”Extreme Weather Impacts on Freight Railways in Europe”
Johanna Ludvigsen ([email protected])
Ronny Klæboe ([email protected])
Institute of Transport Economics,
Oslo, Norway,
www.toi.no
Abstract
Four cases were studied to assess how the harsh 2010 winter weather affected rail freight operations in
Norway, Sweden, Switzerland and Poland and what reactive behaviours the rail managers mobilised to
reduce the adverse outcomes. These results were utilized in the fifth case assessing proportion of
freight train delays in Finland during 2008-2010, modeling the odds for freight trains‟ delays and
tardiness duration as a function of changes in met-states on Finish network and/or weather-induced
infrastructure damage.
Result show that rail operators were totally unprepared to deal with powerful and cascading effects of
three harsh weather elements, long spells of low temperatures, heavy snowfalls and strong winds
which affected them concurrently and shutdown large swathes of European rail infrastructure and
train operations. Rail traffic disruptions spread to downstream and upstream segments of logistics
channels causing shippers and logistics operators to move the freight away from rail to road carriage.
As a result railways lost market for high-value container cargo, revenues and long-term business
prospects for international freight movement. Analyses of measures employed to mitigate the
immediate damage showed that managers improvised their ways of crisis rather than drew on a priori
contingency–fighting and crisis management skills.
Modelling of co-variation between extreme weather and freight train delays in Finland during 20082010 revealed that 60% of arrival tardiness was related to winter weather. Further, a combined effect
of temperature below – 7 Celsius and 10-20 cm change in snow depth coverage from one month to
the next explained 62 % of variation in the log-odds for freight train delays. Also, it has been shown
that changes in number of days with 10-20 cm snow depth coverage explained 66 % of variation of
train arrival lateness contributing to 626 minutes, or 10 1/2 additional delay hours. Changes in number
of days with snowfalls over 5 mm accounted for 77% variation in train arrival lateness implying that
each additional day with this snow fall could contribute to 19 1/2 delay hours. Finally, a combination
of increase in mean number of days with 5 mm snowfalls and temperature below - 20 Celsius
explained 79 % of variation in train arrival tardiness contributing to 193 minutes or 3 ¼ delay hours.
All results were significant (p=.00).
1
1 Introduction
Extreme weather events may threaten individual companies, their personnel, and
collaborative arrangements such as supply chains. Managerial literature indicates that
there is no one best way for overcoming negative impacts of these occurrences. One
reason for that is that such events fall into high-impact/low probability risk category
and therefore there is a scarcity of historical data needed for devising universally
effective prevention, containment and mitigation tools. Another reason is that such
low-frequency incidents are hard to predict and thus difficult to allocate resources to
proactive risk management. If this risk never materializes, the costs incurred are hard
to justify to the company leadership and/or shareholders (Zsidisin et al. 2004).1
Yet empirical evidence indicates that weather-induced disasters tend to occur more
frequently and with increased damage severity. In relation thereto, the report on
“Performance Measures for Freight Transportation” (Transportation Research Board
2011) clearly states that the above approach is reminiscent of “sub-optimization” in
managerial decision making where the focus is inordinately upon achieving narrow,
easily and immediately justified sub-goals to the detriment of broader business
objectives such as long-term operational continuity through resistance development.
In this connection a question arises: why some organizations cope far better than
others with both the prospects and the impacts of weather-induced adversities?
The organizations in focus do not have a common secret formula or even many of
the same processes for dealing with weather-generated risk, but share one critical
trait: resilience. Conceptually resilience is an anti-thesis of vulnerability which Svensson
(2002) defined as “…unexpected deviations from the norm and their negative consequences”.
Mathematically vulnerability may be measured in terms of “risk”, a combination of a
likelihood of an event and its potential severity (Sheffi 2001; 2005). The notion of
organisational resilience entails functional and structural preparedness. Functional
resilience implies that a given entity is capable of efficient and effective dealing with a
given adversity and may recover unscathed by drawing on internal resources. On the
other hand, structural resilience is an organizational ability to absorb and/or
withstand external risks and/or perturbations thanks to built-in robustness and
internal reserves (Bundschuh et al. 2003; Holmgren 2007; Lai et al. 2002).
There is no doubt that supply chain disruptions are costly. In order thus to prevent,
mitigate and neutralize negative consequences of chain ruptures one needs to
understand how an abrupt cessation of goods movement or stoppage of material
flows may affect not only the focal transport operator but also other supply chain
segments (Hendriks and Shinghal 2005). A recent example is the earthquake in Japan
which in March 2011 damaged several plants producing microchips and other
electronic components for equipment manufacturers in the US and Taiwan. This
contagion has spread to Europe causing transient shortages of smart phones, tablets
and other high-tech consumer electronics (Financial Time of April 23rd 2011). As
1
As Qiang et al. (2009) have shown in numerical modeling of changes in supply chain risk level invoked by
transport disruptions, this statement indicates that manufacturers, retailers and transport carriers within a given
supply network place zero weights on disruption risks (page 108).
2
summarised by Shefi (2005; 74), one of the main characteristics of disruptions in
large-scale supply networks is the “high-level transmission between vulnerabilities
stemming from the large systems‟ inter-connectivity”.
Yet, the risks caused by weather-related disruptions in Europe are surprisingly
scarcely addressed by supply chain management literature (Kleindorfer and Saad
2005). Even worse, the consequences that extreme weather events exerted on freight
transport operations in Europe received hardly any attention from researchers in this
field (Wilson 2007).
One reason could be that as compared to disruptions paralyzing manufacturing
plants and/or warehouses which result in large supply shortages, a rupture in
movement of goods within a supply pipeline may be potentially less contagious
because it halts only a transfer of merchandise and/or materials within a given
conduit. The uniqueness of transportation disruption consists thus in that although
the goods in transit have been stopped, the remaining supply network operations
may still function undisturbed2.
However, as observed by Gunipero and Eltantawy (2004) as well as Adegoke and
Gopalakrishnan (2009), this is very far from being true. Transport interruption is a
risk that can quickly cripple the entire supply chain because in addition to halting the
flow movement, the stoppages in materials and/or goods transfer spread quickly to
the upstream and/or downstream supply chain segments causing stock outs,
inventory depletion, production downtimes, unfilled customer orders, information
distortion and/or back-log of goods in transit.
2 Purpose Statement
Against this backdrop, this article sought to assess
1) How the different extreme weather events affected the European rail freight
systems, and
2) What types of action the affected parties have mobilized to mitigate and
neutralize the resultant impacts.
We anticipated that results from this enquiry would allow identifying the most
severe vulnerability areas within the entire European freight rail system and the
types of managerial and physical assets efficient at improving the sector‟s overall
preparedness. Unfortunately, multiple searches for literature on measures
proficient at managing the impacts of weather hazards on European freight
transport industry, and particularly the railways, did not produce tangible results.
As a consequence, our work had to be revised and broken down into five more
specific explorations, as follows.
2
Although a disruption in transportation will certainly delay the arrival of goods at destination, a distinction is
made here between a transportation disruption and a transportation delay which fall into two different risk
categories. Because of larger element of surprise and lower preparedness level, Chopra and Sodhi (2009)
maintained that risk drivers for a delay are much smaller than those of disruptions which may last longer and hit
several supply segments simultaneously. This distinction was also useful for determining the conditions of
supply network robustness and strategies for dealing with disruptions caused by natural hazards.
