Assessing economic and operational feasibility of a designed and lab demonstrated robotic platform for omnichannel logistics

Abstract

As omnichannel services, such as buy online pickup in store and home delivery, grow in popularity, many brick-and-mortar retailers have adopted a store fulfillment strategy, where the same inventory on the store shelves is used for both online and in-store customers. These omnichannel offerings shift the in-store logistics once done by shoppers to retailers. Thus, the 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 designed and built a new picker-to-stock robotic platform to automate piece-level pick, sort, and place tasks in retail environments. Lab demonstrations of the robotic platform confirm the feasibility to robotically pick items from retail shelves and was able to achieve picking performance of 20 s per unit picked once in front of the shelf location. 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 the lab demonstrated values and with improved performance, these resource levels are then 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.