Using internet-of-things point-of-consumption data for smart replenishment under inventory record inaccuracy

Internet-of-Things-enabled systems that monitor usage and inventory are the latest technological advancement in demand forecasting and inventory control. Unlike traditional systems that record sales via cash registers or RFID technology at the point-of-sale, these novel systems can track product usage via smart, connected devices at the point-of-consumption, i.e., directly at the end user. This usage data promises to be a valuable basis for smart, automated replenishment services. We study such a service in the context of commercial coffee machines through collaboration with a large manufacturer in the coffee industry. Our data set contains information on more than 75 million drinks recorded since late 2017 by nearly 6,500 IoT-enabled coffee machines for commercial customers such as office kitchens, restaurants, and gas stations. The nature of the problem and data at the point-of-consumption warrants the development of synergetic models for demand forecasting, inventory control, and correction of inventory record inaccuracy. The resulting models are distinct from the state-of-the-art approach at the point-of-sale as they are uniquely integrated and involve an alternative strategy to mitigate inventory record inaccuracies. Overall, we contrast different approaches to manage smart replenishment systems, test their forecasting, inventory control, and inaccuracy correction performance, and pave the path to implementation in the field. Our findings suggest important implications for manufacturers who wish to engage in direct relationships with the end users of their products.