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A story of why causal AI is necessary for root cause analysis in manufacturing

by Dr. Tobias Hatt


Traditional machine learning is designed for prediction and often struggles with root cause analysis. The article presents a short story demonstrating how causal AI overcomes this problem.

Why causality is needed for decision-making

Data-driven decisions are paramount to stay competitive in today’s manufacturing. However, for effective decisions, we need tools that transform data into actionable insights. Traditional machine learning tools, while great for predictions, fall short in decision-making due to their inability to grasp cause and effect relationships. They fail to understand how different decisions impact outcomes. To make truly informed decisions, understanding these cause and effect dynamics is crucial.

Causal AI provides manufacturers with entirely new insights by going beyond the prediction-focused scope of traditional machine learning. It seeks to uncover the causes behind outcomes, which enables us to assess and compare the outcomes of different decisions. This offers crucial insights for more informed root cause analysis. For manufacturers, this means not only predicting what will happen, but which decision can be taken now that leads to a better outcome in the future.

What is causal AI?

Causal AI, at its core, is an advanced form of artificial intelligence that seeks to understand and quantify cause-and-effect relationships in data. In particular, causal AI aims to understand how one variable A influences another variable B. This is important for decision-making, since if we want to change A with the goal to increase B, we need to know how A influences B. Traditional machine learning only uses A to predict B, but cannot answer what happens to B if we change A as we will see in an example below. However, the answer to this question is important for decision-making, in particular in the context of root cause analysis in manufacturing.

This article looks into the task of root cause analysis for quality improvement. The focus is to maximize “good” quality and minimize “bad” quality outcomes. Simply predicting when quality will drop is not enough in this setting. The objective is to identify and adjust specific production parameters (like adjusting a machine setpoint) when bad quality is observed, to restore good quality. Therefore, understanding the cause-and-effect relationships between these production parameters and the product quality is key. This knowledge allows us to pinpoint which parameters are causing quality issues and make necessary changes to achieve desired quality levels consistently. In the following, we tell a short story to demonstrate the capabilities of causal AI in this context.

Causal AI for root cause analysis

Let’s imagine a manufacturing company specializing in plastic Christmas trees, a seasonal product where quality and timeliness are key. The company faced a peculiar challenge: a noticeable drop in the quality of their plastic trees. Naturally, they turned to data for answers.

Their initial investigation was led by a skilled data scientist, who collected data about the production process. The production process consists of two steps: First, the plastic branches are sourced from a supplier. Second, the branches are put through a machine which attaches the branches to the trunk. There are two possible suppliers, A and B, and two possible machines, M1 and M2.

The data scientist used traditional machine learning techniques, which focused on predicting the quality based on the collected data. This led to an intriguing conclusion: The machine learning model suggested that machine M1 produced worse quality than M2. Based on this analysis, the data scientist recommended stop using the machine M1, which would lead to a substantial reduction in throughput and, hence, reduced production capacity. However, the story took a twist when the company decided to scrutinize both machines. To their astonishment, there was no recognizable difference in the settings of the machines or the machines themselves. This puzzling situation called for a deeper analysis, beyond what traditional machine learning could offer.

Luckily, a friend of the company’s data scientist is a renowned causal AI expert. The expert developed a tailored causal AI algorithm for the production process, seeking not just good predictions, but to understand the underlying cause-and-effect relationships in the production process. The causal AI model revealed an unexpected insight: the root cause of the quality drop was not the machine, but the supplier. In fact, it revealed that Supplier A delivered branches of worse quality than Supplier B. After talking to the factory workers, the company found out that the workers always put the branches of Supplier A through machine M1 and the branches of Supplier B through machine M2. They did this simply because the machines were closer to the boxes with the corresponding branches. Hence, all the low-quality branches of Supplier A ran through machine M2, which made machine M2 look like it is causing the drop in quality. 

But why did the traditional machine learning model fail to identify the true root cause? The reason is that its objective is prediction and, for this, knowing which machine the branches went through was enough to predict the quality perfectly. In particular, since the traditional machine learning model didn’t understand the underlying cause-and-effect relationships, it simply used all available parameters. However, by doing so, it also used the machine as a parameter, which, in this example, is a so-called mediator. By using this mediator, it “blocked” any indirect influence from the supplier via the machines. As a result, the influence of the supplier got lost. Since the causal AI understood the underlying cause-and-effect relationships, in particular the relationship between supplier and machine, it could correctly identify the true root cause.

Armed with this causal insight, the company informed Supplier A about the quality of their branches, which they ultimately were able to improve with new specifications. As such, leveraging causal AI averted a prolonged production stop of machine M1, which would have cost the company a lot of money. All of this just because the traditional machine learning model focuses on prediction, but not on understanding the underlying cause-and-effect relationships. Only a causal AI model could identify and rectify the true root cause of the quality issue.

In this simplified scenario, it would be easy to carefully check all parameters and production steps manually. But imagine a real-world scenario, in which we have hundreds or even thousands of parameters across many process steps. In such a setting, the clear association between machine M1 and quality, identified by traditional methods, can easily be mistaken for a root cause. And manually checking for other influence factors would be tedious, if not impossible. In this case, causal AI can identify the root cause immediately and, as such, saves a lot of time and costs.

Opportunities and challenges of causal AI in manufacturing

The opportunity of causal AI is clear: it offers new ways for manufacturing to identify the true root causes of problems. This depth of insight empowers manufacturers to make decisions that address core issues, leading to enhanced efficiency, quality, and competitive advantage. 

However, the adoption of causal AI is challenging. One significant hurdle is the absence of off-the-shelf software, which can be used without being a data scientist. Moreover, as the above example showed, even seasoned data scientists often lack the experience with causal AI. This is mainly because causal AI is a relatively new field. Despite these challenges, the potential gains in operational understanding and performance are substantial. 

If you’re interested in finding out how causal AI can help your problem-solving efforts, we invite you to book a demo and experience the impact firsthand.

Dr. Tobias Hatt

Tobias Hatt is a Machine Learning Engineer at EthonAI, where he works on root cause analysis. He is particularly interested in applications of causal machine learning that help optimizing manufacturing processes. Tobias holds a PhD from ETH Zurich, where his research focused on causal machine learning.