Bridging data gaps in the food industry – sensor-equipped metal food containers as an enabler for sustainability

Abstract

In recent years, Machine Learning (ML) applications for manufacturing have reached a high degree of maturity and can be considered as a suitable tool for improving production performance. In addition, ML applications can be used in many other production areas to enhance sustainability within the manufacturing process. One area is storing and transporting bulk materials with metal Intermediate Bulk Containers (IBC). These IBCs are currently used solely for their primary purpose of storage and transportation of raw and finished goods. Hence, while in use , IBCs are often a black box that does not provide additional value to manufacturers. Equipping IBCs with sensor technology can provide such value: new data can be generated along the entire supply chain and production processes, taking the sustainability of production to a new level. Within the research project smart.CONSERVE, we use this additional data, for example, to monitor the stored food's critical characteristics or to establish predictive maintenance for IBCs. Thus, storing produced goods in defective IBCs can be avoided and wasting resources can be prevented. This publication describes how smart IBCs in the food industry can increase supply chain visibility and reduce food waste. To illustrate this, we present possible use cases enabled by new data availabilities. Additionally, we provide insights into how these use cases can be transferred to other industries. Besides, we exemplify the many opportunities for manufacturers to develop new smart services and ML applications based on the collected data - and how this can support manufacturers in achieving higher levels of sustainability.