Calculate a simple moving average demand forecast by averaging the last N periods. Smooth out noise and project the next period's demand.
The Simple Moving Average (SMA) is one of the most straightforward demand forecasting methods. It calculates the average demand over the last N periods and uses that average as the forecast for the next period. By averaging multiple periods, random fluctuations are smoothed out, revealing the underlying demand trend.
The key parameter is N — the number of periods to include. A larger N produces a smoother forecast that is less responsive to recent changes. A smaller N tracks recent demand more closely but is more volatile. Choosing the right N depends on the stability of demand and how quickly you need the forecast to react.
This calculator lets you enter demand values for up to 12 periods and choose the number of periods (N) to average. It outputs the forecast for the next period along with the average demand value.
Supply-chain managers, warehouse operators, and shipping coordinators rely on precise moving average forecast data to maintain efficiency and control costs across complex distribution networks. Revisit this calculator whenever conditions change to keep your logistics plans aligned with real-world performance.
Simple moving averages are easy to understand, implement, and explain to stakeholders. They provide a reasonable baseline forecast for items with relatively stable demand. This calculator eliminates spreadsheet work, instantly computing the forecast from your demand history. Real-time recalculation lets you model different scenarios quickly, ensuring your logistics decisions are backed by accurate, up-to-date numbers.
Forecast = (D_{t-1} + D_{t-2} + ... + D_{t-N}) / N Where D_{t-i} is the demand i periods ago and N is the number of periods.
Result: Forecast = 113.3
Using the last 3 periods: (105 + 120 + 115) / 3 = 340 / 3 = 113.3. The forecast for the next period is approximately 113 units.
SMA works best for items with relatively stable, stationary demand — no strong trend and no pronounced seasonality. Common examples include maintenance supplies, office products, and commodity components with predictable consumption patterns.
SMA assigns equal weight to all N periods, meaning a demand spike several periods ago has the same influence as last period's demand. It also lags behind trends and ignores seasonality. For demand with these patterns, exponential smoothing or seasonal decomposition methods are more appropriate.
Even when using advanced forecasting models, SMA serves as a useful benchmark. If a complex model cannot consistently beat a 3-period SMA, the added complexity may not be justified. Always compare sophisticated forecasts against simple baselines before deploying them.
A simple moving average calculates the arithmetic mean of the last N demand values. Each period has equal weight. It smooths out short-term fluctuations to reveal the general demand level.
A smaller N (2–4) responds quickly to changes but is noisy. A larger N (6–12) is smoother but slower to react. Start with N = 3 and adjust based on forecast error metrics like MAD or MAPE.
SMA struggles with strong trends because it averages past data equally. For trending demand, consider double exponential smoothing (Holt's method) which incorporates a trend component.
SMA gives equal weight to every period. A weighted moving average assigns higher weights to more recent periods, making it more responsive to recent demand changes while still smoothing older data.
SMA does not inherently handle seasonality. If demand is seasonal, you should deseasonalize the data before applying SMA, or use a seasonal forecasting method like Holt-Winters.
You need at least N periods of historical data. For best results, have at least 2× to 3× N periods available so you can evaluate forecast performance over multiple cycles.