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A clustering approach for modularizing service-oriented systems

Author

Listed:
  • Omar Ezzat

    (Mines Saint-Etienne, Univ Clermont Auvergne, CNRS, UMR 6158 LIMOS, Henri Fayol Institute)

  • Khaled Medini

    (Mines Saint-Etienne, Univ Clermont Auvergne, CNRS, UMR 6158 LIMOS, Henri Fayol Institute)

  • Xavier Boucher

    (Mines Saint-Etienne, Univ Clermont Auvergne, CNRS, UMR 6158 LIMOS, Henri Fayol Institute)

  • Xavier Delorme

    (Mines Saint-Etienne, Univ Clermont Auvergne, CNRS, UMR 6158 LIMOS, Henri Fayol Institute)

Abstract

Companies are seeking more and more to offer customized goods and services to customers to be able to satisfy their needs. Several methods emerged to fulfill the needs of customization without affecting the performance of the company. Modularity has been considered as an effective method to address the challenges regarding variety management in the product and service domain. It has been addressed in the product domain but rarely in the service domain. This paper aims to provide a method to modularize a service-oriented system that consists of products and services. The method uses a set of modularization criteria and clustering techniques to form service-oriented system modules (product and/or service modules). The output of the clustering process is evaluated using indicators to provide decision-makers with insights into potentially preferred clustering alternatives. A test case is presented in order to show the applicability of the method.

Suggested Citation

  • Omar Ezzat & Khaled Medini & Xavier Boucher & Xavier Delorme, 2022. "A clustering approach for modularizing service-oriented systems," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 719-734, March.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:3:d:10.1007_s10845-020-01668-w
    DOI: 10.1007/s10845-020-01668-w
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    References listed on IDEAS

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    1. Aurelio Montalto & Serena Graziosi & Monica Bordegoni & Luca Di Landro & Michael Johannes Leonardus Tooren, 2020. "An approach to design reconfigurable manufacturing tools to manage product variability: the mass customisation of eyewear," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 87-102, January.
    2. Na Zhang & Yu Yang & Yujie Zheng & Jiafu Su, 2019. "Module partition of complex mechanical products based on weighted complex networks," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1973-1998, April.
    3. Lau Antonio, K.W. & Yam, Richard C.M. & Tang, Esther, 2007. "The impacts of product modularity on competitive capabilities and performance: An empirical study," International Journal of Production Economics, Elsevier, vol. 105(1), pages 1-20, January.
    4. Piran, Fabio Antonio Sartori & Lacerda, Daniel Pacheco & Camargo, Luis Felipe Riehs & Viero, Carlos Frederico & Dresch, Aline & Cauchick-Miguel, Paulo Augusto, 2016. "Product modularization and effects on efficiency: An analysis of a bus manufacturer using data envelopment analysis (DEA)," International Journal of Production Economics, Elsevier, vol. 182(C), pages 1-13.
    5. Zhang, Min & Guo, Hangfei & Huo, Baofeng & Zhao, Xiande & Huang, Jianbo, 2019. "Linking supply chain quality integration with mass customization and product modularity," International Journal of Production Economics, Elsevier, vol. 207(C), pages 227-235.
    6. Samyeon Kim & Seung Ki Moon, 2019. "Eco-modular product architecture identification and assessment for product recovery," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 383-403, January.
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    Cited by:

    1. Sihan Huang & Guoxin Wang & Shiqi Nie & Bin Wang & Yan Yan, 2023. "Part family formation method for delayed reconfigurable manufacturing system based on machine learning," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2849-2863, August.

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