3
1) Which impacts the extremely bad weather inflicted on rail infrastructure and
operations in Poland, Sweden, Norway and Switzerland during harsh winter
2010,
2) What reactive behaviors the managers in the affected companies have
mobilized to counteract and/or contain the ensuing outcomes,
3) What was the proportion of weather-induced delays in all freight train arrival
delays in Finland during 2008-2010
4) How the harsh weather events affected the odds for freight train delays on
Finnish network during the above period, and
5) How the duration of freight train delays in Finland co-variated with harsh
winter weather during 2008-2010.
3 Methodology
3.1 Research Approach and Design
Since this study focused on natural disasters whose tangible impacts on rail freight
companies and their clients in Europe had not yet been adequately recorded,
analyzed and assessed, since these phenomena invoked different material and
temporal damage, and since researchers could neither manipulate the independent
nor the dependent variables a case study method was chosen for this exploration
(Yin 1994, 23).
This research approach was also supported by Snyder and Swann (1978) who
maintained that case study method is an appropriate form of empirical enquiry when
the impacts of determinants studied vary between the targets, when the researchers
neither can manipulate the causes nor the specific outcomes and, when the current
body of knowledge does not allow for making cross-contextual predictions.
Procedures foreseen for scientifically sound case studies recommend that first a
descriptive definition of the phenomena studied is developed. Subsequently actors
with pertinent experience and data are identified and interviewed, and finally an
understanding of the phenomena in focus is constructed which facilitates statistical
tests of more specific causal relationships (Yin 1994, 29).
Pursuant to the above, four case studies on how the rail freight operators in Norway,
Sweden, Switzerland and Poland reacted to harsh winter in 2010 were performed.
Results were then used in a Finnish rail modeling case which assessed 1) the
proportion of delays in freight train arrivals attributed to extreme weather and/or
weather-inflicted technical damages on Finland‟s network during 2008-2010, 2) the
odds for train arrival delays inflicted by harsh weather, and 3) the duration of train
arrival tardiness attributed to bad weather impacts.
3.2 Data Sources
3.2.1 European Cases of Harsh Weather
Interviews with managers of rail cargo companies from Poland, Norway and the
Netherlands provided data for the first three case studies; one of the interviewees
was a state-owned incumbent from Norway while two others private rail
undertakings. Data for the fourth case came from an interview with director of an
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Association of Swedish Train Operators which represents seventeen rail carriers in
Sweden. Inclusion of private rail undertakings was justified by the fact that these
entities are relatively small as compared to national incumbents, and therefore, quite
vulnerable to all types of hazards. Yet, experience shows that despite the small size
and the relative resource scarcity, private operators are capable of rapid adaptations
to environmental dynamics. These two factors made them interesting objects for
studying managerial reactions to extreme weather adversities.
3.2.2 Finnish Case of Harsh Weather
The VR Group Ltd., the Finnish Transport Agency and the Finnish Meteorological
Institute provided data which included two sets of aggregated indicators. The first
was a register of eighty different causes of freight trains delays and these delays‟
duration including thirty seven reasons related to bad weather. The second
comprised monthly registrations of maximum and minimum temperatures,
precipitation, and the numbers of days in each month within 2008-2010 with specific
temperature and precipitation levels. These monthly temperatures and precipitation
indicators were measured at a number of meteorological stations all over Finland and
then averaged monthly.
3.3 Data Collection
3.3.1 European Railway Cases
Face-to-face interviews with executives responsible for management of rail
operations provided data for the European cases. Each interview lasted from one to
two hours and recorded 1) self-reports on targets‟ exposure to harsh weather events
and impacts-triggering mechanisms that affected rail operations 2), type of adversities
experienced and measures mobilized during and immediately after a given extreme
weather instance in order to cope, neutralize and/or reduce the resultant
consequences, and 3) strategic adjustments that the targets have undertaken and/or
planned to introduce in order to improve the overall preparedness for weatherinduced damage at the company and/or supply channel levels.
3.3.2 Analyses of European Case Data
The interview audio-records were transcribed verbatim into an interview protocol,
whose content was then analyzed as regards the types of adversity that the affected
parties experienced, the actions mobilized and resources employed to counteract the
most immediate impacts and the long-term repercussions.
4 Findings from European Cases
4.1 Vulnerability of Rail Operators to Winter Disruptions
The four European cases revealed the instantaneous impacts that the extremely harsh
winter weather in 2010 inflicted on rail disruptions, the aftershocks to other logistics
segments, and the long-term consequences for railway business.
An executive at the Swedish Associations of Rail Operators described the different
stages in the weather-triggered crisis
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“During winter 2010 the south-western Sweden was affected by unusually low temperatures, winds and heavy snow
falls which begun in December 2009 and lasted until March 2010. We have been taken aback by a combination of very
low temperatures and heavy snow storms. Strong winds formed clouds of light snow which stuck to wagon
undercarriages, immobilized vehicles, and blocked track lines between Halsberg intermodal terminal and the main
Swedish harbors. Snow accumulated under undercarriages has dramatically amplified the vehicle weights causing the
wheel axles to break. We lacked the buffer stocks of spare wheels and personnel capable of replacing the broken units
on short notice. So, we had to sign new agreements with repair workshops. The smallest rail operators have of course
suffered the most because of considerable resource scarcity. In addition, wagon brakes lost the grip on slippery track
surface and many trains had to be stopped. This reduced the network traffic. Further, many tonnes of temperaturesensitive goods have frozen while still in wagons because trans-shipment to trucks was delayed”.
The above shows that the targets were neither able to avert nor neutralize the
weather–imposed harms. Efforts used to contain the impacts of infrastructure
shutdowns and traffic breakdowns reveal that both the operators and the
infrastructure managers improvised their way out of crisis rather than drew on the a
priori available preparedness measures and/or crisis management skills. Another
excerpt from this interview reveals the dramatic scope of negative consequences for
the entire Swedish rail freight industry.
“During this (winter) period the volume of rail cargo in Sweden was reduced by entire 20 percent. The Halsberg
marshalling yard which is a centre of Sweden’s rail freight operations was closed for 14 days. This shutdown alone
has cost between 200 and 250 million SEK. This amount has been further attenuated by phasing out of at least 20 rail
shuttles between Halsberg and Gothenburg and re-location of large cargo volumes to road haulage “
One interesting finding was that although all the rail freight companies studied
function in the northern Europe where harsh winters are quite common, still none of
them has anticipated the combined impacts of extraordinarily long spells of low
temperatures, heavy snowfalls and strong winds which in 2010 brought their
operations and infrastructure to standstill. The Norwegian rail freight manger‟s
encounter with unusually harsh winter in 2010 is reported below
“The 2010 winter weather brought about a rare combination of unusually heavy snow falls, low temperatures and
strong winds. This resulted in a range of infrastructure shutdowns and rolling stock breakages. First, Infrastructure
Managers lacked enough snow ploughs to keep all tracks and interchanges snow-free. Second, low temperature and
heavy snow caused that wheels on some of our flatcars went to pieces. These disruptions reduced our flatcar fleet and
supply reliability to 60 p.c. (from standard 90 p.c.) meaning that considerable number of containers was not delivered
on time. Our customers were aghast; they had to shift goods supply to road haulage. Further, we had to renew our
stock of spare wheels immediately and that showed difficult. As a consequence of this but also due to the
accumulation of ice deposits on tracks, the deceleration time and braking distance for wagons increased
considerably. As a result, we were forced to run fewer and shorter trains. Yet despite fewer trains the manpower at
terminals has to be increased to fight technical emergencies. More people had to step in so that we did not breach the
working time regulation. As a consequence we faced two adversary impacts at the same time: our operational costs
skyrocketed while our cargo volumes along with customers’ trust plummeted”
The Dutch rail manager, whose company suffered from 2010 winter traffic
breakdown provided the following account.
