Forecasting product returns in closed‐loop supply chains

Abstract

If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to provide a generic forecasting approach for predicting product returns in closed-loop supply chains. Design/methodology/approach -The approach is based on Bayesian estimation techniques. It permits to forecast product returns on the basis of fewer restrictions than existing approaches in CLSC literature. A numerical example demonstrates the application of the proposed approach using return times drawn from a Poisson distribution. Findings -The Bayesian estimation approach provides at least 50 percent higher accuracy in terms of error measures compared to traditional methods in all scenarios examined in the empirical part.Hence, more precise results can be obtained when predicting product returns.Research limitations/implications -The flexibility of the proposed approach allows for numerous applications in the field of CLSC research. Areas that depend on the results from a forecasting system, such as inventory management, can embed our estimation procedure in order to reduce safety stocks. Further research should address the incorporation of the quality of returned products and its impact on the actual utilizable amount of product returns. Originality/value -The generic character of the proposed forecasting approach leaves degrees of freedom to the user when adapting it to a specific problem. This adaptability is enabled by the following features: first, an arbitrary function is allowed for capturing the customers' demand. Second, the stochastic timeframe between sale and product return may follow an arbitrary distribution. Third, by adjusting two parameters finite as well as infinite planning horizons can be incorporated. Fourth, no assumptions regarding the joint distribution of product returns are necessary.

Distribution systems in omni-channel retailing

“…While there is extensive literature on return policies to minimize customer returns in online retailing (e.g., Su 2009;Asdecker 2014;Gelbrich et al 2015) and estimate return volume (e.g., Krapp et al 2013), very few publications exist that deal with how retailers organize the backward process of customer returns (Min et al 2006). Only a few studies address the number and location of return centers where returned products are collected and reprocessed.…”

The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems

“…Clottey and Benton (2014) used a similar approach to model Gamma distributed returns in an adaptation of the procedure accommodating longer lags. Krapp, Nebel, and Sahamie (2013) used a DLM for a Poisson (returns) delay function. Again, these can be seen as different approaches to Method C, when some manipulation of item-level data is used to derive the returns distribution.…”

Combining or separating forward and reverse logistics

“…High product variation results in higher process complexity, which is best handled in a separated set-up Low product variation decreases the complexity enabling low-skilled workers in the forward supply chain to handle the reverse flow Huang and Su (2012) Kumar et al, 2014;Liang et al, 2014;Ma and Kim, 2016;Clottey et al, 2012;Krapp et al, 2013;Li et al, 2010;Shankar, 2015) Page 1 of 23…”

“…Several quantitative models have been presented for forecasting the volume of returned goods (Kumar et al, 2014;Liang et al, 2014;Ma and Kim, 2016;Clottey et al, 2012;Krapp et al, 2013;Li et al, 2010). When Adenso-Diaz et al (2011) analyses how RSC affect the bullwhip effect the only significant reverse logistics factor is defined as the percentage of units returned.…”