Measuring Forecast Accuracy in a Pharmacy

Results and Discussion

Least Square Regression Model

Table 4. Actual and Forecasts using Least Squared Model

Month (x) Actual Sales (y) (million) XY X2 Forecast IEI IEI2 ǀEǀ/Y x100
1 25 25 1 32.92 7.92 62.73 31.68
2 29 58 4 32.42 3.42 11.70 11.79
3 28 84 9 31.92 3.92 15.37 14
4 35 140 16 31.42 3.58 12.82 10.23
5 33 165 25 30.92
2.18 4.75 6.61
6 32 192 36 30.42 1.58 2.50 4.94
7 36 252 49 29.92 6.08 36.97 16.89
8 41 328 64 29.42 11.58 134.10 28.24
9 45 405 81 28.92 16.08 258.57 35.73
10 20 200 100 28.42 18.42 339.30 92.1
11 23 253 121 27.92 4.92 24.21 21.39
12 15 180 144 27.42 12.42 154.25 82.8

78 362 2282 650

a= 33.42 b= -0.5

Therefore the regression line equation for forecast is F =

y = 33.42 + (-.5)X and (X=1=12) to generate the forecast for the 12 months.

The forecast accuracy performance measures are:

$\mathrm{MAD}=\Sigma / \mathrm{E} / / \mathrm{T}=92.1 / 12=7.675=7.68, \mathrm{MSE}=\Sigma / \mathrm{E} /{ }^{2} \mathrm{~T}=1057.27 / 12=88.11$

MAPE = (Absolute error / Actual Observed Value) × 1 00 = 356.4/12 = 29.7

Table 5. Summary of the results of the Forecast Accuracy Measures

Measure of Accuracy Moving Average Method Exponential Smoothing Method Least Cost Method
MAD 5.79 4.84 7.68
MSE 66.31 40.34 88.11
MAPE (%) 23.56 18.74 29.7

Table 5 reveals that the values of MAD, MSE and MAPE under moving average method are 5.79, 66.31 and 23.56% respectively. For exponential smoothing method, the values of MAD, MSE and MAPE are 4.84, 40.34 and 18.74%, respectively. While the value of MAD is 7.68, MSE is 88.11 and MAPE is 29.7% under least cost method. In performance accuracy comparison, it was observed that exponential smoothing method is the best technique because it generates the optimal forecast accuracy. That is, exponential smoothing method having the least values of MAD (4,84), MSE (40.34) and 18.74 (%) indicates that it has least error and more accurate forecast than the other two methods. Therefore, the pharmacy is advised to consider the exponential smoothing method for accurate demand forecasting.