“In Switzerland we had to stop all our rail container traffic for one week in January 2010 because the track was
blocked by unusually heavy snow falls. However, information about infrastructure shutdown reached us in advance
so we were able to re-position our locos, flatcars and containers, and reduce the costs of the standstill… In Sweden
the situation was different because there several of our trains were trapped by snow falls blocking connections
between the feeder and the trunk lines and could not return to the main operations depot after discharge of container
loads at customers sidings. Besides persistent low temperature of minus 20°Celsius have damaged the rubber linings
on our flatcar brake’s cables which stopped all vehicles. This forced us to stay put until the cables were replaced. This
operation took quite many man-hours”.
The harms inflicted on the Polish rail operator were equally dramatic, although for
different reasons.
“Heavy falls of wet snow was a nuisance because they broke catenaries and fell trees along the track lines that
blocked the network pathways. In addition, the freezing fog glazed catenaries and broke pantographs on several
locos. Surprisingly, the cold in the range of minus 15 Celsius and below did not inflict much harm on locos’ technical
fitness, but temperature in the range of -1 /+ 1 Celsius combined with high humidity caused shortcuts in locos’
electrical wiring. We protected our locos from freezing by keeping engines on empty runs before and after each
journey. As a result, our loco drivers had to put extra working hours in emergency shifts…”
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These excerpts indicate that at least two circumstances amplified the attack severity
and the scope of damage. The fist was a combination of three different harsh
weather components, low temperature, snowfalls and strong winds. As these
elements coincided in space and time, they produced cascading effects which
immobilized long stretches of European rail infrastructure and brought all freight
transfer to a halt (Delmonaco 2006). The second factor derived from the unusually
large scope and power of the knock-on impacts which caught the targets unprepared
and made them virtually helpless within very short time.
Because of the railways‟ specific position in logistics supply system, disruptions in rail
cargo operations produced a chain of contagion which quickly spread to other
segments of logistics channels. However, the railways bore the major brunt of
weather-inflicted damage because they could not substitute the rail freight transfer
with alternative modes as did shippers, forwarders and logistics network integrators.
Adding to the complexity was the fact that being the government utilities, rail
infrastructure administrators, were not liable for direct business losses and other
disutilities that infrastructure shutdowns imposed on rail undertakings, cargo owners
and logistics companies. As a consequence, in addition to sharp spikes in operations
and manpower costs, railways have also suffered from the loss of customers,
reputation and jeopardised business prospects in the European freight market. The
Norwegian operator summarized this problem succinctly.
“In order to reduce the risks of stock out and the amount of unfilled orders at distribution centers, we had to reposition our resources. This required higher operational back-up and closer collaboration with Infrastructure
Managers. However, our hands were tied: our trains were stopped by infrastructure shutdowns. Our customers
demanded compensations for unfilled supply orders. Therefore, we started discussions with the Ministry of
Transport and Communications who owns rail infrastructure in Norway to grant us the rights to charge infrastructure
provider with penalties for track closures and/or pay lower user charges after several track lines were out of
operations which delayed our freight trains’ arrivals. Still, paying considerable delay compensations was not the
biggest harm to us. The loss of traffic which our clients re-located to road transport and the customer trust in our
ability to deliver on time were considered as much more serious setbacks because they seriously threatened our
business’ future”.
The Dutch rail manager summarized the losses that his company suffered due to
infrastructure shutdown in the following manner
“Recently, we have discussed with the Swedish Rail Infrastructure Administration what harms the network
shutdowns and reduction of network serviceability inflicted on our operations. We have presented them with bills for
losses imposed by infrastructure closures. The Swedish people have launched a full-blown investigation into factors
causing infrastructure downtime which, we hope may improve infrastructure security next winter. Still, we expect
more harsh winters to come, and with that more infrastructure closures. We will simply need to live with that and be
better prepared”.
The cases studied made it readily visible that the infrastructure closures rendered the
railways‟ crisis management efforts quite futile. They have also underscored that the
continuity of rail operations and the punctuality of cargo train arrivals are heavily
dependent on infrastructure functionality. In order to avert the risk of stock outs
and/or supply shortages at wholesale and retailer outlets, logistics integrators moved
cargo transfer away from rail to road. The Swedish operators applied the following
measures to contain the damage inflicted by infrastructure shutdown
“Shippers, rail operators and Trafikverket (The Swedish Transport Infrastructure Administration) formed task forces
to jointly combat these damages. To be effective, our decisions had to be based on real-time information.
Trafikverket fed us with information about impending and/or already imposed infrastructure closures, lines open for
detour and serviceability conditions on the remaining network segments. To speed up the most critical
consignments, our clients handed us a list of most urgent shipments and the goods’ physical conditions (i.e.,
tolerance for cold and longer transit time). These data helped Trafikverket to re-assign traffic to a considerably
downsized network using three priority rules 1) trains that had to be given green light immediately, 2) trains that had
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to be re-scheduled to new time windows and new track paths within the next 12 hours, and 3) trains that could be
kept at sidings and/or marshalling yards longer than 12 hours. Eventually we got the most critical tonnage of cargo
traffic out and moving”.
These examples are instructive. On the one hand they have revealed that
mobilization of ad hock damage containment showed effective at dealing with the
unfolding course of disaster events. On the other hand, however, the lack of back-up
systems and preventive skills magnified the scope of damage and the costs of
adversity abatement. The patterns of crisis-fighting behaviours revealed that all
managers strove to alleviate the most immediate impacts on their operations domains
without making efforts to increase the operational robustness of the entire supply
system. Constrained by the rolling stock damages, infrastructure shutdowns and
shortages of vital components destroyed by extremely harsh exploitation conditions,
the targets turned to in-house human resources because this type of assets was
readily available and effective at quelling the most immediate harms. The statement
of the Polish manger underscores the need for an internal “flexibility” without,
however, specifying how this could be achieved.
“Our operational flexibility was the main asset that helped us to absorb the consequences of and to deal with these
(traffic) disruptions. Today, all these happenings seem as if they have taken place in a distance past. Now-a-day we
are facing and dealing with quite new and different challenges. We have realized however, that we need more
operational flexibility in our system to be able to withstand the similar adversities in future”.
Although all railway operators deployed extraordinary resources, they still could not
stop the aftershocks spreading to the upstream and downstream chain segments.
However, none of these targets acknowledged the need for improving resilience at all
channel tiers. Neither a necessity for systematic risk assessment or specialised crisis
management skills capable to address the different hazards affecting the different
supply chain segments was recognised. Nor was the building of long-term strategic
preparedness for level-headed handling of future crises considered as a long-term risk
containing investment.
Only the manager at Norwegian state-owned cargo carrier recognised the need for
strategic overhaul of his company‟s command and control system in order to
withstand the negative “domino effect” between the infrastructure shutdown and the
operations breakdown
“This experience has humbled us. We have to regain the customers’ trust by making our freight dispatch system
more robust. That means that several elements of our command and control system have to be re-engineered while
collaboration with infrastructure provider reinforced. However, before we re-launch a more reliable container
dispatch system we need a guarantee from Infrastructure Managers as regards higher standards of network
reliability. And that’s the critical area on which we are working right now”.
