What is Root Cause Analysis in manufacturing?

On the production floor, Root Cause Analysis (RCA) is the process of identifying factors that cause defects or quality deviations in the manufactured product.

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.

Common examples of root cause analysis in manufacturing include methodologies such as the “Fishbone” diagram and the “5 Whys”. The simplicity of these methods is also their strength, but how effective are they in dealing with the complexity of today’s manufacturing processes?

“Fishbone Diagram” created by Kaoru Ishikawa (Quality Manager at Kawasaki) in the 1960s.
“Fishbone Diagram” created by Kaoru Ishikawa (Quality Manager at Kawasaki) in the 1960s.

Root cause analysis is undergoing a new interpretation in light of the Industry 4.0 and Smart Manufacturing revolutions. With the rise of Predictive Quality and Artificial Intelligence, it’s natural that
manufacturers are progressing to more advanced root cause analysis methods.

Why is it so hard to find the root cause?

Sometimes, it’s relatively simple to solve a particular problem – particularly when that problem stems from a single cause, or a factor that’s easy to spot. For example, if the temperature at a particular point in the line strays from the permitted range, then a process expert will usually be able to identify and solve the problem themselves fairly quickly.

But very often this is not the case. Sometimes, the problem is caused by a complex combination of factors, making the root cause more difficult to understand. This is particularly true if the cause is rooted in the interrelationships between numerous tags, and their place within the process. This can mean no single tag is behaving problematically, but that the problem is rooted in the process itself
– for example the speed at which the raw material travels from Tag A to Tag B; or its temperature or concentration at a specific point within the process; or the pressure it is subjected to between point A and point B, and so on. The options are endless.

Then of course there is the fact that the problem identified is actually just a symptom of a more fundamental issue, and the root cause may actually be the second or third derivative. Perhaps the issue
began further upstream in the production process, and only became noticeable later on.

In any of these scenarios, nothing is amiss to the naked eye. This is why most manufacturers simply come to accept a certain amount of production losses: they’ve applied their smartest, most talented process experts and advanced tools to the problem, and simply couldn’t see what was wrong. What else can they do?

Shortcomings of Traditional Root Cause Analysis

As mentioned above, the general approach currently used by many manufacturers when it comes to root cause analysis is to rely on on-site expert knowledge, aided by a range of analytics tools.

Experience and process expertise is of course invaluable.

The problem is, for many complex processes, it isn’t humanly possible to analyze all the combinations of all the data tags on a production line, all the time.

In our conversations with hundreds of manufacturing executives, including many from leading global brands, the same limitation was consistently highlighted: advanced analytics are excellent for validating existing theories, but are much less useful for discovering the root causes of persistent production problems. That’s because, even with the most sophisticated such platforms, there is always an inevitable blind spot, as the process expert needs to select a handful of tags based on their own human intuition and biases.

It’s natural that even experts can be biased towards certain ideas. Even if the root cause of the problem is roughly identified, there may be inaccuracies in the definition of the problem, making it difficult to come up with an intelligent and lean solution.

Other disadvantages of manual root cause analysis include:

  • Often, most RCA information isn’t shared across manufacturing sites, as manual analysis doesn’t scale. This leaves factories of the same company – or even individual lines within the same factory – to repeat each other’s mistakes, leading to losses that could have been avoided.
  • Manual RCA is conducted on an ad-hoc basis. But as manufacturing processes are dynamic, the data is constantly changing, so the analysis can quickly become redundant.

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The Power of Automated Root Cause Analysis

Automated root cause analysis harnesses the power of Machine Learning – a subfield of Artificial Intelligence that focuses on developing and researching algorithms that learn from data. The algorithms exist in the form of models which are trained with historical data in a way that allows them to make predictions and decisions based upon new data.

Thanks to significant advances in machine learning and Big Data analytics, root cause analysis can be performed using automated methods. These methods are unbiased and based purely upon historic and real-time data from the production floor, infused with process expertise (more on that later). Just as importantly, they take the Sisyphean task of analyzing and interpreting data away from the people on the factory floor, thereby enabling them to focus on actually optimizing the processes and improving performance.

If you had to summarize the value of machine learning in root cause analysis, it would be:

Supervised Learning vs. Unsupervised Learning

To perform RCA using machine learning, we need to first establish the type of problem we are analyzing. In general, there are two categories of Machine Learning that we need to be aware of here in the context of manufacturing.

Unsupervised learning

When there are relatively few examples of a problem (e.g. a machine breaking down once a year).

Since there aren’t enough examples to establish a pattern, the model works backwards, figuring a pattern out by registering each anomaly, and extrapolating from that behavior that an incident may be occurring.

An example of this would be predictive maintenance.

Supervised learning

For when there are many examples of a particular problem (e.g. waste or quality losses, or regular downtime caused by inefficiencies in the process).

The model is trained to understand a good outcome vs. a bad outcome – i.e. learning what the process looks like during optimal performance, vs. during an incident.

An example of this would be Process-Based Artificial Intelligence.

In our context, automated root-cause analysis is used to identify the causes of regular inefficiencies in the manufacturing process, and prevent them from occurring in the future. This is a classic use case for supervised machine learning. Unlike human beings, a supervised machine learning algorithm can continuously analyze all the data and interrelationships between all the tags on any given line. By doing so, it can learn what optimal performance “looks like”, compared to how the data looks when an incident is about to occur.

Essentially, automated root cause analysis can be used as a basis to predict and prevent problems from occurring in the future, by providing manufacturing teams with an “early warning system”.

The root causes are right under your nose — but finding them is complex

With the rise of Industrial Artificial Intelligence, manufacturers have an unprecedented opportunity to eliminate the root causes of their most complex and stubborn production losses.

“Traditional” methods of root cause analysis leave a major blind-spot, as process experts can’t keep track of all the data, including tag interrelationships – particularly given the uniquely complex and dynamic nature of manufacturing data.

By contrast, automated root cause analysis can reveal even the most complex root causes, harnessing the power of supervised machine learning, and coupling that with process expertise. This in turn leaves process experts and manufacturing teams free to work more efficiently, by relieving them of the endless, Sisyphean task of tracking and analyzing all their data.

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