L.L Bean Inc Paper Essay
L.L Bean Inc Paper
This paper has adjusted the 5 problems that shown in this case. First paragraph is to adjust how L.L Bean uses the previous year’s demand to determine how many units of product to order. Second paragraph is to adjust how many units of item L.L Bean should purchase under the relationship between the item costs and revenues. Third paragraph is to adjust what information should Scott Sklar have in order to help him to forecast for a particular style of men’s shirt that is a new catalog item. Fourth paragraph is to adjust the method that Mark Fasold used in the case to solve the number of items purchased. Lastly, the final paragraph is to adjust the improvement that L.L Bean should do in forecast process. L.L Bean uses different determinations and calculations to forecast and decide how many units of items to stock. The first thing is using the frozen forecast, which comes from the forecasting department.
Buyers, product people, and inventory buyers meet to forecast item sales by book and rank various items in terms of expected dollar sales while they assign dollars in accordance with the ranking. They have to make a judgment when there is new item added. They judge the total of forecasted item sales and check it for reality based on the book forecast by comparing the previous data. The second thing is using a calculation of A/F, which is actual demand divided to forecast demand. It helps to calculate the range of inventory that the product would be in the coming year and the frequency distribution of these errors was compiled across items. For example, assume there was a 50% forecast errors, and the new item ratio was between 0.7 and 1.6 in last year, if the frozen forecast for an item were 1000 units, so the actual demand for that item would be between 700 and 1600 units. The last thing is the calculation of profit margin. For instance, an item cost $15, and sells for $30, and the gain of selling would be $30-15=$15. If the liquidation is sold for $10, so the loss for failing to sell the marginal unit would be $15-5= $5.
These can use to calculate the optimal order size and the fractile. The fractile helps to find the units of items to be stocked, estimates the costs, and the actual order size. Therefore, L.L Bean uses frozen forecast, A/F calculation and profit margin calculation to decide the number of units to stock. Demand of the products affects the decision of how many units of inventory the company should have. Based on the demand, the company is able to forecast and predict the number of stocks to order. In addition, the cost to make items, the price to sell the items and the liquidated cost for the items should also be considered. From those concerns, the company can probably estimate the profits and the losses for selling the inventory. Under the items’ costs and revenues, L.L Bean can figure out how many units of inventory they have to purchase in order to obtain a profitable situation. Demand forecast is an activity of estimating the quantity of a product that consumers will purchase; thus, as a buyer, Scott Sklar should create an accurate demand forecast.
However, according to the article, Scott mentioned that he would gather his inventory buyer, some product people, and himself to judge the new catalog item and decide if the new catalog item generates incremental demand. If not, they would see what items were going to steal the demand, and then those items would need to be adjusted accordingly. This shows that Scott was deciding the demand forecast by the team’s personal judgment but not based on the professional forecasting data. Hence, the demand forecast that Scott made may not accurately show the real demand. If the estimation is not accurate, there may be an under-stock and over-stock which can directly affect company’s profit. Accordingly, Scott needs to generate a lot of information: potential customers, and the sales of similar item’s data of L.L Bean’s competitors. Moreover, Scott should decide who will be the potential customers of the new catalog item.
He can obtain this information by reviewing the past customers’ purchasing record and generate the purchasing habit. Scott can also obtain this information by sending our e-mail to search for more potential customers. Once he obtains the list of potential customers, he should send catalog to these potential customers and determine the demand of the new item. In additional, Scott can also use the sales data of similar item from its competitors to determine the treat of the similar style items. By using all these information Scott should able to create an accurate demand forecast for the particular style of men’s shirt that is a new catalog item. The number of items purchased usually exceeds the number forecast. Mark Fasold worried about the wide dispersion in forecast errors, both for “never-out” and “new” items. By doing some calculations on the cost under-stocking and cost of over-stocking, he found that the cost of under-stocking is greater than the cost of over-stocking. As a result, he decided to purchase more stocks than the number of forecasting, which can minimize the lost.
However, with this decision, it created other problems to the company. Thus, it is important to let Mark understand the number of forecast is closely matched with the number of needs in the reality. To do so, L.L Bean should create an accurate system to address the demand forecast with very small forecasting error. Also, they should make up a backup plan when under-stocking is happening, promotion plane. Since it is mostly impossible to perfectly match the stock with the real demand, it is essential to sell all the over-stock to minimize the lost. L.L Bean should create an effective promotion plane to sell as much over-stock as they can. In conclusion, L.L Bean can improve and adjust its forecasting process in numerous ways. Retail industry is a fast-paced, changing industry where product’s demand and preferences rapidly change. The first problem appeared with L.L Bean’s forecasting process is that they only use the past dates to predict future forecasts.
They should instead constantly update their forecasts system based on latest data that gained from market research or resources, in order to determine a more accurate approach. Also, the major catalogs L.L Bean introduces in each spring, summer, fall, and Christmas can be attached optional questionnaires. By doing this, they can have a better way to predict demand, and a better insight into consumer behavior in general. Nevertheless, appropriate adjustment can be made while they capture customers’ preferences and how customer rank L.L Bean “new” in comparison to “never out”. Lastly, in this case, L.L Bean can work close with suppliers so that they can be able to complete more “quick responses” and address the customers’ demand. They need to establish close relationships with the suppliers or reduce dependence on foreign vendors to generate a quicker supply.
University/College: University of Chicago
Type of paper: Thesis/Dissertation Chapter
Date: 4 January 2017
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