Supporting hurricane inventory management decisions with consumer demand estimates

Abstract

Matching supply and demand can be very challenging for anyone attempting to provide goods or services during the threat of a natural disaster. In this paper, we consider inventory allocation issues faced by a retailer during a hurricane event and provide insights that can be applied to humanitarian operations during slow-onset events. We start with an empirical analysis using regression that triangulates three sources of information: a large point-of-sales data set from a Texas Gulf Coast retailer, the retailer's operational and logistical constraints, and hurricane forecast data from the National Hurricane Center (NHC). We establish a strong association between the timing of the hurricane weather forecast, the forecasted landfall position of the storm, and hurricane sales. Storm intensity is found to have a weaker association on overall inventory decisions. Using the results of the empirical analysis and the NHC forecast data, we construct a state-space model of demand during the threat of a hurricane and develop an inventory management model to satisfy consumer demand prior to a hurricane making landfall. Based on the structure of the problem, we model this situation as a two-stage, two-location inventory allocation model from a centralized distribution center that balances transportation, shortage and holding costs. The model is used to explore the role of recourse, i.e., deferring part of the inventory allocation until observing the state of the hurricane as it moves towards landfall. Our approach provides valuable insights into the circumstances under which recourse may or may not be worthwhile in any setting where an anticipated extreme event drives consumer demand.

Empirically grounded research in humanitarian operations management: The way forward

“…Increasingly critical donors demanding ever more impact for their dollar may use these results to reflect on how they themselves may be causing inefficiencies. Similarly, the smarter hurricane inventory management decisions of the retailer, as discussed in Morrice et al. (2016), illustrate how private companies can help state authorities better prepare for disasters.…”

“…(2016) is being implemented by UNHCR and integrated with their ERP system. Morrice et al. (2016) have led the retailer to participate in a state‐wide initiative to improve disaster preparedness in the state of Texas, USA.…”

“…” Moreover, the authorities are implementing the learning. As the authors state: “ Ultimately, our work, in conjunction with other initiatives, enabled the retailer to participate in a state‐wide initiative to improve hurricane disaster preparedness in the state of Texas ” (Morrice et al., 2016).…”

Role of Analytics for Operational Risk Management in the Era of Big Data

“…In the literature, analytical methods are being used in various ways, including a combination of methods from these major areas of analytics. For example, predicting geospatial spread of diseases (predictive) for better resource allocation (prescriptive) is critical for pandemic response (Araz, Lant, Jehn, & Fowler, 2013; Huang et al., 2017); understanding emerging trends in consumer behavior during natural disasters such as hurricanes is critical for optimal inventory management (Morrice, Cronin, Tanrisever, & Butler, 2016). Other examples can be found in analyzing social networks and predicting the role of social media on public behavior (Morss, Cuite, Demuth, Hallman, & Shwom, 2018), managing traffic flows during catastrophic events (Tuydes‐Yaman & Ziliaskopoulos, 2014), and optimizing location of relief facilities for maximum coverage and safety (Salman & Yücel, 2015; Battara et al., 2018; Choi et al., 2018).…”

Analytical Hierarchy Process (AHP), Economic Order Quantity (EOQ), and Reorder Point (ROP) in Inventory Management System

“…These problems can lead to potential pitfalls, from both cost and service perspective (Disney, Maltz, Wang, & Warburton, 2016). In US, Morrice, Cronin, Tanrisever, and Butler (2016) built an inventory management model by combining the hurricane prediction and consumers' demand estimation. This example shows that inventory management is thoughtfully designed to ensure the sustainability of a company.…”