New Frontiers of Risk Analysis in Industrial Companies: Benefits and Challenges

Nowadays the quantitative risk analysis approaches in various fields are widely used at different business case stages: geology, engineering, construction and operations. You have probably heard of the QRA, Cost and Schedule Risk Analyzes, Reliability and Availability Modeling, Reliability Centered Maintenance etc., which have shown their value, added at different stages. These methods are aimed at improving the decision quality (DQ).

However, classically these methods give recommendations to decision makers with some delay, and sometimes it even happens that the recommendations are not relevant. Given the reduction in cost of the real-time data extraction, transmission and processing infrastructure, the risk analysis toolset has received a new set of characteristics that significantly improve several stages of DQ.

Within this workshop, speaker will demonstrate theoretical and practical benefits, challenges of machine learning in real-time risk analysis. Speaker also will present the machine learning use case at the refining process allowing in real time to eliminate the HSE risk and increase operational efficiency.