FMCG Glossary

What is Forecast Accuracy?

Forecast Accuracy (MAPE / BIAS)

Definition

Forecast Accuracy measures how closely demand forecasts match actual sales. It is typically measured using MAPE (Mean Absolute Percentage Error) or BIAS. MAPE measures the average error as a percentage, while BIAS indicates whether forecasts are systematically over or under actual demand.

Examples of Forecast Accuracy in FMCG

  • A demand planner achieving 88% forecast accuracy (12% MAPE) at SKU/week level
  • A positive BIAS of +5% meaning forecasts are consistently 5% higher than actual sales (over-forecasting)
  • Improving forecast accuracy from 75% to 90% reducing excess inventory by 30%

Forecast Accuracy in the FMCG Industry

Forecast accuracy is one of the most critical supply chain metrics in FMCG. Poor forecast accuracy leads to either excess stock (cash trapped in inventory) or out of stocks (lost sales). World-class FMCG businesses target <15% MAPE at SKU/monthly level. Forecast accuracy improvement is a constant focus of S&OP processes and supply chain teams.

Why Forecast Accuracy Matters for Your FMCG Career

Demonstrating forecast accuracy improvement on a CV is one of the strongest supply chain achievements a demand planner or supply chain professional can cite. Specific metrics (e.g., 'improved MAPE from 22% to 13% in 12 months') are compelling evidence of analytical capability and commercial impact.

Related FMCG Terms

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OSA

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