I nternational Journal of EmergingTrends& Technology in Computer Science (I JETTCS) Web Site: www.ijettcs.org Email: editor@ijettcs.org, editorijettcs@gmail.com Volume 2, Issue 6, November – December 2013 ISSN 2278-6856 Volume 2, I ssue 6 November – December 2013 Page 266 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.. Keywords: Retail Forecasting, Data Mining, Soft Computing, Neural Network. 1. INTRODUCTION Data mining techniques have been used to uncover hidden patterns and predict future trends and behaviours in retail markets. The competitive advantages achieved by data mining include increased revenue, reduced cost and much improved marketplace responsiveness and awareness. Data mining has been applied to a number of Retail applications, including development of trading models, investment selection, inventory control and so on. Knowledge provides power in many retail context enabling and facilitating the preservation of valuable heritage, learning new things, solving intricate problems, creating core competencies and initiating new situations for both individuals and organizations now and in the future [1]. In most sectors’ retailing is extremely competitive and the financial margins that differentiate between success and failure are very tight, with most and established industries needing to compete, produce and sell at a global level. To master these trans-continental challenges, a company must achieve low cost production yet still maintain highly skilled, flexible and efficient workforces who are able to consistently design and produce high quality, low cost products. In higher-wage economies, this can generally only be done through very efficient exploitation of knowledge [2-3]. However knowledge can take many forms and it is necessary to identify the kind of knowledge to be mined when examining the huge amount of data generated during retail. Understanding the underlying demand patterns for a particular product mean that outlets can be re-stocked in sufficient time to cope with changes in consumer demand .For products those are perishable or have a short shelf- life, this issue is more critical than the slower moving products with smaller demand requirements . The super market chains face additional problems in terms of number of stores, slow and erratic sales for many items at the store level, assortment instability, and promotional activity and price changes [4]. Modern demand forecasting systems provide new opportunities to improve retail performance. Although the art of deterministic demand estimation of individual merchant may never be replaced, it can be augmented by an efficient, objective and scientific approach to forecasting demand. Nevertheless it remains a difficult task. Point of sales automation through large- scale system may be an aid to handle the mass of retail data - organizing it, mining it and projecting it into future customer behavior. An ecommerce network facilitates this automated data collection, which is further facilitated by statistical analyzer. 2. DATA MINING TECHNIQUES 2.1 Classification and Issues of Data mining in Retail Application The objective of data mining is to discover hidden knowledge, unknown patterns , and new rules from large databases that are potentially useful and ultimately understandable for making crucial decisions. Based on the type of knowledge that is mined, data mining can be mainly classified into the following categories Retail Forecasting using Neural Network and Data Mining Technique: A Review and Reflection Archana Kumari 1 , Umesh Prasad 2 , Pradip Kumar Bala 3 1,2 Department of Computer Science & Engineering, BIT Ext Lalpur, Ranchi-834001, India 3 Indian Institute of Management, Ranchi Ranchi-834001; India