Demand Forecast Accuracy Calculator
Measure forecast accuracy with MAPE, MAD, bias, and tracking signal. Quantify the cost of forecast errors and improve your demand planning.
Enter Your Forecast Data
Input forecasted and actual demand for each period to analyze accuracy
Cost Parameters (optional)
Comprehensive Forecast Accuracy Metrics
Analyze your demand forecasts with industry-standard accuracy metrics
MAPE & WMAPE
Calculate mean absolute percentage error and weighted MAPE to measure forecast accuracy as a percentage across all periods.
Bias & Tracking Signal
Detect systematic over- or under-forecasting with bias analysis and monitor model drift with tracking signal alerts.
Cost of Error
Quantify the financial impact of forecast errors by calculating overstock holding costs and stockout opportunity costs.
What is demand forecast accuracy?
Demand forecast accuracy measures how closely your predicted demand matches actual customer demand over a given period. It is typically expressed as a percentage (100% - MAPE) and is a critical KPI for supply chain management. High forecast accuracy reduces inventory holding costs, prevents stockouts, improves customer satisfaction, and enables better production planning. Companies with best-in-class forecasting achieve accuracy rates above 85%, while the industry average typically ranges from 60-80%.
Key Forecast Accuracy Metrics Compared
MAPE
Mean Absolute Percentage Error. Scale-independent percentage metric ideal for comparing accuracy across different product lines or business units.
MAD
Mean Absolute Deviation. Measures average error in the same units as demand. Useful for setting safety stock levels and understanding absolute error magnitude.
WMAPE
Weighted Mean Absolute Percentage Error. Gives more weight to high-volume periods, making it more representative for products with uneven demand.
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