Measuring Forecast Error Explained — MAD, MSE & MAPE

Continue your forecasting and operations management learning with this in-depth walkthrough of forecast error measurement techniques. In this video, Operations University instructor Brent Bolton explains how organizations evaluate and compare forecasting models using Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). You’ll learn how to calculate each forecast error metric, why they produce different results, and how they help determine which forecasting method performs best. A full example compares three forecasting techniques—moving averages and exponential smoothing—to show how forecast accuracy is measured and improved. 👉 Get Certified (Lean Six Sigma) Watch the full Lean Six Sigma playlist, then complete your certification at OperationsUniversity.org to earn your Lean Six Sigma certificate. 💰 Advance Your Credentials Certification & Pricing = Yellow Belt $99 • Green Belt $499 • Black Belt $899 — or all 3 for $1,199 (save when purchasing together). Employer packages: Bulk enrollments & reporting available. 💡 What You’ll Learn in This Video: • What forecast error is and why it matters • How to calculate MAD, MSE, and MAPE • Key differences between forecast error metrics • How forecast error affects model selection • Comparing multiple forecasting methods • How to improve forecasts using error analysis 📢 Call to Action ✅ Subscribe for more Lean Six Sigma & operations content ✅ Visit OperationsUniversity.org to get certified ✅ Share this video with forecasting or planning teams #LeanSixSigma #Forecasting #ForecastError #MAPE #MSE #MAD #OperationsManagement