THE NETWORK DASHBOARD:
INCREASING THE OPERATING MARGIN AND CUSTOMER
SATISFACTION BY INTEGRAL MANAGEMENT OF
PRODUCTION COSTS AND TRAFFIC REVENUES
J.D.F. Gemke MSc
Head of Market Research, NS Commercial
L. Stellingwerff MSc
Head of Order Service Centre, Logistics, NS Operations
Abstract
On one of the most densely operated networks in the world, Netherlands
Railways (NS) runs the Dutch trunk route system via a franchise that expires
in 2015. As a railway company NS thus operates in a capital-intensive sector,
characterized by small operating margins that have come under even greater
pressure due to the recent recession.
It was during this recession that a decision had to be made on a long-term
investment of € 4.5 billion by the Dutch government to prepare the rail
infrastructure for the growing market demand. This involves the HighFrequency Rail Transport Programme 1 enabling six Intercities and six allstation trains to run hourly on the busiest routes of the country.
The combination of an expiring franchise, a considerable long-term
investment, on top of decreasing operating margins resulting from a
recession, made it crucial that decisions be made on the future of Dutch rail
transport in sound collaboration with the Dutch Ministry of Infrastructure and
Environment, the infra-provider ProRail and the major market operator NS.
At a time when there is also broad consensus on the level of economic
growth remaining at a lower level for the long term, it is even more important
that decision- making on long-term investments is well considered. This
article addresses the Network Dashboard; a model which NS has
implemented and which advocates integral management of production costs
and traffic revenues in order to better match capacity with market demand.
Results show that the operating margin and customer satisfaction improve
significantly.
From an organizational perspective it has been clearly shown how Operations
and Commercial can co-operate more closely, but also how the Network
Dashboard enables NS to move from operational volume-driven operation of
the network to a more financially driven operation of the network, without
neglecting customer satisfaction.
© Association for European Transport and Contributors 2011
Finally, an additional elaboration has been introduced in order to give the
operational line management a tool to get a tangible grip on the twenty major
corridors of the NS network in the Netherlands, in turn affording a further local
optimization of operating results and customer satisfaction.
Introduction
With 16.5 million inhabitants, the Netherlands is one of the most densely
populated countries in Western Europe. Until 2015, Netherlands Railways
(NS) has the exclusive right in this country to operate the trunk route
franchise and occupies a market share of approximately 85% of public rail
transport. On weekdays over 1 million people in the Netherlands travel by
train and at peak hours the market share within the Randstad (the
conurbation in the west of the country) is more than 60%. With a punctuality
of 92.5%2, 75% of its customers award a mark 7 or better (out of 10) for
general customer satisfaction. On an annual base the revenues from the
trunk route franchise in 2010 (see figure 1) are about € 1.8 billion with an
operating result of about € 240 million.
Figure 1: The NS network in the Netherlands
including other operators (dashed
lines)
In order to be able to accommodate the expected growth in public transport of
approximately 27% in the period to 2020, a basic agreement was reached
with the government in the summer of 2010 on the High-Frequency Rail
Transport Programme. This will enable 6 Intercities and 6 all-station trains to
run hourly on the country’s busiest routes and will also accommodate extra
rail freight traffic. The quality improvements thus afforded will mean that the
number of passengers can increase by about 40% as compared with 2008.
© Association for European Transport and Contributors 2011
This will occur at a time when the liberalization agenda for public transport
throughout Europe will continue unabated: after the liberalization of freight
transport, Dutch regional public transport followed in 2007 and in 2011 public
urban transport will likewise be released for competition as will increasingly
international public rail transport. It is thus to be expected that public rail
transport in Europe is on the brink of further cross-border mergers, takeovers
and other forms of partnerships. This will almost certainly lead to a further
consolidation of the sector with the goal of providing customers with higher
quality at lower costs. To play a key role in this process, it is a precondition to
achieve a competitive cost level, which first starts with consistently managing
production costs and traffic revenues.
Against this background the ambition of NS is to be a customer-oriented
European multimodal service provider, which will, in accordance with the
trunk route franchise agreement, operate the network for its own risk and
expense. Consequently, NS is increasingly evolving toward a commercial
organisation with an important social function, with operating results and
customer satisfaction as guiding principles.
To this end, NS has developed the Network Dashboard that distinguishes
itself from existing management information in the following way. First, it
improves the linking between operational processes and financial results.
