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

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.

[1]  Yasuhiro Monden,et al.  Toyota Production System: An Integrated Approach to Just-In-Time , 1993 .

[2]  Matthias Ehrgott,et al.  Multiple criteria decision analysis: state of the art surveys , 2005 .

[3]  Lamia Berrah,et al.  DECISION-AIDING FUNCTIONALITIES FOR INDUSTRIAL PERFORMANCE IMPROVEMENT , 2007 .

[4]  Christopher A. Voss Learning from the first Operations Management textbook , 2007 .

[5]  Lamia Berrah,et al.  Global vision and performance indicators for an industrial improvement approach , 2000 .

[6]  George B. Dantzig,et al.  Linear Programming 1: Introduction , 1997 .

[7]  Shlomo Globerson,et al.  Issues in developing a performance criteria system for an organization , 1985 .

[8]  Umit Bititci,et al.  Quantitative models for performance measurement system , 2000 .

[9]  Maurice Bitton Ecograi : méthode de conception et d'implantation de systèmes de mesure de performances pour organisations industrielles , 1990 .

[10]  Yves Ducq,et al.  Coherence analysis methods for production systems by performance aggregation , 2001 .

[11]  Z. Babic,et al.  Ranking of enterprises based on multicriterial analysis , 1998 .

[12]  L. Berrah,et al.  Information aggregation in industrial performance measurement: rationales, issues and definitions , 2004 .

[13]  U. Bititci Modelling of performance measurement systems in manufacturing enterprises , 1995 .

[14]  Lamia Berrah,et al.  Monitoring the improvement of an overall industrial performance based on a Choquet integral aggregation , 2008 .

[15]  Thomas J. Crowe,et al.  An integrated dynamic performance measurement system for improving manufacturing competitiveness , 1997 .

[16]  Yasutaka Kainuma,et al.  A multiple attribute utility theory approach to lean and green supply chain management , 2006 .

[17]  B. Mareschal,et al.  BANKADVISER: An industrial evaluation system , 1991 .

[18]  Danny J. Johnson,et al.  A framework for reducing manufacturing throughput time , 2003 .

[19]  M. Punniyamoorthy,et al.  A framework to arrive at a unique performance measurement score for the balanced scorecard , 2009, Int. J. Data Anal. Tech. Strateg..

[20]  Christophe Labreuche,et al.  The Choquet integral for the aggregation of interval scales in multicriteria decision making , 2003, Fuzzy Sets Syst..

[21]  Leonard L Fortuin,et al.  Performance Indicators — Why, Where and How? , 1988 .

[22]  M. Grabisch The application of fuzzy integrals in multicriteria decision making , 1996 .

[23]  Andrea Rangone,et al.  An analytical hierarchy process framework for comparing the overall performance of manufacturing departments , 1996 .

[24]  A. Tversky,et al.  Foundations of Measurement, Vol. I: Additive and Polynomial Representations , 1991 .

[25]  Andy Neely,et al.  Performance measurement system design , 1995 .

[26]  George Mavrotas,et al.  A multicriteria approach for evaluating the performance of industrial firms , 1992 .

[27]  G. Mauris,et al.  Quantitative expression and aggregation of performance measurements based on the MACBETH multi-criteria method , 2007 .

[28]  W. Deming Quality, productivity, and competitive position , 1982 .

[29]  Umit Bititci,et al.  Strategy Management through Quantitative Modelling of Performance Measurement Systems , 2001 .

[30]  Michel Grabisch,et al.  K-order Additive Discrete Fuzzy Measures and Their Representation , 1997, Fuzzy Sets Syst..

[31]  Steven A. Melnyk,et al.  Metrics and performance measurement in operations management: dealing with the metrics maze , 2004 .

[32]  John Bradford,et al.  A non-linear redesign methodology for manufacturing systems in SMEs , 2002, Comput. Ind..

[33]  Daniel T. Jones,et al.  The machine that changed the world : based on the Massachusetts Institute of Technology 5-million dollar 5-year study on the future of the automobile , 1990 .

[34]  C. Kahraman,et al.  Multi-attribute comparison of advanced manufacturing systems using fuzzy vs. crisp axiomatic design approach , 2005 .

[35]  Jaap Van Brakel,et al.  Foundations of measurement , 1983 .