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Augmenting measure sensitivity to detect essential, dispensable and highly incompatible features in mass customization

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  • Heradio, Ruben
  • Perez-Morago, Hector
  • Alférez, Mauricio
  • Fernandez-Amoros, David
  • Alférez, Germán H.

Abstract

Mass customization is the new frontier in business competition for both manufacturing and service industries. To improve customer satisfaction, reduce lead-times and shorten costs, families of similar products are built jointly by combining reusable parts that implement the features demanded by the customers. To guarantee the validity of the products derived from mass customization processes, feature dependencies and incompatibilities are usually specified with a variability model. As market demand grows and evolves, variability models become increasingly complex. In such entangled models it is hard to identify which features are essential, dispensable, highly required by other features, or highly incompatible with the remaining features. This paper exposes the limitations of existing approaches to gather such knowledge and provides efficient algorithms to retrieve that information from variability models.

Suggested Citation

  • Heradio, Ruben & Perez-Morago, Hector & Alférez, Mauricio & Fernandez-Amoros, David & Alférez, Germán H., 2016. "Augmenting measure sensitivity to detect essential, dispensable and highly incompatible features in mass customization," European Journal of Operational Research, Elsevier, vol. 248(3), pages 1066-1077.
    • Handle: RePEc:eee:ejores:v:248:y:2016:i:3:p:1066-1077
    DOI: 10.1016/j.ejor.2015.08.005
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    References listed on IDEAS

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    Cited by:

    1. Li, Wei & Sun, Hui & Tong, Meng & Mustafee, Navonil & Koh, Lenny, 2024. "Customizing customization in a 3D printing-enabled hybrid manufacturing supply chain," International Journal of Production Economics, Elsevier, vol. 268(C).
    2. Na Liu & Pui-Sze Chow & Hongshan Zhao, 2020. "Challenges and critical successful factors for apparel mass customization operations: recent development and case study," Annals of Operations Research, Springer, vol. 291(1), pages 531-563, August.
    3. Choi, Tsan-Ming & Ma, Cheng & Shen, Bin & Sun, Qi, 2019. "Optimal pricing in mass customization supply chains with risk-averse agents and retail competition," Omega, Elsevier, vol. 88(C), pages 150-161.
    4. Vladimir Modrak & Zuzana Soltysova & Jan Modrak & Annamaria Behunova, 2017. "Reducing Impact of Negative Complexity on Sustainability of Mass Customization," Sustainability, MDPI, vol. 9(11), pages 1-16, November.
    5. David Fernandez-Amoros & Sergio Bra & Ernesto Aranda-Escolástico & Ruben Heradio, 2020. "Using Extended Logical Primitives for Efficient BDD Building," Mathematics, MDPI, vol. 8(8), pages 1-17, July.

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