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

Value of information in closed loop supply chains

We explore the value of information (VOI) in the context of a firm that faces uncertainty with respect to demand, product return, and product recovery (yield). The operational decision of interest in matching supply with demand is the quantity of new product to order. Our objective is to evaluate the VOI from reducing one or more types of uncertainties, where value is measured by the reduction in total expected holding and shortage costs. We start with a single period model with normally distributed demands and returns, and restrict the analysis to the value of full information (VOFI) on one or more types of uncertainty. We develop estimators that are predictive of the value and sensitivity of (combinations of) different information types. We find that there is no dominance in value amongst the different types of information, and that there is an additional pay-off from investing in more than one type. We then extend our analysis to the multi-period case, where returns in a period are correlated with demands in the previous period, and study the value of partial information (VOPI) as well as full information. We demonstrate that our results from the single period model (adapted for VOPI) carry-over exactly. Furthermore, a comparison with uniformly distributed demand and return show that these results are robust with respect to distributional assumptions