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.
Solution: MAD(Feb) = 0.3*(187 – 151)+0.7*18 = 23 —> MAD(Mar) = 29, MAD(Apr) = 26, MAD(May) = 33, MAD(Jun) = 31, MAD(Jul) = 23, MAD(Aug) = 37, MAD(Sept) = 45, MAD(Oct) = 37, MAD(Nov) = 43.
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.
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.
