Factory Digitalization Ver09. “When AI Prescriptions Fail to Reduce Defect Rates”
- shigenoritanaka3
- 4月18日
- 読了時間: 2分
Apr 18, 2026
Hello everyone,
Today, I'd like to share an effective approach for a situation many factories face: AI has been introduced to reduce defect rates, yet a specific product's defect rate simply does not improve.
AI‑driven prescriptions are convenient, but they are not perfectly effective. In fact, “We introduced AI, but the defect rate hasn't gone down” is one of the most common concerns I hear from manufacturing sites.
One major reason is that AI can only propose optimal ranges based on past production history. Conditions that do not exist in the historical data are outside AI's search space. In other words, AI cannot propose unknown or unexplored parameter regions. (Related: Factory Digitalization Ver06. “AI Is Weak with New Products.”- So What Should Manufacturers Do?)
So, how can we break through this limitation?
🟦 Move the Parameters That Have Been Kept Static
When defect rates do not improve, one effective approach is to intentionally adjust parameters that have long been operated as fixed values (Static parameters).
However, the important point is this:
Do not adjust them randomly.
🟦 Produce Within the “Textbook Ranges” Defined by Process Experts
For each product type, process experts (application engineers) have accumulated decades of knowledge and defined:
“If you are making this product, these are the ranges you should be using.”
These are the textbook parameter ranges.
Yet in real factories, due to:
Past success patterns
Equipment quirks
Operator habits
Conservative settings to avoid trouble
many parameters have been kept at fixed values outside the original textbook ranges for years.
These untouched regions are exactly where AI has no knowledge, and where improvement opportunities may be hidden.
🟦 New Production History Makes the AI Model Stronger
So, what happens when you intentionally produce within the textbook ranges?
If successful → Defect rates decrease
If unsuccessful → You clearly identify the “do-not-touch” regions
In either case → New production history is added
This new history becomes valuable training data when the AI model is refreshed, leading to significantly more accurate prescriptions.
Ultimately, the one who breaks through AI’s limitations is not AI itself— it is the human who expands the exploration space.
🟦 Summary
AI can only propose optimal ranges based on past production history
AI will never propose conditions outside that history
When improvement stalls, review the parameters that have been kept static
Do not adjust randomly—use the textbook ranges defined by process experts
New production history strengthens the next AI model
When AI-driven improvement hits a ceiling, the breakthrough lies “outside the history.”
🟦 Contact
For inquiries regarding factory digitalization, AI implementation, or quality improvement, please feel free to contact us at:
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