These results provided background for two questions
1) Why all the affected parties were so badly prepared to tackle the extreme weather
impacts despite being well accustomed to harsh north European climate?
2) Why the executives studied did not recognize a priori the high risks that harsh
winter weather might inflict on their operations, personnel, infrastructure, and
subsequently, brand reputation and long-term business prospects?
4.2 Conclusions from European Cases
One reason behind the rail operators‟ inability to preempt and avert extreme weather
damage was the element of surprise that caught them unprepared for unusually
powerful and commensurate knock-on effects that brought the entire rail system to a
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temporal paralysis. This “cascading effect” triggered “ failure in a system of
interconnected elements whose entire service provision depends on functionality of
the preceding segments, and whose preceding segments can exert failure on the
successive parts” (Kappes and al. 2012,)
However, the above did not explain the low general level of strategic preparedness
and the absence of weather risk awareness on the part of the managers studied. In
search for more-in depth explanation, we turned to literature on managerial attitudes
towards risk in general and risk perceptions in particular. A classical study that March
and Shapira carried out in 1987 provided some indication by showing that
1. Managers were quite insensitive to estimates of probabilities of possible
outcomes
2. Managers tended to focus on critical performance targets which affected the
way they managed the risk, and
3. Managers made a sharp distinction between risk-taking and gambling.
The first finding could be explained by the fact that manager do not trust, do not
understand, or simply do not use probability estimates when making tactical or
strategic decisions (Kunreuther 1976; Fishhof et al.1981).
Since managers were insensitive to probability estimates, they were most likely to
define the risk in terms of magnitude of losses such as “maximum exposure‟‟ or
„„worst case‟‟ instead of a broader scale of compound harms. The second conclusion
is based on an observation that the quality of managerial accomplishments is
measured by a set of performance targets. These metrics cause that managers
become more risk averse (or risk prone) when their performance is above (or below)
a desired level. Finally, the third conclusion is based on the fact that managerial
rewards are tied to attainment of „„good outcomes‟‟, but not to making „„good
decisions‟‟. The more fragmented and specialized a given service provision chain
becomes, the more focus is on each actor‟s slot in a value chain and the less on
system-wide functionality. Consequently, the patterns of managerial contracts and
incentives structures follow this line.
Case studies performed by Closs and McGarrel (2004), Rice and Caniato (2003) and
Zsidisin et al. (2004) show that the pervasiveness of attitudes undermining the needs
for dealing with risk of supply chain disruptions prevented managers from carrying
out a risk tolerance appraisal and assessing risk tolerance threshold. Yet, some few
companies recognized the importance of risk assessment and used different methods
to measure supply chain risk through formal quantitative models and/or informal
qualitative plans. However, these companies apportioned very little time and
resources to mitigation of all supply risks, not to mention the risk induced by natural
hazards. Several factors have underlain this behavior
1. Due to few data points, good estimates of probability of occurrence of any
particular disruption were difficult to obtain. This hindered performance of
cost/benefit analyses and/or realistic estimation of losses on returns on the
assets damaged needed for honing of risk reduction skills, holding
contingency-reducing assets and reserve capacity
9
2. In the absence of an accurate supply chain risk assessment, the firms have
generally underestimated the risk of sequential disruptions. As a consequence
many managers ignored the impacts of unlikely events and removed natural
hazards from their strategic decision agenda (Tang 2006). This may explain
why so few firms took commensurable actions to mitigate the risk of
disruption in proactive manner. Finally, as aptly summarized by Repenning
and Sterman (2001), firms seldom invest in proactive programmes because
“nobody gets credit for fixing a problem that never occurred”.
Still another explanation could be found in competitive pressures, ubiquitous search
for higher operational efficiency and lower capital costs that companies all over the
world pursue with great vigour. The well-known managerial terms such as “lean
production” and “tightly coupled” supply chain systems with high intra-channel
interconnectivity and “just-in time” manufacturing and supply regimes reveal that
there is not much room for operational slack, “wasteful reserves” or doubling of
sourcing and/or manufacturing outlets.
To deal with considerable technical and market uncertainties, both the private and
the state-owned freight railways adopted lean production technique which prevents
them from keeping large stock of locomotives, wagons, spare-parts and reserve
components as the company possessions.
As the price of multisystem locomotives in Europe reaches these days 3 million €
while purchase of multifunctional rail wagon would require at least 0.5 million €,
hardly any small rail undertaking or even a state-owned incumbent has financial
capacity to keep proprietary equipment and spare parts buffers on its balance sheet.
Market uncertainty evidenced by seasonal and corridor-dependent spikes and slopes
in demand for freight transfer makes that many new entrants neither own
locomotives nor wagons, basing their entire service provision on time-limited lease
contracts with specialty rolling stock and traction companies. This is also fortified by
many countries‟ depreciation rules in the tax code legislation where lower tax rates
are charged on asset-free service providers.
As a rule, service, maintenance and repairs of rolling stocks, traction and IT
equipment are also outsourced to external contractors. As it is widely admitted in
managerial literature and practice, outsourcing may reduce the current costs of
service provision but in return will also reduce the levels of operational robustness
and output security due to delayed responses and/or longer waiting times for
emergency deliveries. As it is evidenced here, this way of doing business became
quite expensive under the weather-induced crises.
Pursuit of financial effectiveness makes that reserves in production capacity, capital
assets and sourcing duplications which constitute the core of functional and strategic
preparedness are deliberately avoided as they show on the companies‟ balance sheets,
reduce operating margins and returns on capital assets. Therefore, they become hardto-justify to board-members and/or shareholders.
Finally, business leaders have many other and equally compelling challenges to attend
to in addition to natural disasters. This was very aptly summarized in 21st Supply
Chain newsletter‟s June 2011 edition: “natural disasters are not the only risks in town”.
10
In addition, the Virtual Strategy Magazine (http://www.virtualstrategy.com/2011/05/10) which published results from the BDO study
(http://www.bdo.com) of risk factors most frequently cited in tax filling reports by
one hundred largest publicly traded US technology companies3 revealed that the risk
of natural hazards ranked as number 12 among the most frequently cited and feared
business threat categories. However, the study has also shown that this risk type has
increased in prominence between 2010 and 2011.
5 Finnish Case: Linkages between Extreme
Weather, Freight Train Delays and
Tardiness Duration during 2008-2010
5.1 Analytical Model
In order to estimate the proportion of weather-induced delays in all freight train
arrivals in Finland during 2008-2010, the odds for train arrival tardiness and duration
of delivery lateness, an analytical regression model was developed (figure 1). This
model utilized results from the four European cases to assess the strength of
covariation between the key weather parameters and the punctuality losses in Finnish
rail freight traffic. However, since this modeling exercise sought also to make more
disaggregated assessments on how the different elements of bad weather and/or
combinations thereof contributed to train arrival delays and tardiness duration, the
aggregated data indicators representing time series with information on
meteorological conditions over Finnish rail network and train arrival delays had to be
transformed to attain these objectives.
Independent Variables
Extreme Weather Events
in Finland over 2008-2010
(temperatures, precipitation and
combinations thereof)
Dependent Variables
Freight Trains’ Arrival Delays
Delay Duration
Figure 1: The study’s analytical model
5.2 Problems with Train Delay Data and Weather Indicators
Data provided by the Finnish Meteorological Institute (FMI) and VR ( the national
Finnish rail operator) as monthly aggregated weather and delay indicators posed
3
BDO Seidman, LLP is the US professional service firm providing assurance, tax, financial advisory and
accounting services to a wide range of publicly traded and privately held companies. The company’s
international arm, BDO International Limited serves multinational clients through global network of 1,138 offices
in 115 countries.
