Decision support models and policy innovations to support automating store fulfillment

Abstract

December 2024School of EngineeringOmnichannel services, such as buy-online-pickup-in-store, curbside pickup, and ship-from-store, have shifted the order-picking tasks that used to be completed by in-store customers doing their own shopping to the responsibility of retailers. To support research on omnichannel serivces,relevant connected in-store and online customer data sets for omnichannel retail research are generated via a mapped categorization of products into product families. Using this mapping to connect previously separate in-store and online customer data sets, these data sets focus on a grocery retail environment, and collect additional data from publicly available websites. These connected data sets contain information about product family data on in-store and online customer demand values, impulse purchases, product dimensions, weight, and price. Additional data is provided on in-store and online customer arrival data. These data sets aid this work in generating numerical insights and can support future grocery retail logistical research. To support omnichannel services, many retailers have deployed a store fulfillment strategy, where online orders are picked from inventory in brick-and-mortar stores. As store fulfillment is currently a labor-intensive operation, this dissertation explores a new policy that relies on the assistance of in-store customers for item extraction from the store shelves and a fleet of Autonomous Mobile Robots (AMRs) to collect and transport them to a designated station. While a set of dedicated pickers and AMRs are manageable by the store, the arrival of in-store customers who are willing to assist an AMR at a given location in the store is out of the store's control, and therefore, uncertain. We model the stochastic order-picking problem with uncertain synchronization times of in-store customers and AMRs, first as a static approach via generating a consensus from multiple scenarios and decisions to visit picking locations are made at the beginning of the picking journey. Then we consider managing resources in a dynamic way, where the store makes new decisions as new information becomes available. We model the dynamic problem as a Markov Decision Process to determine how a retailer should dynamically assign tasks to a set of AMRs and dedicated pickers. We develop a heuristic solution framework that generates a set of initial assignments and routes for picking resources and dynamically updates them as the actual synchronization times between AMRs and in-store customers unfold. We analyze multiple strategies to generate the initial set of task assignments and routes as well as update such decisions based on the system state. We test our proposed approaches using actual online grocery data. Computational results illustrate the potential for AMRs and in-store customers augmenting the dedicated pickers to achieve equivalent pick rates compared to systems with only dedicated pickers. We further demonstrate that it is more effective for achieving higher picking performance to have in-store customers help the AMRs compared to a warehouse like environment where dedicated pickers are synchronized with AMRs. Moreover, our proposed policy improves the operating margin of the store compared to utilizing only dedicated pickers. Lastly, our solution approach is capable of generating high-quality solutions at a pace suitable for practical settings. In addition to fulfilling online customer requests, omnichannel retailers also must support in-store customers, who want to interact with products and often drive sales through impulse purchases and customer loyalty. Yet, how best to support both online and in-store customer channels efficiently and seamlessly is a current challenge for retailers. Thus, the second focus of this work is to explore whether new material handling equipment has the potential to be deployed in a retail store environment to support omnichannel services. To do so, we utilize pick performance data from a newly designed and built picker-to-stock robotic platform suitable for piece-level pick, sort, and place tasks in retail environments. Then an agent-based simulation model is created to mimic a store's logistical operations that integrates data from the robotic platform's lab demonstrations and data from online and in-store customer demand. An iterative process determines the minimum amount of manual and robotic resources needed to operate the store that satisfies a given service level for online order fulfillment and replenishment tasks. Then, to assess the economic viability of deploying such a robotic platform with currently achieved values and improved performance, these resource levels are combined with operational metrics obtained from the simulation and various cost aspects via an economic analysis model. Computational experiments show that deploying the robotic platform for picking and restocking goods in a store environment is operationally and economically viable for retail grocery stores providing omnichannel services using a store fulfillment strategy.Ph