Reverse channel selection for commercial product returns under time-to-market and product value considerations

The advent of mobile channels have changed retail business models, the choice of retail mix, and shopper behavior. As consumers do not differentiate among the channels where they try, purchase and/or take delivery of their product, they also expect maximum flexibility in the product returns process. On average, retailers forecasted returns to reach about 16.6% of the total merchandise that customers purchased in 2021, according to the National Retail Federation, which is an increase from an average return rate of 10.6% in 2020. The resulting cost of returns amounted to $761 billion worth of merchandise in 2021 (Repko in A more than $761 billion dilemma: retailers’ returns jump as online sales grow. https://www.cnbc.com/2022/01/25/retailers-average-return-rate-jumps-to-16point6percent-as-online-sales-grow-.html . Accessed 17 June 2022, 2022). For retailers and manufacturers, integration of different reverse channels is extremely important to deliver the seamless experience demanded by today’s discerning consumer while ensuring the profitable handling of the returned products as well as ensuring the environmental sustainability of the retailing operations. Regardless of which channel receives a return, the reverse logistics network should have the flexibility and the capability to remarket or to recover the value in the returned product in a cost efficient and timely manner that maximizes firm profitability. To the best of our knowledge, this paper is one of the first studies that develops a linear programming model with profit maximization objective to help determine how to optimally decide the returned product touch point(s) in the reverse logistics network. Unlike the extant literature, our model explicitly incorporates the marginal value of time for returns, product characteristics as well as the underling reverse logistics network configuration in return channel selection strategy. We present a comprehensive analysis on how and to what extent the return channel selection is dependent on the product characteristics such as time-based value decay rate, defective rates, and disposal rates as well as the network structure. Using data from HP and Bosch Power tools operations as well as real geographical US data, we show that our decision model can effectively help determine the reverse logistics network and the type of facility where a product is returned as a function of product characteristics and economic parameters. Our work emphasizes that product retur