Abstract
Online shopping is growing fast due to the increasingly widespread use of digital services. During the COVID-19 pandemic, the desire for contactless shopping has further changed consumer behavior and accelerated the acceptance of online grocery purchases. Consequently, traditional brick-and-mortar retailers are developing omnichannel solutions such as click-and-collect services to fulfill the increasing demand. In this work, we consider the Buy-Online-Pick-up-in-Store concept, in which online orders are collected by employees of the conventional stores. As labor is a major cost driver, we apply and discuss different optimizing strategies in the picking and packing process based on real-world data from a German retailer. With comparison of different methods, we estimate the improvements in efficiency in terms of time spent during the picking process. Additionally, the time spent on the packing process can be further decreased by applying a mathematical model that guides the employees on how to organize the articles in different shopping bags during the picking process. In general, we put forward effective strategies for the Buy-Online-Pick-up-in-Store paradigm that can be easily implemented by stores with different topologies.
Highlights
Online shopping has become popular with the development of digitization over the past decade [1]
It is not surprising that the retailer providing data for this study had little to no optimization in place for the Buy-Online-Pick-up-in-Store concept (BOPS) in-store operations: the employees receive shopping lists in a random order, they go through the store similar to regular customers to pick up all the items, and go to the cashier to scan the products and place them in the final bags
We discuss different optimization methods to solve these major issues in terms of the picking and packing process, and we propose a comprehensive optimization strategy that reduces the overall human resource time required and guarantees the quality of packaging to prevent potential damage
Summary
Online shopping has become popular with the development of digitization over the past decade [1]. It is not surprising that the retailer providing data for this study had little to no optimization in place for the BOPS in-store operations: the employees receive shopping lists in a random order, they go through the store similar to regular customers to pick up all the items, and go to the cashier to scan the products and place them in the final bags Following this description, two major issues emerge: the non-optimized picking path, which has a longer travel time, and the need to pass through the cashier, both of which result in an inefficient use of the available human resources.
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