Integral Logistics Management — Operations Management and Supply Chain Management Within and Across Companies

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.

Figure showed the effect of different values of the smoothing con­stant α . Figure shows the necessary relationship between the num­ber of observed values and the smoothing constant α. You can view the comparison using the interactive exercise below.

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. Play with the different parameters and use the calculate-button to see the according changes.
The initial setting marks the 11th and 12th month of the current yeat as unknown (="-"). You may also change these parameters.

In the red section at the top of the Web page, you can choose different values for the smoothing constant α . In the lower, green section you can choose either a different value for the smoothing constant a for comparison with the red curve or choose the number of values for the moving average forecast and compare the results of the technique with exponential smoothing (the red curve). Clicking on the “calculate” icon executes your input choice.

Course section 10.8: Subsections and their intended learning outcomes

  • 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.