necessity of working in short blocks results in a higher delay, as
explained above. This extra delay is assumed to be two nights, or
fourteen hours, resulting in a total delay of 20 hours;
nss Maintenance at night in short blocks can still be scheduled well in
advance. Therefore there is no change in this attribute;
saf This attribute is not quantified, as explained in Section 5.4. Therefore
the attribute value is still 0. However, as mentioned above the safety
risk increases due to more switching moments from maintenance to
operational area;
efc Because of the short and nightly maintenance blocks, and the fact that
the maintenance area remains airside, aircraft barely need to make
detours. However, due to the inevitable effect on taxiways, the attribute
value is taken as 10 percent of the reference value, resulting in extra
fuel costs of e50, 000;
env Due to extra heating and lighting necessary because all work is done
during the night, the environmental impact increases to 3;
res Compared to the reference situation, the preference ‘maintenance as
short as possible’ is no longer fulfilled, but the preference ‘no maintenance during weekends’ is; this results in an attribute value of 4;
hou, hap As mentioned above, these attributes do not change.
Summer maintenance
dus In this scenario, no work is done during the weekends, but the runway
remains non-operational. In the three week maintenance period, there
are two weekends, so four days, that must be added to the total number
of maintenance days. So this attribute becomes 21 + 4 = 25 days;
opp Maintenance in June, during the day and night, results in an operational
penalty of 6 according to Table 5.4;
cos For this project, total labour costs are 25 percent lower when no activities
are done during the weekend. It is assumed that the total maintenance
costs are equally divided over labour costs, equipment costs and material costs. This means that the total maintenance costs for this scenario
are eXX, 000, 000 · (1 − 25% · 13 ) ≈ XX, 000, 000;
impact of alternatives on attributes
del In June, weather conditions are a little better, resulting in a lower delay.
It is assumed that delay for this scenario is 3 hours;
nss This attribute does not change in this scenario;
saf This attribute is not quantified, as explained in Section 5.4. Therefore
the attribute value is still 0. Qualitatively, there is a small risk reduction
because in this scenario the complete maintenance area is landside;
efc Because in this scenario the complete maintenance area is landside,
detour fuel costs for aircraft increase. It is assumed that this increase is
10 percent, resulting in a total of e550, 000;
env This attribute does not change in this scenario;
res Because there is no work done during the weekend, the preference ‘no
maintenance during weekends’ is fulfilled, but the preference ‘maintenance as short as possible’ is no longer fulfilled. Therefore this attribute
value remains at 4;
hou, hap As mentioned above, these attributes do not change.
163
I
S E N S I T I V I T Y A N A LY S E S
To validate the value model and its results, a number of sensitivity analyses
are performed. Their results and implications for the validity of the value
model are discussed in Chapter 8.
The sensitivity tests are not performed for all alternatives, as this adds a
lot of charts without a lot of extra information. Instead, three alternatives
with varying qualitative results have been selected for testing:
• The alternative ‘June–September’: the qualitative analysis of this alternative results in mixed results for the stakeholders, and ∆V is small;
• The alternative ‘night’: this alternative results in a high positive ∆V
for most stakeholders and is the basis for the quantitative scenario
analysis;
• The alternative ‘split up activities’: this alternative results in a high
negative ∆V for most stakeholders.
Figure I.1 tests the sensitivity of the average value function for the inclusion of passengers’ preferences. Therefore, only the results for the average
stakeholder for the three alternatives is shown.
Figure I.2–I.4 test the sensitivity of the value model for the objective weight
factors. For this test, the ∆V for each stakeholder is shown for the original set
of objective weight factors; a random set of weight factors; and the inverse
set of weight factors. All three sets of weight factors are shown in Table I.1.
Figure I.5–I.7 test the sensitivity of the value model for the attribute weight
factors. For this test, the ∆V for each stakeholder is shown for the original
set of attribute weight factors and the set for which all attributes belonging
to the same objective are weighted equally. The two sets of attribute weight
factors are listed in Table I.2.
