# L.L.Bean case Study

Bean had a gestation period of about 9 months. Its creation included merchandising, design, product, and inventory specialists. The first step of its creation process is initial conceptualization followed by the preliminary forecasts of total sales. Then forecasts were developed by book. After the layout and pagination of the books, initial commitments to vendors were made.

The subsequent step is that item forecasts were repeatedly revised and finally the items were fixed.

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When catalogs were in the hands of customers, inventory managers decided on additional commitments to vendors, scheduled replenishments, handle backorder, etc. Inventories which cannot be sold at that time might be liquidated, marked down and sold through special L. L. Bean promotions, or carried over to the next year. 2.

The company determine their actual demand based on historical forecast errors. The historical forecast errors were computed for each item in the previous year and the frequency of these errors.

The urgency of past forecast errors was used as a probability distribution for the future errors. For example, in the past year, If there were 50% of the forecast errors for “new” items were between 0. 7 and 1.

6. Then the company can assumed that the forecast errors for “new’ item in the current year also would be between 0. 7 and 1. 6 with the possibility 50%. If the frozen forecast for an Item is 1000 units, we can assume that with the probability 50%, the actual demand of the item would fall between 700 and 1600 units.

3.

After forecasting the demand based on historical forecast errors. The company will determine the Item’s commitment quantity by balancing the Individual Item’s contribution margin If demand against Its liquidation cost if not demanded. For example, If a cost of an Item Is \$15, and It would be sold for \$30. For the inventory left over could be sold at liquidation for \$10. The profit for that Item would be \$30-\$15=\$15; the loss for falling to sell that Item Is \$15-\$10=\$5.

Then the optimal order size would be \$1 5/ (\$15+\$5) =0. Fractals of the Item’s probability distribution of demand. Suppose the 0. 75 Fractals of the distribution of forecast errors was 1. 3 and the frozen forecast for that Item was 1000 units.

The company would make a commitment for 1000* 1. 3= 1300 units. The company makes their ordering decision based on the actual demand distribution which was determined by forecast errors. But the wide dispersion exists In forecast errors. At the same time, the company also cannot convinced that they estimate their contribution margin and ululation cost correctly.

So using this method need to take some risks.

L. L. Bean case Study By Chihuahuas errors. For example, in the past year, if there were 50% of the forecast errors for “new’ items were between 0. 7 and 1.

6. Then the company can assumed that the with the possibility 50%. If the frozen forecast for an item is 1000 units, we can forecast errors. The company will determine the item’s commitment quantity by balancing the individual item’s contribution margin if demand against its liquidation cost if not demanded.

For example, if a cost of an item is \$15, and it would be sold for item would be \$30-\$15=\$15; the loss for failing to sell that item is \$15-\$10=\$5. Then the optimal order size would be \$1 5/ (\$15+\$5) =0.

75 practice of the item’s probability distribution of demand. Suppose the 0. 75 practice of the distribution of forecast errors was 1. 3 and the frozen forecast for that item was 1000 units. The company forecast errors. But the wide dispersion exists in forecast errors.

At the same time, liquidation cost correctly. So using this method need to take some risks. 