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16 series in. 4 alerts out.
A mid-size manufacturer, 60 days of goods receipts, 16 series across four suppliers. On day 39 one supplier’s lead-time variance opened up. On day 44 the mean followed. The all-supplier on-time rate in the QBR deck never moved. Find the supplier, and note which of its two series went first.
Every number on this page is computed in your browser right now, by the same CUSUM and Bayesian changepoint code that runs in production. Nothing here is a screenshot.
The comparison that matters
Against the rule most teams actually ship
The standard configuration is a percentage or sigma threshold against a trailing mean, evaluated per metric. Run it over the exact same 16 series and count what lands in the channel.
The false-alarm count is not a judgement call. The incident was injected at a known index, so every naive alert before that index is wrong by construction.
What the engine found
Detection run
| Metric | Trend | Baseline | Now | Change | Confidence | Method |
|---|---|---|---|---|---|---|
Lead-time variance supplier=Kestrel Machining | 4.343 | 11.001 | +153% | 99.9% | CUSUM | |
Actual lead time supplier=Kestrel Machining | 18.513 | 26.968 | +45.7% | 99.9% | CUSUM | |
Incoming defect rate supplier=Kestrel Machining | 1.34% | 3.85% | +186% | 99.9% | CUSUM | |
On-time delivery rate supplier=Kestrel Machining | 94.3% | 75.3% | −20.2% | 99.9% | CUSUM |
Show your work
Lead-time variance · supplier=Kestrel Machining
Baseline mean μ = 0.0197 and σ = 0.1530, both computed from the first two thirds of the day-over-day percentage changes. The slack k = 0.0765 is half a sigma, and the decision boundary h = 0.6121 is four. The accumulator runs on percentage changes rather than raw levels so a healthy growing series cannot drift across the boundary on its own.
alert if |Δ| > 15% vs 7-day mean fired 10 times across this one metric, including 3 before anything was actually wrong. The statistical pipeline sent one message, on the day the regime actually changed.
What lands in Slack
One message, with the work already done
- 1.Call Kestrel before the next release: ask what changed on the line in the week to Aug 9.
- 2.Re-plan safety stock on Kestrel parts against the new lead-time distribution, not the promised 18 days.
- 3.Pull incoming inspection records for Kestrel lots received after Aug 13.
The written cause is generated only after the statistics confirm the change. The model never decides whether something is an anomaly — it explains one that has already been established. Getting that order backwards is how these products hallucinate.