L.L.Bean Case Memo

L.L.Bean, Inc. has been a trusted source for quality apparel and reliable outdoor equipment. Founded in 1912 by Leon Leonwood Bean, the company began as one-man operation selling Maine Hunting Shoe. L.L.Bean’s Golden rule had been “Sell good merchandise at a reasonable profit, treatment your customers like human being, and they will always come back for more.” High customer satisfaction rate distinguished L.L.Bean from other competitors. L.L.Bean mainly focused on mail-order and telephone order services, only one retail store was available.

Each catalog had a gestation period of around nine month, forecasting demand at Item Sequence level was very difficult and not very accurate. There were always some items far exceeding the demand forecasts and some items sell way below expectation. The estimated annual costs of lost sales and back order were $11 million and cost associated with having too much of wrong inventory were an additional $10 million.

To determine inventory level, L.L.Bean first freezes a forecast for its demand for the upcoming season based on the book forecast and past demand data. The frozen forecast is then used together with the historical forecast errors, A/F ratios for each item. The frequency distribution of past forecast errors was computed and used as a probability distribution for the as yet unrealized future forecast errors. Then each item’s commitment production quantity was determined by balancing the individual item’s profit liquidation margin if demanded against its scrap value if not demanded.

L.L.Bean was able to capture the real demand and the consequences of understocking. Historical sales and demand data were available to determine the forecast. However, the Inventory System Manager is concerned about treating the errors associated with all items as equally representative of the forecast demand of any item. Also, the critical ratio estimated using the contribution margin and liquidation cost might not be the accurate. Additional costs such as loss of customer goodwill associated with understocking of items and inventory carrying costs associated with overstocking were not considered in this critical ratio. The wide dispersion in forecast errors was also problematic.

There could be several improvements that L.L.Bean can incorporate in the production forecasting process. Instead of balancing the estimates of contribution margin and liquidation value to determine production quantity, other costs associated with overstocking and understocking should be included as well. The cost of understocking should include not only the contribution margin, but also include loss of future business due to customer dissatisfaction. For example, if a particular item is not in stock, the customer might cancel the entire purchase and go elsewhere. The cost of overstocking should include the inventory carrying cost as well as the salvage value, and customer dissatisfaction costs. For example, customer might be dissatisfied if an item goes to liquidation with a much cheaper price shortly after their purchase of that same item. Instead of forecasting using only the bottom up approach, L.L. could considering using a mid-level approach and top-down approach combining with the bottom up approach for better forecast. The fashion industry is fast changing thus the product demands change very rapidly. So only using the historical data to predict demand is not enough. L.L.Bean should continuously updating its forecast based on latest data. “Customer behaviours is very hard to predict”, L.L.Bean could involve SMEs in the whole process. With experts’ opinion and data analysis combined together, L.L.Bean would able to forecast demand better. Finally, L.L.Bean could consider vertical integration of suppliers or incentivise venders to corporate with the “Quick Response” initiative and address customers’ demand.