*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.