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Ll Bean Case

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Submitted By moupia22nd
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L.L. Bean has adopted a two stage ordering process for products with “one-shot” commitments
(i.e. products that they get to order only once because of long supplier lead times). First they determine a forecast for an item and then they have a process for converting that forecast into an order quantity.

Questions
1. How significant (quantitatively) of a problem is the mismatch between supply and demand for L.L. Bean?

From the first page of the case we have an estimate of $11 million cost of lost sales and backorders and $10 million associated with having too much of the wrong inventory. These costs are stated as being a conservative estimate.

2. On the course website is an Excel file that contains demand and forecast data for a collection of items. Suppose those are the data L.L. Bean will use to plan their next season. Consider an item that retails for $45 dollars and costs L.L. Bean $25 dollars. The liquidation price for this item will be $15. The sales forecast for this item is 12,000. What order quantity would L.L. Bean choose for this item?

Using L.L. Bean’s current methodology, our first step is to understand the frequency distribution of past forecast errors. We compute the error by dividing the actual by the forecast such that a number above 1 represents that the item was under forecasted. The plot below provides a histogram of these errors. As an example, from this plot, 50% of the errors are between 0.49 and 1.04 or 66.6% of the errors are between 0.49 and 1.17 Our second step is to compute the contribution to margin against the liquidation value if not demanded. From the information above we have the margin contribution as $45 - $25 = $20 and the liquidation value as $25 - $15 = $10. This gives us a 20/(20+10) = 0.66 fractile.

From the chart above we assume that forecast error for any item at the 0.66 fractile is 1.17
This means L.L.

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