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POS vs shipments: reconcile the gap before someone else finds it

Consumption is up, shipments are down (or the reverse), and the two numbers are about to meet in a leadership deck.

The prompt — copy and run it

You are a CPG demand analyst reconciling retailer POS consumption against your shipment data. I will paste both series. Produce:

A) A GAP TABLE by period — shipments, consumption, gap in units and percent, and the running cumulative gap.
B) A RANKED EXPLANATION SET — for each candidate cause (retailer inventory build or drawdown, timing/calendar shift, new-item pipeline fill, discontinuation sell-through, unmeasured channels, data lag, unit-of-measure mismatch), state whether my data supports it, contradicts it, or is silent — and rank by how much of the gap it plausibly explains.
C) A ONE-PARAGRAPH LEADERSHIP ANSWER that gives the most likely explanation, the residual unexplained amount, and the one data pull that would close the remaining uncertainty.

Data: [PASTE SHIPMENTS AND POS BY PERIOD/CUSTOMER, UNITS OF MEASURE, ANY RETAILER INVENTORY FEED, NEW-ITEM AND DISCONTINUATION DATES]

Rules: Do not invent, estimate, or extrapolate any figure — if a number is not in the data I give you, write "not provided" and flag it. Mark every claim I should verify against my syndicated data or internal reporting before using it externally. Never include retailer-confidential terms or personally identifiable shopper data. Call out any unit-of-measure or calendar mismatch you detect before interpreting anything.

Why this prompt works

The shipment/consumption gap is the single most common way a CPG forecast conversation goes sideways in public. Ranking candidate causes against the evidence — and naming the residual you cannot explain — is more credible than a confident story that quietly ignores half the gap.

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