# 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.

The results of moving average and first-order exponential smoothing are comparable, to the extent that the mean age of the ob­served values corresponds mutually. Figure 10.2.3.1 shows the relationship between the number of observed values and the smoothing constant α.

Fig. 10.2.3.1       Formulas for the relationship between α and n.

Figure 10.2.3.2 shows the same relationship between α and n, using a tabular comparison of individual values.

Fig. 10.2.3.2       Relationship between α and n in tabular form.

Exercise: Comparing Moving Average Forecast versus First-Order Exponential Smoothing Forecast
This exercise demonstrates the different types of demand forecast.
The first graph calculates the forecast using first-order exponential smoothing while the second is calculated by a method of your choice. Get used to the effects of N (the number of considered periods in the past) as well as the smoothing constant α, by chosing different values for these variables.
The initial setting marks the 11th and 12th month of the current yeat as unknown (="-"). You may also change these parameters.

## 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.