Optimizing Inventory for Manufacturing Efficiency: A Data Science Approach to Balance Supply and Demand

Manikandan Natarajan

Abstract: Effective inventory management plays a critical role in sustaining operational efficiency and profitability within the dynamic realm of manufacturing where maintaining a delicate balance between supply and demand is paramount. Based on our supply chain project experience, we observed that major manufacturing companies might hold inventory of up to a month (30 days). Moreover, un - optimized buffers created for safety stock, these companies may end up with 80% more safety stock than required for manufacturing. This can negatively influence cash flow, as money is stuck in assets that generate low returns. Investing in assets with better returns can create opportunities for companies and reduce potential losses. Conversely, stockouts pose risks of lost sales, diminished customer satisfaction, and increased expenditures linked to order expediting and emergency shipments. This paper aims to shed light on the pivotal role of inventory optimization using a novel data science - based technique.

Keywords: Inventory Optimization; Supply Chain Management; Machine Learning; Deep Learning; Review; Analysis; Enabling Safety Stock; Forecasting; Ordering System Enhancement