China AI Retail Visits: Audit the Inventory Recommendation
A retail inventory recommendation matters only after constraints, overrides and stock outcomes are reconciled. Follow one replenishment decision through the workflow.
Direct answer. Follow one replenishment recommendation through the ordering constraints and the buyer's final decision. A plausible suggested quantity is not evidence that the recommendation can be executed profitably or consistently.
Pick a decision with competing constraints
For a China retail AI visit, request an anonymized example with a supplier minimum order, limited shelf space or a short selling window. The example should expose a trade-off rather than merely repeat the forecast. Do not infer access to a retailer's commercial information from an invitation to a showroom.
Ask whether the model predicts demand, recommends orders or automatically commits a purchase. These functions require different evidence and permissions. A recommendation can be useful even when a buyer retains control, provided the operating workload and accountability are understood.
Reconstruct the order
| Step | Question to ask |
|---|---|
| Prediction | What demand horizon and unit does the estimate cover? |
| Constraint | Which order, shelf-life and stock limits change the proposal? |
| Override | Who changed the quantity, and why? |
| Outcome | Was the ordered stock sold, carried over or written off? |
Keep promotion effects and unavailable stock visible in the discussion. Recorded sales during a stockout may not represent unrestricted demand. Conversely, a promotion can create a demand pattern that will not recur. Ask how the host marks these periods rather than asserting that a particular adjustment is always correct.
The trade-off framing follows the benefit-and-cost questions in NIST AI RMF Core. NIST does not prescribe a retail replenishment formula. The trace above is our original worksheet for examining the demonstrated process.
Request an outcome definition before a pilot
A pilot should name the stock and service measures that your retailer accepts: availability, obsolete inventory, intervention effort and the period over which they are evaluated. Avoid attributing all improvement to AI when pricing, assortment or supplier service changed at the same time.
A useful visit note records the recommendation's scope and the manual decisions still required. Use the executive due-diligence checklist to capture those dependencies, then discuss a retail-focused itinerary with the buying and operations teams.