# 10.2.2b The Smoothing Constant α, or Alpha Factor

### Intended learning outcomes: Describe how the smoothing constant α determines the weighting of the past. Present an example of first-order exponential smoothing.

Continuation from previous subsection (10.2.2)

The choice of smoothing constant α or alpha factor determines the weighting of current and past demand according to the formula in Figure 10.2.2.3.

Figure 10.2.2.5 shows the effect of α = 0.1, a value often chosen for well-established products, and α = 0.5 for products at the beginning or the end of their life cycles.

Fig. 10.2.2.5       The smoothing constant α determines the weighting of the past.

Figure 10.2.2.6 shows the behavior of the forecast curve with various values of the smoothing constant α. A high smoothing constant results in a rapid but also nervous reaction to changes in demand behavior. See also Sections 10.2.3 and 10.5.1.

Fig. 10.2.2.6       Forecasts with various values of the smoothing constant α.

Using exponential smoothing techniques, we can determine the uncertainty of a forecast by extrapolating the forecast error. To do this, we calculate the mean absolute deviation (MAD). Figure 10.2.2.7 is an example of expo­nential smoothing with smoothing constant α = 0.2. It was chosen in a way similar to the example of moving average calculation in Figure 10.2.1.4.

Fig. 10.2.2.7       First-order exponential smoothing with smoothing constant α = 0.2.

## Course section 10.2: Subsections and their intended learning outcomes

• ##### 10.2 Historically Oriented Techniques for Constant Demand

Intended learning outcomes: Describe the moving average forecast. Explain the first-order exponential smoothing forecast. Differentiate between the moving average forecast and the first-order exponential smoothing forecast.

• ##### 10.2.1 Moving Average Forecast

Intended learning outcomes: Explain mean and standard deviation in the moving average forecasting technique. Disclose the average age of the observed values. Present an example of determining the forecast value using moving average.

• ##### 10.2.2 First-Order Exponential Smoothing Forecast

Intended learning outcomes: Identify the weighted mean as well as exponential demand weighting. Explain first-order exponential smoothing: mean, MAD, and standard deviation. Disclose the average age of the observed values.

• ##### 10.2.2b The Smoothing Constant α, or Alpha Factor

Intended learning outcomes: Describe how the smoothing constant α determines the weighting of the past. Present an example of first-order exponential smoothing.

• ##### 10.2.3 Moving Average Forecast versus First-Order Exponential Smoothing Forecast

Intended learning outcomes: Disclose formulas for the relationship between α and n. Present the relationship between α and n in tabular form. Present an example of linear regression.