For anyone just learning about root cause analysis, the name probably makes the methodology sound a lot more complex than it actually is.
Basically, root cause analysis (RCA) is about getting to the “root cause” of a problem. So many times in business or in other organizations, it’s very easy to place blame on peripheral evidence such as an employee doing something incorrectly. But upon further investigation, there’s normally an underlying reason for the error, such as inadequate instructions or poor lighting or a machine malfunction or any number of more significant reasons why someone might make an error in the first place.
By peeling back the layers of an issue, root cause analysis eventually pinpoints the problem’s genesis, which then makes a permanent fix possible.
Nowhere has this approach been more effective than in the manufacturing sector. The term “root cause” refers to the most primary reason for a production line’s drop in quality, or a decrease in the overall equipment effectiveness (OEE) of an asset.
In the past, manufacturers deploying RCA leaned on two specific methodologies: the fishbone diagram (a useful tool in determining the most likely causes of a quality problem) and the 5 whys (a series of questions leading to the underlying cause of a problem).
However, as technology advances, manufacturers are now looking at artificial intelligence (AI) to assist with RCA. Artificial intelligence, specifically in the form of machine learning, catapults root cause analysis into another realm of asset management.
That’s because AI has the ability to formulate predictions relating to machine performance and health, instead of waiting for disaster to strike. This of course introduces a whole range of benefits that affect the bottom line.
Some examples of the direct benefits of automated root cause analysis in manufacturing are:
- Early detection of safety issues
- Reduced emissions due to accurate monitoring of the entire production process
- Identification of complex process disruptions, such as inefficiency of a reactor
- More efficient electrical consumption through anomaly detection
- Predicting quality deviations and adjusting processes to prevent the waste of raw materials
Learn more about root cause analysis in Root Cause Analysis Training for Beginners, a 2-day course that covers the tools and techniques to trace problems down to the root cause.