Forecasting in LL Bean's Supply Chain


Added on  2019-09-26

2 Pages633 Words1484 Views
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 and satisfies customer orders from the available inventory. In the past, this has often put the firm at risk of overstocking. At present, the JDA Demand Tool is used to develop and maintain demand forecasts at LL Bean. For items which are well-established in terms of their robust demand, the forecasting system uses past trends to forecast future demand. There is often seasonality that is observed and thus enough information is available to forecast how much quantity of the item will be required and at what point in time. Appropriate adjustments to the system parameters are done as warranted by any in-season trends in the sales of items. For items which lack sufficient demand information, the forecasting system allows for computation of the ratio of actual demand to forecast demand (the A/F ratio) based on whatever limited past data is available for similar competing items, and computes each such item’s profitability as well as the costs of overstocking and the costs of understocking, and finally determines the appropriate 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 of sales as its items are purchased by customers and move from the shelves. This real-time information 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 reduce excess inventories for its seasonal items, improve the availability of those items which are sold 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 model parameters, such as trends and seasonality, with any new demand information. Thus, in a static forecasting model, once the parameters are established, they remain unchanged (or static) 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 model parameters, such as trends and seasonality, adapt to new demand information in an appropriate manner. Thus, any model parameter established earlier would be adjusted to incorporate the effects of new demand observation. Thus, if demand for a product were to be impacted 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 stocking strategy. Retrieved October 01, 2017, from http://www.supplychainquarterly.com/topics/Strategy/201104llbean/
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