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A Methodology for Interoperability Evaluation in Supply Chains based on Causal Performance Measurement Models

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Part of the book series: Proceedings of the I-ESA Conferences ((IESACONF,volume 5))

Abstract

This paper proposes a framework and a methodology for evaluating and improving the interoperability for each partner collaborating in a supply chain. The definition of this framework is based on two principles. The first one is that there are two kinds of activities in a business process: non-value-added (NVA) activities and business activities. In our work, NVA activities are those dedicated to interoperability alignment. The second principle is that process Performance Indicators (PIs) can be used to measure interoperability. The framework uses a causal performance measurement model (CPMM) to allow an understanding of how interoperability can influence the achievement of all the partners’ objectives. The methodology is based on the framework. It is aimed to provide support for managing the evolution of the supply chain towards interoperability. An application of the methodology to an industrial case study is presented.

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References

  1. Ford, T., et al., The Interoperability Score, in 5th Conference on Systems Engineering Research. 2007B, Stevens Institute of Technology: New Jersey.

    Google Scholar 

  2. Blanc, S., Contribution a la caracterisation et a l'evaluation de l'interoperabilite pour les entreprises collaboratives. 2006, University of Bordeaux 1.

    Google Scholar 

  3. Ducq, Y. and D. Chen, How to measure interoperability: Concept and Approach, in 14th International Conference on Concurrent Enterprising. 2008, A New Wave of Innovation in Collaboration Networks: Lisbon.

    Google Scholar 

  4. Huynh, T.V. and J.S. Osmundson, A Model for Assessing the Performance of Interoperable, Complex Systems, in the 2006 Conference on Systems Engineering Research. 2006, Stevens Institute of Technology: New Jersey.

    Google Scholar 

  5. Kasunic, M. and W. Anderson, Measuring Systems Interoperability: Challenges and Opportunities, in Software Engineering Measurement and Analysis Initiative. 2004, Carnegie Mellon University.

    Google Scholar 

  6. Panetto, H., Towards a Classification Framework for Interoperability of Enterprise Applications. International Journal of Computer Integrated Manufacturing, 2007. 20(8): p. 727–740.

    Article  Google Scholar 

  7. Tolk, A. and J. Muguira, The Levels of Conceptual Interoperability Model, in the 2003 Fall Simulation Interoperability Workshop. 2003, IEEE CS Press: Orlando.

    Google Scholar 

  8. Morris, E., et al., System of Systems Interoperability (SOSI): Final Report., in The Software Engineering Institute. 2004, Carnegie Mellon University.

    Google Scholar 

  9. Kingston, G., S. Fewell, and W. Richer, An Organizational Interoperability Agility Model, in the 10th Command and Control Research and Technology Symposium. 2005, Command and Control Research Program: Virginia.

    Google Scholar 

  10. Lebreton, B. and C. Legner, Interoperability Impact Assessment Model: An Overview, in Enterprise Interoperability II: New Challenges and Approaches, R.J. Gonçalves, et al., Editors. 2007, Springer.

    Google Scholar 

  11. Grandin-Dubost, M., et al., Using IIAM to Assess Interoperability Investments: A Case Study, in Enterprise Interoperability II. New Challenges and Approaches, R.J. Gonçalves, et al., Editors. 2007, Springer.

    Google Scholar 

  12. Chen, D., B. Vallespir, and N. Daclin, An Approach for Enterprise Interoperability Measurement, in International Workshop on Model Driven Information Systems Engineering: Enterprise, User and System Models 2008B: Montpellier.

    Google Scholar 

  13. Ford, T., et al., A Survey on Interoperability Measurement, in 12th International Command and Control Research and Technology Symposium. 2007A, A New Wave of Innovation In Collaborative Networks: Lisbon.

    Google Scholar 

  14. Dudoit, S. and M.J. Van der Laan, Asymptotics of cross-validated risk estimation in estimator selection and performance assessment. Statistical Methodology, 2005. 2(2): p. 131-154.

    Article  MathSciNet  Google Scholar 

  15. Cantamessa, M. and E. Paolucci, Using organizational analysis and IDEFO for enterprise modelling in SMEs International Journal of Computer Integrated Manufacturing, 1998. 3(1): p. 416–429.

    Google Scholar 

  16. Niven, P.R., Balanced Scorecard Step by Step. 2002: John Wiley & Sons.

    Google Scholar 

  17. Buytendijk, F., Dealing with Dilemmas: Where Business Analytics Fall Short. 2010: John Wiley and Sons.

    Google Scholar 

  18. Kaplan, R.S. and D.P. Norton, Strategy maps: converting intangible assets into tangible outcomes. 2004: Harvard Business Press.

    Google Scholar 

  19. Epstein, M.J. and R.A. Westbrook, Linking Actions to Profits in Strategic Decision Making. MIT Sloan Management Review, 2001. 42(3): p. 39–49.

    Google Scholar 

  20. Hamschera, W., M.Y. Kiangb, and R. Langc, Qualitative reasoning in business, finance, and economics: Introduction. Decision Support Systems, 1995. 15(2): p. 99-103.

    Article  Google Scholar 

  21. Sproles, N., Formulating Measures of Effectiveness. Systems Engineering, 2002. 5(4): p. 253–263.

    Article  Google Scholar 

  22. Chen, D., G. Doumeingts, and F. Vernadat, Architectures for enterprise integration and interoperability:Past, present and future. Computers in Industry, 2008A. 59(7).

    Google Scholar 

  23. ALCTS. Description and Access Task Force on Metadata. 2010; Available from: http://www.libraries.psu.edu/tas/jca/ccda/tf-meta3.html.

  24. Kuipers, B., Qualitative Simulation, in Encyclopedia of Physical Science and Technology (Third Edition), R.A. Meyers, Editor. 2004, Academic Press.

    Google Scholar 

  25. Kuipers, B., Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge. 1994: MIT Press.

    Google Scholar 

  26. Bredeweg, B., et al., Towards a structured approach to building qualitative reasoning models and simulations. Ecological Informatics, 2008. 3(1).

    Google Scholar 

  27. Doumeingts, G., N. Malhéné, and C. Villenave, GEM: GRAI evolution method: a case study. Int. J. Technology Management, 2001. 22(1/2/3).

    Google Scholar 

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Correspondence to Mamadou Camara .

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© 2012 Springer-Verlag London Limited

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Camara, M., Ducq, Y., Dupas, R. (2012). A Methodology for Interoperability Evaluation in Supply Chains based on Causal Performance Measurement Models. In: Poler, R., Doumeingts, G., Katzy, B., Chalmeta, R. (eds) Enterprise Interoperability V. Proceedings of the I-ESA Conferences, vol 5. Springer, London. https://doi.org/10.1007/978-1-4471-2819-9_1

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  • DOI: https://doi.org/10.1007/978-1-4471-2819-9_1

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2818-2

  • Online ISBN: 978-1-4471-2819-9

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