(47c) The Shift to Dynamic and Simulated Process Safety Risk Management

or every incident that takes place, some estimate that more than 10,000 errors or failures do as well. Complex facilities constantly migrate toward higher-risk states, and it is often not noticed until a significant accident or incident occurs.

Operational Risk Management (ORM) systems have become commonplace within high-risk industries. PHAs, bowties and other hazard identification and assessment tools help specify prevention and mitigation safeguards to minimize the likelihood of experiencing a major hazard event. Once in place, these safeguards are monitored and maintained by numerous business processes and associated systems, including EHS, Maintenance Management, data historians, Asset Performance Management, control of work and more.

Yet these tools do not jointly analyze asset-level data. This leaves organizations vulnerable to Major Accident Hazard (MAH) risk. In fact, Sphera research shows that just 37% of organizations are taking a proactive approach to managing process safety risk, which means an incident or potential incident could be developing and they probably wouldn’t even realize it.

That’s why it’s time to connect the dots on risk-related data to offer prescriptive and predictive remedies to mitigate incidents or near-misses before they take place.

There’s a perceptible shift in ORM adoption, with 52% of organizations making investments in solutions to integrate data sources to be able to model and simulate real-time threats based on the health of their critical safeguarding barriers and minimize the possibility of a hazardous event occurring. The next natural step is to use ORM systems to predict future risk status based on trends or planned changes that may be introduced to the facility.

During this presentation, Simon Jones will share how leading organizations can:

• Share the results of the 2020 Process Safety and Operational Risk Management Industry Survey
• Discuss process safety gaps identified in industrial operations
• Explore data sources and underpinning models for managing operational risk

• Illustrate the shift to dynamic, real-time process safety risk management by sharing a case study of a recent ORM system at a major international refinery and chemical complex