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Configuration of changeable logistics structures in value chain networks: Identification and analysis of change drivers
6th IFAC Conference on Management and Control of Production and Logistics The International Federation of Automatic Control September 11-13, 2013. Fortaleza, Brazil
Configuration of changeable logistics structures in value chain networks: Identification and analysis of change drivers Mehmet Emin Özsahin*. Susanne Schukraft** BIBA - Bremer Institut für Produktion und Logistik at the University of Bremen, Bremen, Germany (Tel: 0049 421 281 50131; e-mail: *[email protected], **[email protected]) Abstract: Focusing on their core competencies companies are increasingly acting in value chain networks (VCN). Within VCN, logistics structures have a special meaning. They support the value-added processes and provide the order processing. Logistics structures in VCN have complex interdependencies and are embedded in dynamically changing environments. In this context, present up-to-date developments in value chain design tries to make logistics structures more flexible and responsive to influencing factors. To some of these influencing factors, a reaction with predefined flexibility and responsiveness is not possible. Accordingly, these influencing factors, the so called change drivers, imply a change demand. To be prepared for these change drivers, changeable logistics structures have to be configured. However, for the selection of the appropriate alternatives, detailed knowledge of actually relevant change drivers and their effects are necessary. This paper describes a procedure for configuration of changeable VCN. Thereby, the focus here lays on the identification of change drivers and the analysis of their effects on logistics structures in VCN. The procedure will be applied to a case study of an Asian-European apparel VCN. the potential of conducting reactive and proactive modifications outside provided flexibility corridors and is specified in (Wiendahl et al., 2007) and (Westkämper, 2009) very detailed. However, for enabling changeability within logistics structures it is essential to gain knowledge about the actually relevant change drivers and their impact.
1. INTRODUCTION Companies in many lines of business and industrial sectors collaborate within VCN. VCN are composed of two or more partners collaborating under a variety of bilateral relationships to handle products with a certain complexity (cf. Jagdev et al., 2000). Nowadays, wide-broaden hierarchical VCN with a predetermined suppliers’ structure and long-term static product programs evolve into flexible dynamic value chain structures. (cf. Ivanov et al., 2010) Shorter product life cycles, increasing variety in small batches and changing customer requirements are properties of those dynamic value chain structures. (Wildemann, 2007) These dynamic structures include interrelations and interdependencies that can lead to complex mechanisms. These mechanisms are partially or completely unknown and difficult to forecast. (Zahn, 2010) Additionally to network caused complex mechanisms and dynamic requirements, VCN are faced with turbulent environments. If the VCN cannot withstand to those influencing factors on their own substance these factors have to be defined as change drivers.
The focus of the paper on hand lays on the analysis of change drivers’ effects on the logistics structure and their impact on the achievement of logistics objectives. The paper will thereby support (i) a changeability procedure with logistics view, (ii) a generic procedure to identify change drivers and (iii) a simulation based approach to analyse the effects of change drivers on logistics structures with the consideration of their interdependencies. For this purpose the paper is organized as follows. It starts with a state-of-the-art analysis in Section 2. In Section 3, the procedure for configuration of changeable VCN and a systematic identification and analysis of change drivers on logistics structures will be described. In Section 4, a case study will be presented and the procedure will be applied. The Paper closes with a conclusion. 2. STATE-OF-THE-ART
The performance of VCN is normally measured by the final customers based on the target achievement of the order processing. Hence, the ultimate goal of a VCN is to meet the customers’ satisfaction concerning to quality and availability of goods. So, besides financial, legal and other aspects, the order fulfilment processes and regarded logistics structures within VCN are of high importance. In this context, Wiendahl describes that the competences of suppliers and logistics providers are a competitive factor of the VCN. (Wiendahl, 2007) For this reason, logistics structures have to be responsive against influences up to a possible redesign, if the achievement of the logistics objectives is not possible with the existing structure. In this case, one way to line up against those change drivers is the configuration of changeable VCN structures. Thereby, changeability describes 978-3-902823-50-2/2013 © IFAC
There are a lot of approaches and concepts for enterprise network planning and design in literature. In (Thoben et al., 2001) various types, relevant dimensions as well as key features by which industrial collaboration of independent enterprises are characterized and classified can be found. (Gunasekaran et al., 2009) e.g. have presented a summary of research in build-to-order supply chains. A considerable compilation of frameworks for the configuration of collaborative supply chain networks were proposed in (Camarinha-Matos et al., 2005). To handle the mentioned dynamic requirements and turbulent environments, concepts for agile and adaptive supply chains have been increasingly developed in the recent years (Goranson, 1999). Besides conceptual approaches for the configuration of adaptive 53
10.3182/20130911-3-BR-3021.00036
IFAC MCPL 2013 September 11-13, 2013. Fortaleza, Brazil
physical distribution, production and procurement. (Swaminathan et al., 1998) Therefore, a differentiation of the considered logistics system will be accomplished in the mentioned structures. From a focal companies’ point of view, the objective of the structural analysis is to determine, which logistics objects and processes do exist and which interdependencies take effect within the logistics system. To understand the behaviour of the determined logistics structure over time and to prepare for the analytical phase, a simulation model is used, based on the order processing data of the considered logistics objects. For this purpose, the logistics functions of the network partners are collated to logistics objectives and parameterized. Further, the upper and lower limits of the objectives are examined and considered as flexibility corridors. The interdependencies of the objectives are set up by their variable correlation in a logistics system of objectives. The variable correlation describes the relationship between two or more objectives by the simultaneous use of target variables. Thus, rectified and contrary interdependencies can be investigated. The parameterized system of objectives represents the logistics order processing and consists of subordinate objectives, such as lead times and delivery dates, that are aggregated in the superior objectives logistics performance and logistics costs. Figure 2 shows examples of interdependencies and the aggregation of subordinate objectives to superior objective, which can represent the logistics goal of fulfillment of the whole VCN.
