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AI is transforming manufacturing. But what is hype, and what are meaningful trends?

We share our hands-on knowledge and real-world experiences.

Material Flow Analysis
Root Cause Analysis
Visual Quality Inspection
The effect of unmeasured root causes in problem-solving

Even with incomplete data, a robust root cause analysis algorithm can find actionable insights to improve your production.

Deploying a Manufacturing Analytics System: On-premises vs. cloud-based solutions

We discuss key aspects to consider when deciding how to deploy a Manufacturing Analytics System.

How modern data analytics enables better decision-making

AI’s potential in decision-making is still underutilized despite impressive advancements in other areas.

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

Explore why causal AI is needed for understanding the underlying cause-and-effect relationships in a production process.

Process Mining: A new take on material flow analysis in manufacturing

Explore how process mining is the new standard for material flow analysis in manufacturing.

What are distributional shifts and why do they matter in industrial applications?

Explore how distributional shifts can deteriorate your ML models’ performance

Industrial anomaly detection: Using only defect-free images to train your inspection model

Why visual inspection should rely on approaches that do not require images of defective products for training.

A terrible idea: Using Random Forest for root cause analysis in manufacturing

Explore the limitations of Random Forests and the effectiveness of graph-based algorithms for root cause analysis in manufacturing.

AI is changing how expert knowledge is used in manufacturing

AI is expected to take over an essential role in troubleshooting complex manufacturing problems. To do so effectively, it will need to be fed with all the expert knowledge we can get.