Defects show up before inspection
Final inspection is too late. How to read the early signs of a quality problem in process data.
If a defect is caught at inspection, that is the good outcome. The problem is that by the time it is caught, the material, the time and the energy have already gone into it. Quality prediction is an attempt to change that order. Instead of checking after the part is finished, it catches the defect while the part is still being made.
From inspection to prediction
Traditional quality control puts its weight on inspecting the finished product. But by the time a defect turns up at inspection it is already too late. And the finished part alone rarely explains why it happened, because the real cause usually sits further upstream in the process.
Quality is already written into the process
Defects rarely appear out of nowhere. Temperature wavers slightly, pressure drifts outside its usual range, a material property shifts a little: the early signs are left in process data first. Quality prediction is the work of reading those signs and warning before the part is finished.
Pinning down a cause takes context
Knowing that “this batch has a high defect rate” is not enough to act on. To narrow the cause, all of it has to be connected: which machine, which material lot, and under what conditions it was made. With an ontology tying process, equipment, material and inspection results into one structure, the conditions correlated with defects can be identified in the data.
From warning to action
The point of quality prediction is not a defect-rate chart but action that reduces defects. When guidance as specific as “current conditions resemble a period that produced defects; check the temperature” arrives with its evidence, the floor can intervene before the part is finished. Here too, when an answer carries its evidence, the shop floor trusts the warning and moves.
Where to start
Pick the single defect type that occurs most often, or costs the most. It starts with gathering the process data likely to relate to it, connecting that to final inspection results, and looking at which conditions came first. Getting one right is faster than trying to predict every defect from the start.
In closing
For connecting scattered process data → Data integration whitepaper
For the groundwork → Five principles for turning industrial data into an asset
To talk through your own process → Contact