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Forecasting product returns

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Reverse logistics activities consist of collecting products from customers and reprocessing them for reuse. Returned products can take the form of end-of-life returns, where the product has been used by the customer, or commercial returns, where the product is returned before use. Some products are leased to customers (e.g. Xerox copiers to corporate customers) and are collected by the manufacturer at the expiration of the lease. In this case, the timing and quantity of products to be returned are known in advance. The major uncertainty is about the condition of the product. Other products are sold to the customer and are returned when their useful life is over or when the customer wants to trade in the product for an upgrade. In the former category are products such as single-use cameras, toner cartridges and tires. In the latter category are durable products such as personal computers, cars and copiers. Predicting the proportion of such returns is important at a tactical level for procurement decisions, capacity planning and disposal management. At an operational level, detailed predictions of the quantities to be returned in each period, as well as the variability of these quantities, is useful, especially for inventory management and production planning. Unlike end-of-life returns that have already been sold for profit and now have the potential of generating additional benefits through value recovery, commercial returns represent a lost margin. In catalog sales, an average return rate of 12% is standard, with return rates varying by product category: 5 – 9% in hard goods, 12 – 18% for casual apparel, 15 – 20% for high-tech products, and up to 35% for high fashion apparel [9]. Commercial returns impose high costs on retailers and manufacturers alike. The Gartner group estimates that the cost of processing returns for Web merchandise in 2000 was twice the value of the merchandise itself [22]. Currently, only 44% of returns are sold as new; 2% are trashed, 13% are liquidated, and 41% are sent back to the manufacturer [19]. Retailers and manufacturers strive to design reverse logistics systems that increase the visibility and speed of the return process to maximize asset recovery for commercial returns, especially for seasonal or short life-cycle products. Firms vary in how they address this problem. For example, Ingram Micro Logistics, the distribution arm of Ingram Micro, opened the first automated returns facility in the US in early 2001 [19].Others increasingly rely on third-party reverse logistics providers such as GENCO Distribution System, UPS, USF Processors, Returns Online [13]. Various software products that are specifically targeted towards returns processing are now available on the market, provided by such companies as Kirus Inc.,, ReturnCentral and The Return Exchange [13]. Like end-of-life returns, an important lever in managing commercial returns is to accurately predict the return quantities for both tactical and operational level decisions. Forecasting product returns, narrowly defined, is predicting the timing and quantity of returns within a given system based on past sales and return data. Methods that have been proposed in the literature for either end-of-life or commercial returns are described and compared in §2. The goal of such forecasting schemes is to provide input at an operational level; this section also reviews the literature on integrating forecasts of returns into inventory management decisions. In this chapter, the author takes a broader view of forecasting product returns. The proportion of products returned depends to a large extent on a number of factors including the design of the product, the collection system, the customer interface, among others. Significant potential for profit maximization therefore lies in understanding what drives the proportion of returns and designing the system accordingly. In §3, she surveys the academic literature, articles from the business press and some case studies to identify factors influencing return rates. In §4, she concludes with directions for future research in exploiting this information for better returns forecasting and management.

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