The Results
Refreshing the forecasting pipeline improved planning confidence and reduced stock-outs.
Placeholder note: Metrics are illustrative and will be updated with confirmed results.
The Challenge
The existing model relied on stale data and manual overrides, leading to inconsistent inventory decisions across regions and channels.
Key Constraints
Late data feeds, missing promotions metadata, and limited visibility into model drift.
The Solution
We rebuilt the pipeline to incorporate real-time sales, promotions, and external signals while introducing monitoring for forecast stability.
Implementation Highlights
Data Pipeline Rebuild
Unified feeds from POS, ecommerce, and promotions to create a single forecasting dataset.
Model Refresh
Adopted a forecasting framework with automated retraining and seasonality controls.
Monitoring & Alerts
Added drift checks and alerting to surface anomalies before they impacted stock levels.
Operational Impact
Merchandising teams shifted from reactive ordering to proactive demand planning, with clear visibility into forecast confidence.
Before
After
Key Takeaways
Better Inputs = Better Forecasts
Incorporating promotions and ecommerce signals delivered more reliable seasonality patterns.
Monitoring Protects Trust
Model drift alerts helped planning teams intervene before forecasts caused stock issues.
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