Risk and Capacity Management in Logistics Networks: The Example of Global Container Operators

Risk and Capacity Management in Logistics Networks: The Example of Global Container Operators Stephan Zelewski, Matthias Klumpp, Susanne Hohmann Prof. Dr. Stephan Zelewski is director of the Institute for Production and Industrial Information Management at the University of Duisburg-Essen. His research interests are in general analysis and development of computer based tools and techniques in the field of operations research and artificial intelligence for solving management problems within business practice by formal and quantitative modelling. Phone: +49 (0) 201-183-4040, E-Mail: stephan.zelewski@pim.uni-due.de Prof. Dr. Matthias Klumpp is professor for business administration at the FOM University of Applied Sciences in Essen and director of the Kompetenz-Centrum Logistik (KCL). His research interests are located in the field of SCM and service production in general. He has conducted research projects concerning e.g. management of logistics services, supply chain management and education services. Phone: +49 (0) 201-8228686, E-Mail: matthias.klumpp@fom.de Dr. Susanne Hohman earned her PhD at the University of Duisburg-Essen (PIM) in the field of Supply Chain Management (bullwhip effect) and works as head of Project Management at IPLPerseco, a member of Alpha Group. Phone: +49 (0) 2065-695-3068, E-Mail: susanne.hohmann@iplperseco.com Keywords: Risk Management, Capacity Management in Logistics Networks Abstract Capacity management modelling is a broad field with a huge number of discussion areas and contributions. Though if applied towards service processes there are even smaller numbers of models and contributions as e.g. gap and yield management models for service production. Further on such model descriptions for logistics services and networks are more seldom. And finally most capacity models focus solely on the use of given resources and assets assuming a demand situation below capacity restrictions and aiming at optimal use of given assets by increasing capacity use towards an maximum of 100%. But very few models discuss situations of capacity demand being considerably higher than capacity supply, especially in logistics services and networks though these situations are increasingly commonplace in international logistics networks as e.g. in global container line shipping. Therefore the authors of the following research contribution try to establish relevant modelling assumptions for the specific situation of logistics services capacities facing an unbearable demand and the choice of rejecting capacity requests by describing relevant practical business situations, evaluating several business cases with online action research methods and finally suggesting a game theory modelling of this specific capacity management situation. 5

1. Introduction A scientific management research view on global supply and transport chains is a prominent and hugely discussed topic in the field of business administration and logistics (cp. Wang et al., 2007; Blanchard, 2007; Olfert, 2005; Göpfert, 2004; Kuhn/Hellingrath, 2002). In this area of logistics networks an emergence of new topics besides the traditional objectives of customer orientation (pull principle) and efficiency increase (reduction of bullwhip effect impacts) in the management concepts of supply chain management can be observed (cp. Klumpp/Koppers, 2008; Koppers/Zelewski/Klumpp, 2008; Klumpp/Jasper, 2007; Fawcett, 2006; Elfing, 2005; Arndt, 2004; Christopher, 2004; Keller, 2004; Pfohl, 2004; Doganzo, 2003; Corsten/Plötzl, 2002; Zäpfel/Wasner, 1999). This can be exemplified by the following questions: How to increase the overall flexibility of logistics networks and supply chains? How to measure and decrease the ecological impact (climate impact) of logistics activities and networks? How to measure and control risks in logistics networks? How can an efficient bottleneck and capacity steering be established in logistics networks and supply chains? How can individual optimisation schemes for single products and customers be ensured in logistics networks and supply chains? The three last named questions are adressed and intertwined in the field of capacity management in logistics networks: Especially for global and therefore intermodal logistics networks e.g. between Asian suppliers and European retailers the simultaneous questions of risk measurement and management (all parties: suppliers, logistics services providers and retailers), bottleneck management as well as efficiency and value optimisation are naturally combined (cp. Grant/Lambert/Stock/Ellram, 2007; Stüger/Unterbrunner, 2007; Jones, 2006; Ernst/Bamford, 2005; Lee, 2005; Schulte, C., 2005; McCormack/Johnson/Walker, 2002; Fleischmann, 2001). The following figure 1 is illustrating this combination. Company A (Procurement, Europe) Supply Chain Co-operation Company B (Supplier, Asia) Capacity Adjustment by Reservations, Delays, Cancellations and Cancellation Fees, Nontarif Measurements Operator, i.e. Logistics Services Provider Transport Way e.g. Hong Kong Antwerp Logistics Capacities Risks -Planning - Scheduling -Fulfillment - Event Management Figure 1: Capacity Management Areas in Global Logistics Networks (Example). Traditional research based on microeconomic theory is assuming that differences or frictions between supply and demand in market systems are regulated through price adjustments and 6

