A hybrid intelligent fuzzy predictive model with simulation for supplier evaluation and selection. (1st November 2016) - Tavana, Madjid
Highlights: Supplier evaluation and selection constitutes a central issue in supply chain management. We develop a novel multi-criteria decision model applicable to supplier evaluation processes. ANFIS is used to determine the most influential criteria on the suppliers' performance. Multi-layer perceptron is then used to rank the suppliers' performance based on these criteria. A case study is used to illustrate the accuracy of several variants of the model in prediction. Abstract: Supplier evaluation and selection constitutes a central issue in supply chain management (SCM). However, the data on which to base the corresponding choices in real life problems are often imprecise or vague, which has led to the introduction of fuzzy approaches. Predictive intelligent-based techniques, such as Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS), have been recently applied in different research fields to model fuzzy multi-criteria decision processes where the understanding and learning of the relationships between the input and output data are the key to select suitable solutions. In this paper, a hybrid ANFIS-ANN model is proposed to assist managers in their supplier evaluation process. After aggregating the data set through the Analytical Hierarchy Process (AHP), the most influential criteria on the suppliers' performance are determined by ANFIS. Then, Multi-Layer Perceptron (MLP) is used to predict and rank the suppliers' performance based on the most effective criteria. A case study is presented to illustrate the main steps of the model and show its accuracy in prediction. A battery of parametric tests and sensitivity analyses has been implemented to evaluate the overall performance of several models based on different effective criteria combinations.