Inventory management and logistics optimization based on data mining

This paper focuses on the importance of effective inventory management and the role of data mining techniques in optimizing logistics and warehouse operations. The objective is to minimize inventory costs while maximizing customer service levels. The current economic landscape, characterized by intense competition and supply chain disruptions, highlights the need for efficient inventory management. Tactical and operational decisions, such as product allocation, storage area zoning, order batch processing, and routing policies, play a significant role in optimizing inventory and supporting storage operations. Warehouse Management Systems (WMS) leverage data analysis to provide timely information for storage, inventory, and SKU movement decisions. Data mining techniques, such as clustering, association rules, and decision trees, can extract valuable insights from large-scale data to enhance inventory management and decision-making processes. Therefore, data mining plays a crucial role in managing inventory and optimizing logistics operations.

Keywords:

inventory management , data mining , logistics optimization , decision-making