Abstract:
Retail Forecasting is a challenging problem in modern world. At the organizational level, forecasts of sales are essential inputs to many decision activities in various functional areas such as marketing, sales, and production/purchasing, as well as finance and accounting. Sales forecasts also provide basis for regional and national distribution and replenishment plans. The importance of accurate sales forecasts for efficient inventory management has long been recognized. A good forecasting model leads to improve the customer’s satisfaction, reduce cost, increase sales revenue and make production plan efficiently. In this paper, we focus on exploring the concept of soft computing and data mining techniques to solve retail problem. This paper describes data mining in the context of retail application from both technical and application perspective by comparing different data mining techniques. This paper also discuss soft computing techniques viz. neural network, genetic algorithm etc. in sales forecasting and inventory management.