therefore readjustments of supply or demand quantities. In day-to-day business and logistics nevertheless there are several non-price adjustment mechanisms: For example in the global logistics and transport network of IPLPerseco (European fast food industry procurement) shipping operators use quantity restrictions or second-round punishment by quantity reductions for existing reservations as reaction on not used reservation quantities. In-depth analysis of such non-price capacity management instruments is crucial and central for the suggested research area. The following research questions are used for contributing in this area: (A) Quantitative value analysis for non-price intruments is currently missing: Do they really provide added value or are they diminishing business value in logistics networks? (B) Though important and informative a game theory approach to such non-price capacity management instruments in logistics networks is not yet existing and shall be provided by the suggested research. Probably for example a Modified SHAPLEY Value as expression for the economic advantages of excluding freight from shipping lines could be conceptualized (cp. Shapley, 1953; Mirman/Tauman, 1982; Benjaafar/Cooper/Kim, 2005). (C) The general question of usefulness and impact of these instruments has not been answered and needs further theoretical as well as empirical (case study) research. This will be established in two research steps: First risk and capacity management in literature (chapter 2.1) as well as in an existing company will be highlighted (chapter 2.2) and results of an online survey will be presented (chapter 3). Second a quantitative theoretical model will be composed with a game theory approach (chapter 4). The expected game theoretical and modelling results for non-price capacity instruments should be capable of providing detailled and quantitative information in different logistics networks and settings. 2. Risks and Capacity Management in Logistics Networks 2.1. Existing Models and Research Results Logistics management concepts and especially the integrated perspective of supply chain management models have achieved a lot of improvements in global productivity and therefore economic wealth with companies and individuals. Nevertheless several hopes e.g. in logistics integration and supply chain management have not yet been fulfilled completely (cp. Speth, 2008). One of several reasons for this expectation gap may be a slight blind spot in operational logistics modelling: Most models focus on technological, co-operative and transparency improvements or increasing use of existing assets (cp. Klumpp/Koppers, 2008; Koppers/Zelewski/Klumpp, 2008; Speth, 2008; Baumgarten, 2008). So for example gap or yield management analysis models aim at increasing use of existing, not yet fully used capacities (cp. Frank/Friedemann/Mederer/Schroeder, 2006; Bhargava/Sundaresan, 2004; de Boer, 2004; Kapteijns/Slager, 2004; Zelewski, 1997). Research areas adjacent to the discussed risk and capacity topic in logistics networks are the folllowing broad fields: (A) Concepts of electronic information technology support for efficiency increases in logistics environments is discussed prominently and leads to innovative instruments in supply chain (event) management systems (cp. Hildebrandt/Schumann/Kiziltoprak/Behrens, 2007; Müller/Meyer-Larsen, 2007; Smirnov/Shilov/Kashevnik, 2006; Zimmermann, 2006; Krol/Keller/Zelewski, 2005; Wannenwetsch/Nicolai, 2004). (B) In a similar context enabling technologies as for example automatic identification technologies (i.e. RFID) or transport and handling technologies support an efficiency increase in logistics networks and with logistics services (cp. Blecker/Huang, 2008; Matheus/Klumpp, 2008; Raghavulu/Yamani/Ravindranath, 2007; Sielemann/Spin, 2007; Cheung/Cheung/Lee/Kwok, 2006). 7

