A decision support system for determining optimal retention stocks for service parts inventories

This paper reports on the development and successful implementation of a decision support system (DSS) for analyzing service parts inventory retention stocks. The DSS was implemented in a Fortune 100 company vzdh an initial $50,000,000 investment in service parts inventory. During the last two years of use, approximately $13,000,000 of service parts have been scrapped (disposed) with the help cf the DSS. Very few of these parts have had to be repurchased from scrap dealers. This has resulted in approximately $6,000,000 in tax savings alone. The system continues‘to be used regularly by the company. The paper contributes to the service parts management literature in three ways. First, the paper proposes a new forecasting model for the retention stock problem. Second, the paper develops a new inventory model which captures a richer operating environment. Third, the paper suggests how these models may be integrated in an interactive, menu-driven, databased DSS. Although the forecasting model, inventory model, and DSS are described in the context of a specific company, the DSS and the embedded models are applicable to managing service parts in a wide variety of environments.