LL Bean methodology Essay

LL Bean methodology Essay
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  • University/College:
    University of Arkansas System

  • Type of paper: Thesis/Dissertation Chapter

  • Words: 489

  • Pages: 2

LL Bean methodology

LL Bean utilizes a probability distribution methodology to help predict the optimal order size of a specific item. The probability distribution is driven by a series of calculations that will predict forecast errors. One of the major concerns is that LL Bean tends to order more inventory than what was predicted in the frozen forecast. Their logic for doing this is that the cost of understocking exceeds the cost of overstocking. According to Marck Fasold (CFO), this methodology leads to major discrepancies with forecasting the demand for their products. Also, this leads to buyers being challenged that products are being ordered that do not align with their forecasting predictions. In addition, Rol Fessenden eludes to the fact that the methodology has issues because they can’t find any real distribution errors among products and he is not convinced about the estimating contribution margins and liquidation costs.

In summary, there are many challenges to LL Bean’s ordering process. LL Bean tends to be okay with just overstocking rather than focusing on making accurate predictions. This approach leads to unwarranted costs that can be eliminated if they focused on refining their ordering process and methodology. Secondly, it seems that buyers make forecasts that are not being applied by the company which turn leads to unsatisfied buyers because they feel their judgments are not being respected. Lastly, LL Bean should allow the distribution forecast errors to be handled by the buyers during their initial forecasting discussion.

The typical forecasting process for LL Bean involves various individuals (including the Inventory Buyer and product “people”) meeting together to make forecasts of items by book. Specially, an Excel spreadsheet is utilized to rank items by expected dollar sales and “discussions” are involved to make adjustments. The buyers tend to use their own personal judgment where they invent a “rule of the thumb” to develop forecasts. Furthermore, they use personal feelings in their forecasting predications which in turn can lead to errors without hard data. The issue with the forecasting process is that it is purely focused on an individual’s personal thoughts rather than the usage of historical sales data as a benchmark.

Also, according to Barbara Hamaluk (a buyer for men’s knit shirts) there tends to be a variance with the item forecasts and the dollar target of that book. In summary, by having a forecasting process that is based on personal judgment versus actual data can lead to issues such as over and understocking items. Also, this can further lose money for LL Bean due to the inaccuracies of ordering too much or too little based on personal “opinion” forecasts. LL Bean can improve this process by having their buyers follow a standardized process that requires them to use historical and valid data to predict forecasts.


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