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

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 the concept of sustainability into supply chain management, criteria based on economic, environmental and social performance have been adopted in evaluating suppliers. However, there are few studies that present sustainable supplier segmentation models in the literature, and none of them makes 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, the objective of this study is 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 dimensions of the triple bottom line (TBL). A pilot application of this model in a sugarcane mill was performed. We implemented 108 candidate topologies by means of the Neuro-Fuzzy Designer of the MATLAB® software. 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 useful to researchers in developing computational solutions for segmenting suppliers, mainly in terms of ANFIS modeling and providing appropriate topological parameters to obtain accurate results.