Measuring downstream supply chain losses due to power disturbances

Published

September 16, 2022

Author(s)

Douglas Thomas, Juan Fung

Abstract

Power outages in the U.S. affect many firms' economic activity and are likely to result in downstream supply chain disruptions. This paper examines the impact of power disturbances in the supply chain for four industries: manufacturing, durable goods manufacturing, nondurable goods manufacturing, and total private industry. This indirect impact includes the losses at downstream firms that might not receive supplies on time due to disturbances at their suppliers' facilities. This is measured using observations of value added before and after power disturbances rather than the more common method of modeling an economy. This paper tests four hypotheses related to the effect of power disturbances with all four of them being supported by the model results. Further, using simulation we estimate the indirect costs of power disturbances. The results suggest that power disturbances have a statistically significant effect on gross domestic product (i.e., value added), particularly in manufacturing where power disturbances in the supply chain had a statistically significant effect. While this industry represents 12.8% of private industry value added, it experiences 36.8% of the supply chain losses due to power disturbances, as suggested by the results of the simulation. Power disturbances in the supply chain affected all four industries with nondurable goods being affected the most. This creates a significant disconnect between the stakeholder that invests in reliability in the power grid and the stakeholders that experience the bulk of losses.

Citation

Energy Economics

Volume

114

Pub Type

Journals

Download Paper

https://doi.org/10.1016/j.eneco.2022.106314

Local Download

Keywords

manufacturing, power disturbance, value added, supply chain

Citation

Thomas, D. and Fung, J. (2022), Measuring downstream supply chain losses due to power disturbances, Energy Economics, [online], https://doi.org/10.1016/j.eneco.2022.106314, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932919 (Accessed April 25, 2024)