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L.L. Bean Case Study

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L.L. Bean Case Study

In the case study for L.L. Bean the primary problem for the company was attempting to forecast item demand. The company seeks to match supply with demand in order to efficiently and effectively keep the right amount of product and not have a shortage or surplus.

Rol Fessenden, Manager of Inventory Systems, knows that forecasting the demand of catalog items is an expected hurdle that any retail company faces. Fessenden, however, formulates his strategic forecasting plan by attempting to detect customer behavior and work with vendors in order to produce the approximate amount of product that will be demanded. Predicting trends in human behavior is a risky business. Human behavior is a product of the surrounding environment, ergo, many variables have differing effects.

One of the strategies that L.L. Bean conceptualized was the creation of seasonal catalogs. This direction complemented that fact that people shop by season, i.e. holidays. People are more prone to buy presents in the fall, which happens to be Christmas. Another strategy was a team was formed consisting of inventory buyers and product personnel. This team ranks items by expected dollar sales. Then if there are any conflicts between team members a resolution is formed. This process proves effective by combining expertise from different departments. Yet another solution to the forecasting dilemma was one of a quantitative approach. Using frequency and probability distribution theory, L.L. Bean, Inventory Specialist were able to accurately estimate product demand with a variance to compensate for errors.

Through all of the aforementioned processes L.L. Bean rose from the rubble of the mail order business to the high rises of the catalog industry. Quantitative and logical human behavior approaches proved to be effective in forecasting the demand for goods pushing the company to the apex of efficiency.



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