*Intended learning outcomes: Calculate forecasts with moving average forecasting technique.*

The person in your firm responsible for forecasting has been absent for three months, so your supervisor asks you to forecast the demand of the most important product. The information you get is a table (see Figure 10.8.2.1) showing the historical data on the demand for the product (January to October) and the forecast for the period January to July based on the moving average forecasting technique.

**Fig. 10.8.2.1** Demand and forecast with moving average forecasting technique.

Moreover, your supervisor asks you to:

a. Forecast the demand just as your colleague does. Therefore, you have to calculate the parameter n from the historical forecast data.

*Solution: *n = 4

b. Calculate the forecast for August, September, and October as well as for the following month, November.

*Solution: *Forecast August = (207+199+175+111) / 4 = 173; forecast September: 145; forecast October: 125; forecast November: 129.

##### c. Compute the standard deviation Sigma of the forecast from January to October and decide if the applied technique fits this product.

*Solution: *Sigma = 53.87 and variation coefficient = 53.87 / 152.6 » 0.35. A variation coefficient of 0.35 stands for a relatively low quality of the forecast. Therefore, the applied technique is not appropriate for this product. Try a value other than n = 4, or with additional seasonal index.

## Course section 10.8: Subsections and their intended learning outcomes

##### 10.8.1 Exercise: Choice of Appropriate Forecasting Techniques

Intended learning outcomes: Propose a forecasting technique for different products to apply to forecast future demand.

##### 10.8.2 Exercise: Moving Average Forecasting Technique

Intended learning outcomes: Calculate forecasts with moving average forecasting technique.

##### 10.8.3 Exercise: First-Order Exponential Smoothing

Intended learning outcomes: Calculate forecasts using first-order exponential smoothing technique.

##### 10.8.4 Scenario: Moving Average Forecast versus First-Order Exponential Smoothing Forecast

Intended learning outcomes: Differentiate between moving average forecast and first-order exponential smoothing using different numbers of observed values or smoothing constants α.

##### 10.8 Scenarios and Exercises

Intended learning outcomes: Choose an appropriate forecasting technique. Calculate an example for the moving average forecasting technique and for the first-order exponential smoothing technique. Differentiate between the moving average forecast and the first-order exponential smoothing forecast.