Managing Safety‐Related Disruptions: Evidence from the U.S. Nuclear Power Industry

Abstract

Low-probability, high-impact events are difficult to manage. Firms may underinvest in risk assessments for low-probability, high-impact events because it is not easy to link the direct and indirect benefits of doing so. Scholarly research on the effectiveness of programs aimed at reducing such events faces the same challenge. In this article, we draw on comprehensive industry-wide data from the U.S. nuclear power industry to explore the impact of conducting probabilistic risk assessment (PRA) on preventing safety-related disruptions. We examine this using data from over 25,000 monthly event reports across 101 U.S. nuclear reactors from 1985 to 1998. Using Poisson fixed effects models with time trends, we find that the number of safety-related disruptions reduced between 8% and 27% per month in periods after operators submitted their PRA in response to the Nuclear Regulatory Commission's Generic Letter 88-20, which required all operators to conduct a PRA. One possible mechanism for this is that the adoption of PRA may have increased learning rates, lowering the rate of recurring events by 42%. We find that operators that completed their PRA before Generic Letter 88-20 continued to experience safety improvements during 1990-1995. This suggests that revisiting PRA or conducting it again can be beneficial. Our results suggest that even in a highly safety-conscious industry as nuclear utilities, a more formal approach to quantifying risk has its benefits.

Prevent or Report? Managing Near Misses for Safer Operations

“…Within the broad operations literature on disruption risk management (e.g., Tomlin 2006, Aydin et al 2010, Kim et al 2010, Serpa and Krishnan 2016, Chod et al 2019, precursor analysis (or near miss management) is a key tool for ensuring safety and improvement of processes, in the face of rare disasters. Given the rarity of disasters, risk management must rely on more frequent near misses (Phimister et al 2003, Blanco et al 2019. The safety pyramid (Heinrich 1931, Phimister et al 2003 and event tree (Yi and Bier 1998) models commonly employed in safety management embody this idea.…”

“…In the context of rare disasters, precursor analysis addresses the challenge that there are few chances to discover the root cause (Blanco et al 2019). This approach views a disaster as a chain of events: the escalation step leading to a disaster is invariably preceded by a precursor.…”

Risk Analysis: Celebrating the Accomplishments and Embracing Ongoing Challenges

“…A review of local hazard mitigation plans in the United States shows that governments have proposed many more resilience projects than can possibly be funded (Greenberg & Schneider, 2017). Risk assessment should help inform risk management to prioritize the most important projects (Alderson, Brow, & Caryle, 2015;Aven & Renn, 2012;Blanco, Caro, & Corbett, 2019;McDaniels et al, 2012;Mukherjee & Neteghi, 2019).…”

Anticipating Energy-driven Crises in Process Industry by AI-based Scenario Planning

“…In research, related work on anticipating energy-driven disruptions in process industry focuses on linear programming models for estimation of energy disruptions from earth quakes (Janev et al, 2021), organizational learning approaches for adaption to climate changes (Orsato et al, 2017) as well as knowledge management in energy data spaces (Rose et al, 1997). In addition to that, there is related work focusing on the energy industry itself, considering probabilistic risk assessment for preventing safety related disruptions (Blanco et al, 2019;Kosai and Unesaki, 2017), anticipation of power generation and outages (Kim et al, 2019;Moghavvemi and Faruque, 1999) as well as outage management approaches (He et al, 2016). Objective of our research is the anticipation of such energy-driven crises in process industry by AI-based scenario planning for improving resilience in manufacturing.…”