Manufacturing Root Cause Analysis with Causal AI

Machine learning and AI are extensively used in manufacturing to optimize processes, enhance quality, and reduce costs. Predictive maintenance algorithms analyze sensor data to anticipate equipment failures, reducing downtime. Quality control systems leverage computer vision to identify defects on production lines in real time, while AI-powered robots automate complex tasks like assembly and welding with high precision.

Root cause analysis is crucial in manufacturing for uncovering the underlying issues that lead to defects, inefficiencies, and failures. By pinpointing the true sources of problems, manufacturers can implement targeted solutions to prevent recurrence, minimize waste, improve product quality, and boost operational efficiency. For instance, in a complex welding process, various factors may affect the quality of the final products. A specific defect might stem from excessive humidity causing temperature fluctuations, leading to an unstable joint, or from an undertrained operator incorrectly adjusting machine settings. Effectively addressing the root cause allows the team to implement targeted measures, ultimately reducing defect rates.

Challenges with traditional machine learning approaches

Many manufacturers rely on traditional machine learning algorithms based on correlations to address this problem. However, these techniques have significant limitations in root cause analysis due to their inability to capture causality. They often fail to distinguish true root causes from mere symptoms, oversimplifying complex manufacturing processes into a tabular dataset while neglecting the manufacturing process flows. By prioritizing predictive power over causal understanding, these algorithms risk misidentifying root causes and can lead to misleading conclusions.

Enhancing root cause analysis with causal AI

Causal AI is a powerful technique that enhances root cause analysis by identifying true root causes rather than symptoms, enabling the precise identification of issues and their origins. It utilizes domain knowledge, often represented as knowledge graphs, and integrates that with observational data to uncover causal relationships among key variables in complex processes. By modeling cause-and-effect dynamics instead of relying solely on correlations, causal AI provides actionable insights for defect prevention and process optimization.

Case study

In a series of notebooks, we demonstrate how causal AI can be applied to perform causal analysis in a manufacturing process using the open-source Python framework DoWhy. We present a fictitious scenario where we are tasked with reducing costs and optimizing the efficiency of a production line. Through this setup, we examine how various factors impact the quality of finished products and explore methods to identify these factors.

Above is a schematic representation of our production line, where raw materials undergo multiple processes such as cleaning, assembling and welding. Along the production line, we collect measurements of various factors that could influence the final product quality. At the end of the process, a quality check determines whether a product is defective or not. This quality depends on several evaluations, including dimensional verification, torque resistance checks, and visual inspections, each influenced by different factors within the processes. For example, the torque resistance checks may depend on the force and torque exerted by a machine during the process, which in turn can be affected by the machine settings or specific material properties. Now, imagine the product quality remains stable for some time but suddenly experiences a significant drop. Why?

Causal AI answers this question by providing deeper insights into how various factors influence product quality and pinpointing the root causes of declines. For a product flagged as defective, traditional machine learning approaches might incorrectly focus on symptoms, such as dimensional check failures or abnormal torque readings, to diagnose quality issues. In contrast, causal AI could reveal that the true root causes are primarily linked to worker skill levels and machine settings, which exert the strongest causal influence on the quality outcome. This level of clarity empowers confident decision-making on effective countermeasures, such as refining machine calibration protocols or implementing enhanced worker training programs, rather than relying on superficial adjustments to quality control thresholds. While real-world production lines are often more complex and involve a broader range of variables, our example provides a practical introduction to the technique.

Why Databricks

Databricks offers an ideal platform for implementing causal AI applications, thanks to its unified platform for all data and models. With Databricks, organizations can benefit from:

• Collaboration
  The platform facilitates seamless collaboration, enabling data scientists, engineers, and domain experts to work effectively on the same project: a key factor for the success of causal AI initiatives.
• Integration with Causal AI Libraries
  With its open standards, Databricks allows easy integration of popular open-source causal AI tools such as DoWhy and causal-learn. This empowers data scientists to leverage state-of-the-art causal AI techniques with minimal friction.
• End-to-End ML Pipeline Support
  Databricks supports the entire AI lifecycle, from data preparation to model deployment. This end-to-end support is particularly valuable for causal AI applications, which often involve complex data preprocessing, model training, and real-time inference.

By combining these features, Databricks provides a robust and flexible environment for developing, testing, and deploying causal AI solutions, making it an excellent choice for organizations aiming to incorporate causal AI into their operational workflows.

Summary

Causal AI is a transformative approach to root cause analysis, enabling the distinction between true root causes and symptoms. Unlike traditional methods that rely solely on correlations, causal AI models cause-and-effect relationships, providing actionable insights for defect prevention and process optimization. With its unified platform, Databricks offers an ideal environment for implementing causal AI applications.

Download the notebooks to explore how causal AI can be implemented on Databricks.