Key Metrics to Track for Capturing Events and Seasonality Impact on Demand Predictions for $10M–$100M Companies

In $10M–$100M companies, many planning teams rely on a single metric such as MAPE to evaluate forecast performance.

However, these metrics often fail to reflect how seasonal demand cycles and promotional events influence inventory decisions.

Weighted Mean Absolute Percentage Error reflects forecast accuracy weighted by volume.

This ensures high-impact SKUs receive appropriate attention.

Forecast bias highlights systematic over- or under-forecasting.

This metric is particularly useful during promotional campaigns.

Error contribution identifies SKUs that drive the largest forecast inaccuracies.

Tracking this metric enables targeted planning improvements.

Service level metrics connect demand predictions with fulfillment outcomes.

Maintaining service levels during seasonal peaks improves customer satisfaction.

Inventory turnover reflects how effectively procurement aligns with consumption patterns.

Improved turnover indicates better capture of event-driven demand variability.

For $10M–$100M companies, tracking the right metrics enables planners to capture seasonal demand variability accurately.

Modern planning systems surface these metrics automatically to support event-aware forecasting.