An integrated analytics model for supplier selection and order allocation with machine learning and multi-criteria optimization

Abstract

Sustainable Supplier Selection and Order Allocation (SSSOA) are critical strategic decisions in supply chain management. The decision-making process becomes complex under uncertainty, especially in a multi-supplier, multi-item, and multi-period environment. This study proposes a four-stage framework to address the SSSOA planning problem. In the first stage, machine learning techniques with the Autoregressive Integrated Moving Average (ARIMA) method are used to determine future product demand. In the second stage, Life Cycle Analysis (LCA) is used to determine the environmental impact of purchased drugs. In the third stage, a fuzzy supplier evaluation model based on the Best-Worst Method (BWM)-Additive Ratio Assessment (ARAS) method is used to determine supplier scores. Finally, a fuzzy probabilistic multi-objective mixed integer linear programming model is developed to determine the optimal drug order. This model aims to minimize the total purchase cost, probabilistic defects, and environmental impacts and maximize the total purchase value of the order allocation. The weighted sum method is used to solve the model. The application of the proposed framework is tested using a real dataset from a teaching hospital in Surakarta, Indonesia. The results show that this model can minimize the purchasing cost by 168.11 million Indonesian Rupiah (IDR), optimizing the total allocation value by 6,519.731 units. Sensitivity analysis of parameters such as holding cost, $\alpha$ value, and supplier capacity reveals that significant changes in these parameters substantially affect the total purchasing cost and order allocation. The implications of this study include improving planning accuracy, reducing environmental impacts, and optimizing supplier selection amid uncertainty, with potential applications in various other industrial sectors.