Figure I.8–I.10 test the sensitivity of the value function for the choice of
factormax. For this test, the objective weight factors for each stakeholder are
recalculated using the comparison matrix with factormax 2.
Finally, Figure I.11–I.13 test the sensitivity of the value model for the
averaging method used to calculate the stakeholder’s objective weight factors.
In these tests, the arithmetic mean is compared to the geometric mean. The
resulting ∆V is shown only for Amsterdam Airport Schiphol, LVNL and the
average stakeholder, as only for these stakeholders multiple assessments are
combined.
The individual stakeholders’ objective weight factors are classified and therefore removed from the public version of this thesis. Only the average weight
factors are public. Also the passengers’ objective weight factors are public, as
these are based on literature.
Moreover, because of this confidentiality the ∆V are not shown for the
individual stakeholders in the public version of this thesis.
165
166
sensitivity analyses
&#$%
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(#$%
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!(#$%
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)*+,!-,./,01,2%
3456/%
=;,285,%>18<,?%
-.74/%*.%89:;4:,<%
=;,285,%>@4/6A*/%.8<<,+5,2<?%
Figure I.1: Comparison of average ∆V including (base) and excluding passengers’ preferences
aas
lvnl
airlines
im
nh
residents
pax
average
40
28
5
4
40
21
base
1
Capacity
2
Costs
3
Predictability
4
Safety
5
20
5
Environment
5
8
6
Nuisance
5
19
random
19
19
17
24
25
23
11
20
Costs
8
25
14
35
8
17
37
21
3
Predictability
4
12
8
10
15
23
2
11
4
Safety
23
13
25
4
5
3
1
11
5
Environment
23
17
6
14
40
15
26
20
6
Nuisance
23
14
29
13
8
19
22
18
5
7
40
44
5
7
40
15
1
Capacity
2
inverse
1
Capacity
2
Costs
3
Predictability
4
Safety
5
Environment
5
16
6
Nuisance
5
11
Table I.1: Objective weight factors in percent for the sensitivity analysis: the original sets (base); a random set; and
the inverse of the base. Not all columns add up to 100 percent because of rounding errors. ‘IM’ and ‘NH’
are the stakeholders ‘national government’ and ‘regional and local governments’ respectively.
sensitivity analyses
167
20%%
10%%
0%%
∆V#
!10%%
!20%%
!30%%
AAS%
LVNL%
Airlines%
June!September%(base)%
Na4onal%gov't%
Local%gov't%
June!September%(random)%
Residents%
Passengers%
Average%
June!September%(inverse)%
Figure I.2: Comparison of ∆V for different objective weight factors, for the alternative ‘June–September’
60%%
50%%
40%%
30%%
20%%
10%%
0%%
∆V#
!10%%
!20%%
!30%%
!40%%
!50%%
!60%%
!70%%
!80%%
!90%%
AAS%
LVNL%
Airlines%
Night%(base)%
Na:onal%gov't%
Night%(random)%
Local%gov't%
Residents%
Passengers%
Average%
Night%(inverse)%
Figure I.3: Comparison of ∆V for different objective weight factors, for the alternative ‘night’
168
sensitivity analyses
20%%
10%%
0%%
!10%%
!20%%
!30%%
∆V#
!40%%
!50%%
!60%%
!70%%
!80%%
!90%%
AAS%
LVNL%
Airlines%
Split%up%ac:vi:es%(base)%
Na:onal%gov't%
Local%gov't%
Split%up%ac:vi:es%(random)%
Residents%
Passengers%
Average%
Split%up%ac:vi:es%(inverse)%
Figure I.4: Comparison of ∆V for different objective weight factors, for the alternative ‘split up activities’
