Moving Average
A Moving Average (MA) is a simple forecasting method that smooths out fluctuations in demand by averaging past sales over a specific period.
It helps identify trends and patterns without being influenced by short-term spikes or dips.
How It Works (Step-by-Step)
- Choose a Time Period: Decide how many past periods (e.g., days, weeks, or months) to include in the average.
- Calculate the Average: Add up sales for the chosen period and divide by the number of periods.
- Update as New Data Comes In: Drop the oldest data point and include the newest one to keep the average “moving.”
Types of Moving Averages
- Simple Moving Average (SMA) – A basic average of past values.
- Weighted Moving Average (WMA) – Assigns more weight to recent data (e.g., June’s sales might be weighted more than April’s).
- Exponential Moving Average (EMA) – Uses a formula to give exponentially higher weight to recent data, making it more responsive to trends.
1️⃣Simple Moving Average (SMA)
How it works:
- Takes the average of past data points over a fixed period.
- Each data point has equal weight
Formula:
Where D = demand (sales) and n = number of periods.
Example (3-Month SMA):
| Month | Sales (Units) |
|---|---|
| April | 100 |
| May | 120 |
| June | 110 |
SMA=(100+120+110)/3=110
Best for: Short-term forecasting when demand is stable.
Weakness: Can lag behind trends and doesn’t react to sudden changes.
2️⃣Weighted Moving Average (WMA)
How it works:
- Assigns more weight to recent periods.
- More responsive to changes than SMA.
Where w = weight assigned to each period
Example (3-Month WMA with weights 1, 2, and 3 for April, May, and June):
| Month | Sales (Units) | Weight |
|---|---|---|
| April | 100 | 1 |
| May | 120 | 2 |
| June | 110 | 3 |
Best for: Detecting trends when recent demand changes matter more.
Weakness: Choosing the right weights requires judgment.
3️⃣Exponential Moving Average (EMA)
How it works:
- Similar to WMA but assigns weights exponentially, so recent data has much more impact.
- Uses a smoothing factor (α) to control how much weight recent data gets.
Where:
- EMAtEMA_tEMAt = current EMA
- DtD_tDt = current demand
- α\alphaα = smoothing constant (between 0 and 1)
- EMAt−1EMA_{t-1}EMAt−1 = previous EMA
Best for: Fast-changing demand patterns and real-time tracking.
Weakness: Needs an initial value and careful selection of α.
Which One Should You Use?
| Type | Use Case | Pros | Cons |
|---|---|---|---|
| SMA | Stable demand, no trends | Simple, easy to calculate | Lags behind trends |
| WMA | Recent data is more relevant | Responds better to trends | Requires choosing weights |
| EMA | Fast-changing demand, real-time | Reacts quickly to shifts | Needs fine-tuning (α) |
If demand is stable, SMA works fine. If trends matter, WMA is better. But if you need real-time, responsive forecasting, go for EMA.
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