11
considerable challenge as regards statistical linking of train arrival delays with extreme
weather events.
The first issue was that the monthly weather indicators depicted Finnish weather as
national averages and thus confounded meteorological states on the different days of
a month with weather conditions prevailing over Finland‟s northern, coastal, inland
and southern regions. Second, since some of the indicators were specific for areas
surrounding the measurement stations, chances existed that weather indicators could
simply reflect weather parameters at a given station, and/or a change in number of
measurement stations needed for satisfying a given selection criterion and not the
actual meteorological conditions.
Further, the average temperatures in Finland differ sharply not only between the
southern and the northern regions but also within a given time period. In addition
season changes in the north and south also occur in a time-lagged fashion. Therefore,
the average records of monthly temperature and precipitation did not provide
information on when and where the weather was the most severe.
Likewise, the freight train delays during 2008 – 2010 were recorded as minute
averages per monthly periods. This hindered assignment of delays to the different rail
corridors, train trips, days of train operations and/or peak traffic hours. Further,
inspection of freight traffic density on the Finnish rail network in 2010 supported an
assumption that chances of freight train delays and duration of arrival tardiness were
higher on lines with high traffic volumes as compared to those with lesser and/or
sparse train movements.
5.3 Addition of Data File with Lagged Weather Indicators
Faced with these challenges, the results from European cases helped us to recognise
that adaptation to sudden bad weather events may be much more challenging than to
periodically state conditions. As a result, we conjectured that the odds for arrival
delays might increase when the weather shifted rapidly and when this shift triggered a
chain of follow-on consequences.
Therefore, to capture the shifts in the levels of weather indicators, one-month-lagged
values were calculated, added to the data representing independent variables and
defined as changes in met-states. Consequently, we have hypothesised that a rapid
accumulation of snow on infrastructure network would probably delay train arrivals
more than the snow cover laying over several days. Therefore changes in the number
of days with a given snow depth from one month to another were also calculated and
served as a proxy for snow accumulation during the period analyzed4. Analyses of
data on changes in snow depth included 35 observations only as the snow cover
depth record for December 2008 was missing.
4
The number of days with a stable snow depth cover could also indicate that measurement was simply
undertaken at the end of winter season and not that the actual depth of snow has changed between the different
time periods.
12
5.4 Data Transformation
5.4.1 Dependent Variable
We assumed that punctuality would be inversely related to adverse weather. To
facilitate the interpretation of regression results, we decided to study delays defined
as a proportion of delayed trips in all train trips during 2008-2010. A worsening of
weather conditions and an increase in harsh weather indicators became thus related
to a positive increase in proportion of delays in freight train arrivals while the
negative parameter estimates would imply the opposite.
Generally, it is a bad idea to apply linear regression on dependent variable defined as
a proportion. Studies of disaggregate data show that such relationships are often
non-linear and S-shaped and may therefore render negative probabilities (below 0%
delay) or more than 100% delay occurrence, making the results meaningless.
A standard procedure for analysis of a dependent variable in the form of probability
or proportion, is to perform a non-linear logit transformation by taking a natural
logarithm of the odds for a delay (Hosmer and Lemeshow 1989) which is equivalent
to running a grouped regression model (Agresti and Natarajan 2001; Long 1997).
 delays 
ln 
  C+b1 X 1  ...bn X n
 1  delays 
Using the logit transformation of dependent variable means that by powering the
value of an estimated parameter, one obtains an estimate of the odds ratio associated
with the unit increase in the independent variable. This implies that eb1 indicates how
much the odds for a delay would increases when a determinant variable representing
a given met-state X1 increases by one unit, producing X new = X1+1, while the other
variables remain unchanged. Based on the above, we established a linear regression
model that linked the transformed dependent variable with the independent ones.
5.4.2 Independent Variables
The temperature indicators (or the number of days below a given temperature) were
overlapping. Therefore, their values were transformed so that it would be possible to
assess the probability of delay and delay duration as a function of each separate low
temperature interval. Hence, one non-overlapping variable category defined as the
change in snow cover was added to the modeling procedure. Consequently, the depth of
snow exceeding 10 cm but not more than 20 cm was used instead of the depth of snow
exceeding 10 cm.
A simple bar chart juxtaposing proportions of delayed freight trains against fivedegree Celsius temperature intervals from – 15 to + 20 centigrade revealed that
delays were not a linear function of temperature changes despite the fact that the
proportion of trains delayed increased when it became colder (figure2)5.
5
Given that our case studies assessed impacts of extreme harsh winter weather only, impacts of extremely
high summer temperatures and/or seasonal flooding were excluded from model analyses. This decision was
13
Figure 2: Delayed freight train arrivals by monthly average temperatures in Finland
2008-2010
However, inclusion of mean temperature indicators into regression model in addition
to those showing low temperatures could easily prove counterproductive because
these two were strongly inter-correlated. The danger was that this combination could
explain random variation rather than systematic co-variation between low
temperatures and train delays. Therefore, the monthly average temperature over
Finland during 2008-2010 were removed from the equations and instead, indicators
of cold weather i.e., the number of days below -7°C and -20°C were used.
5.4.3 Extraordinary Delays from March to May 2010
After having included the number of days with cold temperatures, the amount of
snowfalls and the depth of snow cover as independent variables, the fit statistics
indicated that the model‟s explanatory factors were not satisfactorily accounting for
variation in freight train delays during the thirty five month period.
A clustered bar chart showing delays per month over 2008-2010 has indicated that
the reason for the model‟s ill-fitting could lie in the unexpected large delays during
the relatively warm period from March to May 2010 (figure3).
also supported by a finding that values of time lost (a product of valuations assigned to on-time arrivals and a
proportion of train cargo arriving late) were highest during late autumn and winter seasons, although delays
occurred all-year-long.
14
Figure 3: Delayed freight trains by month, Finland 2010
When enquired, VR explained that delays during these months were caused by the
lagged-effects of damages that harsh winter weather inflicted on rail infrastructure,
which lead to imposition of train speed restrictions from March to May 2010.
In addition, it has also been established that an industrial action in the Finnish
transport sector took place in March 2010. However, it was not clear how this event
affected the train arrival tardiness since during the strike period the entire train traffic
was suspended. Yet, one could expect that delays caused by the post-strike
accumulation of cargo back-logs imposed the needs for catch-up with late
consignment deliveries. Since the strike started at the beginning of March and lasted
for 16 days, it still could have had time-lagged effect damaging train arrival
punctuality in April and May 2010.
So, to capture the effects of the time-lagged delays between March and May 2010, a
dummy variable “Lagged/other” denoting “1” for delays during these three months,
and “0” otherwise was constructed to neutralize the delaying effects of lower train
speed limits in the aftermath of winter infrastructure damage and impacts of nonweather-related event (industrial action).
15
5.5 Modelling Results
5.5.1 Proportion of Weather-related Delays in All Train Arrivals
Proportion of delays induced by bad weather in all freight train delays in Finland was
determined by deriving delays recorded under thirty seven freight train delay
categories attributed to bad weather and/or technical damages imposed by these
events from an inventory of eighty different delay categories registered by VR during
2008-2010. Subsequently, an Anova regression analysis was run which showed that
weather-related delays accounted for 60 % of variation in all trains delays. The
model‟s goodness of fit was not very high (9.6%) implying that many other causes
have also contributed to train delays in addition to bad weather. However, all results
were statistically significant (p =.00).
