School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
10.24200/j65.2024.63431.2378
Abstract
Supplier selection is a crucial aspect of supply chain management. Traditionally, multi-criteria decision-making methods and experts' experience have been the go-to approaches for this process. However, in today's highly competitive business environment, making decisions with both speed and accuracy has become paramount. Consequently, the utilization of innovative data-driven technologies and machine learning methods has gained significant importance. Surprisingly, the combination of simulation and machine learning has received limited attention in research endeavors. In this study, supplier performance is evaluated based on specific characteristics utilizing a combination of simulation and machine learning techniques. The research investigates its applications in data-driven decision support for supplier selection. we tackled the supplier selection challenge by simulating the problem using Arena software. The dataset generated from the simulation served as input for our machine learning model. We employed different algorithms, namely Decision Tree (DT), K-Nearest Neighbor (KNN), and Logistic Regression (LR), to analyze the data. Our research demonstrates the remarkable effectiveness of machine learning algorithms in supplier selection. Based on the results, the DT algorithm with 99% accuracy, the LR algorithm with 98% accuracy, and the KNN algorithm with 96% accuracy assign orders to suppliers who have the highest probability of success in delivering them on time. Our approach proves invaluable in analyzing the supplier base and identifying critical suppliers or combinations thereof, minimizing disruptions caused by adverse supplier performance. These findings underscore the potential of integrating advanced computational methods to significantly enhance decision-making processes within supplier selection in supply chain management. Our analysis highlights the pivotal role of integrating simulation and machine learning techniques, offering a robust framework for improving supplier selection methods in the fast-paced and competitive landscape of modern industries. This approach not only improves existing methods but also promises a new era in supply chain management. The synergy of simulation and machine learning can create a revolution in the way businesses choose strategic suppliers and ensure speed and accuracy in decision-making processes.
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