How the weighted moving average works
A weighted moving average assigns different importance levels to each period in the averaging window rather than treating all periods equally. Recent months get higher weights, older months get lower weights. This makes the weighted moving average more responsive to demand shifts than a simple moving average while remaining more transparent than exponential smoothing.
For FBA sellers running 50 to 200 SKUs, the weighted moving average works well when you want more control over how much influence each period has on your demand forecast. You explicitly choose the weights, so the logic is auditable. If your category buyer or operations manager asks why you ordered 600 units instead of 400, you can show exactly how the weighted moving average calculation arrived at that number.
The trade-off is maintenance. With a weighted moving average, you need to decide on a weighting scheme for each SKU or product group, and those weights may need to change as demand patterns evolve. That manual tuning overhead is why many sellers eventually graduate to exponential smoothing, which achieves a similar effect with a single alpha parameter.
Weighted moving average formula
| Variable | Meaning |
|---|---|
w1...wn | Weights assigned to each period (higher = more influence). Common scheme: 3-2-1 for 3 periods. |
D1...Dn | Actual demand for each period (D1 = most recent) |
n | Number of periods in the window |
Example: a $45 fitness product
You sell a resistance band set at $45 ASP with a 55-day lead time from Guangzhou. Your last 3 months of sales: Month 1 (oldest) = 350, Month 2 = 420, Month 3 (most recent) = 480. Let’s compare equal weights vs. a 3-2-1 weighted moving average:
| Method | Calculation | Forecast |
|---|---|---|
| Simple MA (equal) | (350 + 420 + 480) / 3 | 417 |
| WMA (3-2-1) | (3×480 + 2×420 + 1×350) / 6 | 438 |
The weighted moving average gives 438 vs. the simple average of 417. That 21-unit difference reflects the upward trend in recent months. With a 55-day lead time (about 1.8 months), the WMA forecasts lead time demand of 788 units vs. 751 from the simple MA. At $45 ASP, the WMA orders an extra $1,665 in inventory to cover the growth pattern.
If you used a 4-3-2-1 scheme over 4 months (even stronger recency bias), the forecast would shift further toward recent actuals. The right weighting scheme depends on how quickly your product’s demand changes.
FBA-specific considerations
The weighted moving average is particularly useful for FBA sellers heading into Q4. From October through December, demand patterns shift fast, and a simple equal-weight average lags behind. Heavier weights on recent periods (3-2-1 or 4-3-2-1) help your forecast catch up to holiday demand without waiting for an entire averaging window to roll over.
Post-Prime-Day cleanup is another strong use case. If Prime Day produced a 3x sales spike, that single inflated period will distort an equal-weight moving average for months. With a weighted moving average, you can either set the Prime Day month’s weight to zero (excluding the spike entirely) or weight it lower than the trailing months that better represent baseline demand.
One FBA-specific gotcha: Amazon’s storage tier rules change quarterly based on your IPI score. A weighted moving average that aggressively up-weights last month can recommend orders too large for your current storage allocation. Always cross-check WMA-driven reorder quantities against your storage limits before placing the PO.
Common mistakes
- Using arbitrary weights without testing. Sellers often pick 3-2-1 because it sounds reasonable, then never validate the weights against actual forecast accuracy. Backtest your weighting scheme by comparing forecasts to actuals over the past 6 months. If 4-3-2-1 produces lower MAPE than 3-2-1, switch.
- Over-weighting recent data during anomalies. If last month was a Lightning Deal month with 2x normal sales, a heavy 3-weight on that period inflates your forecast. The next month’s reorder quantity will be too high, leading to excess inventory after the promotion fades. Either exclude promotional months or temporarily reduce their weight.
- Not adjusting weights seasonally. The optimal weighting scheme for a stable summer month is different from the optimal scheme heading into Q4. Many sellers set weights once and forget them. Review weights at least quarterly, or use exponential smoothing with adaptive alpha to handle the adjustment automatically.