A new approach to prevent accidents in the steel industry

Abstract

The steel industry has faced extraordinary changes over recent years incorporating new technologies and processes to make it more competitive and safer based on market requirements, regulations, and community concerns. Safety, in particular, has been an important topic in which the industry has put remarkable efforts to improve its performance. Traditional safety models currently used to analyze and prevent accidents have been in use for decades. However, the complexity of systems has substantially increased over this time and has reshaped the way people perform their activities. The limitations of traditional models are becoming more evident as system complexity increases, especially when it comes to understanding the interactions between many system elements, incomplete or otherwise flawed requirements, design errors, and human behavior and contextual factors. Today, there is general recognition that a new approach is needed to address the complexity of modern systems and to address the deeper systemic causes that are leading to accidents. This thesis evaluates and demonstrates how new approaches based on Systems-Theoretic Accident Model and Processes (STAMP) can be applied using a real case from the steel industry. Using these approaches, organizations can gain a broader perspective to understand the full range of factors contributing to accidents and create more effective measures to prevent future accidents.

This thesis examines a high-risk incident in a steel plant and compares a traditional Root Cause Analysis that was performed with a new systems approach called Causal Analysis based on System Theory (CAST). Causes and recommendations from both methods are compared. In addition, a systems approach for hazard analysis called Systems Theoretic Process Analysis (STPA) is evaluated to determine whether it could have anticipated the behaviors and contextual factors that led to the incident and whether it could have been prevented. These methods were found to be extremely effective in analyzing past accidents and in preventing future accidents, providing significant insights for organizations to understand the reasons behind accidents and to define the necessary steps to prevent them.