Journal of Intelligent Manufacturing

, Volume 30, Issue 1, pp 47–58 | Cite as

The Contribution concept for the control of a manufacturing multi-criteria performance improvement

  • L. Berrah
  • V. ClivilléEmail author
  • J. Montmain
  • G. Mauris


By dealing with an overall manufacturing performance improvement context, we introduce in this paper the “improvement contribution” concept. A framework that integrates such a concept to the quantification of a multi-criteria interacting performance is proposed. The improvement contribution is defined as a new intelligent functionality that quantifies the impact of the improvement of a single (or a set of) mono-criterion performance(s) on the improvement of an overall performance. When performances are interacting, the quantification of such a contribution cannot be direct. The proposed approach consists of an extension of a previously developed Performance Measurement System (PMS). The considered PMS integrates an aggregation operator—the Choquet Integral (CI)—for the expression of an overall performance by handling weights and interactions between the mono-criterion performances. The principles of the improvement contribution and its quantification are thus presented in addition to the way the improvement contribution can be used for helping decision-makers in their manufacturing improvement control. As an illustration, the use of these contributions within successive iterations of improvement actions is shown using a case study submitted by the SME Fournier Company.


Manufacturing performance Improvement contribution Multi-criteria and interacting performance Performance measurement system Choquet integral 


