Crowdsourced order‐fulfillment policies using in‐store customers

Omni-channel services, such as buy-online-pick-up-from-store, transfer the in-store logistics once completed by shoppers to retailers. To cost-effectively meet the high demands for such pickup services, we introduce a crowdsourced order-fulfillment policy that deploys in-store customers to pick items for online orders while completing their own personal shopping. As opposed to existing store fulfillment policies, this new concept utilizes in-store customers to help, not constrain, dedicated pickers. Empirical data indicate that a high percentage of surveyed in-store shoppers would be willing to occasionally participate in such a program. In-store customers willing to participate were observed to be heterogeneous in their efforts, with variability in how much extra time they would be willing to provide and would prefer picking tasks that had only a small deviation from their personal shopping. Motivated by these empirical results, the decision problem of how to assign picking tasks for arriving online orders with a given service commitment, to a set of arriving in-store customers or an abundant set of dedicated pickers, was formalized to capture the uncertainty and heterogeneity of using in-store customers for in-store picking tasks. We propose a tractable decision-making methodology to determine whether an order will meet both service commitment feasibility and in-store customer availability with a probability at least equal to a target threshold. This method captures dynamic order placements and in-store customer arrivals and stochasticity in in-store customers' shopping baskets. Extensive computational experiments for varying operational conditions of a grocery store dynamically matching actual online orders to arriving in-store customers helps answer open questions from practitioners. Compensating in-store customers based on their additional efforts reduced costs of fulfillment by greater than 30%, on average, compared to a baseline that uses only dedicated pickers for store fulfillment. Using the past five shopping baskets of participating in-store customers to estimate assignment decisions can achieve both high online order service commitments and in-store customer availability requirements. Our results suggest that in-store customers should be assigned smaller orders than dedicated pickers.