Firms at various levels of the supply chain are implementing reverse logistics systems to maximize the value captured from products flowing backwards from customers to suppliers. However, due to the sporadic and unpredictable cash outflows associated with returns, firms must take care to avoid liquidity problems. Previous work addressing reverse logistics liquidity issues has considered long-term expectations, uncertainty, and shock potential inherent in the retail reverse logistics process, but the impact of the expected returns volumes and random return quantities within fixed-scale systems has yet to be explored. The current paper addresses these concerns.
The challenge of dealing with products moving backward through the supply chain is significant for modern firms, and increases in both difficulty and importance each year. The annual costs of dealing with the nearly $100 billion of returned products in the US market has been estimated as over $35 billion (Feuling, 2009 and Council of Supply Chain Management Professionals, 2009), with consumer returns representing slightly more than half of the total (Angrick, 2009). Similarly, the value of products remanufactured into saleable form is estimated to exceed $50 billion annually in the US market (Guide and Van Wassenhove, 2003). In light of these actualities, and given the relevance of reverse logistics activities to both the firm’s financial position and customer relations (e.g., Daugherty et al., 2005), the need for further research addressing reverse logistics implications for the firm has never been more vital.
Serrato et al. (2007) observe that an abundance of empirical work has already addressed reverse logistics topics from an operational standpoint, but few analytical models have been offered that adequately represent the current state of reverse logistics practice. The few exceptions have limited their focus to the implications of reverse logistics on production (Fleischmann et al., 2001 and Nakashima et al., 2004) and on inventory policy (i.e., Dobos, 2003 and Minner, 2001). There is a relative lack of analytic research addressing how best to manage reverse logistics product flows, especially from a financial standpoint; additional work is required that models reverse logistics from a financial perspective. The limited extant research aims to help managers better understand how to best achieve cost reductions and profit maximizations from reverse logistics activities (i.e., Kannan et al., 2009, Guide et al., 2006 and Mukhopudhyay and Setoputro, 2004). However, though these models address reverse logistics outcomes from an eventual profit-and-loss perspective, they generally fail to account for a more pressing concern of the reverse logistics financial process: reversed cash flows paid out in remuneration for product returns, which are problematic to the firm due to their impact on firm liquidity in the short- to medium-term.
While the impact of liquidity as a constraint is well understood with regard to outbound inventory policy (Kashyap et al., 1994, Hendel, 1996 and Carpenter et al., 1998), the relationship between reverse product flows and liquidity is less understood. While Horvath et al. (2005) examined uncertainty, shock, and long-term impacts of potential illiquidity in retail reverse logistics system, no research has yet assessed random return volumes at different supply chain echelons, nor operational and financial system design constraints. This omission in the literature is problematic. The current paper addresses these gaps in the financially oriented reverse logistics stream with a model designed to assist firms in accounting for the unpredictable quantity of returns and processing cash costs and inflows at each stage of the reverse logistics process. The model helps managers synchronize the activities of operations with finance to more proactively and accurately plan for short- and long-term liquidity needs, thereby better facilitating the integration of the firm’s operations and finance functions.
This study contributes to the reverse logistics operations literature by providing additional analysis related to the financial management of the reverse logistics/returns processes. Adopting a liquidity-based perspective on reverse logistics operations management, the current paper better incorporates the contingencies associated with the operation of a reverse logistics system. This paper builds on past research while providing a more thorough evaluation of the short-term impacts and a more complete model of the dynamic reverse logistics process. The benefits of this model include the introduction of random numbers of units entering the system, as well as random numbers of units present within the various states of the system. Secondly we consider the system states operating above, at, or below design capacity, and the variable effects this will have on the reverse logistics system. Finally we consider the costs associated with each state behaving as a random variable, and the impacts it will have on returns and liquidity management.
Ultimately the paper contributes to the management planning process by facilitating the development of confidence intervals associated with the distributional characteristics identified with the Markov chain analysis. The different confidence intervals outlined in the managerial implications section allow managers to estimate the likelihood that next period’s results will occur within a particular range. This allows firms to better understand short-term needs and plan for longer-term liquidity needs, given the dynamic nature of the returns process and the varying nature of the associated costs.
