A supplier price file that arrived on schedule, parsed cleanly, and loaded with a success log was, according to one engineer’s published account, the reason a business sold below cost for eight days. The supplier had switched from reporting prices in pence to reporting them in pounds. The file’s numbers were still valid numbers, so nothing objected. The business’s checks confirmed that the transfer had worked, not that the prices made sense.
What the account reports
The incident comes from a DEV Community article by Serguey Shinder, which appeared with the posting date “Sep 24”. The version available for this article shows no year, so no publication or incident date is assigned here. Every figure below is the author’s own report. None has been independently verified, and no outside source confirming the episode was found.
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- A supplier sent a price file every Tuesday. The supplier changed the system that produces the file, and the new output expressed prices in pounds where the old output had used pence.
- 1,400 lines were affected and arrived at one hundredth of their real cost.
- The business sold at those prices for eight days to about 90 trade customers.
- The resulting loss was a little over £31,000, which the author says could not be recovered.
- The subsequent review found four other unchecked feeds, including credit limits and a carrier’s surcharges.
How a file can be successful and still wrong
The arithmetic the author describes explains the scale of the problem. A price of 4.50 meant as £4.50 is, to a system expecting pence, 4.50p. That is one hundredth of the intended value. The change was systematic, because every affected line carried the same unit shift, so the error was not a scattering of bad rows that a spot check might catch.
The file itself gave no obvious signal. The transfer completed, the file parsed, the row count sat within its normal range, and the load logged success. Each of those checks answered a narrow question: did the mechanism work? The mechanism did work. The question nobody had built a check for was whether a price of 4.50 still made sense for the product it described.
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The only detection the author records came from a human. The depot manager noticed an unusually low ladder price and called to ask about it. That call is an illustration of the weakness here: a live price that is wrong by a factor of 100 was caught by a person who happened to look, not by any control in the pipeline.
Why this was a governance gap, not a software crash
The author’s central point is about ownership rather than code. Prices typed in by internal staff were subject to tolerance checks and approvals. Supplier data, by contrast, had been treated as infrastructure: something that flows in automatically and is trusted because it is technical plumbing. The file was never governed as a business change, even though it changed what the business charged and what it paid for stock.
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Two passages from the account state this clearly:
- “Nothing in the chain objected, and that is the part worth writing down.” (Serguey Shinder)
- “Every check we had was a check on whether the mechanism had worked, and the mechanism had worked perfectly.” (Serguey Shinder)
The author also argues that most of what a business’s systems act on is now written by other systems, while governance effort has concentrated on the smaller share that staff type in by hand.
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The author describes a response built on three layers. The sequence below is the order in which a file passes through them.
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- Stage the file. Inbound files are placed in a staging area rather than going straight to live prices.
- Compare with the previous version. Each incoming line is compared against the prior file.
- Apply change thresholds. A single line changing by more than 20%, or a file in which more than 5% of rows changed, holds the entire load.
- Require an accountable approval. A category manager reviews a summary and approves the load before anything goes live.
Staging and comparison against the previous file
Staging separates “arrived” from “live.” Comparison supplies the baseline that the original checks lacked. A file that is internally consistent can still be wildly different from what the business has been charging for the same products, and only a comparison against a known-good version exposes that gap.
Change thresholds that hold the whole batch
The thresholds are the author’s reported settings: 20% at line level and 5% at file level. When a threshold is exceeded, the entire load waits, rather than only the offending rows being rejected. That choice matters. Holding only the suspicious lines would leave the rest of a changed file live, possibly with the same unit mismatch applied to other fields.
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Approval as an assigned task
The approval was assigned as a task instead of sent as an email. The author’s reasoning is that an email depends on someone noticing it, while a task sits in a queue with an owner and a record of who approved what. This is the piece that turns a validation rule into a governed decision.
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|---|---|---|
| Staging area before go-live | Inbound files not live on arrival | Yes, provided the load was held rather than released by default |
| Comparison with previous file | Line-level comparison | Yes. A change of about 99% on affected lines is far beyond any 20% line threshold |
| Line-level threshold | More than 20% change on one line | Yes, on the reported figures, because a hundredfold shift is a change of roughly 99% |
| File-level threshold | More than 5% of rows changed | Yes, since 1,400 affected lines would be a large share of a file if the file’s total row count is not much larger |
| Assigned approval task | Category manager reviews a summary | Depends on the reviewer checking the summary; the control is only as strong as that review |
The table reflects arithmetic against the reported thresholds. It does not show that these controls were tested against this incident, and the author’s account does not give the total size of the file, which would determine the file-level result.
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The wider feed problem
The review found four other feeds that were not checked. Two examples the author gives are credit limits and a carrier’s surcharges. Those feeds are not prices, but they change what a business can extend to a customer and what it costs to ship. An incorrect credit limit or surcharge can cause the same kind of silent, systematic loss as a wrong price.
The lesson the author draws is to apply ownership and validation to any externally produced data that drives a business decision, not only to values that employees type. An inventory of feeds, with an owner and a validation rule for each, is the practical starting point.
A checklist for supplier price files
The following checks follow from the failure described above. They are editorial suggestions drawn from the controls in the account, not a published standard.
- Check units and currency explicitly. Ask the supplier to state the unit and currency in the file header or a schema document, and reject files whose declared unit has changed without approval.
- Compare every price with the previous version. Flag large movements at line level, and compare each line’s price with its cost so that a price below cost is an exception in its own right.
- Test the batch, not just the rows. Count how many lines changed and by how much, and hold the whole load when the total exceeds your tolerance.
- Assign an owner who approves a summary. The reviewer should see the largest changes first and sign off as a recorded task.
- Inventory every external feed. Include credit limits, surcharges, and any other value that changes money, and give each feed an owner.
- Treat a successful load as a claim to be checked. Logs confirming that a transfer completed say nothing about whether its contents are correct.
Where the thresholds need judgement
The 20% line threshold and 5% file threshold are the author’s settings for a particular business. They are not universal values. A feed with naturally volatile prices may produce constant false alarms at 20%, while a feed with stable prices may miss a meaningful error at the same level. The right numbers depend on the product mix, how often prices move, and what a missed error costs. Those thresholds should be set from the business’s own price history, then reviewed when the supplier, the product range, or the file format changes.
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