Stochastic modeling of manufacturing-based interdependent inventory for formulating sector prioritization strategies in reinforcing disaster preparedness

摘要

The production of commodities and provision of services in today's infrastructure and economic sectors have become heavily driven by their intrinsic interdependent relationships. Natural and man-made disasters have been known to render a number of these sectors inoperable. And with minimum levels of inventory, the delivery of the expected products and services that fulfill the total production input requirements of other sectors is disrupted. Hence, disaster consequences are propagated across these interdependent sectors — ultimately leading to amplified losses and diversified system inoperability. This research investigates how inventory levels of manufacturing sectors impact system capability of absorbing demand for input requirements in the event of a disaster. A unique contribution of this research is the formulation of a stochastic model of interdependent inventory to provide more realistic estimates of economic losses and sector inoperability. Inventory modeling and simulation are utilized using empirical cumulative distribution functions of inventory levels generated from the inventory-to-sales database of the Bureau of Economic Analysis, which spans 14 years with 168 observations for each of the 21 manufacturing and trade sectors considered. Results of the study are incorporated into a Dynamic Inoperability Input-Output Model that provide insights into the formulation of sector prioritization policies. A Dynamic Cross Prioritization Plot identifies the prioritized set of critical sectors for inclusion in an inventory-enhancement plan to improve system recovery. Risk assessment without factoring inventory was found to have overestimated total economic loss by an average of 21.86% or $136M for a moderate intensity hurricane scenario in Virginia. To complement the manufacturing-based inventory enhancement study explored in this paper, further work is recommended to evaluate the resilience of the service sectors.