This paper addresses a fundamental short-run resource-allocation problem confronting retail distribution: simultaneously finding the specific brands, from many, that should be displayed, and the amount of retail product-display area that should be assigned to these brands, in order to maximize the retail institution's profit. The paper decomposes total market demand according to the various levels of brand preference that could conceivably exist in final markets, and then, employs an algorithm, similar to the one used to solve the fixed-charge problem, to find the optimal brand mix and display-area allocation.
This paper extends a previously accepted model, used to estimate the warranty reserves required for nonrepayable products, by discounting future warranty costs to their present value, and by adjusting for expected changes in the general price level. The estimation equations are derived and their implications are discussed.
Inventory managers are charged with keeping inventory costs as low as possible, while still maintaining an acceptable level of service. More accurate demand forecasts should always allow the inventory manager to better fulfill this goal.Billions of dollars are spent every year on replacement parts and warranty claims inventory. Exponential smoothing (ES) and weighted moving average (WMA) are two of the traditional tools used to forecast demand for these parts. These methods are simple to implement and require relatively little data.If a company tested a part to destruction or kept a database of consumer use failures, this information could be used to estimate the failure function, the probability of a part lasting a given amount of time before failing. As this paper will explain, the failure function could be coupled with the sales history of the product using the part itself to create a more accurate forecast of demand for it. However, the cost of testing and data collection information could offset any reduction in inventory cost due to improved forecasting. A parts inventory manager must be able to answer the question: when will the benefits of the better forecast outweigh the additional costs?Generally, a part fails because of normal wear, accidents, or abuse. In the case of normal wear, replacement demand depends upon the age of the products in use, and their level of use. In the case of accidents or abuse, a wide variety of joint, random factors make it difficult to determine the demand distribution. Although the proposed method can be applied to any type of failure cause, this paper will limit its discussion to demand caused by normal wear, in which the usage rate of the product is constant across customers. For example, a refrigerator is plugged in and used at a similar rate by all consumers, whereas the wear on an automobile component varies from consumer to consumer, depending on the hours of use, number of uses, and conditions of use, including driving style.If the firm performs laboratory testing, an approximation of the product's failure function can be constructed. If accurate records regarding past failures of products in use have been kept, this information could also be used to estimate a failure function. In either case, there is a cost associated with collecting this information.
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