supply chains various numbers of mathematical investigations exists. These investigations can be divided into optimization, heuristics and simulation. Optimization is an analysing method that determines the best possible design of a particular supply chain. In the case, that optimization cannot be used due to the quantity of variables and their combinations, another approach is heuristics. Heuristics are intelligent rules that often lead to sufficient, but not necessarily best solutions. Simulation as an alternative mathematical approach is the imitation of the behaviour of one system with another. (Ivanov et al., 2010) Regarding the adaptive supply chain, complex adaptive systems and multi agent systems are the most used simulation techniques. (Swaminathan et al., 1998) Thereby, supply chain investigation is the optimization of logistics processes for the management of goods and material flows. Supply chain processes affecting activities mainly outside the enterprises in a network. Supply chain activities may, but need not be value-adding. In general, it’s more about cost-minimizing logistics solutions. Value chain in contrast refers, additionally to the goods and material flow, internal value adding in the provision of service performance. This service performance may be the production of goods or the service itself. Compared to the supply chain view, the value-added view on a network is more complex, caused by the consideration of its value adding partners and processes. So, by the investigation of changeability in VCN, firstly the VCN elements, their comprehensive interlinking and their structural dynamics have to be considered. In addition, the effects of change drivers have to be known to initiate targeted changes of the logistics structure. Thus, investigation in VCN is investigation in a high dynamically system. Besides multi agent systems another approach, which is often applied to network dynamics investigation is systems dynamics (Sterman, 2000). The systems dynamics approach will be used for the procedure in this paper. 3. THE PROCEDURE
Fig. 2. Logistics system of objectives and their interactions (c.f. Lödding, 2005)
In this Section we describe a procedure for the configuration of changeable logistics structures in VCN. For the identification of change drivers and their future characteristics the systems dynamics approach is implied into the procedure. Fig. 1 shows the configuration of changeable logistics structures in VCN based on a five-phase approach.
Following up is the environmental analysis. The overall goal of this phase is the identification of change drivers by the detection of possible influencing factors and their impact on the logistics objectives. For the detection of possible influencing factors workshops are proposed within the logistics organizational units of an enterprise. The identified influencing factors can then be evaluated in change drivers’ modes and effects analysis (CMEA). The CMEA is a tool for the qualitative and subjective assessment of influencing factors based on a failure mode and effects analysis. The influencing factors can be evaluated in terms of their occurrence probability, their expected impact significance and the discovery space. These assessment criteria can be consolidated to a, so-called, CMEA indicator that serves as a rate for the criticality of the considered influencing factors. Thus, the CMEA enables a first, qualitative assessment of the identified influencing factors and the exclusion of apparently uncritical factors. The CMEA is respectively described in (Scholz-Reiter et al., 2012). Only critical estimated influencing factors will then be transferred into the
Fig. 1. Procedure for the configuration and validation of changeable logistics structures in VCN The first phase of the procedure is the structural analysis. Conventionally, VCN are described by the structure of 54
IFAC MCPL 2013 September 11-13, 2013. Fortaleza, Brazil
are finished they will be transported in standard containers to the distribution centres in Germany. The distance between the production plants and the distribution centres is normally bridged by ship, in urgent cases air transport may be used, too. Transports are executed completely by external service providers and are by default routed through logistics hubs, where transport routes and means can be switched. Procurement lead times for raw materials are roughly 40 days. The production throughput time is roughly 10 days. Transport times by container vessel are 30 to 40 days, depending on the route. Air transport times are only two or three days, but increases transport costs of the same volume and over the same distance by a factor of three to ten compared to transport by container vessel. After arrival at the distribution centre, incoming containers are unloaded and packages are put on palettes, stored and dispatched to the customers. The described VCN is similar to the generic apparel reference supply chain described by (Bruckner et al., 2003). The described VCN has been investigated concerning its logistics structure. By an analysis of the order processing data, the logistics objectives were identified and are mainly the following:
simulation model of the VCN with its logistics objectives. A forecast of the potential change drivers’ characteristics and the appropriation of the change drivers to logistics target variables can bring out the possible quantitative impact of the change drivers on the logistics structure in the simulation model. This step will be specified by a concrete example in Section 4 (case study). Since this paper focuses the identification and analysis of change drivers the subsequent steps of the procedure is subject of future researches. Nevertheless, they are briefly described as follows. In the attainment analysis, possible alternative logistics structures will identified and analyzed regarding their adequacy to handle the impact of occurring change drivers. The result out of this analysis is a number of possible alternative logistics structures. Thereby, the solution alternatives can be: changes of strategies for order processing, alternative geographical variations of the structural elements but also functional enrichments, substitutions and eliminations of the logistics processes. The next phase is the value analysis. Based on the identified structural alternatives, the related change processes have to be analyzed. Thereby, the change process contains all working steps to get from the current state of the logistics structure to a solution alternative and thus, describes the change effort. The evaluation results in a recommendation, which alternative logistics structure suits best in the case of occurring change drivers. Finally, the enabling analysis involves the identification of change enablers. Change enablers enable the logistics structure to get quickly and cost efficient from an actual situation into a determined solution alternative. In this phase, existing logistics structure elements in the VCN should be awarded with the property to be prepared to the identified change drivers.
The objective total costs include the costs of all specific logistics processes like production, transportation, personal and material costs in the VCN. The objective stock includes all stored materials and products within the process of procurement, production and delivery and indicates the companies’ flexibility to dis-turbances. It is also a rate for the goal conflict between security of supply and inventory costs. The objective delivery reliability marks the processing of detailed orders and evaluates their adherence to agreed delivery dates. The throughput time (flow time, lead time) is the needed time of an entity for passing the VCN. The calculation of the processing time depends on the respective VCN and entity type. For the development of the simulation model actual processing order data and the VCN specific settings were used, which are mentioned in the case study description. This includes the quantitative distribution of the order processing to the different production plants and of the transportation alternatives. It is adopted a medium term period of 60 months for the simulation, which concurs with the forecast of the companies’ controlling department. For the system dynamics simulation the software tool Vensim PLE of VENTANA systems was elected. Figure 3 shows the case studies’ system dynamics model. To keep the model clear the shadow variables were banned. Despite the restrictions on the logistics structure and the areas procurement, production and distribution it can be seen that the model is already complex. In the following, the environmental analysis of the procedure will be executed based on the introduced simulation model.
