An Approach for Sustainable Supplier Segmentation Using Adaptive Network-Based Fuzzy Inference Systems

Ricardo Antonio Saugo, Francisco Rodrigues Lima Junior (), Luiz Cesar Ribeiro Carpinetti, Ana Paula Duarte and Jurandir Peinado
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Ricardo Antonio Saugo: Postgraduate Program in Administration, Federal University of Technology—Paraná, Av. Sete de Setembro, 3165, Rebouças, Curitiba 80230-901, PR, Brazil
Francisco Rodrigues Lima Junior: Postgraduate Program in Administration, Federal University of Technology—Paraná, Av. Sete de Setembro, 3165, Rebouças, Curitiba 80230-901, PR, Brazil
Luiz Cesar Ribeiro Carpinetti: Production Engineering Department, School of Engineering of São Carlos, University of São Paulo, Av. Trabalhador Sancarlense 400, Saint Carlos 13566-590, SP, Brazil
Ana Paula Duarte: Federal Institute of Education, Science and Technology of the Southeast of Minas Gerais, Muzambinho Road, Morro Preto, Muzambinho 37890-000, MG, Brazil
Jurandir Peinado: Postgraduate Program in Administration, Federal University of Technology—Paraná, Av. Sete de Setembro, 3165, Rebouças, Curitiba 80230-901, PR, Brazil

Mathematics, 2025, vol. 13, issue 19, 1-33

Abstract: Due to the globalization of supply chains and the resulting increase in the quantity and diversity of suppliers, the segmentation of suppliers has become fundamental to improving relationship management and supplier performance. Moreover, given the need to incorporate sustainability into supply chain management, criteria based on economic, environmental, and social performance have been adopted for evaluating suppliers. However, few studies present sustainable supplier segmentation models in the literature, and none of them make it possible to predict individual supplier performance for each TBL dimension in a non-compensatory manner. Moreover, none of them permits the use of historical performance data to adapt the model to the usage environment. Given this, this study aims to propose a decision-making model for sustainable supplier segmentation using an adaptive network-based fuzzy inference system (ANFIS). Our approach combines three ANFIS computational models in a tridimensional quadratic matrix, based on diverse criteria associated with the triple bottom line (TBL) dimensions. A pilot application of this model in a sugarcane mill was performed. We implemented 108 candidate topologies using the Neuro-Fuzzy Designer of the MATLAB ® software (R2014a). The cross-validation method was applied to select the best topologies. The accuracy of the selected topologies was confirmed using statistical tests. The proposed model can be adopted for supplier segmentation processes in companies that wish to monitor and/or improve the sustainability performance of their suppliers. This study may also be helpful to researchers in developing computational solutions for segmenting suppliers, mainly regarding ANFIS modeling and providing appropriate topological parameters to obtain accurate results.

Keywords: sustainable supplier segmentation; ANFIS; supplier categorization; sustainable supply chain management (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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