Every manufacturing process has its inefficiencies — and these inefficiencies often lead to losses that harm their bottom line.
These losses take many different forms: be it from yield, quality, waste, throughput, energy, emissions, factory downtime, or something else. Usually, the more complex the process, the more complex the problems tend to be — and that’s where root cause analysis (RCA) comes in.
Process experts or process engineers use root cause analysis to trace a particular loss to its cause, in order to eliminate it. Of course, while that sounds pretty straightforward, the reality is often quite different.
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 hidden 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.
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.