4. CASE STUDY The case study in this paper is a supplier company in the apparel industry. The suppliers’ work domain is the production and distribution of casual and leisure wear. The VCN includes raw material suppliers and a garment production plant situated in southern China. Additionally, there are garment production plants in Bangladesh and Tunisia, a procurement agency situated in Hong Kong and three distribution centres situated in Europe (Germany). The customers are settled predominantly in Germany and Western Europe. A low quota is delivered, with rising trend, to customers in Eastern Europe and Asia. Besides bulk buyers like department stores and mail-order companies, numerous retail sellers and specialist suppliers are also amongst the customers. Further network partners are service providers for the transportation of the products. Besides the standard business model “never out of stock”, a conventional from stock delivery with the commitment to be able to deliver at any time, the case study offers a seasonal delivery business model. Here, the customers can chose twice a year (March and July) different article types, which are designed for actual apparel trends. The process focus lays on the logistics distribution processes and the order processing. After the incoming customers’ orders the demands are transmitted to the procurement agency, which organizes the procurement of raw materials to the production plant. After the apparel goods
4.1 Identification and forecasting of change drivers Several influencing factors have been determined in workshops with employees of different departments. For this purpose, together with experts in the areas of procurement, production planning and distribution, existing weaknesses were identified and analyzed. In addition, interviews were conducted with the senior management to identify additional, industry-specific factors. The result of this analysis is a list of influencing factors, evaluated by the mean of the CMEA. 55
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PC wage cost PC Tunisia stock production PC-costs internal Bangladesh Tunisia losses of new customer total picking costs wage cost PC-costs production logistic BD customer total total wage cost production costs apiece Ospinter China clearance CN PC-costs pasteboard articles, Bangladesh total PC China logistic costs cost apiece transport costs clearance TN shipping material carriage PC-costs manufacture internal transport apiece manufacture internal costs shipping outwards clearance BD internal transport to transport cots personal costs warehouse costs costs picking container BGD process capacity of courtesy PC-costs internal PC-costs internal production & faults production logistic production processes production logistic TN production logistic CN costs fit costs material procurement internal transport to stock China production logistic additional defect PC-costs Container TUN external costs new customer service picking faults Reset purchase RW external stock processes stock CHN number of apiece Bangladesh new customer material defect costs picking components apiece losses of costs for fit BGD customer CHN faults courtesy costs container by internal transport to Finishing stock costs distance truck per km Container CHN apiece CHN-CHN production avearge transport further faults costs container by warehouse costs production losses of China time to GER delay of costs further ship per km procurement costs delay transport costs logistic costs Bangladesh customer BGD warehouse cost stock Tunisia delivery faults delivery procurement procurement distribution move to average waiting time in components per new customer production China Delivery of losses of TUN distance distribution stock until container move to production components seaport customer TUN delivery to customer distance BGD-CHN average order Bangladesh supplier transport to TUN-CHN quantity deliveries move to GER not in due time demand Distribution production Tunisia not in due time deliveries production Tunisia processes delivery to customer schedule deviation procurement production delay in delivery process capacity of procurement delay in delivery processes per CD 1: losses of annual demand normal distribution internal distribution logistic throughput time component of supplier processes customer transport time production distribution in due time throughput time switch variable process capacity of throughput time Reset delivery deliveries procurement CD 1 price procurement logistic distribution switch value switch throughput time delivery CD 2: increasing processes delay in total profitability reliability salary in Bangladesh demand total process costs procurement delivery turnover total CD 2: increasing process costs pieces per total costs delay in delivery salary in Tunesien distribution delivery container total CD 3: increasing reliability total in due time delivery CD 4: political &legal CD 2: increasing process costs transport costs to transports and logistics to customer stability delivery reliability salary in China Reset demand procurement & costs GER level of quantity total distance production distance switch variable service switch variable switch variable CHN-GER distance TUN-GER CD 4 CD 2 CD 3 BGD-GER
Fig. 3. System dynamics model of the case study The occurrence probability in the CMEA includes the comparison of the influencing factors by analyzing their conditional probability in a cross impact analysis, too. The investigation results in four critical influencing factors, the potential change drivers. In the following, these potential change drivers and their expected characteristics for the next 60 months will be illustrated. The expected characteristics will be implied into the system dynamics model. The first influencing factor which is evaluated as being critical in the CMEA is the possibility of increasing customer losses in the retail sales due to rising internet sales. Fig. 4 shows the expected order losses concerning to losses of retail customers for the case study. Thereby, the customer losses are Gaussian distributed during the lead time. Here, the direct impact in the simulation model is modelled on the variables stocks of the production plants and the distribution centres.
Fig. 6. Expected salary increase in China [in €/month] (own forecast depending on Jahnke, 2013)
Fig. 4. Expected customer losses in the retail sale [in percent] (forecast of the case study, 2013) The next influencing factor is increasing salaries due to rising prosperity in developing countries. Each production country of the case study has other expectations but all are increasing inversely proportional, as seen in Fig. 5, 6 and 7. The direct impact in the simulation model is modelled on the variables processing costs of procurement and production.
Fig. 7. Expected salary increase in Bangladesh [in €/month] (own forecast depending on Fau, 2013) Furthermore increasing transport and retail costs because of rising crude oil prices is evaluated in the CMEA as critical. For the forecast of the crude oil prices we have used the crude oil prices of the past 60 months and have operated a time series analysis for the next 60 months (Fig. 8). Concerning to the financial crisis and the resulting decrease of crude oil prices in 2008 the forecast has an outlier between the month 24 and 30. The direct impact of this potential change driver is modelled on the variable logistics costs of transportation in the whole VCN.
Fig. 5. Expected salary increase in Tunisia [in €/month] (own forecast depending on Tunispro, 2012) 56
IFAC MCPL 2013 September 11-13, 2013. Fortaleza, Brazil
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Fig. 10. Stock of total production with and without change drivers effects [pieces/time]
Finally the qualitative influencing factor political & legal stability problems are carried out as a potential change driver. The workshop participants expect that these problems in developing countries can cause instabilities in production countries. On investigation of political & legal stability indicators e.g. of the Worldbank we have recognized that this problem is only critical for the location in Tunisia. Figure 9 shows the forecast for political & legal situation in Tunisia. The impact is modelled on the variable production rate in Tunisia, whereas the expectation for the future is operated by past consultations.
