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As of 13 August 2026, AI can only partly trace a product batch through your supply chain.
This still needs a person who signs their name to it.
Can you do it?
15 minutesto a draft.
1 hourto something you’d act on.
Cost, all in£0
Skill neededpower-user
Who has to check ita colleague
What the alternative costsA specialist traceability system or manual reconciliation is the alternative; no price is supplied here.
If this goes wrong, you may make a faulty stock, release, withdrawal or recall decision because a missing or mismatched batch link looked complete.
What to actually do
Hand it to a person
The route this page recommends
A person who owns the outcome does this end to end, worth it when the failure is dear.
Use a tool built for this
Second choiceDo it yourself
The distant thirdA chat interface, power-user skill, and roughly 1 hour until you can act on the result.
How to actually do it
- Open your stock, purchasing, goods-in, production, delivery and sales systems and export the records that contain the target batch or lot identifier.
- Gather the matching supplier documents, goods-in notes, production records, stock movements, delivery notes and customer shipment records, keeping each source reference and date.
- Remove unrelated personal information, then paste the records into the prompt with the target batch or lot ID, the direction of trace and the date range filled in.
- Ask the model to produce the chronological trace and to keep confirmed, inferred and missing links in separate sections.
- Compare every row in the drafted trace with its named source record, checking identifiers, quantities, dates, locations and any split or merge against the original systems.
- Send each flagged gap or conflict to the relevant supplier, warehouse, production or quality colleague and add their confirmed evidence to the records.
- Run the prompt again with the new evidence, then have a quality or operations colleague approve the final trace before using it for stock decisions, release, withdrawal or recall.
Prompt
Trace the product batch through the supply-chain records below. Treat the records as the only source of truth and invent nothing. Batch or lot to trace: [BATCH OR LOT ID] Scope of trace: [SUPPLIER TO CUSTOMER, CUSTOMER TO SUPPLIER, OR BOTH] Date range: [DATE RANGE] Records: [PASTE PURCHASE ORDERS, SUPPLIER LOT RECORDS, GOODS-IN RECORDS, PRODUCTION RECORDS, STOCK MOVEMENTS, DELIVERY NOTES, SALES ORDERS, CUSTOMER SHIPMENTS AND ANY RELEVANT QUALITY RECORDS HERE] Produce: 1. A chronological trace table with one row for each confirmed movement or transformation. 2. For every row, include the source record name or reference, date, item, batch or lot identifier, quantity and location or organisation where available. 3. Separate confirmed links, inferred links and missing links. Do not present an inference as fact. 4. Flag duplicate identifiers, quantity mismatches, date conflicts, unexplained splits or merges, and records that cannot be joined. 5. State whether the batch can be traced forward, backward or both, and identify the exact point where traceability stops. 6. List the specific records or people needed to resolve each gap. 7. End with a short exception summary for a quality or operations colleague. Do not estimate quantities, dates or destinations. Quote the relevant source reference for every conclusion. If the records do not support an answer, write "not established from the supplied records".
Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.
What it gets wrong
- AI cannot access records that are held in systems, emails or paper files you have not supplied.
- AI cannot prove that a batch identifier was entered correctly at the point of receipt, production or dispatch.
- AI cannot resolve a missing or conflicting link without evidence from the responsible supplier, warehouse, production or quality colleague.
- AI cannot take responsibility for releasing stock, stopping distribution, withdrawing goods or initiating a recall.
- AI cannot replace a controlled traceability system with reliable permissions, audit history and live operational data.
What caps this at PARTLY: private data access, verification cost and stakes of error.
How we scored this
Five axes, each scored nought to two by hand: ten means AI carries the task cleanly, and the thresholds that turn a total into YES, PARTLY or NO are published in the methodology. Each axis name links to its definition.
| Axis | Score (0–2) |
|---|---|
| Output | 2 |
| Inputs | 1 |
| Verification | 1 |
| Liability | 1 |
| Effort delta | 1 |
| Total | 6 / 10 |
The methodology and its thresholds are published in full.
FAQ
- Can ChatGPT trace a batch through my supply chain?
- Partly. It can reconcile batch, lot, stock and shipment records that you provide, but it cannot access missing systems or prove that the records are complete. A colleague should check every link before the trace supports an operational decision.
- What data does AI need to trace a product batch?
- Give it the batch or lot identifier plus supplier records, goods-in records, production or transformation records, stock movements, delivery notes and customer shipment records. Include dates, quantities, locations and source references, and label any gaps rather than filling them with estimates.
- Can AI find where a batch traceability record is broken?
- Yes, if the relevant records are supplied in a consistent form. It can flag unmatched identifiers, quantity differences, date conflicts and unexplained splits or merges, but a person must obtain evidence to explain or correct each exception.
- Is AI safe to use for a product recall?
- Use it to organise evidence and expose gaps, not to make the recall decision on its own. Your quality or operations team remains accountable for confirming affected stock, customers and actions before a withdrawal or recall.
Nearby answers
- Can AI analyse my quality control data?YES
- Can AI check my product's UKCA marking requirements?PARTLY
- Can AI create a quality control checklist for my business?YES
- Can AI detect product defects from photos?PARTLY
- Can AI identify snagging issues from building photos?PARTLY
- Can AI investigate the root cause of product defects?PARTLY
Assessed by gpt-5.6-luna (gpt-5.6-luna) on 2026-08-13, second-checked by an independent model. Wrong somewhere? Email [email protected] and it gets re-checked.
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