5.5.2 Freight Trains Delays and Delay Duration as a Function of Bad
Weather
5.5.2.1 Odds for Freight Train Delays
Since several independent variables contained temperature measurements in one
form or another plus data on snowfalls and snow depth which were a function of
below-zero temperatures, we had to decide which indicators to use. Given the small
data set and the exploratory nature of the study, this decision could not be solely
based on statistical records but on experience of rail freight managers from Poland,
Sweden, Norway and the Netherland who fought weather disasters and inputs from
VR and FMI professionals. Taking stock of the above, three explanatory weather
variables were included into regression model which assessed impacts of weather
conditions on train punctuality: 1) a dummy variable composed of fixed values of
delays during March-May 2010, 2) the mean number of days with temperature below
-7 Celsius, and 3) the change from one month to the next in number of days with
snow depth of 10-20 cm. These variables explained 62% of variation in the reported
train delays.
The odds for freight train delays were obtained by calculating eC = 13%, which
translated into an approximately 12% probability of delay. The compound lagged
effects of damage of infrastructure caused by 2010 harsh winter and (possibly)
industrial action in March 2010 have increased the odds of delays by 75% and were
calculated by multiplying the odds associated with the fixed values of these variables
by e0.557 = 1.77 or approx. 175%. A unit increase in the number of days with 10 - 20
cm of snow cover from one month to the next has raised the odds for freight train
delays by about 8%. Similarly, each additional day of temperature below -7 Celsius
increased the odds for a train delay by about 3%. The increases in odds were
calculated “ceteris paribus”, i.e., under the assumption that values of the remaining
variables stayed unchanged.
16
5.5.2.2 Duration of Weather-attributed Delays
The subsequent models analysed the duration of delays associated with the different
delay causes as coded by VR. Several weather-related delay causes were initially
inserted into regression models. However, only results from model testing the
relationships between delay determinants and delay durations which both were
significant and made good sense are reported here. Good sense meant that they were
in accord with managerial assessments of freight train delay-causing factors, and
these factors‟ combined impacts on service punctuality. As a consequence, only
three linear ordinary regression models presented below assessed duration of train
delays as a function of three independent variables (and their interactions)
representing changes in weather conditions.
Delays Attributed to Fog, Cold Weather and Leaves on Track
One additional mean day in the number of days with 10 - 20 cm snow cover as
compared to the previous month emerged as a significant explanatory factor. The
model including variable denoted as “change in the snow cover” explained 66% of
variation in freight train delays. A unit increase in the average number of days with
snow cover between 10 and 20 cm from one month to the next might contribute to
629 minutes or 10 ½ hours in duration of train delays attributed to this weather
condition.
Delays Attributed to Snow Barriers
A unit increase in the number of days with snowfalls over 5 mm explained 77% of
variation in train delays attributed to snow barriers. This implies that each additional
mean day with snow fall may contribute to an additional train delay of 19 ½ hours.
Delays Attributed to Faults at Switch Stations
A unit increase in the mean number of days with more than 5 mm snowfalls might
contribute to additional 342 minutes in freight trains delays or nearly 6 hours of extra
tardiness. Each additional day with temperature -20 Celsius or below might increase
duration of train delays by 193 minutes or roughly 3 hours and 15 minutes. This
regression model explained 79% of the variation in duration of train delays attributed
to faults in track switches.
5.6 Conclusions from the Finnish Modeling Case
The runs of univariate ANOVA regression model revealed that about 60% of all
arrival delays tardiness could be attributed to bad weather and /or weather- related
technical damages of the network.
Assessment of statistically valid relationship between bad weather and occurrence
and duration of freight train delays was difficult due to small data set and aggregation
of weather and delay records as monthly averages. For establishing the odds for
delays, a dependent variable was converted into the log odds through non-linear logit
transformation. This allowed the odds for the occurrence of a delay to be calculated.
As regards the independent variable, the monthly changes in the number of days
with a given snow depth and snowfalls combined with occurrence of negative
temperatures were used as model parameters. This model explained 62 % of
17
variation in the occurrence of the reported train delays. Afterwards, three linear
regression models assessed the strength of statistical co-variation between the
monthly changes in the number of days with snow depth of 10 - 20 cm, the different
categories of bad weather, the weather-related-infrastructure and/or rolling stock
damages, and duration of freight trains delays.
The three models explained, respectively 65%, 77% and 79% of variation in duration
of freight train delays. It also appeared that train delays attributed to snow barriers were
the most severe punctuality impediments as these might have contributed to
additional 19 ½ hours of arrival lateness.
These results indicate that, statistically, it was quite difficult to establish clear causal
relationships between bad weather and occurrences of freight train delays and/or
bad-weather-induced technical problems affecting delay duration. This does not,
mean, however that such relationship could not be detected with better quality of
met-data and train delay counts, and generally better understanding of interactions
between changes in weather conditions and freight trains‟ arrival dynamics.
6 Suggestions for Future Work
Based on results presented we admit that obtaining empirically sound results took
more than just a simple analysis of raw meteorological weather indicators and rail
delay data. Without prior knowledge of managerial experience from fighting bad
weather disasters, this task could not have been accomplished meaningfully. In order
thus to reduce the risks of future delays and improve the freight trains‟ arrival
punctuality, a better match between weather indicators and data on rail operations is
needed. This in turn requires a continuous dialogue between the met-professionals,
rail infrastructure managers and traffic supervisors. Unfortunately, this collaboration
was not feasible within the current study.
Therefore, to improve the understanding for how the different classes of adverse
weather may affect punctuality of freight train operations, five suggestions are
presented below.
To assess the impact that various weather conditions may exert on train punctuality
and devise models discerning the effects of low temperatures, wind, icing, snowfalls,
and snow accumulation, more detailed data are needed. To attain this goal, rail
practitioners need to assess in detail how the different categories/combinations of
bad weather affect the punctuality of train traffic. In research on weather impacts on
humans, a compound indicator “chill-factor” is often used which measures how
strong winds, low temperature and precipitation conjointly affect human bodies.
Could a similar “chill factor” be constructed to measure how low temperatures,
precipitation and infrastructure exposure to bad weather affect punctuality of rail
operations? Could the track clogging by leafs, icing of switch stations, snow
accumulation on rail wagon undercarriages or other traffic impediments be captured
through more comprehensive indicators? Will a change in snow depth from one
hour to the next, one day to the next or one month to the next provide more
18
adequate time frame for defining how the different components of adverse weather
may threaten freight train arrivals?
Further, in order to establish which combinations of snowfalls and low temperature
may critically damage infrastructure and rail operations these insights may need to be
discussed with met-experts and people with knowledge of rail infrastructure
topography and topology in different regions and/or countries.
A second step would involve design and calculation of multi-hazard scorecards
incorporating conjoint impacts of snowfalls, strong winds, hail and low temperatures
which collectively immobilise rail infrastructure and operations. By adding built-in
weights to multiple hazard components, these indices could be tailored to the
different geographical areas enabling to determine how the shifts in weather
conditions might affect traffic on particular networks, corridors, or lines crossing the
exposed territories.