The total costs (fig. 11) consist of single costs of the different areas, like logistics manufacturing costs, total production costs, internal transport costs etc. Wage costs are subject to heavy fluctuations and increase during simulation. Increases in total costs depend as well as from increases in wage costs, which are differently for all production plants and the development of the crude oil price. total costs 6M
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Fig. 11 Total costs of the logistics processes in the case study with and without change driver effects [€/time]
Fig. 9. Expected political & legal situation in Tunisia [in percent to the actual indicator] (own forecast depending on Worldbank, 2013)
Because of rising production stocks the throughput time of the order processing is rising, too. This effect can be seen in Fig. 12.
4.2 Effects of identified change drivers on logistics target values of the VCN
throughput time total
The consistent set of potential change drivers which are entered to the target variables as described above have common effects on the logistics objectives. So depending to the change drivers and their expected characteristics in the middle term future the following scenario for the case study is emerging.
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The production stocks raise high according to the change drivers (Fig. 10). The main reason is the lead time for production and distribution. So the workshop participants expect that one third of customer losses are happening during the production of goods. Generally, finished goods will transported instantly with containers to the seaport, that’s why stock costs in the simulation without change drivers are neglected.
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Fig. 12. Flow time of the order processing with and without change driver effects [month/time] The delivery reliability is measured twice a year, once for the first demand in March and once for the second demand in 57
IFAC MCPL 2013 September 11-13, 2013. Fortaleza, Brazil
July (seasonal delivery). As seen in Fig. 13 the change drivers have no effect on the delivery reliability.
Journal of Operational Research, 195, 2 (1), 319334ECB, Hernández, R. (2002) Systematik der Wandlungsfähigkeit in der Fabrikplanung, Dissertation Leibniz University Hannover, VDI Verlag GmbH, Düsseldorf, Ivanov, D., Sokolov, B., Kaeschel. J. (2010) A multistructural framework for adaptive supply chain planning and operations control with structure dynamics considerations. European Journal of Operational Research, 200 (2010), 409-420 Jagdev, H. S., Thoben, K.-D. (2001) Anatomy of enterprise collaborations. Production Planning & Control, 12(5), 437-451 Jahnke (2012) http://www.jjahnke.net/rundbr19.html, accessed on 10.12.2012 Knof, Heide Lore (1192) Identifizierung organisatorischen Flexibilitätspotenzials. In: WiSt – Wirtschaftswissenschaftliches Studium, 21 (1192) 3, S. 143-147 Lödding, H. (2005) Verfahren der Fertigungssteuerung – Grundlagen, Beschreibung, Konfiguration, SpringerVerlag, Berlin Scholz-Reiter, B., Schukraft, S., Özsahin, M. E., Lo, L. (2012) Identifikation von potenziellen Wandlungstreibern – Potenzielle Wandlungstrieber am Beispiel eines Unternehmens der Bekleidungsindustrie. In: Industrie Managment, 28(2012)6, S. 19-22 Scholz-Reiter, B., Özsahin, M. E., Schukraft, S. (2012) Procedure for analyzing effects of changeability on logistics structures in value chain networks. In: POMS 2012 PROCEEDINGS, Chicago, Illinois Swaminathan, J.M., Smith, S.F., Sadeh, N.M. (1998) Modelling supply chain dynamics: a multi agent approach. Decision Science, 29 (3), 607-632 Sterman, J.D. (2000) Business dynamics: systems thinking and modeling for complex world. McGraw-Hill Thoben, K.-D., Jagdev, H. S. (2001) Typological issues in enterprise networks. Production Planning & Control: The Management of Operations. Vol. 12, No. 5, 421-436 Tunispro, (2012) http://www.tunispro.de/tunesien/ sozialsystem-in-tunesien.html, accessed on 10.12.2012 Westkämper, E. (2009) Wandlungsfähige Produktionsunternehmen. Das Stuttgarter Unternehmensmodell, (Zahn, E., Westkämper, E.) (Ed.) Springer Verlag, Berlin/Heidelberg Wiendahl, H.-P. (2007) Betriebsorganisation für Ingenieure, Volume 6, Hanser Fachbuchverlag, München. Wiendahl, H.-P., ElMaraghy, H.A., Nyhuis, P., Zäh, M.F., Wiendahl, H.-H., Duffie, N. and Brieke, M. (2007) Changeable Manufacturing – Classification, Design and Operation. In: Annals of the CIRP, Vol. 56/2/2007, 783809 Wildemann, H. (2007) Fertigungssegmentierung. Leitfaden zur fluss- und logistikgerechten Fabrikgestaltung. Vol. 11, TCW, München Zäh, M. F.; Moeller, N.; Vogl, W. (2005) Symbiosis of Changeable and Virtual Production, In: 1st International Conference on Changeable, Agile, Reconfigurable and Virtual Production. (Zäh, M. F.) (Ed.) CARV 2005, Utz, München
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Fig. 13. Delivery reliability with and without change driver effects [per cent/month] 5. CONCLUSIONS VCN are confronted with dynamically changing influences. Some of these influences (change drivers) force the value chain network to reconfigurations beyond existing flexibility corridors. For this reason, our research aims at providing methods and procedures for the configuration and optimization of changeable VCN. The paper on hand describes a procedure to analyse the effects of change drivers on logistics structures of VCN. In doing so a system dynamics model for the quantitative analysis of possible change drivers’ effects is illustrated. For the investigation we have used logistics target variables, which we have derived from real order processing data of a case study. The determination of the effects of specific change drivers shows that the procedure can illustrate the effects of influences. To respond to these effects possible logistics structure alternatives have to be deducted and evaluated according to their target variable characteristics. Following up is the evaluation of specific change enablers that enable the logistics structure to react to change drivers and examine the effects of it on their VCN. ACKNOWLEDGEMENT The research described in this paper is funded by the German Federal Ministry of Education and Research (BMBF) as part of the project POWer.net – Planning and Optimization of Changeable Global Value Chain Networks. REFERENCES Bruckner, A., Müller, S. (2003) Supply Chain Management in der Bekleidungsindustrie. Forschungsstelle der Bekleidungsindustrie, Cologne, Germany Camarinha-Matos, L. M., Afsarmanesh, H., Ortiz, A. (2005) Collaborative Networks and Their Breeding Environments. (Ortiz, A.) (Ed.), Springer, Boston European Central Bank, Statistical Data Warehouse, http://sdw.ecb.europa.eu/browseTable.do?node=2120782 &start=&end=&trans=N&dvfreq=M, accessed on 12.02.2013 Gunasekaran, A., Ngai, N.W.T., (2009) Modeling and analysis of built-to-order supply chains. European 58
Configuration of changeable logistics structures in value chain networks: Identification and analysis of change drivers Mehmet Emin Özsahin*. Susanne Schukraft** BIBA - Bremer Institut für Produktion und Logistik at the University of Bremen, Bremen, Germany (Tel: 0049 421 281 50131; e-mail: *[email protected], **[email protected]) Abstract: Focusing on their core competencies companies are increasingly acting in value chain networks (VCN). Within VCN, logistics structures have a special meaning. They support the value-added processes and provide the order processing. Logistics structures in VCN have complex interdependencies and are embedded in dynamically changing environments. In this context, present up-to-date developments in value chain design tries to make logistics structures more flexible and responsive to influencing factors. To some of these influencing factors, a reaction with predefined flexibility and responsiveness is not possible. Accordingly, these influencing factors, the so called change drivers, imply a change demand. To be prepared for these change drivers, changeable logistics structures have to be configured. However, for the selection of the appropriate alternatives, detailed knowledge of actually relevant change drivers and their effects are necessary. This paper describes a procedure for configuration of changeable VCN. Thereby, the focus here lays on the identification of change drivers and the analysis of their effects on logistics structures in VCN. The procedure will be applied to a case study of an Asian-European apparel VCN. the potential of conducting reactive and proactive modifications outside provided flexibility corridors and is specified in (Wiendahl et al., 2007) and (Westkämper, 2009) very detailed. However, for enabling changeability within logistics structures it is essential to gain knowledge about the actually relevant change drivers and their impact.
