Production codes are not incidental data. Marker names and related identifiers often carry manufacturing knowledge about the work being performed. Leaving them as opaque strings forces every user and every downstream feature to interpret that knowledge again.
Classification makes the meaning explicit. A useful model translates recognizable patterns into practical categories while preserving the original value that supported the classification. The result is easier to analyze, explain, and review than a collection of unexplained labels.
The difficult cases matter most. When a code is ambiguous, conservative classification is better than confident guessing. An unknown or reviewable result preserves uncertainty; an incorrect label can quietly distort filters, comparisons, and later reasoning.
Domain knowledge is therefore part of the software, not background context outside it. Encoding that knowledge explicitly and auditably turns local conventions into reliable system behavior without pretending the conventions are more certain than they are.