Data Mining for Inventory Control for Blessed Superstores Limited: A Pilot Study

ABSTRACT

As the nation’s economy has tightened, managers of retail businesses have been greatly challenged in recent years to become more strategic in their planning. These managers need fast, reliable and interpretable analysis of different transaction data in order to effectively assess the performance of their business organizations. The introduction of computerized Point-of-Sale terminals has made it possible for retailers to easily accumulate large stores of sales data. This wealth of data provides the required knowledge for competitive advantage, through the application of data mining. Among other benefits, data mining can be used to enhance inventory control. With data mining, retailers are able to conduct detailed analysis of sales transactions to accurately forecast demands that match customer profiles. The purpose of this thesis is to explain how the data mining technology can be used in small retail firms to significantly increase sales and improve inventory control. The Blessed Superstores Limited, a small retail outfit, is used as a pilot study. As a necessary first step to data mining, a computerized Point-of Sale system, which is to be used to collect all transaction data at the superstore, has been developed. The data mining process would only commence after sufficient data has accumulated in the database.