  1. Bana e Costa, C., & De Corte, J. M. (2012). MACBETH. International Journal of Information Technology and Decision Making, 11(02), 359–387.CrossRefGoogle Scholar
  2. Berrah, L., Montmain, J., Mauris, G., & Clivillé, V. (2011). Optimising industrial performance improvement within a quantitative multi-criteria aggregation framework. International Journal of Data Analysis Techniques and Strategies, 3(1), 42–65.CrossRefGoogle Scholar
  3. Bititci, U. S. (2001). Strategy management through quantitative modelling of performance measurement systems. International Journal of Production Economics, 69, 137–147.CrossRefGoogle Scholar
  4. Bosch-Mauchand, M., Siadat, A., Perry, N., & Bernard, A. (2012). VCS: value chains simulator, a tool for value analysis of manufacturing enterprise processes (a value-based decision support tool ). Journal of Intelligent Manufacturing, 23(4), 1389–1402.CrossRefGoogle Scholar
  5. Bourne, M., Mills, J. F., Wilcox, M., Neely, A. D., & Platts, K. W. (2000). Designing, implementing and updating performance measurement systems. International Journal of Operations Production management, 20(7), 754–771.CrossRefGoogle Scholar
  6. 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), 171–189.CrossRefGoogle Scholar
  7. Gallasso, F., Ducq, Y., Lauras, M., Gourc, D., & Camara, M. (2016). A method to select a successful interoperability solution through a simulation approach. Journal of Intelligent Manufacturing, 27(1), 217–229.CrossRefGoogle Scholar
  8. Ghalayini, A. M. (1996). The changing basis of performance measurement. International Journal of Operations and Production Management, 16(8), 63–80.CrossRefGoogle Scholar
  9. Ghalayini, A. M., Noble, J. S., & Crowe, T. J. (1997). An integrated dynamic performance measurement system for improving manufacturing competitiveness. International Journal of Production Economics, 48(3), 207–225.CrossRefGoogle Scholar
  10. Globerson, S. (1985). Issues in developing a performance criteria system for an organisation. International Journal of Production Research, 23(4), 639–646.CrossRefGoogle Scholar
  11. Grabisch, M. (1996). The application of fuzzy integrals in multicriteria decision making. European Journal of Operational Research, 89, 445–456.CrossRefGoogle Scholar
  12. Grabisch, M. (1997). k-ordered discrete fuzzy measures and their representation. Fuzzy Sets and Systems, 92, 167–189.CrossRefGoogle Scholar
  13. Imai, M. (1986). Kaizen: The key to Japan’s competitiveness. New York: Mac Graw-Hill Higher Education.Google Scholar
  14. Johnson, H. T. (1975). Management accounting in early integrated industry – E. I. Dupont de Nemours Powder Company 1903–1912. Business History Review, Summer, 48(2), 184–204.Google Scholar
  15. Kaplan, R. S.,  & Norton, D. P. (1992). The balanced scorecard: Measures that drive performance. Harvard Business Review, 70(1), 71–79.Google Scholar
  16. Kaplan, R. S., & Norton, D. P. (1996). The Balanced Scorecard: Translating Strategy into Action. Boston: Harvard Business School Press.Google Scholar
  17. Kocaoğlu, B., Gülsün, B., & Tanyaş, M. (2013). A SCOR based approach for measuring a benchmarkable supply chain performance. Journal of Intelligent Manufacturing, 24(1), 113–132.CrossRefGoogle Scholar
  18. Krantz, D. H., Luce, R. D., Suppes, P., & Tversky, A. (1971). Foundations of measurement (Vol. 1)., Additive and Polynomial Representations Cambridge: Academic Press.Google Scholar
  19. Labreuche, C., & Grabisch, M. (2003). The Choquet integral for the aggregation of interval scales in multicriteria decision making. Fuzzy Sets and Systems, 137, 11–26.CrossRefGoogle Scholar
  20. Montmain, J., Mauris, G., & Akharraz, A. (2005). Elucidation and decisional risk in a multicriteria decision based on a Choquet integral aggregation- a cybernetic framework. International Journal of Multi-Criteria Decision Analysis, 13(5–6), 239–258.CrossRefGoogle Scholar
  21. Neely, A., Gregory, M., & Platts, K. (1995). Performance measurement system design: a literature review and research agenda. International Journal of Operations and Production Management, 48(4), 80–116.CrossRefGoogle Scholar
  22. Nudurupati, S. S., Bititci, U. S., Kumar, V., & Chan, F. T. S. (2011). State of the art literature review on performance measurement. Computers and Industrial Engineering, 60(2), 279–290.CrossRefGoogle Scholar
  23. Ohno, T. (1988). Toyota production system: Beyond large-scale production. Boca Raton: Productivity Press.Google Scholar
  24. Ounnar, F., & Pujo, P. (2012). Pull control for job shop: Holonic manufacturing system approach using multicriteria decision-making. Journal of Intelligent Manufacturing, 23, 141–153.CrossRefGoogle Scholar
  25. Oztemel, E. (2010). Intelligent manufacturing systems. Artificial Intelligence techniques for networked manufacturing enterprises management (pp. 1–41). Berlin: Springer.Google Scholar
  26. Saaty, T. (2000). Fundamentals of the analytic hierarchy process. Pittsburgh: RWS Publications.Google Scholar
  27. Schneidermann, A. M. (1988). Setting quality goals. Quality Progress, 21, 51–75.Google Scholar
  28. Shah, L. A., Etienne, A., Siadat, A., & Vernadat, F. (2016). Decision-making in the manufacturing environment using a value-risk graph. Journal of Intelligent Manufacturing, 27(3), 617–630.Google Scholar
  29. Singh, S., Olugu, E., Musa, S., & Mahat, A. (2015). Fuzzy-based sustainability evaluation method for manufacturing SMEs using balanced scorecard framework. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-015-1081-1.
  30. Suwignjo, P., & Bititci, U. S. (2000). Quantitative models for performance measurement system. International Journal of Production Economics, 64, 231–241.CrossRefGoogle Scholar
  31. Waggoner, D. B., Neely, A. D., & Kennerley, M. P. (1999). The forces that shape organizational performance measurement systems: An interdisciplinary review. International Journal of Production Economics, 60–61, 53–60.Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • L. Berrah
    • 1
  • V. Clivillé
    • 1
    Email author
  • J. Montmain
    • 2
  • G. Mauris
    • 1
  1. 1.LISTICUniversité Savoie Mont BlancAnnecy le Vieux cedexFrance
  2. 2.ENSTIMA Site EERIE – parc scientifique Georges BesseNîmesFrance

Personalised recommendations