Root Cause Analysis
What is root cause analysis?
Root cause analysis (RCA) is the process of identifying the original cause for any drop in product quality, plant or asset OEE, or for a part failure. It’s a vital part of dealing with any incident or fall in efficiency within the plant, because if you don’t know what has gone wrong, you won’t be able to fix it effectively or prevent it from occurring again.
Finding the root cause of an incident can be simple, like noticing that the temperature inside a furnace has fallen and tracing that to a faulty heating element. But more often, it’s a complex and taxing process. Sometimes the root cause is hiding in the interplay between a number of elements, and examining just one will lead you to the wrong conclusion. At others, what appears to be the root cause is just a symptom caused by some other issue further upstream, or even upstream of that.
Advanced sensors and smart devices provide more information about varying parameters within an asset or process, which helps guide process engineers to the actual root cause of an incident. But as big data piles up, it becomes too much for manual root cause analysis to handle.
Thanks to industry 4.0 and AI/ML in manufacturing, root cause analysis is now more sophisticated and powerful. Even when using advanced analytics, manual root cause analysis can’t evaluate all the hundreds of data points that are collected for any given incident. Human operators have to select a certain number of tags in order to keep the dataset manageable for manual analysis, but natural human bias will skew their choices. However, AI-powered, automated RCA can handle the flood of information, remove bias, and examine both historical and real-time data without getting overwhelmed.
Why is root cause analysis important for process manufacturing plants?
Process manufacturing companies occupy a competitive sector. Process systems are complex, markets fluctuate rapidly, and customer demands can be insistent. Successful plants invest in advanced root cause analysis so that they can swiftly spot inefficiencies and anomalies within the system and act quickly to address them.
With advanced, AI and ML-powered root cause analysis, process plants can:
- Fix failing parts and correct inefficiencies to maintain high product quality and operational excellence
- Understand the right way to correct an incident so that it doesn’t happen again within a short space of time
- Save time and resources that would be wasted fixing the symptoms while leaving the core problem unaddressed
- Prevent downtime from recurring problems
- Share RCA data across plants within the organization so they can spot common causes and prevent an incident from occurring in a different plant
How can process plants apply root cause analysis?
Improve data collection
Successful root cause analysis rests on sufficient data. Even the most advanced automated RCA tool will produce unreliable conclusions if you don’t have information from the right sensors. Often, the root cause lies in a combination of more than one tag, so if one is missing you’ll end up with inaccurate results.
Appoint the right stakeholders
It’s important to clarify who is responsible for carrying out root cause analysis and making sure that they have the time and training to use RCA tools correctly. You might need to offer upskilling to some of your maintenance engineers, or hire new talent to free up existing employees from other tasks.
Strengthen trust in the tools
AI and ML are still relatively new to the process manufacturing world, and AI-powered root cause analysis can produce “black box” results that lack transparency. Employees need to learn to trust your new RCA tools, so take time to explain the process and demonstrate the benefits before handing them over.
How do process plants benefit from root cause analysis?
With advanced, automated root cause analysis, process manufacturing plants can cut downtime, save money, and ensure consistently high product quality that raises their reputation among customers and helps drive revenue and profits.