Second, it enables the transfer from network management on a national
scale to network management at corridor scale. Third, at corridor level, a
relationship is made between realization and forecast. Fourth, it contributes to
more successfully dimensioning product capacity to market demand. The
Management of the NS passenger business discusses the Network
Dashboard at least twice a year.
Theory
Railway companies, like postal services, power networks, aviation facilities
and fixed telecom, are part of the capital-intensive public infrastructure and
thus have their own rate of development and unique business model. Typical
of these sectors is that they often have to cope with structural overcapacity.
This is partly due to the perishable nature of the business and non-profitable
market segments which are often obliged to operate in accordance with public
interest or franchise conditions. Particularly the capital-intensive character
mostly demands public investments, as costs cannot solely be carried by
individual (market) parties. From a public perspective, and against this
background, it is crucial that the operation of railway companies be as
profitable as possible. At a time when market liberalization continues without
abatement, and pressure on costs increase, it is thus of paramount
importance to improve the relationship between operational processes and
financial results. Key concepts for a railway company in this context are: load
factor, break-even load factor, utilization, customer satisfaction and average
system speed, which will be elaborated on below.
© Association for European Transport and Contributors 2011
Load factor
The load factor (see figure 2) is defined as the number of passenger
kilometres traffic as a percentage of the seat kilometres production during a
certain period. This implies that the load factor is a volume-driven variable
because a relationship is only made between traffic and production and not
between production costs and traffic revenues. Of course the (objective) load
factor shows a strong inverse proportion to (subjective) customer satisfaction
scores on “seat availability” thereby emphasizing how vital it is to strike the
right balance between customer and company interests.
Break-even load factor
The break-even load factor is defined as the load factor at which the traffic
revenues, in terms of revenues per passenger kilometre, equal the production
costs, in terms of costs per seat kilometre (see figure 2). Profit is made if the
break-even load factor is lower than the actual load factor. Conversely, a loss
is made if the latter is higher than the break-even load factor. In the Network
Dashboard the difference between load factor and break-even load factor is
referred to as the margin and is a profit measure that should, however, not be
confused with the operational margin in a profit-and-loss account.
Revenues
Traffic
€, mrd
rkm, mrd
Operational
driven
Production
Costs
ask, mrd
€ , mrd
Load factor
%
Traffic
revenues
€/rkm
Production
costs
Financial
driven
€/ask
Break-even
Load factor
%
Figure 2: Difference between operational driven load factor and financial driven
break-even load factor (rkm: revenue seatkilometers, ask available seatkilometers)
Utilization
In accordance with the trunk route franchise agreement, NS is obliged to
generate sufficient production capacity to guarantee a reasonable chance on
a seat during peak hours. This means that capacity investments are mainly
driven by traffic demand during peak hours. Consequently, peak hour
capacity drives fixed costs. They could be partly balanced by a higher fare but
should always adhere to Trunk Route Franchise conditions. Maximizing
utilization during peak hours is crucial and contributes both to operating
results and customer satisfaction.
© Association for European Transport and Contributors 2011
With off-peak traffic, on the other hand, one can definitely speak of
overcapacity. Consequently, variable costs can be reduced by dimensioning
the capacity to lower traffic demand, despite the continued fixed costs of nonutilized rolling stock. Moreover, traffic demand can be stimulated by marketing
propositions. To recapitulate from a financial perspective; peak traffic is driven
by utilization and off-peak traffic by load factor. Of course labour agreements
and crew assignment issues are an important issue in this context.
