(13a) Overview of Risk-Based Decision-Making Methodologies and Scenario Prioritization in Bow Tie Analysis: Unveiling Benefits and Challenges

Process safety is paramount to ensuring efficient and secure industrial operations. Dynamic barrier management plays a crucial role in identifying and controlling potential risks in complex industrial processes. In this study, we provide an overview of methodologies for risk-based decision-making and scenario prioritization within the context of Bow Tie analysis.

This research is particularly significant for large process facilities managing multiple Bow Ties, associated barriers, and crucial elements, as they require structured methodologies that support decision-making in cases of barrier degradation or unavailability.

The choice among the existing methodologies for the company must consider aspects such as the company's safety culture, technological infrastructure, corporate standards, maintenance and inspection software, human resources, among other factors.

Our critical analysis delves into both traditional and innovative approaches for decision-making, with a specific focus on the Bow Tie method and its qualitative and quantitative applications. This includes the qualitative approach that relies on robust verification, monitoring activities, and robust critical analysis, as well as the Bow Tie - LOPA method and the AHP method.

We emphasize the fundamental benefits of these methodologies, particularly their capacity to prioritize and track multiple consequences within a single scenario, thereby increasing their efficacy in implementing crucial safeguards.

Furthermore, we address the challenges inherent in implementing these methodologies, such as the need for reliable data, the complexity of modeling cause-and-effect relationships, the software requirements to implement these methodologies for a large number of Bow Ties, and the human resources needed to process this data. We highlight the significance of robust data collection and analysis strategies to enhance the accuracy and reliability of risk analyses.

In addition to these methodologies, we debate the increasing relevance of integrating Big Data and Artificial Intelligence technologies into the process of risk-based decision-making. We highlight the main role these technologies play in uncovering hidden patterns, predicting risk scenarios, and optimizing preventive and corrective decisions.

This study offers a comprehensive and critical analysis of key methodologies for risk-based decision-making and scenario hierarchization within Bow Tie analysis. We conclude by emphasizing the continuous significance of research and development in this domain, with a focus on enhancing process safety and strengthening practices in industrial risk management.