Lesson 52 of 1550
Supply Chain Anomaly Detection: Patterns Humans Miss
Supply chain data is too dense and too noisy for humans to monitor in real time. AI anomaly detection surfaces the signals — when scoped to actionable thresholds.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1Anomaly is in the eye of the beholder
- 2anomaly detection
- 3baseline
- 4false positive
Concept cluster
Terms to connect while reading
Section 1
Anomaly is in the eye of the beholder
An order spike is an anomaly to inventory and a celebration to sales. The same datapoint, two interpretations. AI anomaly systems fail when they don't know whose anomaly they're flagging. Define the consumer first, the detector second.
Baseline matters more than algorithm
- 1Pick a baseline window that matches the seasonality of the data — daily, weekly, holiday-aware
- 2Decide what 'normal' includes: do you count last year's pandemic surge as normal?
- 3Choose between absolute thresholds and relative ones based on stakes
- 4Track baseline drift — your normal of three years ago isn't your normal today
False positive cost is the real metric
An anomaly detector firing 50 alerts a day with one real one hidden inside is worse than no detector. The team learns to ignore alerts. Tune for the cost of being wrong: how much human time does each false alarm consume, and how much does each miss cost?
Key terms in this lesson
The big idea: detection is a triage tool, not an action tool. Surface signals; humans decide moves.
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