Customer satisfaction
Every year, NS circulates approximately 80,000 questionnaires to measure
customer satisfaction on circa 40 aspects. In this customer satisfaction
survey, passengers are also asked to award a general score. As a quality
measure, NS takes the percentage of passengers who award a mark 7 of
higher (see figure 3). Besides the customer satisfaction survey, which is also
used as a justification to the franchise grantor, additional analyses are
conducted. By doing so, NS is continuously and actively engaged in
improving its passenger-related performance. Various conceptual models
from marketing and other quality literature, however, demonstrate that the
customer evaluation of services is strongly determined by the difference
between expectation and perception of the rendered quality.3 If the quality
improves, then so, too, does the service the customers expect. This means
that an ever higher perceived quality is required to keep customer evaluations
stable, even if the quality is objectively improving. However, both from a
social perspective and from business economics perspective, there is an
efficient maximum as regards improving customer satisfaction.4
Overall customer satisfaction and its major drivers:
Punctuality - Information - Seat availability
95 %
% of customers giving a 7 or higher (%)
Objective punctuality (< 3 min)
General satisfaction
75 %
Seat availability
perception (peak)
55 %
Information during
disruptions
Subjective punctuality
(customer perception)
35 %
jan-07
jan-08
jan-09
jan-10
jan-11
Period
Figure 3: Overall customer satisfaction and its major drivers. Note that objective
punctuality % represents percentage of trains
Moreover, the general customer evaluation appears to be a good indicator of
the desire to travel less or more with NS and is thus an important business
economics factor. As improvements to punctuality generally involve
© Association for European Transport and Contributors 2011
considerably higher investments than improvements to customer satisfaction,
the hypothesis might be that - after having achieved a certain basic level investments in customer satisfaction cost less and have a greater effect
Average system speed
The average system speed is defined here as the average speed at which
trains run on the network. It is the average train speed that determines to a
large degree the costs of the network because of its direct influence on rolling
stock and crew assignment. Moreover, the average system speed also largely
determines both the timetable and customer appeal and thus also the
revenues.
Databases & Models
Measuring on the Train (MotT 5)
Measuring on the Train (MotT) determines how many passenger kilometres
are generated per business segment and per type of ticket. Researchers
check approximately 1 in every 65 trains and record a number of basic details
per passenger. On the basis of these samples it is possible to calculate the
traffic levels on each route.
Origin-Destination Matrix
MotT also feeds the Origin-Destination Matrix, a database that - for all
stations in the Netherlands - holds various journeys passengers make and
thus forms an important foundation for operational management. These
journeys can be broken down into peak, off-peak and weekend. This matrix is
annually reviewed in order to provide an updated basis. Other sources of
input for the Origin-Destination Matrix are the relationship-specific ticket
sales, the customer satisfaction survey and station related passengers
counts.
Forecast model
For the forecast model 6 a calculation is used that enables the volume of
passenger traffic by rail to be forecast (see figure 4). Input parameters include
economic growth 7, employment, demography and various unique trainrelated data such as timetable, price and customer satisfaction. With this
model, issues can be answered with various time and spatial resolutions. The
time resolution can vary from 1 to 10 years in the future, and the spatial
resolution varies from station to nationwide.
Tool for Rail Assignment to Network System (TRANS)
Fed by the Origin-Destination Matrix and based on passenger numbers, travel
times and interchange facilities, the TRANS model distributes passengers
over the network according to journey options and travel purpose. This
affords a starting point for line and thus also schedule design. In the context
of this article it can be stated that the forecast model forecasts a future OriginDestination Matrix which then serves as a basis for TRANS to develop lines
based on a certain schedule.
© Association for European Transport and Contributors 2011
Train Series Model (TSM)
Besides the aforementioned, predominantly traffic- and revenue-oriented
models and databases, the Train Series Model (TSM) is employed to feed
the cost and production side of the Network Dashboard. In the TSM, the
production costs, infrastructure charges and indirect costs are broken down
per line. Also included are the costs of salaries, shunting, energy,
maintenance, depreciation, insurance etc.
Leeuwarden
Groningen
Den Helder
Hoorn
Zwolle
Lelyst.
Schiphol
A’dam
Enschede
A’foort
Leiden
Deventer
Utrecht
Arnhem
Den Haag
Gouda
Nijmegen
Den Bosch
Rotterdam
Hoek v. Holland
Breda
Tilburg
Vlissingen
Roosendaal
Boxtel
Eindhoven
Venlo
Sittard
Maastricht
Figure 4:
Heerlen
Fictitious corridor forecast of high (black lines) and
low (grey lines) marketgrowth
Corridors
Based on natural market flows, corridors are developed which are based on
one or more clustered lines. A line is defined by a route that is operated by an
Intercity or an all-station train according to a fixed hourly schedule throughout
the day. According to this concept, twenty corridors have been defined which
together form almost the entire national NS network. 8
Network Dashboard
The Network Dashboard (see figure 5 where numbers correspond to subparts
in this section) enables the management to simultaneously assess the
financial impact and customer satisfaction, as integral trade-offs between
commercial opportunities and operational constraints can be made.
