1. What role does forecasting play in the supply chain

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1. What role does forecasting play in the supply chain of a mail order firm such as LL Bean?Historically, the mail order firm LL Bean has followed the made-to-stock philosophy andsatisfies customer orders from the available inventory. In the past, this has often put the firmat risk of overstocking.At present, the JDA Demand Tool is used to develop and maintain demand forecasts at LLBean. For items which are well-established in terms of their robust demand, the forecastingsystem uses past trends to forecast future demand. There is often seasonality that is observedand thus enough information is available to forecast how much quantity of the item will berequired and at what point in time. Appropriate adjustments to the system parameters aredone as warranted by any in-season trends in the sales of items. For items which lacksufficient demand information, the forecasting system allows for computation of the ratio ofactual demand to forecast demand (the A/F ratio) based on whatever limited past data isavailable for similar competing items, and computes each such item’s profitability as well asthe costs of overstocking and the costs of understocking, and finally determines theappropriate quantity of the item that must be made available at a point in time.The firm’s demand forecasting system is able to get real-time information from all points ofsales as its items are purchased by customers and move from the shelves. This real-timeinformation on the demand for their items allows them make better item-line forecasts.With a much better demand forecasting system than before, the firm has been able to reduceexcess inventories for its seasonal items, improve the availability of those items which aresold at all times during the year, and lower its costs of warehousing of its items.2. How do static and adaptive forecasting methods differ?A static forecasting method, as the name suggests, would not see any change in modelparameters, such as trends and seasonality, with any new demand information. Thus, in astatic forecasting model, once the parameters are established, they remain unchanged (orstatic) regardless of what the new demand observation happens to be. The parameters need noadjustment and continue being used for all forecasts going forward. Due to the static nature ofthe estimated values of the said parameters, a static forecasting method works well only wheninsignificant variances are expected to occur in the model parameters over time. In contrast,an adaptive forecasting method, as the name suggests, would ensure that the modelparameters, such as trends and seasonality, adapt to new demand information in anappropriate manner. Thus, any model parameter established earlier would be adjusted toincorporate the effects of new demand observation. Thus, if demand for a product were to beimpacted in a significant manner because of advent of any disruptive technology, the adaptiveforecasting method would have an immediate appropriate response.References:James A. Cooke | From the Quarter 4 2011 issue. (n.d.). L.L. Bean's smarter stockingstrategy. Retrieved October 01, 2017, fromhttp://www.supplychainquarterly.com/topics/Strategy/201104llbean/
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