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Optimising industrial performance improvement within a quantitative multi-criteria aggregation framework

Author

Listed:
  • L. Berrah
  • J. Montmain
  • G. Mauris
  • V. Cliville

Abstract

The major industrial control purpose is the reaching of the expected performances. In this sense, improvement processes are continuously carried out in order to define the right actions with regard to the objectives achievement. Thus, in order to better monitor the performance continuous improvement process, we consider a quantitative model for performance assessment. The industrial performance being multi-criteria, the proposed model is thus based on the one hand, on the MACBETH method to express quantitatively elementary performances from qualitative expert pair-wise comparisons and, on the other hand, on the Choquet integral to express the overall performance according to subordination and transverse interactions between the elementary performances. Then, the main focus concerns the decision-maker's requirements for optimising the improvement of the overall performance versus the allocated resources. In this view, we propose useful pieces of information first for diagnosis, then for overall performance improvement optimisation versus the costs of elementary performance improvements. Finally, the proposed approach is applied to an industrial case looking for optimising the improvement of the lean objective satisfaction related to the throughput time of hydraulic component manufacturing.

Suggested Citation

  • L. Berrah & J. Montmain & G. Mauris & V. Cliville, 2011. "Optimising industrial performance improvement within a quantitative multi-criteria aggregation framework," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 3(1), pages 42-65.
  • Handle: RePEc:ids:injdan:v:3:y:2011:i:1:p:42-65
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    Cited by:

    1. L. Berrah & V. Clivillé & J. Montmain & G. Mauris, 0. "The Contribution concept for the control of a manufacturing multi-criteria performance improvement," Journal of Intelligent Manufacturing, Springer, vol. 0, pages 1-12.

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