"Which conditions produced this rejected part?" sounds like a straightforward question. The answer depends on how the quality result connects to the production history, not just whether both exist.
A dashboard can show the rejection total. A useful investigation needs to know which product, lot or operating period it belongs to and which other records are genuinely relevant.
Check the link before building the explanation.
An illustrative example: the inspection record names a part but not its lot. The machine log records a time, but the inspection system uses a different clock. Material was scanned on arrival, but not when it reached the production step. A tool change was recorded separately.
Those gaps do not prove that an analysis is impossible. They show what has to be resolved before treating a comparison as reliable.
Start with the outcome and work backward.
Select the quality outcome you want to explain. Check how consistently it is classified. Then identify the records that can be connected to it through a part, lot, order or production period.
Ask which links are reliable, which depend on an assumption and which are absent. The answer should be visible in the analysis, not hidden in an integration script.
Collect only the context the question needs.
A material question may need lot history. A process-drift question may need measurements over time. A test-failure question may require fixture and test-condition records.
Requiring every plant to provide every possible source can delay a useful piece of work. Ignoring a necessary source can produce a confident but weak answer. The quality question is what distinguishes the two.
Use the readiness check to choose the next step.
If the records support the investigation, proceed with the comparison. If a specific link is missing, decide whether it can be resolved or whether a narrower question is more useful.
The same discipline applies to AI. An earlier-warning model needs information that was actually available before the outcome was known. Linking in the final inspection result may make a model look impressive while making the test useless.
The goal is not perfect data everywhere. It is evidence strong enough for the decision you want to make.