The third step would encompass coding the different delay causes more precisely so
that these could be better linked to specific weather conditions. Data on the different
classes of bad weather developed at the first stage could be used here. Since the
analyses reported above indicate the immediate/acute, mediated, lateral and timelagged types of weather impacts on infrastructure and train operations, indicators of
short-term and lasting damages need to be developed and connected to the different
bad weather categories.
The fourth step would involve making sure that sufficiently large data sets are
available for determining the relationships between more detailed model parameters.
Ideally, data on varying weather conditions on networks cutting through the different
geographical regions would be desired. In this manner, the need for long time
observation series might be somewhat, but not entirely reduced. Inter-temporal
differences are not the same as inter-regional differences during the same time.
However, with data on both the inter-temporal and inter-regional differences
available, the partial impacts of bad weather components and their interactions could
be established to determine whether the composite weather indicators from the
primary steps are more useful for explaining causes of train delays as compared to
single factors.
Finally, the fifths step will consist in application of multivariate regression and/or
structural equation models for assessing how train arrival delays could be causally
linked to multi-hazard weather impacts and forecasted for different settings.
As suggested, a dialogue between the met-scientists, behavioural researchers and rail
operations managers whose daily experience from coping with weather-related and
other types of train punctuality threats is needed. This collaboration will provide
guidance for data definitions, data processing, designing of analytical models and
formulation of empirically validated findings.
7 Discussion
Conclusions from railway cases from Poland, Finland, Switzerland and Norway are
relatively clear. Managers from logistics and rail freight industry are well aware of
extreme events impacts on their business and reputation, but the tools and means to
19
mitigate the impacts are limited. The industry leaders and managers prioritise other
things than weather, which in the end is an “act of God”, and not something they
can influence through better business skills. They expect and hope, however, that
infrastructure managers improve their performance to have pathways clear even
during or at least soon after an extreme weather incidence.
However, it would be wrong to conclude that rail and logistics business managers do
not care or are not prepared for extreme events. The simple fact is that these issues
are not too high on their agenda. One obvious reason for this behaviour could be
that such preparations are not included in management contracts – it is shareholder
value that matters, not necessarily smooth and uninterrupted operations. One may also with
good reason question whether service functionality should not be included in
management and performance contracts of public sector executives, namely
infrastructure managers. If neither side is assuming responsibility, the reliability of
rail-based logistic operations may falter further.
Also the modern lean-production thinking which shuns reserves in capital, people,
equipment and inventories provides little room for preparedness-building. The
higher are the efficiency requirements on business and public organisations, the less
room there will be for extreme weather preparations, at least at operational level.
However, on strategic level, the situation could be different. Choices of production
locations, topography and topology of global logistics channels and gateway hubs,
contractual guarantees on supply chains effectiveness, inventory availability and
similar decisions are examples of strategic choices into which the long-term
resilience-building could be incorporated. However, as these judgments have
profound long-term financial implications, they need to be taken by the highest
corporate echelons.
The results of the modelling exercise were also quite instructive. Cold days (below 20 Celsius) and snow falls (≥ 5 mm) explained the majority of winter time freight
train delays in Finland and when these phenomena are known – as they usually are –
with reasonable certainty a few days beforehand, preparations can take place – if seen
worthwhile. However, even when the knowledge is there it does not necessarily mean
that it will be taken into practice unless the benefits are explicit, measurable and longterm. Infrastructure managers may need to understand that rail operators will loose
freight carriage to road hauliers because shippers‟ competitiveness depends on
reliability of goods supply. Market losses of rail companies had time-lagged scope
and duration which by far exceeded the bad weather occurrence. This will not only
affect the rail infrastructure usage but also the entire socio-environmental profile of
European freight transportation.
Modelling of relationship between changes in met-states and freight train delays
revealed large mismatch between the types and the quality of available data and the
needs for rail operators to assess probability of weather-related train arrival delays.
Paradoxically, it appeared that icing of traction lines, heavy snow falls and low
temperatures may damage the market position of environmentally friendly rail
transport much more than other service shortages. Hence, to ascertain that weather
and/or other natural hazards do no harm the rail freight competitiveness, it is
necessary to understand how these adversities affect transport and logistics
20
operations as well as how the managers in these industries deal with natural hazards
risks. Therefore, in order to prevent the immediate and the long-term damage the
met-information needs not only to be on time, but also in format that will help
business people to incorporate weather-risk forecasts in building of long-term
resilience.
To this end the current modelling exercise was quite useful. Hopefully, it may
provide guidance for assessing transportation impacts of weather phenomena from
other climatological zones. Winter phenomena in Finland, Norway, Poland, Sweden
and Switzerland are obvious but heat waves are not. In southern Italy the conditions
could be quite different.
Needless to say, both transport and meteorological/climatological scientists need to
join forces to successfully accomplish these tasks.
21
8 References
Adegoke O., and Gopalakrishnan, M. (2009): Managing Disruptions in Supply
Chains: A Case Study of a Retail Supply Chain. International Journal of Production
Economics 118, pp. 168-174.
Agresti, A., and Natarajan, R. (2001): Modelling Clustered Ordered Categorical Data:
A Survey. International Statistical Review. Vol.69, Issue 3, pp. 345-371.
Altay, N., Ramirez, A. (2010): Impact of Disasters on Firms in Different Sectors:
Implications for Supply Chain. International Journal of Supply Chain Managemen.
Vo.46, no.4, pp. 59-80
Ahuja, S.,van Vuren, T., Porter, S., and Fearon J.(2002): Assessing Measures which
Reduce Incident Related Delays and Travel Time Variability. European Transport
Conference 2002, Homerton College, Cambridge, England, September 9-11, 2002.
Bates, J., Black, I., and Fearon, J. (2002): Supply Models for Use in Modelling the
Variability of Journey Times on the Highway Network. European Transport
Conference 2002, Homerton College, Cambridge, England, September 9-11, 2002.
Bundschuh, M. Klabjan, D., Thurston, D.L. (2003): Modelling of Robust and
Reliable Supply Chains. Organization Online e-print. htt://www.optimizationonline.org. Department of Mechanical and Industrial Engineering, University of
Illinois at Urbana-Champaign, Urbana, IL.
Canadian Competition Bureau (2006): Competition Bureau Concludes Examination
into Gasoline Price Spike Following Hurricane Katrina.
http://www.competitionbureau.gc.ca.
Chapman, L., and Andersson A. (2010): Climate Change and Winter Road
Maintenance:
Will Complacency Be the New Killer? Proceedings of European Transport
Conference, 2010.
Closs, D., McGarrel, E. (2004): Enhancing Security through the Supply Chain. IBM
Center for the Business of Government. Special Report Series.
Copley, G., Murphy, P., and Pearce D. (2002): Understanding and Valuing Journey
Time Variability. Proceedings of European Transport Conference 2002, Homerton
College, Cambridge, England, September 9-11, 2002.
Cochran, I. (2009): Climate Change Vulnerabilities and Adaptation Possibilities for
Transport Infrastructure in France. Climate Report, Issue no18, September 2009.
Dacy, D.C., Kunreuther, H (1969): The Economics of Natural Disasters:
Implications for Federal Policy. The Free Press New York.
Daganzo, C.F.(2002): A behavioral Theory of Multi-lane Traffic Flow : Long
Homogenous Freeway Sections. Transportation Research Part B (36). pp. 131-158.
De Jong, G., Bakker, S., Pieters, M and Wortelboer-vam Dosselaar, P. (2004): New
Values of Time and Reliability in Freight Transport in Netherlands. in: Proceedings
of the European Transport Conference 2004.
