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1 Introduction
Supply chain management systems and intelligent systems for forecasting have grown significantly during the last two decades. However, the growth of these two has mostly taken place independently. On one hand, we have very sophisticated supply chain management systems and on the other, we have very sophisticated forecasting systems. However, we rarely come across the combination of two sophisticated systems. At the same time, retailing has gone through a period of unprecedented change as customers' demands and competition amongst the retailers have intensified in the last 25 years in most countries. Over this period, the retail industry has seen a transition from manual merchandise control systems to the computerized systems. Retailers with the sophisticated computerized systems for better forecasting and improved inventory management have an edge over the others in terms of profitability. Initially, these sophisticated systems were being used only by the supermarkets. Gradually, other retailers found it necessary to remain competitive in their businesses. India is also not an exception to this movement and it has witnessed a sea change in retail business in the last ten years.
In a typical retail outlet of grocery items, number of stock keeping units (SKUs) is in the range of a few thousand and in a large supermarket, it is generally more than 50,000 SKUs. Retailers buy these items from a large number of distributors and sometimes directly from manufacturers. For each item, inventory managers are to decide when to purchase, how much to purchase and from whom to purchase. Efficient forecasting for future demand is the key to success for inventory management. Future demand of an item depends on a large number of factors and it has been a challenging task for the retailers to predict the future demand.
The current research work suggests a data mining-based business intelligence model for demand forecast and its application in enhancing supply chain performance in an Indian retail outlet. The model suggests the use of clustering-based segmentation of the customers as an input to forecasting. Based on customers' demographical profiles and other details, segmentation of customers is done in clusters using data mining software, SPSS-Clementine 12.1.
2 Related work
Improved demand forecasting accuracy can result in monetary savings, greater competitiveness, enhanced channel relationships, and customer...