Optimizing Supply Chain Operations in the Electronics Industry Using Machine Learning and Integer Linear Programming

Efficient operations, within the supply chain are vital for the electronics industry. In this study, we focus on optimizing the supply chain network of a hypothetical company ABC Electronics, an entity crafted for the purposes of this research paper. The company faces challenges related to production, inventory management, and transportation logistics. To address these challenges we employ machine learning (ML) and integer linear programming (ILP) techniques. Our primary objectives include enhancing demand predictions through ML, optimizing production schedules and inventory using ILP, and devising strategies for transportation logistics. Our research makes a contribution toward reducing costs and improving efficiency at ABC Electronics enabling them to stay competitive in the electronics market. Furthermore, our findings have implications, for industry standards, sustainability practices, and customer satisfaction.