As customer choice options increase and relative margins shrink, retailers are increasingly coming to realize the value of a well-conceptualized and managed product returns process. Traditionally, product returns have been viewed by retailers as an inconvenience or afterthought, and the scant attention paid to product returns has often been the result of the need to be more “customer friendly” or as a result of legislative requirements related to recycling or waste disposal. However, modern retailers are coming to realize that proper returns management can also serve to recoup portions of profits that could otherwise be lost. Given the ever-increasing rates of retail returns, which can commonly reach as high as 10-15% for some products and/or retail formats, returns management should be a focus now and for the foreseeable future. Many retailers are responding via the introduction of reverse logistics programs that serve to collect,handle, and dispose of returned product.

The regular, forward movement of product down the supply chain, through the retailer,and to the customer, is generally somewhat predictable. On the other hand, reversed flows of product,  information, and value possess greater degrees of variability, and given the cash-intensive nature of reverse logistics processes, the retailer is exposed to significant financial risk as a result of their returns handling operations. Of particular interest in the current article is the problem of illiquidity, i.e., the availability of cash when needed to respond to market needs. The reverse logistics process generates significant cash outflows that must be accounted for, and the failure to track and time these flows carefully can result in situations where the retailer is strained in meeting cash obligations. Unfortunately, to date there are no known models that are useful for assessing the cash flow position of the retailer as products move back into and through the retail firm.

The current article is an initial attempt to model liquidity issues as returned product moves through the reverse logistics process, from one return state to another. Units that are moving through the reverse logistics process temporarily lose significant value, as they are both unavailable to customers and yet generate costs associated with returns handling, and so it is important to minimize time within the system. To assess this problem, a Markov model is applied to the retail reverse logistics situation. Two questions were of interest:

• How long can a unit be expected to remain in the reverse logistics system before being disposed of or restocked?

• What is the probability that a unit becomes disposed of or restocked after a given amount of time?

Importantly, each of these questions is evaluated in three products returns contexts: regular, variable, and highly volatile returns rate scenarios are considered. Field data are applied to the Markov model so that its validity can be established. An illustration of the model’s utility is offered via the examination of the reverse logistics process of a sporting and outdoor goods chain. Through this application, it is possible to examine the retailer’s movement-by-movement returned product transitions, and identify locations in the process where inefficiencies occur. More importantly, though, the impact of reverse logistics on the periodic cash flows of the retailer is illustrated within the normally distributed, variable, and highly skewed scenarios, and the retailer is advised ofremediative actions that can be taken to solidify their cash position in response to each scenario.