WAPE (Weighted Absolute Percentage Error) | What it means and how to use it

Last updated 2020-07-01 Read time 4 min

Summary

WAPE measures absolute error relative to the total actual volume.
Calculate WAPE on demand forecasts and see how it is reported in dashboards.
Understand instability when total volume is small and how to combine WAPE with MAE.

MAE & RMSE — understanding this concept first will make learning smoother
MAPE — understanding this concept first will make learning smoother

1. Definition #

$$ \mathrm{WAPE} = \frac{\sum_{i=1}^n |y_i - \hat{y}_i|}{\sum_{i=1}^n |y_i|} \times 100\% $$

Scales the absolute error by total actuals, making it comparable across items of different magnitude.
Unlike MAPE, it does not explode when individual actuals hover near zero.

2. Implementation in Python #

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import numpy as np

def wape(y_true: np.ndarray, y_pred: np.ndarray) -> float:
    """Weighted Absolute Percentage Error."""
    return float(np.sum(np.abs(y_true - y_pred)) / np.sum(np.abs(y_true)))

print("WAPE:", round(wape(y_test, y_pred) * 100, 2), "%")

Guard against cases where the total actual volume is ~0: remove such segments or normalise by mean demand instead.

3. Comparing WAPE and MAPE #

Metric	Characteristics	Caution
MAPE	Mean percentage error per sample	Diverges when actual ≈ 0
WAPE	Total absolute error ÷ total actual volume	Heavily influenced by high-volume items
sMAPE	Uses average of actual & predicted	Handles zeros but harder to interpret

WAPE is popular in CPG/retail because it summarises overall demand accuracy without overemphasising low-volume SKUs.

4. Practical usage #

Inventory planning: track WAPE as a KPI (e.g., target WAPE ≤ 10%). Compare across regions or categories.
Model comparison: MAE depends on units, but WAPE (percentage) makes models comparable even across different scales.
Drill-downs: global WAPE can hide poorly performing SKUs—slice WAPE by brand, region, or segment.

5. Caveats #

If long periods have zero demand, compute WAPE only on active periods or switch to MAE.
Always evaluate on identical periods and item sets when comparing across time.
Complement with item-level MAE/MAPE to spot individual problem SKUs.

Summary #

WAPE expresses absolute error as a share of total demand, making it easy to communicate as a KPI.
It mitigates MAPE’s zero-demand blow-up, yet still needs MAE/other metrics for item-level diagnosis.
Combine overall WAPE with segmented views to prioritise improvement work.

FAQ #

What does WAPE mean? #

WAPE stands for Weighted Absolute Percentage Error. It measures forecast accuracy by summing all absolute errors and dividing by the total actual demand volume:

$$ \mathrm{WAPE} = \frac{\sum |y_i - \hat{y}_i|}{\sum |y_i|} \times 100\% $$

The “weighted” refers to weighting by volume: high-demand items naturally contribute more to the overall error, so WAPE reflects business impact better than a simple average of per-item percentage errors.

What is a good WAPE value? #

There is no universal threshold, but common industry benchmarks in demand forecasting are:

WAPE	Typical interpretation
< 10%	Excellent
10–20%	Good
20–40%	Acceptable
> 40%	Needs improvement

Context matters: stable, high-volume products should achieve lower WAPE than seasonal or promotional items. Always compare against a naive baseline (e.g., last period’s actuals) rather than an absolute target.

What is the difference between WAPE and MAPE? #

MAPE averages the percentage error per individual item:

$$ \mathrm{MAPE} = \frac{1}{n} \sum \frac{|y_i - \hat{y}_i|}{|y_i|} \times 100\% $$

WAPE pools errors across all items before dividing. Key consequences:

MAPE explodes when any individual actual is near zero; WAPE is stable as long as total volume is non-zero.
MAPE weights every item equally regardless of volume; WAPE weights high-volume items more heavily.
WAPE is preferred in retail/CPG for overall KPI reporting; MAPE is used when per-item accuracy is equally important regardless of size.

When should I use MAE instead of WAPE? #

Use MAE (Mean Absolute Error) when you need item-level diagnostics in the original unit (e.g., units, revenue). MAE does not depend on actual values being non-zero and is easy to interpret per SKU. WAPE gives a single relative number suitable for executive dashboards and cross-category comparisons. Use both together: WAPE for the headline KPI and MAE (or MAPE on positive-demand periods) for item-level root-cause analysis.

How do I handle zero demand in WAPE? #

When the denominator (total actual volume) is zero for a segment, WAPE is undefined. Practical solutions:

Exclude zero-demand periods from the calculation scope.
Segment separately: compute WAPE only on active SKUs, and track zero-demand separately as a coverage metric.
Switch to MAE for items or periods with intermittent demand (Croston’s method or ADIDA are designed for these cases).