(C) Modelling in the areas of operations research and game theory in logistics try to establish broad guidelines for efficient business conduct in logistics and supply chain networks (cp. Zelewski/Peters, 2008; Rabbani/Zadeh/Delasay/Gharegozli, 2007; Zelewski, 2007; Zhang, 2007; Killat/Laue/Vogeley, 2006; Saiz/Castellano/Besga/Zugasti/Eizaguirre, 2006). (D) Moreover external impacts as for example innovation, outsourcing, customer orientation and political concepts and decisions have to be discussed with their specific influence for efficiency in logistics networks (cp. Kersten/Hohrath/Koch, 2007; Pfohl/Elbert/Röth, 2007; Kersten/Rall/Meyer/Dalhöfer, 2006; Lamsali, 2006). 2.2. Practical Description of Relevant Capacity Restrictions and Reactions The example of the company IPLPerseco presents day-to-day logistical risk and restriction. IPL Perseco is a member of the Alpha Group, operating in the European zone, but on a global level, functionally fully integrated into Havi Global Solutions. IPLPerseco is striving to offer fully globally leveraged Outstanding Promotion + Packaging + Purchasing Services to its customers in Europe. Customers receive more than 720 million promotional items in around 44 countries and 1,250 million packaging items in around 25 countries per year. IPLPerseco is located in 7 European countries with a team of 70 employees. IPLPerseco and its forwarders plan and execute annually about 3,000 import containers and 100 export containers (40 feet size). Import containers are mainly arriving from Far East, China and Vietnam. As a consequence of the recent China Boom with risen imports from China container space to Europe is becoming shorter and shorter. Therefore container transports have to be planned as early as possible in order to get the required space and to ensure the European supply chain maintenance. IPLPerseco uses several carriers for container transportation from Far East. Within these are three main carriers with guarantee for space commitments and fixed allocations. The guarantee for space commitments is agreed between the carrier and the Alpha Group forwarding company. The space commitment required from the carrier is just a rough indication about the yearly demand of container respectively TEU. The guarantee for space commitment does not include any indication about weekly demand, port of departure, date of arrival or vessel. Allocations mean the breakdown of previous agreed space commitment guarantee. Allocations are fixed for one year on a weekly basis. They are arranged for determined ports mainly in the south of China and are fixed for vessels. As a reaction of container shortage one of the main carrier introduced a new planning tool to fix allocations to customers. Customers like IPLPerseco and the forwarding company have to report their weekly transport requirements approximately two months in advance. These requirements have to include the specific departure and arrival port. If the announced capacities are not fully utilized, the carrier may reduce the space commitment. In case no other sea freight is available on time IPLPerseco would have to use sea/air or air freight. Advantage of this procedure is that space commitments are offered and even more or less predictable and reliable. Other carriers do not guarantee any space commitments and allocations are not fixed on a yearly base. These carriers just decline transport request or postpone already booked container space without further explanation. They provide a smaller degree of transparency. On the other hand those carriers who offer space commitments are among the high priced shipping companies which makes the loss of urgently required container space even more challenging and expensive. The alternatives for sea freight can roughly be calculated as follows: For a standard transport route South of China Central Europe costs for a 40 feet container are about 2,300 $ for sea transport, 20-30,000 $ for sea/air transport (depending on volume and weight e.g. via Dubai) and 40-50,000 $ for air transport (depending on volume and weight). 8

3. Risk and Capacity Management Action Research Data 3.1. Case Study Instrument and Methodology Presented case studies were collected through an online questionnaire, described in detail in a separate paper (Klumpp, 2008). The main topics were risks, quality and capacity management in logistics. Questions combined ranking of selected given answers and free text answers for each topic section providing for a broad range of feedback possibilities. Research was conducted in an online interview tool of KCL with an empirical research institute (inomic), open from 15th of April to 5th of May 2008 (20 days). The short research period prevents for the main representativity problem with most online surveys: The proliferation danger of invited contacts through mail spreading is limited. Business contacts of FOM/KCL in different industries were invited as described below (case characteristics, figure 2). No Case Id Industry Employees (2007) Turnover (2007) 1 21 Food Industries - (not reported) - (not reported) 2 23 Automotive & Electronics 1-50 - 3 24 Service Industries - - 4 25 Service Industries 51-250 - 5 28 Textile Industries 501-5,000-6 30 Textile Industries > 5,000 > 1 Mrd. Euro 7 31 Textile Industries - - 8 32 Automotive & Electronics 501-5,000 > 1 Mrd. Euro 9 33 Industry Products 501-5,000 100 Mio. Euro-1 Mrd. Euro 10 36 Automotive & Electronics - - 11 37 Defense Industries > 5,000 > 1 Mrd. Euro 12 38 Industry Products - - 13 39 Service Industries - - 14 40 Service Industries > 5,000 100 Mio. Euro-1 Mrd. Euro 15 42 Automotive & Electronics - - 16 43 Service Industries - - 17 45 Automotive & Electronics 51-250 100 Mio. Euro-1 Mrd. Euro 18 46 Industry Products > 5,000 > 1 Mrd. Euro 19 47 Service Industries - - 20 48 Service Industries 501-5,000 100 Mio. Euro-1 Mrd. Euro 21 49 Automotive & Electronics - - 22 50 Service Industries 501-5,000 > 1 Mrd. Euro 23 51 Industry Products 501-5,000-24 53 Automotive & Electronics 251-500 - 25 54 Defense Industries 501-5,000 100 Mio. Euro-1 Mrd. Euro 26 55 Food Industries 501-5,000-27 56 Defense Industries 501-5,000-28 57 Defense Industries 501-5,000-29 58 Service Industries > 5,000 > 1 Mrd. Euro 30 59 Industry Products > 5,000 > 1 Mrd. Euro 31 60 Service Industries 501-5,000 10-100 Mio. Euro 32 61 Defense Industries > 5,000 > 1 Mrd. Euro 33 62 Service Industries 1-50 1-10 Mio. Euro Figure 2: Action Research Cases by Industry and Company Size 9

