Customers shifting from stationary to online grocery shopping and the decreasing mobility of an ageing population pose major challenges for the stationary grocery retailing sector. To fulfill the increasing demand for online grocery shopping, traditional bricks-and-mortar retailers use existing store networks to offer customers click-and-collect services. The current COVID-19 pandemic is substantially accelerating the transition to such a mixed offline/online model, and companies like the one behind this study are facing the urgent need of a re-design of their business model to cope with the change. Currently, a majority of the operations to service online demand consists of in-store picker-to-parts order picking systems, where employees go around the shelves of the shop to pick up the articles of online orders. The optimization of such operations is entirely left to the experience of the staff at the moment. Since in-store operations are a major cost-driver in retail supply chains, this paper proposes optimization ideas and solutions for these in-store operations. With experimental simulations run on a real store with real online orders, we show that a simple optimization tool can improve the situation substantially. The method is easy to apply and adaptable to stores with complex topologies.
Chou, X., Loske, D., Klumpp, M., Gambardella, L., Montemanni, R. (2022). In-store Picking Strategies for Online Orders in Grocery Retail Logistics. In R. Cerulli, M. Dell'Amico, F. Guerriero, D. Pacciarelli, A. Sforza (a cura di), Optimization and Decision Science ODS, Virtual Conference, November 19, 2020 (pp. 181-189). Springer Cham [10.1007/978-3-030-86841-3_15].
In-store Picking Strategies for Online Orders in Grocery Retail Logistics
Chou, Xiaochen;
2022
Abstract
Customers shifting from stationary to online grocery shopping and the decreasing mobility of an ageing population pose major challenges for the stationary grocery retailing sector. To fulfill the increasing demand for online grocery shopping, traditional bricks-and-mortar retailers use existing store networks to offer customers click-and-collect services. The current COVID-19 pandemic is substantially accelerating the transition to such a mixed offline/online model, and companies like the one behind this study are facing the urgent need of a re-design of their business model to cope with the change. Currently, a majority of the operations to service online demand consists of in-store picker-to-parts order picking systems, where employees go around the shelves of the shop to pick up the articles of online orders. The optimization of such operations is entirely left to the experience of the staff at the moment. Since in-store operations are a major cost-driver in retail supply chains, this paper proposes optimization ideas and solutions for these in-store operations. With experimental simulations run on a real store with real online orders, we show that a simple optimization tool can improve the situation substantially. The method is easy to apply and adaptable to stores with complex topologies.
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