L.L. Bean, Inc. Case Study

Custom Student Mr. Teacher ENG 1001-04 22 March 2016

L.L. Bean, Inc. Case Study

1. How does L.L. Bean use past demand data and a specific item forecast to decide how many units of that item to stock?

L.L. Bean uses different type of calculation to determine the number of units of a particular item it should stock (new item or never out item). First we detect a frozen demand forecast for the item in the upcoming season. This figure is a result of an agreement between product people, merchandising, design and inventory specialists. Then, we analyze the historical forecast errors (named A/F ratios) and the frequency distribution of these errors for each individual item by using the historical demand and forecast data. Once the historical forecast errors is determined, we define future forecast errors by using frequency distribution of past forecast errors as probability distribution. Finally we find the service level based on a profit margin calculation: determine by balancing contribution margin if demanded against its liquidation cost if not demanded. We can notice that for new items it is more complicated to have good prevision because we know very little about them.

2. What item costs and revenues are relevant to the decision of how many units of that item to stock?

Principally, L.L. Bean will need 3 types of data to decide how many units of an item to stock. First, they need to know the buying cost of the item. Then, they need the selling price of the item. With these 2 figures, they can calculate the profit margin and the costs of understocking. The 3rd figure they need is the liquidation cost of an item. With the liquidation cost, they can calculate the costs of overstocking. With all these data, we can decide the final amount of items to stock by comparing the understocking costs and overstocking costs.

3. What information should Scott Sklar have available to help him arrive at a demand forecast for a particular style of men’s shirt that is a new catalog item?

Scott Sklar should have data about actual and forecasted demand of new item that were previously introduced. With these data, he can know the different costs of launching a new item. Then, he should have an idea of the selling price given by marketing, sales and production department. With that, he has to know cost of sales, commissions provided for sales, stock outs and backorders cost. He can also compare this new item to the competitor and get sales information. It will help him to understand the existing market trends for that new item. Following that he should know the level of buffer stock he should have to avoid stock outs by matching stock out costs and over-stocking costs. Finally he should precise the service level by calculating the profit margin and observe if new products are pulling customers away from existing products. All of this will help him to forecast the demand for a new catalogue item.

4. What should L.L. Bean do to improve its forecasting process?

L.L. Bean has 5 important things if the company wants to improve its forecasting process: They have to have more than they have market researches to their products they will sell. Actually, they will be understand clearly all news tendencies so they can adapt their stock to the others They don’t have to understand their real demand, because the goal of the real demand is to increase the profitability In the business world, a company has to understand and find a solution ton maintain the accurate and also a timely data that supports the business decisions The company has to have a forecasting discipline. This one will include a commitment to guide the forecasting process in the firm. Moreover, forecasting is strength and an element of strategic decision-making. Right people have to be involved. In fact, the forecasting management involves that people need to have an easy access to input their intelligence for the forecast, for those who have market information. This intelligence has to be used because this will provide information on future demand spikes and troughs.


  • Subject:

  • University/College: University of California

  • Type of paper: Thesis/Dissertation Chapter

  • Date: 22 March 2016

  • Words:

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