Mitigating risks of perishable products in the cyber-physical systems based on the extended MRP model

In the supply chain of fresh fruit and vegetables, large losses may incurred throughout the whole farm to fork route. Food supply chain management is faced with challenges of minimizing the post-harvest loss, while delivering the items directly to the refrigerators in smart homes (i.e. domotics). A substantial value can therefore be added to the criterion function by an immediate, real-time detection of changes in perishability dynamics, including a real-time calculation and communication of the remaining shelf life during transportation from one chain node to another. The changes in the estimated remaining shelf life can, therefore, be matched with the expected remaining transportation time, and so the critical moment can be avoided with a given probability. This can be done by dynamic rerouting in real time, based on previous net present value (NPV) criteria. Such criteria could then we include in the contractually stipulated remaining shelf life requirements at the delivery point. This paper focuses on a novel concept of moving activity cells which represent the moving cargo between the fixed activity cells in the extended material requirements planning (EMRP) model. The changes in NPV are calculated dynamically from the expected shelf life changes. Such real-time calculations and early reports are enabled by the Internet of Things (IoT) infrastructure, where there is a smart device that tracks ambient conditions like temperature, humidity, and gas concentrations. These early estimations allow a better decision making based on first-expired-first-out (FEFO) cold chain management strategies for perishable products. Therefore, the model includes the possibility to deliver the items to the local market if the expected contractually stipulated shelf-life losses become too high. The paper does not intend to discuss the details of IoT or analyse different sensors, but it wishes to show how the EMRP theory can be used to estimate the changes in NPV when moving activity cells are included in the model. The smart measurement devices embedded in moving activity cells of cyber-physical system measure the ambient data and broadcast decay acceleration factors and postharvest loss of cargo to the decision support system. The numerical example shows how smart measurement devices embedded in moving activity cells of cyber-physical system help to reduce the post-harvest loss in a supply chain by rerouting when necessary. The paper additionally shows how much such a cyber-physical system improves NPV by the development of decision-making processes in the real-time, using the IoT as infrastructure, including automatic rerouting in postharvest logistics.