Quantitative supply chain segmentation model for dynamic alignment





Supply chain segmentation, Supply chain management, fuzzy inference system


Companies deal with different customer groups, requirements differ among them, which makes it important to define the service level precisely and improve customer service through different supply chain strategies for each group. An alternative to deal with imprecision related to the segmentation processes suggested by either the Leagile or the Dynamic Alignment Schools is the application of fuzzy set theory. The objective of this work is to develop a quantitative model that uses the fuzzy set theory and, based on sales data, assess the company’s supply chain(s). The model's aim is to facilitate managers' decision-making processes to achieve the dynamic alignment. It was possible to identify the supply chains that serve the client groups evaluated, providing answers faster than the analysis proposed by the models found in the literature. The application in two real situations validated the model since the results obtained were consistent with the reality pointed out by the experts of the companies assessed. The model indicates possible actions for the realignment of the supply chain by their managers. Results obtained should improve practice, preparing managers to cope with the organizations` multiple supply chains. This study is the first one that aims to segment quantitatively supply chains on a company applying fuzzy set theory, providing a novel approach to align operations and supply chain strategy dynamically.


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Author Biographies

Rafael Alves Ferreira, University of São Paulo

Production Engineering Department

São Carlos School of Engineering

Lucas A. S. Santos, University of São Paulo

Production Engineering Department

São Carlos School of Engineering

Kleber F. Espôsto, University of São Paulo

Production Engineering Department

São Carlos School of Engineering


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How to Cite

Alves Ferreira, R., A. S. Santos, L., & Espôsto, K. F. (2022). Quantitative supply chain segmentation model for dynamic alignment. International Journal of Production Management and Engineering, 10(2), 99–113. https://doi.org/10.4995/ijpme.2022.16494