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Measuring Forecast Error (MAD, MAPE & Bias)
Quantify accuracy and bias so a forecast can be trusted and improved.
Inside this lesson
- Define forecast error mathematically as Actual minus Forecast and understand its importance in supply chain management.
- Calculate Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE) to assess forecast accuracy.
- Analyze forecast bias and tracking signals to detect systematic over- or under-forecasting behaviors.
- Compute MAD and MAPE across a multi-period dataset and determine the sign of the forecast bias.
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Educational training content only - not legal, financial, customs, or professional advice, and not a promise of any operational or business result. Examples are illustrative and use hypothetical data with stated assumptions and units. Trade-term and regulatory context is general and jurisdiction-neutral; always confirm current rules with a qualified source. Third-party names are descriptive and imply no affiliation.