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The 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. End customers include most large retailers such as Macy's (USA), Breuninger (Germany), Harrods (UK), Galeries Lafayette (France), and De Bijenkorf (Holland), as well as many other smaller retail outlets.
METHODOLOGY
POS data is transferred via EDI (Electronic Data Interchange), which is to say, it is transferred by way of computer-to-computer data transfers. Retail POS data is transmitted from retail stores to our computer facility once each week. The POS data is modeled for seasonality patterns and then an annual (single number) forecast of expected sales to POS accounts is produced which is then broken down into 52 weekly periods. The derived annual forecast of retail POS sales is inflated for non-POS customers, suchas international and military customers. Due to the difference in seasonality between retail POS sales and shipments...