Explore why causal AI is needed for understanding the underlying cause-and-effect relationships in a production process.
Explore how process mining is the new standard for material flow analysis in manufacturing.
Explore how distributional shifts can deteriorate your ML models’ performance
Why visual inspection should rely on approaches that do not require images of defective products for training.
Explore the limitations of Random Forests and the effectiveness of graph-based algorithms for root cause analysis 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.
Explainable AI enhances manufacturing jobs by augmenting human intelligence and fostering better collaboration without replacing human roles.
Virtual Design of Experiments allow optimizing manufacturing processes without interfering with the actual processes, which can save a lot of time and money.
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.