22
Delmonaco, G., Margottini C., Spizzichino D. (2006):ARMONIA methodology for
mulit-risk assessment and the harmonization of different natural risk maps.
Deliverable 3.11., ARMONIA.
Fabrikant, J.I. (2009): The Chernobyl Disaster: An International Disaster.
Unpublished manuscript from University of California. Berkeley and San Francisco.
Fishhoff, B., Lichtenstein, S., Slovik, P., Derby S., Keeney, R. (1981): Acceptable
Risk. Cambridge University Press, NY.
Fowkes, A.S., Firmin, P.E., Tweedle, G., and Whiteing, A.E. (2004): How Highly
does the Freight Transport Industry Value Journey Time Variability – and for What
Reasons? International Journal of Logistics 7(1) pp. 33-43.
Gardner, J.T. and Cooper M.C. (2003): Strategic Supply Chain Mapping Approaches.
Journal of Business Logistics. Vol. 24, No.2, pp. 37-64.
Gassebner, M., Keck A., and Teh R. (2006): Shaken. Not Stirred: the Impact of
Disasters on International Trade. KOF Working Paper No. 139.
Goklany, I.M.(2009): Deaths and Death Rates from Extreme Weather Events: 19002008. Journal of American Physicians and Surgeons. Vol.14. No.4. Winter 2009.
Gunipero, L.C., Eltantawy, R.A., (2004): Securing the Upstream Supply Chain: A
Risk Management Approach. International Journal of Physical Distribution and
Logistics Management 9 (34). pp. 698-713.
Halse Harkjarr, A., Samstad, H., Killi, M., Flügel, S., and Ramjerdi, R. (2010):
Valuation of Transport Time and Reliability in Freight Transport. Report 1083/2010
by Institute of Transport Economics. Oslo, Norway.
Hendricks, K.B., Singhal, V.R. (2005): An Empirical Analysis of the Effect of Supply
Chain Disruptions on Long-term Stock Price Performance and Risk of the Firm.
Production and Operations Management. 14. pp. 35-52.
Holmgren, A.J. (2007): A Framework for Vulnerability Assessment in Electric Power
Systems. in Murray, A., Grubesic, T. (eds.): Critical Infrastructure – Reliability and
Vulnerability. Springer, New York.
Hosmer, D.W., Lemeshow S. (1989): Applied Logistic Regression. New York. John
Wiley and Sons.
Kappes, M.S., Keiler M., von Elverfeldt K., and Glade T. (2012):Cahllenges of
Analyzing Multi-hazard Risk: A Review. Natural Hazards DOI 10.1007/s11069-0120294-2.
Kleindorfer, P.R., Saad, G.H., (2005): Managing Disruption Risks in Supply Chains.
Production and Operations Managementr 14 (1) pp. 53-68.
Knemeyer, A. M., Corsi, T.M., Murphy P.R. (2003): Logistics Outsourcing
Relationships:
Customer Perspectives. Journal of Business Logistics. 24 (1), pp. 77-109.
23
Koetse, M.J., and Rietveld P. (2009): The Impact of Climate Change and Weather on
Transport: An Overview of Empirical Findings. Transportation Research Part D 14,
pp. 205-221.
Kunreuther, H. (1976): Limited Knowledge and Insurance Protection. Public Policy
24, pp. 227-261.
Lai, K.H., Ngai, E.W.T., Cheng, T.C.E. (2002): Measures for Evaluating Supply
Chain Performance in Transport Logistics. Matekon 13, pp. 35-49.
Long, S.J. (1997): Regression Models for Categorical and Limited Dependent
Variables. Sage Publications, London.
March J., Shapira Z. (1987): Managerial Perspectives on Risk and Risk Taking.
Management Science 33, pp. 1404-1418.
McKinsey Quarterly at http://www.mckinseyquarterly.com/2007.
Odgaard, T., Kelly, C.E. and Laird, J.J. (2005): Current Practice in Project Appraisal
in Europe – Analyses of Country Reports. HEATCO Work Package 3 (Stuttgart:
IER).
Oke, A. and Gpalakrishnan M. (2009): Managing Disruption in Supply Chains: A
Case Study of a Retail Supply Chain. International Journal of Production Economics.
118 (200), pp. 168-174.
Qiang, Q., Nagurnet, A., and Dong J.( 2009): Modelling of Supply Chain Risk under
Disruption with Performance Measurement and Robustness Analysis in Wu. T., and
J. Blackhurst (eds.): Managing Supply Chain Risk and Vulnerability, Springer, New
York.
Repenning, N., Sterman, J.(2001): Nobody Ever Gets Credit for Fixing Problems
That Never Happened. California Management Review, 43, pp. 64-88.
Rice, B., Caniato, F.(2003): Supply Chain Response to Terrorism: Creating Resilient
and Secure Supply Chains. Supply Chain Response to Terrorism Project Interim
Report. MIT Center for Transportation and Logistics. MIT, Massachusetts
Sheffi, Y. (2001): Supply Chain Management under the Threat of International
Terrorism. International Journal of Logistics Management. 12 (2), pp. 1-11.
Sheffi, Y. (2005): The Resilient Enterprise. Overcoming Vulnerability for
Competitive Advantage. MIT Press. Cambridge, Massachusetts.
Snyder, M. and Swann W.B. (1978): Hypothesis-testing Processes in Social
Interactions. Journal of Personality and Social Psychology, 36(11), pp.1202-1212.
Svensson, G., (2002): A Typology of Vulnerability Scenarios towards Suppliers and
Customers in Supply Chains Based upon Perceived Time and Relationships
Dependencies. International Journal of Physical Distribution and Logistics
Management, 32 (3), pp. 168-187.
Tang, C. S.(2006): Perspectives on Supply Chain Risk Management. International
Journal of Production Economics, 103, pp. 451-488.
24
Toya, H., and Skidmore M. (2002): Economic Development and the Impacts of
Natural Disasters. Unpublished Paper by University of Wisconsin-Whitewater,
Department of Economics.
Transportation Research Board (2011): Proceedings of Annual Meeting, 2011,
Washington DC.
Wagner, S.M., Bode, C. (2006): An Empirical Investigation into Supply Chain
Vulnerability. Journal of Purchasing and Supply Management, Volume 12, pp. 301312.
Wilson, M.C. (2007): The Impact of Transportation Disruptions on Supply Chain
Performance. Transportation Research E 43, pp. 295-320.
Yin, R.K. (1994) Case Study Research: Design and Methods. Sage Publications,
Thousand Oaks
Zamparini, L. and Reggiani, A. (2007): Freight Transport and the Value of Travel
Time Savings: A Meta-analysis of Empirical Studies. Transport Review, Vol.27, No.5,
pp. 621-636.
Zsidisin, G., Elram, L., Carter, J., Cavinato, J. (2004): An Analysis of Risk
Assessment Techniques. International Journal of Physical Distribution and Logistics
Management 34 (5), pp. 397-413.
Xia, Y., Van Ommeren, J.N., Rietveld, P., and Verhagen W.(2011): The Effects of
Extreme Weather on the Rail Sector. Unpublished manuscript from VU University
Amsterdam.
Xu, L. and Beamon, B.M, (2006): Supply Chain Coordination and Cooperation
Mechanisms: An Attribute-Based Approach. Journal of Supply Chain Management.
Volume 42, Issue 1, pp. 4 – 12.
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