Dynamic clustering of inventory parts to enhance warehouse management

Abstract

Inventory management in today's complex manufacturing environments has become increasingly challenging. Ineffective management of inventory can lead to material shortages, excessive inventories, long lead times, waste of space, and poor customer service. Nowadays, various companies are using information systems to establish effective linkages to suppliers, customers, and other agents in the supply chain. These information systems include comprehensive data warehouses that integrate operational data within the supply chain including part usage, customer demand, defect rates, etc. The data can be used in analytics models to improve warehouse operations and inventory management. In this research, an approach is proposed for warehouse inventory management based on part clustering. The proposed approach categorises inventory parts based on their pick frequency, age, price, and sensitivity to transportation. Part grouping helps the decision makers to identify whether to keep the part in the warehouse, move it to an offsite inventory storage, or scrap it. The approach also determines when and how many parts should be moved from the offsite storage to the internal warehouse in order to balance the inventory and minimise the transportation costs. Dynamic reports are generated on a regular basis to effectively manage the inventory. [Received 3 September 2016; Revised 9 March 2017; Accepted 13 March 2017]