1. INTRODUCTION Companies in many lines of business and industrial sectors collaborate within VCN. VCN are composed of two or more partners collaborating under a variety of bilateral relationships to handle products with a certain complexity (cf. Jagdev et al., 2000). Nowadays, wide-broaden hierarchical VCN with a predetermined suppliers’ structure and long-term static product programs evolve into flexible dynamic value chain structures. (cf. Ivanov et al., 2010) Shorter product life cycles, increasing variety in small batches and changing customer requirements are properties of those dynamic value chain structures. (Wildemann, 2007) These dynamic structures include interrelations and interdependencies that can lead to complex mechanisms. These mechanisms are partially or completely unknown and difficult to forecast. (Zahn, 2010) Additionally to network caused complex mechanisms and dynamic requirements, VCN are faced with turbulent environments. If the VCN cannot withstand to those influencing factors on their own substance these factors have to be defined as change drivers.
The focus of the paper on hand lays on the analysis of change drivers’ effects on the logistics structure and their impact on the achievement of logistics objectives. The paper will thereby support (i) a changeability procedure with logistics view, (ii) a generic procedure to identify change drivers and (iii) a simulation based approach to analyse the effects of change drivers on logistics structures with the consideration of their interdependencies. For this purpose the paper is organized as follows. It starts with a state-of-the-art analysis in Section 2. In Section 3, the procedure for configuration of changeable VCN and a systematic identification and analysis of change drivers on logistics structures will be described. In Section 4, a case study will be presented and the procedure will be applied. The Paper closes with a conclusion. 2. STATE-OF-THE-ART
The performance of VCN is normally measured by the final customers based on the target achievement of the order processing. Hence, the ultimate goal of a VCN is to meet the customers’ satisfaction concerning to quality and availability of goods. So, besides financial, legal and other aspects, the order fulfilment processes and regarded logistics structures within VCN are of high importance. In this context, Wiendahl describes that the competences of suppliers and logistics providers are a competitive factor of the VCN. (Wiendahl, 2007) For this reason, logistics structures have to be responsive against influences up to a possible redesign, if the achievement of the logistics objectives is not possible with the existing structure. In this case, one way to line up against those change drivers is the configuration of changeable VCN structures. Thereby, changeability describes 978-3-902823-50-2/2013 © IFAC
There are a lot of approaches and concepts for enterprise network planning and design in literature. In (Thoben et al., 2001) various types, relevant dimensions as well as key features by which industrial collaboration of independent enterprises are characterized and classified can be found. (Gunasekaran et al., 2009) e.g. have presented a summary of research in build-to-order supply chains. A considerable compilation of frameworks for the configuration of collaborative supply chain networks were proposed in (Camarinha-Matos et al., 2005). To handle the mentioned dynamic requirements and turbulent environments, concepts for agile and adaptive supply chains have been increasingly developed in the recent years (Goranson, 1999). Besides conceptual approaches for the configuration of adaptive 53
10.3182/20130911-3-BR-3021.00036
IFAC MCPL 2013 September 11-13, 2013. Fortaleza, Brazil
physical distribution, production and procurement. (Swaminathan et al., 1998) Therefore, a differentiation of the considered logistics system will be accomplished in the mentioned structures. From a focal companies’ point of view, the objective of the structural analysis is to determine, which logistics objects and processes do exist and which interdependencies take effect within the logistics system. To understand the behaviour of the determined logistics structure over time and to prepare for the analytical phase, a simulation model is used, based on the order processing data of the considered logistics objects. For this purpose, the logistics functions of the network partners are collated to logistics objectives and parameterized. Further, the upper and lower limits of the objectives are examined and considered as flexibility corridors. The interdependencies of the objectives are set up by their variable correlation in a logistics system of objectives. The variable correlation describes the relationship between two or more objectives by the simultaneous use of target variables. Thus, rectified and contrary interdependencies can be investigated. The parameterized system of objectives represents the logistics order processing and consists of subordinate objectives, such as lead times and delivery dates, that are aggregated in the superior objectives logistics performance and logistics costs. Figure 2 shows examples of interdependencies and the aggregation of subordinate objectives to superior objective, which can represent the logistics goal of fulfillment of the whole VCN.
