Journal of Intelligent Manufacturing

, Volume 23, Issue 6, pp 2459–2469 | Cite as

Overall performance measurement in a supply chain: towards a supplier-prime manufacturer based model

  • Vincent Clivillé
  • Lamia Berrah


This study deals with the supply chain (SC) overall performance expression. The developed idea concerns more particularly the performance of the manufactured products. Indeed, two companies or more contribute to the manufacturing of products that are generally assembled by the prime manufacturer company. Moreover, in the industrial practice, performance scorecards are defined with regard to each process; and the overall performance is neither expressed for each company, nor for the whole SC. We propose here to identify the SC overall performance to the combination of the performances of the different involved companies in the SC. Thus, in order to obtain a definition of such performance, we choose to focus first on the performance of the prime manufacturer. In this sense, the approach is based on the SCOR model for the handling of the main processes around the considered product manufacturing. The prime manufacturer performance is then defined as the aggregation of its involved processes’ performances. While the prime manufacturer performance is strongly dependent on the suppliers’ performance, we suggest the integration of the impacting supplier performance into the prime manufacturer scorecards. From an operational point of view, the MACBETH methodology is used to coherently express both processes and overall performances. More precisely, the Choquet aggregation integral operator is applied in order to model mutual interactions between processes. Finally, the expression of a bearing’s manufacturer performance illustrates the proposition.


Multi-criteria performance evaluation Supply chain management SCOR model MACBETH aggregation methodology Choquet integral operator 


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  1. Angerhofer B. J., Angelides M. C. (2006) A model and a performance measurement system for collaborative supply chains. Decision support system 42(1): 283–301CrossRefGoogle Scholar
  2. Ayers, J. B. (2000). Handbook of supply chain management, 400 p APICS series on resource management. Alexandria, Virginia, USAGoogle Scholar
  3. Bana e Costa C., Vansnick J. C. (1997) Applications of the MACBETH approach in the framework of an additive aggregation model. Journal of Multi-Criteria Decision Analysis 6: 107–114CrossRefGoogle Scholar
  4. Bana e Costa C., De Corte J. M., Vansnick J. C. (2004) On the mathematical foundations of MACBETH, 409–442. In: Figueira J., Greco S., Ehrgott M. (eds) MCDA multiple criteria decision analysis. Kluwer, Boston/Dordrecht/London, p 1040Google Scholar
  5. Barut M., Faisst W., Kanet J. J. (2002) Measuring supply chain coupling: An information system perspective. European Journal of Purchasing & Supply Management 8: 161–171CrossRefGoogle Scholar
  6. Beamon B. (1998) Supply chain design and analysis: Models and methods. International Journal of Production Economics 55: 281–294CrossRefGoogle Scholar
  7. Berrah L., Mauris G., Vernadat F. (2004) Information aggregation in industrial performance measurement: Rationales, issues and definitions. International Journal of Production Research 42: 4271–4293CrossRefGoogle Scholar
  8. Berrah L., Clivillé V. (2007) Towards an aggregation performance measurement system model in a supply chain context. Computers in Industry 58(7): 709–719CrossRefGoogle Scholar
  9. Bititci U. S. (1995) Modelling of performance measurement systems in manufacturing enterprises. International Journal of Production Economics 42: 137–147CrossRefGoogle Scholar
  10. Bititci U. S., Suwignjo P., Carrie A.S. (2001) Strategy management through quantitative modelling of performance measurement systems. International Journal Production Economics 69(2): 15–24CrossRefGoogle Scholar
  11. Chan F.T. (2003) Performance measurement in a supply chain. International Journal of Advanced Manufacturing Technology 21: 534–548CrossRefGoogle Scholar
  12. Clivillé, V., & Berrah, L., (2006). Overall performance measurement in a supply chain. In Proceedings of 12th IFAC symposium on information control problems in manufacturing (INCOM 2006). CD-ROM, Saint-Etienne, France, May 2006, 6 p.Google Scholar
  13. Clivillé V., Berrah L., Mauris G. (2007) Quantitative expression and aggregation of performance measurements based on the MACBETH multi-criteria method. International Journal of Production Economics 105(1): 171–189CrossRefGoogle Scholar
  14. Diakoulaki D., Mavrotas G., Papagyannakis L. (1992) A multicriteria approach for evaluating the performance of industrial firms. Omega 20: 467–474CrossRefGoogle Scholar
  15. Folan P., Browne J. (2005) A review of performance measurement: Towards performance management. Computers in Industry 56: 663–680CrossRefGoogle Scholar
  16. Globerson S. (1985) Issues in developing a performance criteria system for an organisation. International Journal of production research 23: 639–646CrossRefGoogle Scholar
  17. Grabisch M. (1997) k-ordered discrete fuzzy measures and their representation. Fuzzy sets and Systems 92: 167–189CrossRefGoogle Scholar
  18. Gunasekaran A., Patelb C., McGaughey R. E. (2004) A framework for supply chain performance measurement. International Journal of Production Economics 87: 333–347CrossRefGoogle Scholar
  19. Gunasekaran A., LaiK. Cheng E. (2008) Responsive supply chain: A competitive strategy in a networked economy. Omega 36(4): 549–564CrossRefGoogle Scholar
  20. Hausman, W. & (2004) In H. T. P. Harrison, H. L. Lee, J.J. Neale (Eds.). The practice of supply chain management: Where theory and application converge. New York, NY: Springer Science & Business MediaGoogle Scholar
  21. ISO. (2001). Qualité et systèmes de management ISO 9000, AFNOR, 581 p.Google Scholar
  22. Kranz, D. H., Luce, R. D., Suppes, P., & Tversky, A. (1971). Foundations of measurement: Additive and polynomial representations. Academic Press, 1971, new edition by Dover edition, 2006 (624 p).Google Scholar
  23. Labreuche C., Grabisch M. (2003) The Choquet integral for the aggregation of interval scales in multi-criteria decision making. Fuzzy Sets and Systems 137: 11–26CrossRefGoogle Scholar
  24. Lohman C., Fortuin L., Wouters M. (2004) Designing a performance measurement system: A case study. European Journal of Operational Research 156: 267–286CrossRefGoogle Scholar
  25. Marichal J. L. (2000) An axiomatic approach of the discrete Choquet integral as a tool to aggregate interacting criteria. IEEE Transactions on Fuzzy Systems 8: 800–807CrossRefGoogle Scholar
  26. Melnyk S. A., Stewart D. M., Swin M. (2004) Metrics and performance measurement in operations management: dealing with the metrics maze. Journal of Operations Management 22(3): 209–218CrossRefGoogle Scholar
  27. Neely A. (1999) The performance measurement revolution: why now and what next?. International Journal of Operations & Production Management 19: 205–228CrossRefGoogle Scholar
  28. Saaty, T. (2004). The analytic hierarchy and the analytic network processes for the measurement of intangible criteria and for decision making, 345–407. In J. Figueira, S., Greco, & M. Ehrgott (Eds.), MCDA: Multiple criteria decision analysis: State of the art surveys (p. 1040). Boston/Dordrecht/London: Kluwer.Google Scholar
  29. SCOR 80 (2007). SCOR 80 overview booklet, supply chain council editions. 2000 organisation avalaible on
  30. Suwignjo P., Bititci U.S. (2000) Quantitative models for performance measurement system. International Journal of Production Economics 64: 231–241CrossRefGoogle Scholar
  31. Villa A. (2001) Introducing some Supply Chain Management problems. International Journal of Production Economics 73: 1–4CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.LISTICUniversité de Savoie, Domaine UniversitaireAnnecy-le-Vieux cedexFrance

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