Reverse cross-docking

Juan Pablo Soto Zuluaga, Marcus Thiell and Rosa Colomé Perales

Omega, 2017, vol. 66, issue PA, 48-57

Abstract: Companies continuously look for new ways to optimize their processes according to their competitive priorities. Returns process management, as a part of reverse logistics, has become an important field of performance improvement, especially in businesses with seasonal demand patterns, like fashion, books, or electronics. Consequently, unsold articles are often commercialized through secondary channels, such as outlet stores. To approach the management of reverse logistics systems, models used to optimize the forward flow of articles have been analyzed and adjusted to cope with the characteristics of reverse flows. Despite the recognized impact of cross-docking in forward logistics, approaches to apply this strategy in the returns context are lacking. This paper demonstrates how cross-docking can be implemented in a reverse logistics context and it proposes a corresponding linear programming model. Results show that the application of “reverse cross-docking” can increase the efficiency of reverse logistics in terms of cost reductions, time savings, and improvement of information management in returns processes. Sensitivity analyses show that a reverse cross-docking system can help companies to improve competitiveness in situations where (a) the outlet flexibility related to products and quantities received is high, (b) the probability of returns from secondary markets is low, or (c) the combination of return and cross-docking costs in comparison with warehousing costs are low. The reverse cross-docking model in its basic form covers the main system characteristics and is flexible for further extensions. An extension presented herein refers to the consideration of heterogeneous article prices, indicating the usefulness of reverse cross-docking, particularly in industries with low price levels.

Keywords: Cross-docking; Reverse logistics; Linear programming (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1016/j.omega.2016.01.010

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