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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.

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Knowledge
Material Flow Analysis
Products
Root Cause Analysis
Technology
Visual Quality Inspection
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.

Augmented Intelligence: How explainable AI is changing manufacturing jobs for the better

Explainable AI enhances manufacturing jobs by augmenting human intelligence and fostering better collaboration without replacing human roles.

Virtual Design of Experiment: How to optimize your production processes through digital tools

Virtual Design of Experiments allow optimizing manufacturing processes without interfering with the actual processes, which can save a lot of time and money.

What data is needed for AI-based root cause analysis?

AI-based root cause analysis starts with setting up a data collection pipeline, and following some basic considerations can leverage the full potential of today’s analytics tools.