Emergency Management Strategies for the Retail Industry

Abstract

Uncertain disruptions complicate inventory management for retailers because it is difficult to determine when and how to adjust order quantities to ensure high service levels before and after a possible disruption. In order to assist retailers with their decision-making, three different models have been developed to determine the importance and effectiveness of considering proactive approaches to disruptions, specifically, forecasted storms. The first model addresses the time horizon when a storm is approaching and it is not known whether a demand surge for emergency items will occur. Minimax decision criterion highlighted the circumstances that constitute increasing the order quantity or encouraging the retailer to adopt a proactive approach instead of the current strategy of "wait-and-see". Ordering strategies also affect the response efforts as shown in the second model, which is comprised of two uncertain disruptions. Minimax and minimax regret decision criteria were used to evaluate the model and provided insight into the inventory management decisions during and after the storm. Minimax regret decision criterion supported holding inventory during the storm to ensure a higher service level after the storm while minimax decision criterion supported the opposite. However, both criteria advocated holding the same order quantity during the second disruption or demand surge after the storm. Lastly, a stochastic programming model was developed to determine pre-positioned quantities for a network of retailers in addition to post-storm shipments between retailers and the manufacturer to alleviate shortages. The results from the commercial software and solution methodology supported pre-positioning items at most retailers despite the manufacturer's location. Overall, the research presented in this dissertation illustrates the importance and cost effectiveness of incorporating inventory strategies into management decisions when a storm is on the horizon.