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

R. Heradio 1 H. Perez-Morago 1 M. Alférez 2 D. Fernandez-Amoros 1 G.H. Alférez
1 UNED - Universidad Nacional de Educación a Distancia
2 DILS - Département Ingénierie Logiciels et Systèmes
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.
Keywords : Service industries Variability model Product platforms Algorithms Binary decision diagrams Competition Customer satisfaction Business competition Lead time Market demand Mass customization Computer aided manufacturing
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Journal articles
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https://hal-cea.archives-ouvertes.fr/cea-01845193
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Submitted on : Friday, July 20, 2018 - 11:10:54 AM
Last modification on : Friday, July 26, 2019 - 11:59:05 AM

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R. Heradio, H. Perez-Morago, M. Alférez, D. Fernandez-Amoros, G.H. Alférez. Augmenting measure sensitivity to detect essential, dispensable and highly incompatible features in mass customization. European Journal of Operational Research, Elsevier, 2016, 248 (3), pp.1066-1077. ⟨10.1016/j.ejor.2015.08.005⟩. ⟨cea-01845193⟩

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