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# Ll Bean

Submitted By br268
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Pages 4
L.L. Bean, Inc.
SINGH Varun
M1, GR21
L.L. Bean is a privately owned mail-order, telephone, retail and online catalog company with headquarters in Freeport, Maine in the United States. It was founded in 1912 by Leon Leonwood Bean, and is currently worth US\$1.5 billion. The major problem faced by the supply chain of the firm is the over reliance of initial forecasting on estimates and speculations. 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 forecasting team at L.L. Bean decides how many units of an item to stock using the following methodology: i. Divide the past demand data by the forecast demand to compute historical forecast errors, expressed in the form of A/F ratios, for each individual item. ii. The frequency distribution of these errors is compiled across items. iii. The frequency distribution of past forecast errors is used as a probability distribution for future forecast errors. iv. The cost of understocking an item is calculated by subtracting the cost price from the selling price. v. The cost of overstocking an item is computed by subtracting the liquidation price from the cost price of the item. vi. The critical ratio CU / (CU + CO) is calculated, which gives the optimal order size as the quantile of the item’s probability distribution of demand. vii. The commitment quantity is calculated as the product of the critical ratio quantile of the distribution of forecast errors and the frozen forecast data.

2. What item costs and revenues are relevant to the decision of how many units of that item to stock?
The relevant cost and revenues for deciding the number of stock units of an item are: * Cost of the item incurred by L.L. Bean from the vendor * Revenue obtained on regular sale of the item * Revenue obtained when the item is liquidated

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?
In order to arrive at the demand forecast for a particular style of men’s shirt new to the catalog, Scott Sklar and his team must have access to the actual demand and forecast data for other new shirts that were introduced in previous years. The trend of the performance of similar new shirts on introduction can provide a realistic estimate for the demand of this particular style of shirt. They should also analyze data for the demand of similar shirts offered by their competitors. They also need to consider the possibility of this shirt cannibalizing on the demand for other shirts. They should look into the past data to find out what proportion of the demand for a new shirt is incremental, and how much of it is stolen from other shirts. This demand can be stolen from shirts of similar styles as well as from shirts of similar color but different styles. The process is not very scientific or quantitative, and relies heavily on past experiences and made-up rules of thumb. 4. What should L.L. Bean do to improve its forecasting process?
L.L. Bean can undertake the following steps to improve its forecasting process: * It might be inaccurate to assume that the error distribution is uniform across products. Instead of compiling the frequency distribution of error in forecast across products, it can be calculated for each individual item across different years. It is possible that different buyers have different levels of errors and therefore, compiling the error frequency distribution for individual products would be more logical. This might also reduce the dispersion in forecast errors for a lot of items. * As fashion is a rapidly changing industry, L.L. Bean should conduct more market research to understand the requirements of the customers. This will be particularly helpful in forecasting the demand for “never out” products. Market research can easily be undertaken by sending feedback forms along with the catalogs. * Reducing dependency on foreign vendors by establishing relationships with local vendors could reduce lead time, thereby facilitating the possibility of placing additional subsequent orders if demand for a product is higher than initially anticipated. * Introduce size of the item as an additional step in their product hierarchy so that forecasting can be done for individual sizes of each product. * Make product catalogs available online and analyze the number of visits to a particular product page in order to update production commitments for that product based on the interest shown by customers. * Perform forecasting at the Demand Center level, which can be used to triangulate the demand for all the items within each Demand Center. * Making sure that the estimations of marginal contribution and revenue on liquidation are as close to the real numbers as possible.

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