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

Product Recalls in the Meat and Poultry Industry: Key Drivers of Supply Chain Efficiency and Effectiveness

There has been a significant increase in the number, size, and severity of food product recalls in the United States in the past decade. Additionally, the pressure to reduce costs has caused many food supply chains to off-shore production activities, making the logistics of recall events more challenging and costly for these supply chains. Thus, there is a strong need for research that can help identify the determinants and key drivers of supply chain efficiency and effectiveness with respect to food recall events. We focus our investigation on meat and poultry supply chains in the United States. Through an empirical analysis of over 500 recall events recorded in the government tracking database during the 2005-2013 period, we identify and test key factors that impact the product recall process in contemporary food supply chains. We conduct a statistical regression analysis to examine the impact of recall strategy, hazard type and the supply chain entity detecting the hazard on the time to recall and the amount of product recovered at closure. Future work also aims to investigate the impact of supply chain structure, reverse logistics and the potential impact of traceability (e.g., RFID) and condition monitoring (e.g., temperature sensors) on recall outcomes