CDPP Demand Planning Metrics & Performance Management 1 — Questions and Answers
Question 1: What does MAPE stand for in demand planning?
- Mean Absolute Percentage Error (Correct answer)
- Monthly Average Planning Estimate
- Moving Average Projection Error
- Maximum Allowable Planning Error
Correct answer: Mean Absolute Percentage Error
MAPE (Mean Absolute Percentage Error) measures forecast accuracy as the average absolute percentage difference between forecasted and actual demand.
Question 2: What is a key limitation of MAPE as a forecast accuracy metric?
- It cannot be calculated for monthly data
- It becomes undefined or skewed when actual demand is zero or very low (Correct answer)
- It measures bias rather than accuracy
- It requires normally distributed demand data
Correct answer: It becomes undefined or skewed when actual demand is zero or very low
MAPE divides by actual values, so when actuals are zero or near-zero the metric becomes undefined or extremely large, distorting the overall accuracy picture.
Question 3: What does forecast bias indicate in demand planning?
- Random variation in the forecast
- A systematic tendency for forecasts to be consistently too high or too low (Correct answer)
- The spread of errors around the mean
- The accuracy of seasonal adjustments
Correct answer: A systematic tendency for forecasts to be consistently too high or too low
Forecast bias reveals a directional pattern in forecast errors—consistently over-forecasting leads to excess inventory while consistent under-forecasting causes stockouts.
Question 4: How is Mean Error (ME) used to detect forecast bias?
- It measures the absolute size of each error
- A positive ME indicates consistent over-forecasting; a negative ME indicates consistent under-forecasting (Correct answer)
- It measures the variance of the forecast errors
- It calculates the percentage error for each period
Correct answer: A positive ME indicates consistent over-forecasting; a negative ME indicates consistent under-forecasting
Mean Error averages signed forecast errors (forecast minus actual), so positive values reveal systematic over-forecasting and negative values reveal under-forecasting.
Question 5: What is the primary difference between MAD and RMSE as forecast error metrics?
- MAD is only used for seasonal products; RMSE for non-seasonal
- RMSE penalizes large errors more heavily due to squaring, while MAD treats all errors equally (Correct answer)
- MAD requires normally distributed data; RMSE does not
- RMSE is a percentage measure; MAD is an absolute measure
Correct answer: RMSE penalizes large errors more heavily due to squaring, while MAD treats all errors equally
Because RMSE squares errors before averaging, large individual errors receive disproportionately higher weight compared to MAD, making RMSE more sensitive to outliers.
Question 6: What is a typical 'best-in-class' MAPE benchmark for fast-moving consumer goods (FMCG) demand planning?
- Less than 5%
- Less than 20% (Correct answer)
- Less than 50%
- Less than 80%
Correct answer: Less than 20%
Best-in-class FMCG demand planners typically achieve MAPEs below 20% at the SKU/location level, though benchmarks vary by category volatility.
CDPP Demand Planning Metrics & Performance Management 1
What does MAPE stand for in demand planning?