supply chains various numbers of mathematical investigations exists. These investigations can be divided into optimization, heuristics and simulation. Optimization is an analysing method that determines the best possible design of a particular supply chain. In the case, that optimization cannot be used due to the quantity of variables and their combinations, another approach is heuristics. Heuristics are intelligent rules that often lead to sufficient, but not necessarily best solutions. Simulation as an alternative mathematical approach is the imitation of the behaviour of one system with another. (Ivanov et al., 2010) Regarding the adaptive supply chain, complex adaptive systems and multi agent systems are the most used simulation techniques. (Swaminathan et al., 1998) Thereby, supply chain investigation is the optimization of logistics processes for the management of goods and material flows. Supply chain processes affecting activities mainly outside the enterprises in a network. Supply chain activities may, but need not be value-adding. In general, it’s more about cost-minimizing logistics solutions. Value chain in contrast refers, additionally to the goods and material flow, internal value adding in the provision of service performance. This service performance may be the production of goods or the service itself. Compared to the supply chain view, the value-added view on a network is more complex, caused by the consideration of its value adding partners and processes. So, by the investigation of changeability in VCN, firstly the VCN elements, their comprehensive interlinking and their structural dynamics have to be considered. In addition, the effects of change drivers have to be known to initiate targeted changes of the logistics structure. Thus, investigation in VCN is investigation in a high dynamically system. Besides multi agent systems another approach, which is often applied to network dynamics investigation is systems dynamics (Sterman, 2000). The systems dynamics approach will be used for the procedure in this paper. 3. THE PROCEDURE
Fig. 2. Logistics system of objectives and their interactions (c.f. Lödding, 2005)
In this Section we describe a procedure for the configuration of changeable logistics structures in VCN. For the identification of change drivers and their future characteristics the systems dynamics approach is implied into the procedure. Fig. 1 shows the configuration of changeable logistics structures in VCN based on a five-phase approach.
Following up is the environmental analysis. The overall goal of this phase is the identification of change drivers by the detection of possible influencing factors and their impact on the logistics objectives. For the detection of possible influencing factors workshops are proposed within the logistics organizational units of an enterprise. The identified influencing factors can then be evaluated in change drivers’ modes and effects analysis (CMEA). The CMEA is a tool for the qualitative and subjective assessment of influencing factors based on a failure mode and effects analysis. The influencing factors can be evaluated in terms of their occurrence probability, their expected impact significance and the discovery space. These assessment criteria can be consolidated to a, so-called, CMEA indicator that serves as a rate for the criticality of the considered influencing factors. Thus, the CMEA enables a first, qualitative assessment of the identified influencing factors and the exclusion of apparently uncritical factors. The CMEA is respectively described in (Scholz-Reiter et al., 2012). Only critical estimated influencing factors will then be transferred into the
Fig. 1. Procedure for the configuration and validation of changeable logistics structures in VCN The first phase of the procedure is the structural analysis. Conventionally, VCN are described by the structure of 54
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are finished they will be transported in standard containers to the distribution centres in Germany. The distance between the production plants and the distribution centres is normally bridged by ship, in urgent cases air transport may be used, too. Transports are executed completely by external service providers and are by default routed through logistics hubs, where transport routes and means can be switched. Procurement lead times for raw materials are roughly 40 days. The production throughput time is roughly 10 days. Transport times by container vessel are 30 to 40 days, depending on the route. Air transport times are only two or three days, but increases transport costs of the same volume and over the same distance by a factor of three to ten compared to transport by container vessel. After arrival at the distribution centre, incoming containers are unloaded and packages are put on palettes, stored and dispatched to the customers. The described VCN is similar to the generic apparel reference supply chain described by (Bruckner et al., 2003). The described VCN has been investigated concerning its logistics structure. By an analysis of the order processing data, the logistics objectives were identified and are mainly the following:
simulation model of the VCN with its logistics objectives. A forecast of the potential change drivers’ characteristics and the appropriation of the change drivers to logistics target variables can bring out the possible quantitative impact of the change drivers on the logistics structure in the simulation model. This step will be specified by a concrete example in Section 4 (case study). Since this paper focuses the identification and analysis of change drivers the subsequent steps of the procedure is subject of future researches. Nevertheless, they are briefly described as follows. In the attainment analysis, possible alternative logistics structures will identified and analyzed regarding their adequacy to handle the impact of occurring change drivers. The result out of this analysis is a number of possible alternative logistics structures. Thereby, the solution alternatives can be: changes of strategies for order processing, alternative geographical variations of the structural elements but also functional enrichments, substitutions and eliminations of the logistics processes. The next phase is the value analysis. Based on the identified structural alternatives, the related change processes have to be analyzed. Thereby, the change process contains all working steps to get from the current state of the logistics structure to a solution alternative and thus, describes the change effort. The evaluation results in a recommendation, which alternative logistics structure suits best in the case of occurring change drivers. Finally, the enabling analysis involves the identification of change enablers. Change enablers enable the logistics structure to get quickly and cost efficient from an actual situation into a determined solution alternative. In this phase, existing logistics structure elements in the VCN should be awarded with the property to be prepared to the identified change drivers.
The objective total costs include the costs of all specific logistics processes like production, transportation, personal and material costs in the VCN. The objective stock includes all stored materials and products within the process of procurement, production and delivery and indicates the companies’ flexibility to dis-turbances. It is also a rate for the goal conflict between security of supply and inventory costs. The objective delivery reliability marks the processing of detailed orders and evaluates their adherence to agreed delivery dates. The throughput time (flow time, lead time) is the needed time of an entity for passing the VCN. The calculation of the processing time depends on the respective VCN and entity type. For the development of the simulation model actual processing order data and the VCN specific settings were used, which are mentioned in the case study description. This includes the quantitative distribution of the order processing to the different production plants and of the transportation alternatives. It is adopted a medium term period of 60 months for the simulation, which concurs with the forecast of the companies’ controlling department. For the system dynamics simulation the software tool Vensim PLE of VENTANA systems was elected. Figure 3 shows the case studies’ system dynamics model. To keep the model clear the shadow variables were banned. Despite the restrictions on the logistics structure and the areas procurement, production and distribution it can be seen that the model is already complex. In the following, the environmental analysis of the procedure will be executed based on the introduced simulation model.
