Review of Applied Studies


The ultimate goal of the decision-making process in a supply chain is improving the efficiency of individual supply chain units, as well as that of the whole supply chain by implementing decision-making results. Additionally, decision-making efficiency substantially depends upon feedback obtained upon evaluation of implementation results. The supply chain configuration problem presents a major challenge in this regard. The road from decision-making to implementation of decisions made, and further to obtaining the feedback, is often very long. Additionally, decisions made often undergo a process of informal adjustment due to various managerial assumptions. Therefore, observation of results is blurred by many complicating factors. These issues create difficulties in the assessment of configuration decisions and, more generally, in assessing the real value of various decision-making methods and tools. Therefore, applied studies on supply chain configuration pose a high degree of interest.


Supply Chain Supply Chain Management Distribution Center Product Family Apply Study 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Abrahamsson M, Brege S (1997) Structural changes in the supply chain. The International Journal of Logistics Management 8:35–44Google Scholar
  2. Arntzen BC, Brown GG, Harrison TP, Trafton LL (1995) Global supply chain management at Digital Equipment Corporation. Interfaces 25:69–93Google Scholar
  3. Arntzen BC, Mulgrew DW, Sjolander GL (1998) Redesigning 3M’s worldwide product supply chains. Supply Chain Management Review 1:16–27Google Scholar
  4. Billington C, Callioni G, Crane B, Ruark JD, Rapp JU, White T, Willems SP (2004) Accelerating the profitability of Hewlett-Packard’s supply chains, Interfaces 34:59–72CrossRefGoogle Scholar
  5. Camm JD, Chorman T, Sill F, Evans J, Sweeney D, Wegryn G (1997) Blending OR/MS judgment, and GIS: Restructuring P&G’s supply chain. Interfaces 27:128–142Google Scholar
  6. Choi TY, Hong Y (2002) Unveiling the structure of supply networks: case studies in Honda, Acura, and DaimlerChrysler. Journal of Operations Management 20:469–493CrossRefGoogle Scholar
  7. Ding H, Benyoucef L, Xie X (2006) A simulation-based multi-objective genetic algorithm approach for networked enterprises optimization. Engineering Applications of Artificial Intelligence, In PressGoogle Scholar
  8. Dogan K, Goetschalckx M (1999) A primal decomposition method for the integrated design of multi-period production-distribution systems. IIE Trans 31:1027–1036CrossRefGoogle Scholar
  9. Eskigun E, Uzsoy R, Preckel PV, Beaujon G, Krishnan S, Tew JD (2005) Outbound supply chain network design with mode selection, lead times and capacitated vehicle distribution centers. European Journal of Operational Research 165:182–206MATHCrossRefMathSciNetGoogle Scholar
  10. Feitzinger E, Lee HL (1997) Mass customization at Hewlett-Packard: The power of postponement. Harvard Business Review 75:116–121Google Scholar
  11. Geoffrion AM, Powers RF (1995) Twenty years of strategic distribution system design: an evolutionary perspective. Interfaces 25:105–128CrossRefGoogle Scholar
  12. Graves SC, Willems SP (2005) Optimizing the supply chain configuration for new products. Management Science 51:1165–1180CrossRefGoogle Scholar
  13. Gupta V, Peters E, Miller T, Blyden K (2002) Implementing a distribution-network decision-support system at Pfizer/Warner-Lambert. Interfaces 32:28CrossRefGoogle Scholar
  14. Harland C (1996) Supply network strategies: The case of health supplies. European Journal of Purchasing & Supply Management 2:183–192CrossRefGoogle Scholar
  15. Ingalls RG, Kasales C (1999) CSCAT: The Compaq supply chain analysis tool. P. A. Farrington, H. B. Nembhard, D. T. Sturrock and G. W. Evans (eds), Proceedings of the 1999 Winter Simulation Conference. Phoenix, pp. 1201–1206Google Scholar
  16. Jayaraman V, Ross A (2003) A simulated annealing methodology to distribution network design and management. European Journal of Operational Research 144:629–645MATHCrossRefMathSciNetGoogle Scholar
  17. Keeney RL (1992) Value Focused Thinking: A Path to Creative Decision-making. Cambridge: Harvard University PressGoogle Scholar
  18. Kirkwood CW, Slaven MP, Maltz A (2005) Improving Supply-Chain-Reconfiguration Decisions at IBM. Interfaces 35:460–475CrossRefGoogle Scholar
  19. Kouvelis P, Rosenblatt MJ, Munson CL (2004) A mathematical programming model for global plant location problems: Analysis and insights. IIE Transactions (Institute of Industrial Engineers) 36:127–144Google Scholar
  20. Lamothe J, Hadj-Hamou K, Aldanondo M (2006) An optimization model for selecting a product family and designing its supply chain. European Journal of Operational Research 169:1030–1047MATHCrossRefGoogle Scholar
  21. Laval C, Feyhl M, Kakouros S (2005) Hewlett-Packard Combined OR and Expert Knowledge to Design Its Supply Chains. Interfaces 35:238–247CrossRefGoogle Scholar
  22. Papageorgiou LG, Rotstein GE, Shah N (2001) Strategic Supply Chain Optimization for the Pharmaceutical Industries. Ind. Eng. Chem. Res. 40:275–286CrossRefGoogle Scholar
  23. Potter A, Mason R, Naim M, Lalwani C (2004) The evolution towards an integrated steel supply chain: A case study from the UK. International Journal of Production Economics 89:207–16CrossRefGoogle Scholar
  24. Ross A, Venkataramanan MA, Ernstberger KW (1998) Reconfiguring the supply network using current performance data. Decision Science 29:707–728Google Scholar
  25. Senter JR, Flynn MS (1999) Changing Interorganizational Patterns in the North American Automotive Supply Chain. Applied Behavioral Science Review 7:59–80CrossRefGoogle Scholar
  26. Sery S, Presti V, Shobrys DE (2001) Optimization models for restructuring BASF North America’s distribution system. Interfaces 31:55–65CrossRefGoogle Scholar
  27. Thomas DJ, Griffin PM (1996) Coordinated supply chain management. European Journal of Operational Research 94:1–15MATHCrossRefGoogle Scholar
  28. Tsiakis P, Shah N, Pantelides CC (2001) Design of multi-echelon supply chain networks under demand uncertainty. Ind. Eng. Chem. Res. 40:3585–3604CrossRefGoogle Scholar
  29. Vila D, Martel A, Beauregard R (2006) Designing logistics networks in divergent process industries: A methodology and its application to the lumber industry. International Journal of Production Economics 102:358–378CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2007

Personalised recommendations