# 10.8.3 Exercise: First-Order Exponential Smoothing

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

When you report to your supervisor that the moving average forecasting technique is not suitable for the product, he remembers that your colleague in charge of forecasting had been working on introducing the first-order exponential smoothing technique for this product. Therefore, your supervisor gives you the information in Figure 10.8.3.1.

Fig. 10.8.3.1      Demand and forecast using first-order exponential smoothing technique.

The Figure shows the demand for the product (January to October) and the forecast using the first-order exponential smoothing technique with α = 0.3 of the product (January to July). To evaluate your supervisor’s suggestion, you execute the following steps:

a. Compute the forecast for August, September, and October and for the following month, November.

Solution:    Forecast August = 0.3*111+0.7*179 = 159; forecast September: 140; forecast October: 134; forecast November: 151.

b. Calculate the mean absolute deviation (MAD) for November assuming MAD(Jan) = 18 and the smoothing parameter α.

c. In the preceding exercise, could you have obtained a result comparable to the one for the parameter α calculated above by changing n, that is, the number of observed values?

Solution:   Yes, by choosing a value of n = (2 – 0.3)/0.3 = 5.67 (see the formula in Figure 10.2.3.1).

d. Decide whether the chosen first-order exponential smoothing technique with parameter α calculated above is appropriate for this product.

Solution:  Since the demand fluctuates, it would be better to increase α. More­over, the first-order exponential smoothing technique does not fit this demand curve well. Therefore, it is worth considering another fore­casting technique, e.g., with short-term seasonality.

e. What can you say in general about the choice of α depending on the product life cycle?

Solution:    At the beginning and the end of the product (market) life cycle, α should be relatively high, e.g., α = 0.5. For a well-established product, the a often chosen α is around 0.1.