Results and Discussion

Sales Forecast using Exponential Smoothing Method

This is an advance method, weighted average method which overcomes the limitations of moving averages. In this study, α is assumed to 0.30 (30%). This implies that that 30% of the forecast will be affected by recent data while the while the older data will be affected by 70%. It is similar to the value of smoothing constant (α) applied in study of evaluating the performance of forecasting methods to determine the level of accuracy.

Table 3. Actual and Forecasts using Exponential Smoothing

Actual sales (y) (million) Forecast IEI EI2 ǀEǀ/Y x100
1 25
2 29 25 4 16 13.79
3 28 25.9 2.1 4.41 7.5
4 35 28.56 6.44 41.47 18.4
5 33 29.89 3.11 9.67 9.42
6 32 30.52 1.48 2.19 4.63
7 36 32.17 3.83 14.67 10.64
8 41 34.82 6.18 38.19
 15.07
9 45 37.87 7.13 50.84 15.84
10 20 36.26 16.26 264.39 81.3
11 23 24.14 1.14 1.30 5.65
12 15 21.40 6.4 40.96 42.67


The forecast accuracy performance measures are:

\mathrm{MAD}=\Sigma / \mathrm{E} / / \mathrm{T}=58.07 / 12=4.84, \mathrm{MSE}=\Sigma / \mathrm{E} /{ }^{2} \mathrm{~T}=484.09 / 12=40.34

MAPE = (Absolute error / Actual Observed Value) × 1 00 = 224.91/12= 18.74