Managing Perishables with Time and temperature History

We address the use and value of time and temperature information to manage perishables in the contextof a retailer that sells a random lifetime product subject to stochastic demand and lost sales. The product’s lifetime is largely determined by the temperature history and the flow time through the supply chain. We compare the case in which information on flow time and temperature history is available and used for inventory management to a base case in which such information is not available. We formulate the two cases as Markov Decision Processes and evaluate the value of information through an extensive simulation using representative, real world supply chain parameters

The value of time and temperature history information for the distribution of perishables

We model a supply chain that transports a perishable product from product origin to a destination market via a waypoint. The operational decision of interest is the transportation mode choice from the waypoint to the destination market, dependent on available information, including time and temperature history via RFID and sensors. We use analytical modeling to derive optimal transportation policies and generate generalizable, managerial insights. We then apply the analytical model in a numerical case study investigating the transportation of vine-ripened tomatoes from the Netherlands to the United States. Our analytical and numerical studies result in a number of interesting findings. First, the quality of sensor measurements may or may not impact the optimal policy and the decision maker can be guided accordingly. Second, better information may enable more profitable transport decisions, but doing so can have a negative impact on product quality at the destination. Third, we show that more stringent quality requirements by retailers may drive salvaging produce at the waypoint and thereby negatively impact service levels, despite penalties. Fourth, we identify the factors that drive the value of information under multiple information scenarios and establish both the direction and magnitude of their effects. Finally, both the analytical and numerical findings indicate that the information value is robust under measurement error. Thus, even if measurements are not perfect, RFID and sensor technology enabled information can be used to dynamically adjust forwarding decisions for perishable products, which can yield significant improvements to operational performance.</p