Altogether there are 33 business cases reported for the study. In 8 out of the 33 cases the respondents were with industry logistics service providers, 4 respondents connected themselves to an industry OEM and further 4 represented trading companies. Therefore a broad range of experts and perspectives can be assumed. 3.2. Action Research Results The resulting research data in the practical cases of logistics, supply chains and procurement are shown below in the following figures adressing experienced risks of companies in global supply chains, expressed objectives for capacity management activities as well as capacity management tools in use in business practice. Id Risk Combined Ranking Points (170 max.) 1 Percentage of Maximum Ranking Points 10 a Quality Risks Procurement 118 69.41% b Quality Risks Production/Sales 116 68.24% c Price Decrease Sales Markets 111 65.29% d Price Increase Procurement Markets 100 58.82% e Service Risks (Missing Information etc.) 96 56.47% f Security Risks internal (Sabotage) 93 54.71% g Image Risks 92 54.12% h Political Risks 82 48.24% i Financial Risks (Financial Markets, Currency Markets) 78 45.88% j Security Risks external (Terrorism) 49 28.82% Figure 3: Risk Ranking from Action Research Case Studies Relevant risks for global logistics networks can be especially seen in parts (c), (d), (e) and (j) according to the respondents answers as reported above (cp. figure 3): As a working capital perspective easily shows a possible price decrease in sales markets is directly linked in its economic loss valuation to the shipping duration or stock volume as central logistics indicators. Further price increases in procurement markets also increase working capital values and therefore the economic impact of shipping times and stock volumes. Risk (e) as service risk is imminent in transportation processes as information provided may increase transparency and reduce this specific risk position and vice versa. And fourth external security risks are a problem of contingency planning in international logistics urging global forwarders and carriers to provide alternative transport solutions in any such event. Altogether the described risks show the importance of increased logistics efficieny and therefore optimal capacity management as all of 1 Only 17 of all 33 respondents completed the full 10 point risk ranking, therefore a hypothetical ranking point sum of 10 times 17 is possible. This would indicate that if all respondents would have ranked one single risk the most important risk this one would have achieved 170 points.

these risk positions will increase with rising capacity problems and goods waiting for transportation.important objectives for capacity management are recognized especially in product/service utility, planning accuracy and service quality. But also trust and flexibility are taken into account according to the case studies. Less important are cost effectiveness, employees satisfaction and environmental considerations (cp. figure 4). Id Objective of Capacity Management Combined Ranking Points (88 max.) 2 Percentage of Maximum Ranking Points a Product / Service Utility 72 81.82% b Planning Accuracy 64 72.73% c Service Quality 60 68.18% d Trust (e.g. to Suppliers) 52 59.09% e Flexibility 48 54.55% f Cost Efficiency / Cost Minimum Objective 38 43.18% g Employees Situation / Satisfaction 32 36.36% h Environmental Impact/Natural Resource Efficiency 30 34.09% Figure 4: Objectives in Corporate Capacity Management Id Capacity Management Tools in Use Head Count Percentage a Customer Reservations / Bookings 7 21.21% b Providers own Capacity Planning 5 15.15% c Scheduling of Customer Bookings 5 15.15% d Other (e.g. According to Historical Data, Market ) 3 09.09% e Spot Price Steering (Price Reductions) 2 06.06% f Strategic Pricing (long-term advance reductions) 2 06.06% g Poenal Payment for No-Shows 1 03.03% h No Capacity Steering at all (Stock Production) 1 03.03% Figure 5: Management Tools in Corporate Capacity Management As analysed in the literature context also the case studies prove that very little rational capacity management tools are in use in business practice though the cases also entail manufacturing 2 Only 11 of all 33 respondents completed the full 8 point objective ranking, therefore a hypothetical ranking point sum of 11 times 8 is possible. This would indicate that if all respondents would have ranked one single objective the most important one this one would have achieved 88 points. 11

