Automated deployment of supply planning models increases demand forecast accuracy and reduces manual intervention

jessica_dev
Mon, 03 Feb 2025 18:33:05 +0000

Great question. Our validation framework runs three test stages:

1. Historical backtest against last 12 months data - new model must achieve MAPE within 2% of current production model

2. Forecast bias check - ensures no systematic over/under forecasting (bias must be between -5% and +5%)

3. Outlier detection - flags any SKUs where forecast variance exceeds 40%

If validation fails, the pipeline automatically rolls back and sends detailed reports to our planning team. We found that setting thresholds too strict initially caused too many false rejections, so we calibrated them over three months based on actual model performance patterns. The key was balancing automation speed with forecast quality assurance.