(86b) “Getting More Out of Your PHAs with Data Analytics”

Pierce, M., DNV GL
De Man, A., ACM Facility Safety
Process Hazard Analysis (PHA) studies are required to identify process and operations risks for process plants. PHAs enable management and process safety staff to make decisions that improve safety performance, minimize downtime, and optimize utilization. 

Operating companies also must make these key decisions under financial capital constraints and ensure maximum return on investment for safety. These decisions require a clear understanding of the interconnectivity of the risks associated with a facility’s operations which are captured in a PHA. Most PHA studies generate recommendations based on residual risk and consequence levels, but they do not directly address how safeguards and recommendations are linked to the hazard scenarios. By improving the way PHA data are analyzed, using data analytics, additional insight is generated that is typically hidden, or not considered, when traditional PHA approaches are followed. These insights include more intuitive identification of critical safeguards (barriers), better prioritization of recommendations from a cumulative risk perspective, and visual aids to improve awareness and communication of risk drivers to operational staff and decision makers.

Traditional PHAs require a substantial investment in time and resources, which results in a potential gold mine of useful data that can be further extracted and exploited. With an ever shifting focus on new technology and innovation, data analytics is the next key step for improving risk management practices within organizations and the industry as a whole. Taking PHA data analytics further, “Big Data” data mining techniques can be utilized to extract information from the collected data and expose trends and patterns in industry PHA data. These trends can be benchmarked across various industries providing an advanced level of decision making insight not available from a single PHA. Statistical analysis of the trends and deviations, along with analysis of incident data can also predict increasing levels of risk, thus creating strong leading indicators to aid in decision making before incidents occur. This paper showcases a case study for a unit within a refinery which demonstrates how application of PHA with data analytics can greatly improve decision making.