4. CASE STUDY The case study in this paper is a supplier company in the apparel industry. The suppliers’ work domain is the production and distribution of casual and leisure wear. The VCN includes raw material suppliers and a garment production plant situated in southern China. Additionally, there are garment production plants in Bangladesh and Tunisia, a procurement agency situated in Hong Kong and three distribution centres situated in Europe (Germany). The customers are settled predominantly in Germany and Western Europe. A low quota is delivered, with rising trend, to customers in Eastern Europe and Asia. Besides bulk buyers like department stores and mail-order companies, numerous retail sellers and specialist suppliers are also amongst the customers. Further network partners are service providers for the transportation of the products. Besides the standard business model “never out of stock”, a conventional from stock delivery with the commitment to be able to deliver at any time, the case study offers a seasonal delivery business model. Here, the customers can chose twice a year (March and July) different article types, which are designed for actual apparel trends. The process focus lays on the logistics distribution processes and the order processing. After the incoming customers’ orders the demands are transmitted to the procurement agency, which organizes the procurement of raw materials to the production plant. After the apparel goods
4.1 Identification and forecasting of change drivers Several influencing factors have been determined in workshops with employees of different departments. For this purpose, together with experts in the areas of procurement, production planning and distribution, existing weaknesses were identified and analyzed. In addition, interviews were conducted with the senior management to identify additional, industry-specific factors. The result of this analysis is a list of influencing factors, evaluated by the mean of the CMEA. 55
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PC wage cost PC Tunisia stock production PC-costs internal Bangladesh Tunisia losses of new customer total picking costs wage cost PC-costs production logistic BD customer total total wage cost production costs apiece Ospinter China clearance CN PC-costs pasteboard articles, Bangladesh total PC China logistic costs cost apiece transport costs clearance TN shipping material carriage PC-costs manufacture internal transport apiece manufacture internal costs shipping outwards clearance BD internal transport to transport cots personal costs warehouse costs costs picking container BGD process capacity of courtesy PC-costs internal PC-costs internal production & faults production logistic production processes production logistic TN production logistic CN costs fit costs material procurement internal transport to stock China production logistic additional defect PC-costs Container TUN external costs new customer service picking faults Reset purchase RW external stock processes stock CHN number of apiece Bangladesh new customer material defect costs picking components apiece losses of costs for fit BGD customer CHN faults courtesy costs container by internal transport to Finishing stock costs distance truck per km Container CHN apiece CHN-CHN production avearge transport further faults costs container by warehouse costs production losses of China time to GER delay of costs further ship per km procurement costs delay transport costs logistic costs Bangladesh customer BGD warehouse cost stock Tunisia delivery faults delivery procurement procurement distribution move to average waiting time in components per new customer production China Delivery of losses of TUN distance distribution stock until container move to production components seaport customer TUN delivery to customer distance BGD-CHN average order Bangladesh supplier transport to TUN-CHN quantity deliveries move to GER not in due time demand Distribution production Tunisia not in due time deliveries production Tunisia processes delivery to customer schedule deviation procurement production delay in delivery process capacity of procurement delay in delivery processes per CD 1: losses of annual demand normal distribution internal distribution logistic throughput time component of supplier processes customer transport time production distribution in due time throughput time switch variable process capacity of throughput time Reset delivery deliveries procurement CD 1 price procurement logistic distribution switch value switch throughput time delivery CD 2: increasing processes delay in total profitability reliability salary in Bangladesh demand total process costs procurement delivery turnover total CD 2: increasing process costs pieces per total costs delay in delivery salary in Tunesien distribution delivery container total CD 3: increasing reliability total in due time delivery CD 4: political &legal CD 2: increasing process costs transport costs to transports and logistics to customer stability delivery reliability salary in China Reset demand procurement & costs GER level of quantity total distance production distance switch variable service switch variable switch variable CHN-GER distance TUN-GER CD 4 CD 2 CD 3 BGD-GER
Fig. 3. System dynamics model of the case study The occurrence probability in the CMEA includes the comparison of the influencing factors by analyzing their conditional probability in a cross impact analysis, too. The investigation results in four critical influencing factors, the potential change drivers. In the following, these potential change drivers and their expected characteristics for the next 60 months will be illustrated. The expected characteristics will be implied into the system dynamics model. The first influencing factor which is evaluated as being critical in the CMEA is the possibility of increasing customer losses in the retail sales due to rising internet sales. Fig. 4 shows the expected order losses concerning to losses of retail customers for the case study. Thereby, the customer losses are Gaussian distributed during the lead time. Here, the direct impact in the simulation model is modelled on the variables stocks of the production plants and the distribution centres.
Fig. 6. Expected salary increase in China [in €/month] (own forecast depending on Jahnke, 2013)
Fig. 4. Expected customer losses in the retail sale [in percent] (forecast of the case study, 2013) The next influencing factor is increasing salaries due to rising prosperity in developing countries. Each production country of the case study has other expectations but all are increasing inversely proportional, as seen in Fig. 5, 6 and 7. The direct impact in the simulation model is modelled on the variables processing costs of procurement and production.
Fig. 7. Expected salary increase in Bangladesh [in €/month] (own forecast depending on Fau, 2013) Furthermore increasing transport and retail costs because of rising crude oil prices is evaluated in the CMEA as critical. For the forecast of the crude oil prices we have used the crude oil prices of the past 60 months and have operated a time series analysis for the next 60 months (Fig. 8). Concerning to the financial crisis and the resulting decrease of crude oil prices in 2008 the forecast has an outlier between the month 24 and 30. The direct impact of this potential change driver is modelled on the variable logistics costs of transportation in the whole VCN.
Fig. 5. Expected salary increase in Tunisia [in €/month] (own forecast depending on Tunispro, 2012) 56
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Fig. 10. Stock of total production with and without change drivers effects [pieces/time]
Finally the qualitative influencing factor political & legal stability problems are carried out as a potential change driver. The workshop participants expect that these problems in developing countries can cause instabilities in production countries. On investigation of political & legal stability indicators e.g. of the Worldbank we have recognized that this problem is only critical for the location in Tunisia. Figure 9 shows the forecast for political & legal situation in Tunisia. The impact is modelled on the variable production rate in Tunisia, whereas the expectation for the future is operated by past consultations.
