With the rise of demand for online delivery or pickup services during and post-COVID-19 pandemic, organizations have deployed a wide range of strategies such as store fulfillment or picking orders at distribution centers and transporting them to stores for customer curbside pickup or courier last mile delivery. This creates an opportunity to leverage in-store customers to fulfill orders from store shelves. Shoppers, who are already at a store, could be given opportunities to fulfill orders, which would augment – and not replace – a workforce of dedicated pickers.
In Crowdsourced Order-Fulfillment Policies Using In-Store Customers, in Production and Operations Management, Culverhouse’s Dr. Iman Dayarian, along with co-author Dr. Jennifer Pazour of the Rensselaer Polytechnic Institute, first conducted a survey to understand customer attitudes about the proposed system. The authors find that the majority of respondents are open to the idea, provided it is not too time-consuming.
The main challenge with the system is how to effectively match incoming orders to available shoppers given their different shopping patterns, behaviors, and time constraints, for which the researchers propose a decision-making methodology. This research finds that a potential in-store customer’s last five shopping baskets can effectively predict their shopping basket and help make assignment decisions, though in-store customers should usually be assigned smaller orders than dedicated pickers.
Finally, the authors apply computational experiments to an existing dataset and show that employing pickers using this system could reduce order fulfillment costs by greater than 30 percent. Dayarian explains, “By compensating customers who partake in fulfillment operations as they shop for their own groceries, this research views in-store customers as an asset to stores, rather than a constraint to avoid—which is the current paradigm.”
[Dayarian, I., & Pazour, J. (2022). Crowdsourced order-fulfillment policies using in-store customers. Production and Operations Management, 00, 1– 20. https://doi.org/10.1111/poms.13805]