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LL Bean Inc Item Forecasting and Inventory Management

Solution Id Length Case Author Case Publisher
2651 1682 Words (6 Pages) Arthur Schleifer Harvard Business School : 893003
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L.L. Bean Inc., founded by Leon Leonwood Bean, started off in 1912. It was a mail-order business that was set up after acquiring a list of non-resident Maine hunting license holders. According to the Golden Rule of L. L., the goods need to be sold at suitable profits so the customers can return for more. L.L. Bean Inc. specialized in men's and women's accessories, footwear, apparel etc., and was known as a catalog manufacturer. However, their major catalogs came out in spring, fall, summer and Christmas. These were distributed among their customers based on their purchasing behaviour. In order to manage the inventory, the initial forecasts were made during the period of December till March. Then, the layouts were made in January and February. Their black and white version came out in July, after which inventory managers were provided with the product line. So, these catalogs helped in creating demand.

Following questions are answered in this case study solution:

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

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

  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? 

  4. How would you address Mark Fasold’s concern that the number of items purchased usually exceeds the number forecast?

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

Case Study Questions Answers

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?

The process for forecasting the demand of a specific item begins by making a list of the items that need to have their forecasting done. Then by taking the expected dollar value, the items are ranked, after which the historical forecast errors are calculated. That is done by taking the ratio of the actual demand to the forecasted demand. For instance, the item under question is a pair of women's shoes, so for forecasting its demand, the actual demand for a pair of new women's shoes in the previous year would be divided by the forecasted demand for the previous year's new pair of women's shoes. The value obtained would be the historical forecast error for the new pair of women's shoes in the current year. After that, this error's frequency distribution is compiled across different items. Now the frequency distribution of the previous year's forecast errors would be used as the frequency distribution of the current year's forecast errors. Taking the example forward, if it were found that 30% of pairs of women's shoes in the previous year had forecast errors of 0.4 and 1.2, that would that with a probability of 0.3, the current year's forecast of women's shoes was also to be between the range of 0.4 and 1.2. So, if the forecast came out to be 1000 items, it would be assumed that the demand for the new pair of women's shoes in the current year would be between 400 and 1200 at a 30% chance rate.

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

For any item order quantity to be determined, the balance of the item's contribution to the margin, if it is demanded of the customers, is taken out against the liquidation cost of it not being demanded of the customers. The liquidation cost is the actual costs incurred while creating/producing that particular item. It can also be said that the quantity is determined by finding a balance between the profit that can be incurred if the item has demand against the loss if the item has no demand. Let's say that an item for Bean costs $15, so its selling cost would be around $30, and the liquidation cost here would be $10. The profit for selling the margin unit would be $30 - $15 = $15, and the loss for not being able to sell that margin unit would be $15 - $10 = $5. Due to these reasons, the optimal order size would be the 75% percentile of the item's probability distribution of demand. Using the example in the previous questions of the pair of women's shoes. Let's say with the 75% percentile, the forecast error was 1.2, and with the demand of 1000 units, the demand distribution would then be 1000 x 1.2 = 1200, so for Bean, the relevant decision of units of a particular item to be stocked would need to be 1200.

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