The Value of Demand Information in Omni-Channel Grocery Retailing

As e-commerce reaches one of the last strongholds of traditional fulfillment, how can grocers leverage the omni-channel trend and stay competitive in today’s changing market landscape? To improve operating outcomes and address food waste concerns, this study investigates various scenarios in which the grocery retailer accepts online orders in advance. We examine the value of advance demand information through a Markov Decision Process-based model, in terms of changes to expected profits, outdating, freshness, and several inventory and service performance metrics. Our results indicate that when the demand lead time is longer than the replenishment lead time, close to 20% safety stock reduction on average can be achieved, leading to a 15% decrease in product deterioration and 26% less outdating. In some cases, we also find that it is possible to profitably offer discounted prices in exchange for the customer’s future demand information

RFID-Enabled Management of Highly-Perishable Inventory: A Markov Decision Process Approach for Grocery Retailers

We address the challenge of managing perishable inventory. One study was conducted to analyze the effects of recapturing unsatisfied demand, and another to estimate improvements in operational metrics through delaying order placements. Our results indicate that significant profit improvements can be achieved under these scenarios, as evidenced by a greater than 30% median increase in profit margin

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