companies (cp. figure 5). No single method is used at least of every fourth case as reported and even Other Tools are only named three times out of all 33 cases. 4. Game Theory Approach to Capacity Management in Logistics In the following a game theory approach will be presented how it could be possible to analyse and quantify the risks and capacity management in Logistics network. A co-operation in any given logistics network may encompass n partners P i (i=1,,n), each demanding in period t the amount d i.t R 0 of a limited logistics resource e.g. an international container vessel with the total capacity supply of c t. Capacity demands d i.t are ex ante not completely known, but depending on day to day business and logistics volume development of each and every partner P i. Using frame contracts solely the following general conditions can be agreed upon: (A) A minimum and maximum capacity demand allowance d i.min resp. d i.max of one partner P i in a time interval [t l ;t u ] with d i.min (t = t l, t l+1,, t u-1, t u ): d i.t d i.max with d i.t as a real capacity demand of partner P i in period t; (B) an average capacity demand d i.ave of one partner P i in time interval [t l ;t u ] with d i.ave = ( (t = t l, t l+1,, t u-1, t u ): d i.t ) : (t u - t l + 1) with d i.t as a real capacity demand of partner P i in period t. As far as in any period t the given capacity c t of limited resources is exceeded by aggregated capacity demand d t = (i=1,,n): d i.t of all partners P i, a restriction has to be imposed in order to align capacity demand and supply. This is executed following the rule that capacity demands d i.t of singular partners P i are neglected in exactly the specific amount until resulting total capactiy demand of all other partners is balanced by given capacity supply c of the limited logistics capacity. Therefore the basic concept of this model can be described as follows: For each capacity demand d i.t exceeding the given and limited capacity supply c in a period t total costs co i.t of buying logistics services in an assumed spot market have to be calculated. The capacity demand d i.t has to be fulfilled as one order on this hypothetical spot market as is is assumed the lot size d i.t can not be broken up in sub-units as logistics services may be viewed as un-dividable complete service packages. This would imply that any capacity demand d i.t can only be fulfilled completely by the discussed limited capacity supply of one logistics network as e.g. a given container vessel ( internal solution ) or completely by an external other logistics service provider outside of the discussed logistics network as e.g. by airfreight ( spotmarket solution ). This restriction may not hold true in logistics transport procedures as lot sizes of container shipments larger than one are divided up in some not so seldom cases. But for further modelling this assumption may equivocally assumed as true without harming general results. Furthermore as always only one specific, representative period t is used in the calculation the period indicator t can be set aside also without loosing important results. For an all-encompasing co-operation N = {1,,n} of all n partners P i with i=1,,n according to the concept of the SHAPLEY Value for profit and cost distributions among co-operative game theory any possible sequence for the partners is calculated. Therefore every single possible sequence is reviewed and checked for the question, whether a single reviewed partner P i belongs to a cooperation S with {i} S N fulfilling the following requirements: For a marginal co-operation S\{i} limited available (transport) capacity supply c is enough to counter total capacity demand of this co-operation whereas for the co-operation S the total capacity demand is exceeding the limited available (transport) capacity supply c. With this regime it may be prudent and rational to require a value compensation from the specific partner P i joining the marginal co-operation S\{i} with an amount equivalent to the costs co(d i ) necessary to buy the 12

