What Is a Risk Matrix?
A risk matrix is used to plot level of risk based on likelihood and consequence of hazards. A risk matrix is a key part of a risk assessment, which is a process used to identify potential hazards and the risks associated with said hazards. Risk assessments and risk matrices are used by asset intensive industries, such as refining, chemical, up and mid-stream oil and gas processing facilities, pharmaceutical production facilities, etc. to identify high-risk assets in order to develop proactive risk-mitigation plans.
Risk Matrix Limitations
Risk matrices are commonly used to define risk; however, solely relying on a standard risk matrix to generate proactive asset strategies will be limiting in the following ways:.
- Risk is subjective.
- The matrix is based on static data representing a snapshot in time.
- There can be order of magnitude differences in risk within the same cell of a matrix, i.e. lack of discrimination
- Different departments have different interpretations of risk and consequence.
- The matrix is based on static data representing a snapshot in time.
What Does the Future of Reliability Look Like?
Asset strategies that utilize risk matrices have helped the industry become more safe, more reliable, and more cost effective in allocating resources. However, the industry is presently experiencing major advancements in data acquisition, warehousing, modeling, and analytics. These capabilities are now providing the opportunity to take the next leap in reliability analysis, allowing us to improve reliability further while decreasing total maintenance and inspection spend.
This leap is being made possible through Quantitative Reliability Optimization (QRO). QRO is an approach to reliability modeling which connects every relevant reliability data point at a complex facility to one integrated model, allowing for near real time complex decision making that allows users to do things like:
- Understand risks objectively, with credible dollar values, and determine the best action(s) based on investment payback to better protect health, safety, the environment, and reliability.
- Optimize, in near-real time all maintenance spend based on short/mid/long-term reliability targets.
- Understand the economic value of every inspection or maintenance activity performed.
- Understand the economic value of every piece of data that is currently being gathered or could possibly be gathered in the future.
- Near real-time scenario modeling, including the implications of moving a turnaround, changing feedstock pricing, or introducing various capital projects.
- Drive effective economic decisions in the event of reliability based operating excursions.
Learn more about Quantitative Reliability Optimization (QRO).