Teambuilding and Conflict Resolution at LL Bean - Case Study Example

? LL Bean Case Study Table of Contents Question 6 Question 2 7 Question 3 8 Question 4 8 References 10 Question The ment is being made in comparison to demand as it is captured or not captured in a physical store. In a physical store, the demand capture data is relative to a certain maximum number, and that number is the amount of physical goods that is present at the store. Beyond that maximum physical number, there is no way for the physical store to find out what the real demand is if all customers potential customers were provided with the amount that they want. If the inventory runs out, then the store can re-order, but there is really no way of knowing whether while the good is out of stock more people want that particular good or not, because there is no catalog or mechanism for the store to demonstrate what an out of stock good looks like, unless there is a physical sample and the physical stores have an supply on demand option. Moreover, in a physical store, the audience for a particular good is limited to those who physically go to the stores to view the merchandise, limiting the potential demand to the shop frequenters. In contrast, LL Bean, by making the catalogs available for the duration of a season, and allowing for orders, is able to capture the demand for a larger subset of potential customers. In this case this demand is more real or comprehensive than the more limited demand subset that the physical store is able to address/cover. On the other hand, one can also say that the audience for the good is limited to those who receive the catalogs. In this sense this demand is arguably not universal enough. Moreover, from the point of view of being able to satisfy all demand, the statement needs to be qualified. Demand forecasting is part art in LL Bean, as has been demonstrated in the study, and even with the use of A/F and probability distributions for probable demand there are many opportunities to overshoot targets. This is especially true when the costs associated with overstocking on an item are larger than the costs of understocking, or being conservative on demand projections. In the case where stocks on hand turn out to be larger than the actual demand, then one can say that LL Bean is indeed able to capture the true demand, with the caveats on the limits of catalog marketing being able to capture all possible demand as discussed above (Schleifer, 1992, pp. 1-5). Question 2 Recording demand for an item that is out of stock may not have much of a bearing in that current season, given that for many of the items there is a long lag time for deliveries that replenishing out of stock items during the current season is often not feasible. First if it were feasible to restock within the season, obviously not being able to capture demand for an item that is out of stock means that the company misses out on any additional sales tied to that non-captured demand. The company is unable to know how many of that out of stock item to reorder. Second, for most items that cannot be replenished during the season, the implications for LL Bean are with regard to being able to record future demand and factor them into the forecasts. This is so because from the case itself, LL Bean uses historical actual demand and forecast demand figures as a weighting factor to determine how many to order of individual items for the coming seasons. If LL Bean is unable to record demand for items that are out of stock, then they are no different from operations that are purely based on physical stores, and their future demand forecasts then become continuously flawed and inaccurate Such inability to record out of stock demand translates to perpetual misses in future forecasts for the demand for that out of stock item. This has consequences for the company’s profits obviously (Schleifer, 1992, pp. 1-5). Question 3 The marginal unit profit for a sale of the shirt is $ 25. This is selling price minus cost. The marginal unit loss for a non-sale is $15. This is cost minus liquidation price per shirt. The most optimum size for the number of shirts to order is tied to the fractile of demand tied to this marginal profit versus loss profile of the shirt’s demand probability distribution. The question says that the forecast is 1,000 shirts. At the 0.75 fractile of the demand probability distribution, the multiplier is 0.3. Going by the prescribed method of the case therefore, Fessenden should order 1,000 x 0.3 or 300 shirts (Schleifer, 1992, pp. 1-5). Question 4 Liquidation costs as far as the current methods are concerned are fixed to the cost of producing the shirt, but there are other costs to consider when computing for liquidation costs, and those include the costs of keeping the stock in inventory and tying the money to that unsold inventory, or cost of money, among other cost items. There are also operational costs that are eaten up along the way, including the costs of transporting the items, and the costs of continuing to house the stock in warehouses, occupying space that could otherwise be used by faster selling goods. These latter costs are essentially on-going overhead costs that are incurred as long as the items remain in the hands of the firm. Marginal contribution too is likewise lacking in these material inputs that add to the costs of the item. In both cases the total costs are understated, so that marginal profits are overstated, while marginal losses are understated. In the case where a particular item remains overstocked in large quantities, for instance, these costs are larger because the multiplication factor is proportional to the number of unsold goods. In other words, poor forecasts that lead to overstocking may lead to higher losses. These currently are not imputed in LL Bean’s computations for the costs of overstocking, and if imputed may dampen enthusiasm for new items and for the propensity to overstock (Schleifer, 1992, pp. 1-5). . References Schleifer, A. (1992). LL Bean Inc. Item Forecasting and Inventory Management Harvard Business School. Read More