exceeding capacity demand of this partner on a logistics services spot market in order to fulfill the capacity demand d i of this partner P i as a whole because the limited available (transport) capacity c of the logistics network is surpassed by the incoming partner s capacity demand for the first time. This established SHAPLEY Value is modified (termed Modified SHAPLEY Value, MSV) as one partner then and only then is awarded with incremental costs, if he belongs to a co-operation S leading to a distinctive transition of the marginal co-operation S\{i} to the co-operation S with total (transport) capacity demand exceeding total capacity supply c for the first time. As long as such a marginal situation with exceeding capacity demand at the transition point from marginal cooperation S\{i} to co-operation S is not on hand for any co-operation S with {i} S N the respective MSV is zero because in the reviewed logistics network no capacity problems exist (all capacity demands can be fulfilled and therefore no excess external spot market comparative costs exist). According to the outlined requirements and definitions a MSV as a variable SV i for each partner P i within the logistics network with i {1,.,n} can be defined with the help of a value function v as follows: SV i with: (v(s) v(s\{i})) (1) (v(s) v(s\{i})) co (d i ); for for d j < c d j > c d j > c d j < c (2) The modification of a conventional SHAPLEY Value is exercised in formula (2) by calculating the specific value of a marginal incoming new co-operation partner P i into the marginal co-operation S\{i}. The complete co-operation N = {1,,n} of all n partners P i has a natural motivation to exclude all those partners in a given (representative) execution period who would experience the lowest external excess costs co(d i ) for their specific capacity demand d i on the spot market for logistics services by third parties outside the logistics network. Therefore the possible partners in a co-operation may be positioned in a specific array with non-increasing usually even falling MSV values SV i. Co-operation partners will be served regarding their (transport) capacity demand due to the mentioned specific array until the available limited (transport) capacity supply is depleted. For any residual capacity demands d i of co-operation partners P i not being able to be served in the discussed logistics transport network those co-operation partners P j whose capacity demand d j is to be servid within the logistics network have to award compensation payments towards the excluded co-operation partners. The amount of compensation payments is determined by the before mentioned minimal excess costs co(d i ). Distribution of these compensation payments burden among the insiders P j is executed in proportion to the mentioned MSV values SV j in formula (1) and (2); this ensures that each co- 13

operation partner P j with a high MSV will be hold liable in proportion to the alternative hypothetical excess costs he would have to face would he have been excluded from the transport capacity (excess transport spot market pricing for the transport capacity demand of this coopertation partner P j ). In addition to the presented basic MSV concept there are some specific extension possibilities: In the general SHAPLEY value concept all sequences of the co-operation partners P i for aligning into the complete and final co-operation in one specific period t, are considered with an identical occurrence probability. This assumption can be reworked according to specific situation requirements as the two following example cases show: (I) For co-operation partners who are willing to fix their specific capacity demand before final decision time a surplus value can be established compared to co-operation partners insisting on their (transport capacity demand) flexibility until the latest possible moment: The specific array of extending the marginal co-operation S\{i} leading towards the cooperation S and the sitiuation of capacity demand exceeding capacity supply may be weighted with a lower occurrence probability if a reviewed co-operation partner P i is willing to fix his capacity demand d i before final decision time. (II) On the other hand co-operation partners with a motivation to hold on to their specific capacity demand flexibilitiy may be charged with an additional flexibility premium : Their specific (transport) capacity demands may be multiplied with a factor α > 1 for the external excess costs (co(d i )) they therefore will be scheduled very high in the MSV array and experience a relatively low probability of loosing out on the reviewed transport capacity in the (internal) logistics network. As price for this premium preference treatment these co-operation partners would have to pay an increased compensation payment share as multiplied with the same factor α to the excluded co-operation partners. 5. Conclusions The following concluding research hypotheses can be obtained from the presented research traits and may be discussed in further detailed research ventures in order to establish a firm understanding of capacity management requirements in global logistics networks. This could benefit all participating companies better than more or less heuristic muddling through concepts do today: (A) Situations with capacity demand surpassing supply in logistics transport (and supposedly also storage) services are distinctively different from assumed situations of standard objectives in capacity management (aiming at the efficient i.e. complete use of existing assets and service capacities). (B) Existing models and methods of logistics and supply chain management respectively capacity management do not provide sufficient management knowledge to rationally handle such situations as can be recognized in the different and more or less hands-on approaches and reactions with e.g. global container carriers or other operators towards those specific capacity problems. (C) The presented Modified SHAPLEY Value may provide a sufficient and innovative approach towards rational solutions in these special narrow capacity situations in logistics networks. (D) Logistics services providers may be inclined to align their reaction tools as e.g. poenal payments with the results of this analysis and calculation concept in order to aim for more rational and pareto efficient (also: multi-stage) capacity management. (E) Though in business practice the needed price transparency i.e. transaction costs for obtaining full data sets in order to achieve these calculations may be prohibitively high. (F) Nevertheless further research on this topic is needed at least in order to establish benchmarking values and conditions for comparable situations as for example simulation 14

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