The total costs (fig. 11) consist of single costs of the different areas, like logistics manufacturing costs, total production costs, internal transport costs etc. Wage costs are subject to heavy fluctuations and increase during simulation. Increases in total costs depend as well as from increases in wage costs, which are differently for all production plants and the development of the crude oil price. total costs 6M
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Fig. 9. Expected political & legal situation in Tunisia [in percent to the actual indicator] (own forecast depending on Worldbank, 2013)
Because of rising production stocks the throughput time of the order processing is rising, too. This effect can be seen in Fig. 12.
4.2 Effects of identified change drivers on logistics target values of the VCN
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The consistent set of potential change drivers which are entered to the target variables as described above have common effects on the logistics objectives. So depending to the change drivers and their expected characteristics in the middle term future the following scenario for the case study is emerging.
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The production stocks raise high according to the change drivers (Fig. 10). The main reason is the lead time for production and distribution. So the workshop participants expect that one third of customer losses are happening during the production of goods. Generally, finished goods will transported instantly with containers to the seaport, that’s why stock costs in the simulation without change drivers are neglected.
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Fig. 12. Flow time of the order processing with and without change driver effects [month/time] The delivery reliability is measured twice a year, once for the first demand in March and once for the second demand in 57
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July (seasonal delivery). As seen in Fig. 13 the change drivers have no effect on the delivery reliability.
Journal of Operational Research, 195, 2 (1), 319334ECB, Hernández, R. (2002) Systematik der Wandlungsfähigkeit in der Fabrikplanung, Dissertation Leibniz University Hannover, VDI Verlag GmbH, Düsseldorf, Ivanov, D., Sokolov, B., Kaeschel. J. (2010) A multistructural framework for adaptive supply chain planning and operations control with structure dynamics considerations. European Journal of Operational Research, 200 (2010), 409-420 Jagdev, H. S., Thoben, K.-D. (2001) Anatomy of enterprise collaborations. Production Planning & Control, 12(5), 437-451 Jahnke (2012) http://www.jjahnke.net/rundbr19.html, accessed on 10.12.2012 Knof, Heide Lore (1192) Identifizierung organisatorischen Flexibilitätspotenzials. In: WiSt – Wirtschaftswissenschaftliches Studium, 21 (1192) 3, S. 143-147 Lödding, H. (2005) Verfahren der Fertigungssteuerung – Grundlagen, Beschreibung, Konfiguration, SpringerVerlag, Berlin Scholz-Reiter, B., Schukraft, S., Özsahin, M. E., Lo, L. (2012) Identifikation von potenziellen Wandlungstreibern – Potenzielle Wandlungstrieber am Beispiel eines Unternehmens der Bekleidungsindustrie. In: Industrie Managment, 28(2012)6, S. 19-22 Scholz-Reiter, B., Özsahin, M. E., Schukraft, S. (2012) Procedure for analyzing effects of changeability on logistics structures in value chain networks. In: POMS 2012 PROCEEDINGS, Chicago, Illinois Swaminathan, J.M., Smith, S.F., Sadeh, N.M. (1998) Modelling supply chain dynamics: a multi agent approach. Decision Science, 29 (3), 607-632 Sterman, J.D. (2000) Business dynamics: systems thinking and modeling for complex world. McGraw-Hill Thoben, K.-D., Jagdev, H. S. (2001) Typological issues in enterprise networks. Production Planning & Control: The Management of Operations. Vol. 12, No. 5, 421-436 Tunispro, (2012) http://www.tunispro.de/tunesien/ sozialsystem-in-tunesien.html, accessed on 10.12.2012 Westkämper, E. (2009) Wandlungsfähige Produktionsunternehmen. Das Stuttgarter Unternehmensmodell, (Zahn, E., Westkämper, E.) (Ed.) Springer Verlag, Berlin/Heidelberg Wiendahl, H.-P. (2007) Betriebsorganisation für Ingenieure, Volume 6, Hanser Fachbuchverlag, München. Wiendahl, H.-P., ElMaraghy, H.A., Nyhuis, P., Zäh, M.F., Wiendahl, H.-H., Duffie, N. and Brieke, M. (2007) Changeable Manufacturing – Classification, Design and Operation. In: Annals of the CIRP, Vol. 56/2/2007, 783809 Wildemann, H. (2007) Fertigungssegmentierung. Leitfaden zur fluss- und logistikgerechten Fabrikgestaltung. Vol. 11, TCW, München Zäh, M. F.; Moeller, N.; Vogl, W. (2005) Symbiosis of Changeable and Virtual Production, In: 1st International Conference on Changeable, Agile, Reconfigurable and Virtual Production. (Zäh, M. F.) (Ed.) CARV 2005, Utz, München
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Fig. 13. Delivery reliability with and without change driver effects [per cent/month] 5. CONCLUSIONS VCN are confronted with dynamically changing influences. Some of these influences (change drivers) force the value chain network to reconfigurations beyond existing flexibility corridors. For this reason, our research aims at providing methods and procedures for the configuration and optimization of changeable VCN. The paper on hand describes a procedure to analyse the effects of change drivers on logistics structures of VCN. In doing so a system dynamics model for the quantitative analysis of possible change drivers’ effects is illustrated. For the investigation we have used logistics target variables, which we have derived from real order processing data of a case study. The determination of the effects of specific change drivers shows that the procedure can illustrate the effects of influences. To respond to these effects possible logistics structure alternatives have to be deducted and evaluated according to their target variable characteristics. Following up is the evaluation of specific change enablers that enable the logistics structure to react to change drivers and examine the effects of it on their VCN. ACKNOWLEDGEMENT The research described in this paper is funded by the German Federal Ministry of Education and Research (BMBF) as part of the project POWer.net – Planning and Optimization of Changeable Global Value Chain Networks. REFERENCES Bruckner, A., Müller, S. (2003) Supply Chain Management in der Bekleidungsindustrie. Forschungsstelle der Bekleidungsindustrie, Cologne, Germany Camarinha-Matos, L. M., Afsarmanesh, H., Ortiz, A. (2005) Collaborative Networks and Their Breeding Environments. (Ortiz, A.) (Ed.), Springer, Boston European Central Bank, Statistical Data Warehouse, http://sdw.ecb.europa.eu/browseTable.do?node=2120782 &start=&end=&trans=N&dvfreq=M, accessed on 12.02.2013 Gunasekaran, A., Ngai, N.W.T., (2009) Modeling and analysis of built-to-order supply chains. European 58