Alerts to possible issues are generated, but not all of them result in scrap or rework decisions. The alerts are sent to technicians who, either through experience, or by consulting a lengthy reference manual, make decisions about the particular lot, based on possible root causes.
This can be time consuming and slows production. The manufacturer theorized that machine learning could help automate decision making.
With this data we explored five different machine learning and deep learning approaches to see how well these could emulate the technicians’ decisions.
The company had done a financial analysis estimating the cost of the technician time in resolving the issues, as well as the impact on productivity. Globally, this was costing them tens of millions of dollar annually.
By implementing deep learning-based root-cause analysis, they are now able to make technicians more efficient, targeting those tens of millions of cost savings.
In no case was it ever suggested that AI would replace technicians, but rather, their skills would be augmented to make them more efficient.