Moreover, the Network Dashboard follows the existing organizational
responsibilities as it conveniently aligns production costs (made by
Operations), and traffic revenues (made by Commercial). At the same time it
establishes a connection between realization and forecast on a regional
© Association for European Transport and Contributors 2011
scale, thus supporting both an evaluative and a steering task on a more local
scale.
Because the entire network is divided into twenty logical corridors, it becomes
possible to evaluate and improve network contributions with the aid of local
expertise without neglecting the integral trade-offs and so arrive at a total
network optimization. The components of the Network Dashboard will be
more closely scrutinized below on the basis of one ( “Zaan-corridor”) of the
twenty corridors.
Figure 5: Fictitious example of one corridor (“Zaan-line”) of network dashboard. Reference is made
to numbers in text
Traffic forecast 
In order to be able to examine the traffic forecast in context, the realization for
the corridor concerned over recent years is also presented. In this way, local
economic developments as well as changes to both the rail infrastructure and
schedules can be addressed in time. The traffic forecast serves as a guideline
for optimization actions to be undertaken.
Production costs versus transport revenues 
The essence of the Network Dashboard is that it enables integral trade-offs to
be made between (Commercial) traffic revenues and (Operational) production
costs. By doing so, a connection is made between operational processes and
financial results via the load factor, the break-even load factor and the margin.
Network contribution 
As Intercities and all-station trains are inextricably linked to one another in a
network, partly due to the feeder function all-station trains have with regard to
Intercities, each corridor distinguishes how these two types of train contribute
to the corridor total. The added value of a individual corridor to the total
network is indicated via the so-called network contribution .To this end,
revenues, costs, profit, traffic, production, load factor, break-even load factor
© Association for European Transport and Contributors 2011
and margin at corridor level are reported. This permits integral network tradeoffs with the aim of maximizing operational result and customer satisfaction.
To evaluate as well as optimize the total network in terms of operating result
and customer satisfaction, a link is made both on network and corridor level
with the business plan. Depending on the past, present and future network
contribution, the business plan is proportionally divided between the twenty
corridors. During the period to which the business plan applies, there are
fixed moments for evaluation as well as for looking ahead and anticipate on
market developments. Such moments also include those in which minor
schedule changes can be made and thus form a means to implement low
hanging fruit optimizations. At NS this takes place at the “Load-factor Board”.
Participants of this board are: those who, on account of their commercial
function, are responsible for traffic revenues, those whose operational role
means they are responsible for production costs, and those whose
independent role makes them accountable for long-term market forecasts. If,
for example, a certain corridor shows an above-average traffic forecast and
another corridor an under-average traffic forecast, then a schedule
adjustment and/or marketing proposition may be considered not only to
optimize the load factor, but more importantly, to lower the break-even load
factor.
Customer satisfaction 
In order to strike the right balance between operating results and customer
satisfaction, the major components in the customer satisfaction survey are
indicated per market segment (Intercities and all-station trains) with the
national total as reference. The general customer evaluation, punctuality and
seating capacity during peak hours are indicated as a percentage of the total
number of passengers awarding a mark 7 or higher. In this way it becomes
clear how a specific corridor performs when compared with the overall
network performance. Also average system speed, utilization and peak share
are shown. The customer satisfaction perspective and the logistical
perspective combined, constitute a valuable means for the management to
make integral trade-offs an by doing so further increase the quality of
business decision making
Average load factor Intercity 
Finally, in order to judge whether market demand can be adequately met, it is
relevant to ascertain when the maximum traffic capacity has been reached
and frequency needs to be increased. To this end, the actual load factor is
measured during the busiest peak hour and compared with the actual
schedule and what the load factor in such a case would have been, if the
maximum possible capacity had been deployed. This illustrates how much
market growth is still possible before frequency steps have to be considered.
Finally, the average load factor is indicated which shows the degree of
peaking.
© Association for European Transport and Contributors 2011
Results and conclusions
The Network Dashboard further increases the quality of decision making on
Board level in the following ways. First, operational processes are better
linked to financial results. Second, it enables the transfer from network
management on national scale to corridor management on a regional scale.
Third, a link is made between realization and forecast at corridor level. Fourth,
it contributes to more successfully dimensioning product capacity to market
demand. Fifth, by comparing corridors with one another it thus becomes
apparent which ones are performing relatively better or worse.
After one year operational use of the Network Dashboard, general guidelines
to improve customer satisfaction and operating result have become more
rigorous and integral trade-offs can be made better, where customer
satisfaction and operating results are leading parameters. With the corridor
approach local experts are better equipped to manage specific local
opportunities and constraints.
