Lesson 10 of 20
PO Price-Variance Audit — From Raw Extract to Three Director Actions
PO & Risk Management · Packaging / MRO
Synthetic case data — evidence from real ChatGPT, Claude, Gemini and Copilot runs.
📥 Download the practice file:
data/case-file.txt— one contract price schedule (two suppliers) and six months of purchase orders, 40 lines. Paste it into your AI tool to follow along — the worked runs below were captured on ChatGPT, Claude, Copilot, and Gemini.
Summit Packaging, corrugated carton 18×14×12: $62.50. Lakeshore Industrial, same carton: $2.65. You have bought from both suppliers for a year and you cannot say which one is cheaper. Not "it's close" — you genuinely cannot tell, because $62.50 is per case of 25 and $2.65 is per each, and the only place that says so is a contract footnote you signed and have not opened since.
That footnote is the whole lesson. Your 40-line PO extract covers about $179,574 of spend across two suppliers and six months. It has every number. What it does not have is the handful of contract rules that decide what those numbers mean — a unit convention, a freight term, a discount threshold, an escalation clause. Miss any one and the variance report is confidently wrong. The skill is three moves: feed the AI those rules explicitly, cross-validate every transaction against the contract clauses, and compress the audit into three actions a director can authorize. The same three moves work on any PO extract, any AI platform.
You’ve read the free three. The other seventeen are where the misses live.
The first lessons show what a general-purpose AI catches on a procurement task. The rest show what it misses — the unit conversion that survives an expert prompt, the two clauses it lists but never connects, the table that contradicts its own arithmetic. That gap is the job.
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