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Demand shifts that matter often start small and look like noise in the weekly report. A Demand Anomaly Detection Playbook reads sales velocity, order patterns, and channel signals to flag the demand anomalies that precede a trend, with the classification and context that tells the team whether to act.
The Challenge
A weekly demand report shows category totals. Inside those totals, a specific SKU or region may have shifted materially. By the time the shift shows up in the aggregate, the inventory and production decisions that should have responded are already 4 to 8 weeks behind.
Demand fluctuates week to week for many reasons. Without a model that separates expected variation from genuine anomaly, the planning team either reacts to every wiggle (creating bullwhip) or ignores all of them (missing real shifts). Neither approach produces clean planning.
Even when an anomaly is detected, the team often does not know whether it is a genuine demand shift, a promotion effect, a competitor outage, or a data quality issue. The response is delayed by investigation rather than acted on the signal.
How eyko Solves It
A Demand Anomaly Detection Playbook reads sales velocity by SKU, region, channel, and customer cohort, compares each series to its own historical pattern and to similar-segment patterns, and flags statistically significant anomalies. It classifies each anomaly by likely cause (trend shift, promotion effect, competitor outage, data quality, seasonal pattern) and routes the alert with the contextualizing data attached so planning teams act on signal rather than chase noise.
The Playbook scored demand anomalies across 4,200 SKUs and 28 regions in the past 30 days. 184 anomalies detected: 38 classified as trend-shift signals (sustained departure from baseline), 64 as promotion effects, 42 as competitor or channel disruptions, 24 as data quality issues, and 16 normal-variation false positives. The trend-shift cohort represents $2.4M in projected revenue impact over the next quarter.
| Metric | Current | Benchmark | Status |
|---|---|---|---|
| Primary indicator | Flagged | Target | Action needed |
| Secondary indicator | Monitoring | Within range | On track |
| Trend direction | Declining | Stable | Review required |
Demand Anomaly Detection watches sales velocity across SKUs, regions, channels, and customer cohorts continuously and flags statistically significant departures from baseline. The Playbook classifies each anomaly by likely cause and routes the alert with the contextualizing data attached so planning teams act on signal rather than chase noise or react to every wiggle in the weekly report.
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FAQ
Everything you need to know about Demand Anomaly Watch.
Demand Anomaly Detection is an AI-driven analysis that watches sales velocity across SKUs, regions, channels, and customer cohorts and flags statistically significant departures from baseline. The Playbook classifies each anomaly by likely cause (trend shift, promotion effect, competitor disruption, data quality, seasonal pattern) and routes the alert with contextualizing data attached so planning teams act on signal rather than chase noise.
The Playbook reads from your ERP or sales system (per-SKU sales velocity, regional breakouts, customer cohort metadata), promotion calendar (planned and active promotions tied to dates), competitive intelligence feeds where available, channel reporting (POS, e-commerce, distributor sell-through), and data ingestion logs for quality cross-reference. At least 18 months of per-SKU history produces useful baselines.
The Playbook compares each SKU's movement to two baselines: its own historical pattern and the pattern of similar SKUs in the same category. An anomaly is flagged only when both baselines are exceeded with statistical significance and the pattern persists across a configurable window. The dual-baseline approach filters out one-off spikes and seasonal effects that single-series thresholds would surface as false positives.
Yes. Each detected anomaly is classified by likely cause using contributing context: scheduled promotion activity, competitive signals, channel-level reporting consistency, and recent data ingestion patterns. The classification comes with a confidence score so planning teams can prioritize trend-shift anomalies (real signal worth a forecast update) above promotion-effect anomalies (already explained) and data-quality flags (require investigation).
Join the enterprises replacing weeks of manual analysis with a single prompt. See what eyko Playbooks can do with your data.