Traffic can be increased by improving the connection between Intercities and
all-station trains and waiting time can be made more pleasant.
Revenues can be increased with targeted marketing propositions and transfer
reliability.
Costs can be decreased by running trains in fixed compositions, developing
partnerships with local bus operators and by deploying rolling stock with
optimized capacity and schedule optimization.
Production capacity can be decreased by increased schedule reductions
during weekend and holiday periods, faster turnaround of rolling stock and by
improved dimensioning of rolling stock to the market demand.
Acknowledgements
Contributions prior making the manuscript have been made by: Suzanne
Brouwer, Koen Dekkers, Wiebo Drost, Wim Fabries, Kim Hauwert, Bart de
Keizer, Bas van Köningslöw, Ramon Lentink, Richard de Roo, Hans van
Uden, Maurice Unck, Vincent Verbeet, Cor van ’t Wout and Gerjan Zweers.
Final note from the authors
Considering company sensitive information, all data used here are fictitious
and any relationship with reality is purely coincidental.
© Association for European Transport and Contributors 2011
Bibliography
Arzac, E. (2005) “Valuation for mergers, Buyouts and Restructuring”, Wiley.
Blommaert, A. et al (2008) “Bedrijfseconomische analyses”, Wolters-Noordhof.
Bradley C. et al (2010) “The ten timeless tests of strategy”, McKinsey & Company.
Bruyn, de M.O. & Gemke, J.D.F. (2009) “Het Meethuis”, Railforum.
Bruyn, M.O. and Spijkerman, P. “Business cycles and future demand: how use of a leading indicator
improves forecasts or rail passenger transport”, ETC 2011 Glasgow
Copeland, T. (1994) “Valuation; measuring & managing the value of companies”, Wiley.
Duijn, J. van (2007) “De groei voorbij”, De Bezige Bij.
Duijn, J. van (2008) “Trends en cycli”, Balans.
Gemke, J. (1999) “Catching up is easier than trail blazing”, Airline business.
Gemke, J. (2009) “Competentieontwikkeling in dienst van bedrijfsresultaat en klanttevredenheid”,
Railforum.
Gilbert, X. (2008) “Smarter execution”, Prentice Hall.
Gleick, J. (1988) “Chaos; making a new science”, Viking.
Guis, N. (2011) “Lijnvoering van de toekomst”, TU Delft.
Jenkins, D. (1995) “Handbook of airline economics”, Aviation Week Group.
Kotter, J. (1996) “Change management”, Harvard Business School.
Minto, B. (1985) “The pyramid principle”, McKinsey & Company.
Peters & Waterman (1982), “In search of excellence”, Wiley.
Peijs, K. (1996) “Vervoersreuzen domineren vervoersmarkt”, DCP-MDV-u06.03282.
Revans, R. (1980) “Action learning: New techniques for management”, Briggs Ltd.
Schevernels, P. (2008) “Hoogspel rond PTT”, Thieme Art.
Slywotzky, A. et al (1999) “Profit patterns”, Wiley.
Slywotzky, A. et al (200) “The profit zone”, Wiley.
Teulings, C. et al (2009) “The grote recessie”, Balans.
Vries, de B. and Willigers J. “On the state of the art demand forecasting model developed by Duch
Railways”, ETC 2011 Glasgow conference
Weick, K.E. (2001) “Making sense of the Organization”, Blackwell.
Notes
1
In the Netherlands known as PHS: Programma Hoogfrequent Spoor
According to the international 5-minute standard
3
Zeithaml, V.A., Parasuramam, Berry, L.L. 1990, Delivering quality service: Balancing customer
perceptions and expectations, New York: Free Press
4
The general customer evaluation for customer satisfaction is driven for nearly 50% by punctuality,
information services and travel comfort.
5
In NS known as Meten in de trein (MidT)
6
See for example Vries, de B. and Willigers, J. “on the state of the art demand forecasting model
developed by Duch Railways”
7
See for example Bruyn, M.O. and Spijkerman, P. “Business cycles and future demand” how use of a
leading indicator improves forecasts or rail passenger transport”
8
One example of a corridor is the Zaan route, which connects the northern half of the Randstad (the
conurbation in the west of the country) with the extreme north of the province Noord-Holland.
2
© Association for European Transport and Contributors 2011