This paper seeks to illustrate the successful development of a warehousing decision support system (WDSS) that aided the Buckeye Cable Vision Incorporated (BCV) in making its strategic warehouse expansion and re‐warehousing decisions. The WDSS utilized by the BCV can be a blue‐print for other companies which would like to improve their warehousing productivity and strengthen the warehousing link to their supply chain.
Warehousing costs make up a significant portion of a company's business expenditures. Thus, many companies are pressured to control warehousing costs by improving their warehousing productivity. However, it is not an easy task to improve warehousing productivity given the increased complexity associated with today's warehousing activities such as value‐added services and cross‐docking operations. To cope with such warehousing challenges, a specific decision support system (DSS) that incorporates several computer‐based models into the warehouse decision‐making process is proposed. These models include: a simulation model based on computer‐aided design, an analytic hierarchy process, and a forecasting technique. To demonstrate the usefulness of the proposed DSS and provide practical guidance for other companies that seek the DSS as a powerful decision‐aid tool, this paper conducted an in‐depth case analysis of the BCV that successfully exploited the WDSS.
Through the BCV's case, it was shown that the WDSS could not only enhance warehousing productivity, but could also improve supply chain visibility. Also, it was found that the WDSS success lay in the company's ability to sustain high data quality by standardizing, cleaning, and updating relevant data on a real‐time basis.
Since the early 1970s, a DSS has attracted attention from both profit and non‐profit organizations. Despite the long history of DSS evolution, it has been rarely applied to strategic warehousing decisions. This paper is one of the first attempts to develop a specific DSS that can assist warehousing managers in documenting warehousing costs, identifying non‐value adding activities, evaluating strategic warehousing alternatives, and utilizing given warehousing resources.