dus
opp
cos
del
nss
saf
efc
env
res
hou
hap
Base
50
50
100
67
33
100
11
89
6
47
47
Equally weighted
50
50
100
50
50
100
50
50
33
33
33
Table I.2: Attribute weight factors in percent for the base value function, and for the case where all attributes are
weighted equally
sensitivity analyses
169
20%%
10%%
0%%
∆V#
!10%%
!20%%
!30%%
AAS%
LVNL%
Airlines%
June!September%(base)%
Na4onal%gov't%
Local%gov't%
Residents%
Passengers%
Average%
June!September%(aEributes%equally%weighted)%
Figure I.5: Comparison of ∆V when attributes are weighted according to Table 5.5 and when all attributes are
weighted equally, for the alternative ‘June–September’
60%%
50%%
40%%
30%%
20%%
10%%
0%%
∆V#
!10%%
!20%%
!30%%
!40%%
!50%%
!60%%
!70%%
AAS%
LVNL%
Airlines%
Night%(base)%
Na8onal%gov't%
Local%gov't%
Residents%
Passengers%
Average%
Night%(aFributes%equally%weighted)%
Figure I.6: Comparison of ∆V when attributes are weighted according to Table 5.5 and when all attributes are
weighted equally, for the alternative ‘night’
170
sensitivity analyses
20%%
10%%
0%%
!10%%
!20%%
∆V#
!30%%
!40%%
!50%%
!60%%
!70%%
AAS%
LVNL%
Airlines%
Split%up%ac8vi8es%(base)%
Na8onal%gov't%
Local%gov't%
Residents%
Passengers%
Average%
Split%up%ac8vi8es%(aGributes%equally%weighted)%
Figure I.7: Comparison of ∆V when attributes are weighted according to Table 5.5 and when all attributes are
weighted equally, for the alternative ‘split up activities’
20%%
10%%
0%%
∆V#
!10%%
!20%%
!30%%
AAS%
LVNL%
Airlines%
Na4onal%gov't%
June!September%(base)%
Local%gov't%
Residents%
Passengers%
Average%
June!September%(factormax%2)%
Figure I.8: Comparison of ∆V for a factormax of 8 (base) and 2, for the alternative ‘June–September’
sensitivity analyses
171
60%%
50%%
40%%
30%%
20%%
10%%
0%%
∆V#
!10%%
!20%%
!30%%
!40%%
!50%%
!60%%
!70%%
AAS%
LVNL%
Airlines%
Na8onal%gov't%
Night%(base)%
Local%gov't%
Residents%
Passengers%
Average%
Night%(factormax%2)%
Figure I.9: Comparison of ∆V for a factormax of 8 (base) and 2, for the alternative ‘night’
10%%
0%%
!10%%
!20%%
∆V# !30%%
!40%%
!50%%
!60%%
!70%%
AAS%
LVNL%
Airlines%
Na8onal%gov't%
Split%up%ac8vi8es%(base)%
Local%gov't%
Residents%
Passengers%
Average%
Split%up%ac8vi8es%(factormax%2)%
Figure I.10: Comparison of ∆V for a factormax of 8 (base) and 2, for the alternative ‘split up activities’
172
sensitivity analyses
10%%
0%%
∆V# !10%%
!20%%
!30%%
AAS%
LVNL%
June!September%(base)%
Average%
June!September%(geometric%mean)%
Figure I.11: Comparison of ∆V when using the arithmetic mean (base) or geometric mean to combine stakeholders’
assessments, for the alternative ‘June–September’
40%%
30%%
20%%
∆V#
10%%
0%%
!10%%
AAS%
LVNL%
Night%(base)%
Average%
Night%(geometric%mean)%
Figure I.12: Comparison of ∆V when using the arithmetic mean (base) or geometric mean to combine stakeholders’
assessments, for the alternative ‘night’
0%%
!10%%
!20%%
∆V#
!30%%
!40%%
!50%%
AAS%
LVNL%
Split%up%ac:vi:es%(base)%
Average%
Split%up%ac:vi:es%(geometric%mean)%
Figure I.13: Comparison of ∆V when using the arithmetic mean (base) or geometric mean to combine stakeholders’
assessments, for the alternative ‘split up activities’