A System-Based Classification and Prioritization Model to Prevent Dead Stock Occurrence in Retail Store Systems

Abstract

Retail chain stores commonly experience dead stock inventory accumulation due to the absence of indicators and decision rules in the inventory management system to track down the impact of potential dead stocks when left “unattended” or “unmanaged” in the warehouse. Potential dead stocks are inventory items that are either near-expiry, near its end-of-season, near the end of its product market life cycle, or simply slow moving which will soon become dead stocks in the warehouse if not managed in a timely manner. Most retail systems have focused on fighting the dead stock fire rather than developing a standardized process to manage inventories and prevent potential dead stocks from becoming dead stocks. The systematic management perspective is to identify the potential dead stocks first and then apply the best strategies to prevent the occurrence of dead stocks. This research aims to develop a standardized potential dead stock identification and prioritization framework that will provide the level of priority for management intervention using decision rules. Literature review is performed to develop the indicators required. The framework is then validated through hypothetical data sets. As a result, the classification phase shows that the data sets produced similar industry findings on dead stock composition as a percentage of total inventory. Next, the prioritization phase shows that considering a 10-4-1 risk weight produces more discriminating ranks than a 9-3-1, adopted from the House of Quality (HOQ) framework. The rank discrimination is an important metric for this to address the primary research objective as it represents the ability of the framework to prioritize intervention given urgency based on criteria and resource constraints. Further research may be performed on enhancing the decision rules used in producing the prioritization output of the developed framework.