Investigate and fix demand anomalies
Spot anomalies, review agent suggestions, and accept or reject corrections.
This guide walks you through identifying demand anomalies, reviewing the corrections proposed by the Demand Anomaly Agent, and deciding whether to accept or reject them.
Spot anomalies via the Outliers view
Open the Demand module and switch to the Outliers view tab. This built-in view surfaces products where the Demand Anomaly Agent has detected unusual demand patterns.
Browse the list and click on a product to open its detail view.
Compare cleaned vs raw demand
In the product detail chart, toggle on both:
Cleaned past demand — The demand after Flowlity has removed anomalies.
Raw past demand — The original, unmodified demand data.
Periods where the two lines diverge are where the agent proposed corrections. The Anomaly adjustments impact row in the data table shows the exact difference per period.
Review agent suggestions
Go to the Agents module and open the Demand Anomaly Agent card. The "How it works" tab shows a summary of all recent adjustments, broken down by type:
Outliers — Unusual peaks.
Shortages — Zero-demand periods caused by stockouts.
Smooth past promotions — Promotional spikes smoothed out.
Each adjustment has a status: Accepted, Rejected, Not treated, or Modified.
Accept or reject corrections
For each anomaly, decide whether the agent's correction is appropriate:
Accept — The correction replaces the raw demand in cleaned past demand, improving future forecast accuracy.
Reject — The original raw value is kept. Use this when the anomaly reflects genuine demand (e.g. a real one-time order, not noise).
Modify — Adjust the agent's proposed value to a number you consider more accurate.
Configure automatic vs manual handling
In the Agents module, open the Demand Anomaly Agent's Configuration tab. You can:
Activate or deactivate anomaly types — Turn off detection for specific types if they aren't relevant.
Automatic vs manual — Set whether corrections are accepted automatically or held for manual review.
For products with sparse demand history, the Similar Products agent can help bootstrap a more reliable forecast. Check the product's forecast strategy in Products settings to see if similar products are configured.
Related pages
Forecast events and demand adjustments — How anomaly corrections and forecast events interact.
Demand forecasting explained — How cleaned demand feeds into the AI model.
Demand — Where anomalies are visible in the product detail view.
Agents — Where to review and configure the Demand Anomaly Agent.
Last updated