Forecasting Demand with Point of Sales Data—A Case Study of Fashion Products

ForeCAstIng DemAnD wItH PoInt oF sAles DAtA—A CAse stuDy oF FAsHIon ProDuCts By Bill Sichel T T he retail industry is faced with increasingly shorter lead times due to changing customer-supplier relationships and overall competitive and profitability pressures. Many retailers utilize weekly POS data to improve forecasting accuracy of their products by store location. In this article we describe methods to improve weekly demand forecasts by using the Point of Sales (POS) data. This data, which represents retail store sales to their final consumers, are captured electronically from retail accounts. In forecasting consumer demand trends, POS data represents the most current indicator of actual consumer demand; in fact, it is the first indicator of changes in consumer demand patterns. In consideration of lead times and the potential short duration of trends, the fashion industry requires a weekly forecasting technique, which detects early changes in consumer demand so that it can quickly respond by revising forecasts, as well as production plans. The Monet Group, acquired by Liz Claiborne, is the world leader in the design, production, and distribution of costume jewelry. ...