Purpose: Online sales have significantly increased, especially in the realm of the COVID emergency. For B2C e-commerce, reverse logistics is critical: it strongly impacts the willingness of customers to buy online, but it is very expensive for online players, who are striving to find ways to reduce the associated costs. This work aims to define a measure of the return cost and to investigate the main factors affecting it. Methodology: This work combines analytical modelling and simulation. The model allows to represent the reverse logistics process and to define the associated cost; simulation is used in testing the model and analysing different scenarios. The used data were collected from both primary and secondary sources. Findings: The return cost includes three main components (usage of the van, time spent by the driver to travel and to perform collection activities). The application of the model to Milan (Italy) resulted in an average unitary return cost of 2.78€. The variables impacting the most on this cost are the collection density and the travel speed. Originality: This research is a first attempt to propose a measure for the cost of B2C e-commerce returns, and to analytically investigate the variables having the greatest impact in determining such cost.

Investigating the return cost for B2C e-commerce

A. Seghezzi;C. Siragusa;A. Tumino;R. Mangiaracina
2021

Abstract

Purpose: Online sales have significantly increased, especially in the realm of the COVID emergency. For B2C e-commerce, reverse logistics is critical: it strongly impacts the willingness of customers to buy online, but it is very expensive for online players, who are striving to find ways to reduce the associated costs. This work aims to define a measure of the return cost and to investigate the main factors affecting it. Methodology: This work combines analytical modelling and simulation. The model allows to represent the reverse logistics process and to define the associated cost; simulation is used in testing the model and analysing different scenarios. The used data were collected from both primary and secondary sources. Findings: The return cost includes three main components (usage of the van, time spent by the driver to travel and to perform collection activities). The application of the model to Milan (Italy) resulted in an average unitary return cost of 2.78€. The variables impacting the most on this cost are the collection density and the travel speed. Originality: This research is a first attempt to propose a measure for the cost of B2C e-commerce returns, and to analytically investigate the variables having the greatest impact in determining such cost.
Proceedings of the Hamburg International Conference of Logistics (HICL) 2021
978-3-754927-71-7
return
logistics
e-commerce
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1209057
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