Every trace question, recall scope, customer complaint, supplier problem, is a walk through a graph: nodes are lots, edges are 'went into.' Plants differ only in when the graph gets built. Some build it during the incident, from binders and batch sheets, under a deadline. Some build it during production, automatically, one link at a time. The second kind answers in seconds because the answer already exists.

Walking the graph

One-back: start at a finished lot, walk edges upstream, every input lot, with quantities and records attached. One-up: start at a suspect input, walk downstream, every batch, lot, and customer it touched, which is recall scope, exact, not padded 'to be safe' because the data was fuzzy. Precision is money: plants over-recall when genealogy is uncertain.

Where the process context multiplies value

A genealogy that also carries process context, which machine, which hour, which operator, which cook profile, answers the question after the trace: not just where the bad lot went, but why it went bad. That is the bridge from compliance to operations: ML ties the defect pattern to the conditions, and the trace layer becomes a quality tool (the category mechanics live in our traceability software guide).

Retrofit reality

Plants assume genealogy requires serializing everything and a year of integration. The additive version digitizes the three link-writing moments in weeks, and backfills history by letting document AI read the paper archive. Serialization gets added only where unit-level identity pays, firearms, appliances, and anywhere warranty claims arrive by serial number (see firearms and windows and doors for what unit-level looks like).