## 4 Results
### Comparisons between GA and Proposed CBGA  
As you see in Table 4, some products' forecasting errors are very high. For example, the product coded S14210 is a good example for this. When we look at the sales volume \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Pred** & **SM** & **MA(2)** & **MA(3)** & **ES** & **LINR** & **HOLT** & **WINT** & **GA** \\ \hline L04231 & 0.075 & 0.12 & 0.086 & 0.086 & 0.07 & 0.08 & 0.09 & 0.05 \\ \hline L04259 & 0.544 & 0.696 & 0.648 & 0.648 & 0.58 & 0.67 & 0.74 & 0.07 \\ \hline L07955 & 0.617 & 1.087 & 0.82 & 0.82 & 0.68 & 0.77 & 0.90 & 0.09 \\ \hline L26969 & 113.78 & 126.10 & 169.23 & 169.23 & 84.62 & 94.97 & 147.44 & 0.05 \\ \hline L28177 & 22.18 & 60.47 & 40.8 & 40.8 & 33.11 & 37.21 & 41.72 & 28.96 \\ \hline L28560 & 2.177 & 2.696 & 2.556 & 2.556 & 2.32 & 2.64 & 2.74 & 1.85 \\ \hline L29392 & 174.54 & 8040.41 & 7829.78 & 7829.78 & 5113.9 & 5770.2 & 12,756.6 & 4798 \\ \hline L29573 & 1.094 & 1.761 & 1.369 & 1.369 & 1.21 & 1.38 & 1.48 & 1.01 \\ \hline L30268 & 0.075 & 0.13 & 0.086 & 0.086 & 0.08 & 0.09 & 0.09 & 0.05 \\ \hline L30391 & 0.157 & 0.261 & 0.181 & 0.181 & 0.16 & 0.18 & 0.17 & 0.16 \\ \hline M2736 & 0.039 & 0.065 & 0.045 & 0.045 & 0.04 & 0.05 & 0.04 & 0.05 \\ \hline S00363 & 0.928 & 1.217 & 1.187 & 1.187 & 0.93 & 1.06 & 1.22 & 0.77 \\ \hline S06845 & 163.7 & 70.587 & 46.583 & 46.583 & 36.72 & 41.20 & 66.79 & 34.77 \\ \hline S08132 & 15,837.6 & 23,187.88 & 28,855.34 & 28,855.34 & 14,925.92 & 16,160.71 & 70,210.23 & 19,708 \\ \hline S08812 & 0.245 & 0.326 & 0.266 & 0.266 & 0.22 & 0.25 & 0.30 & 0.16 \\ \hline S09775 & 207.278 & 289.533 & 297.062 & 297.062 & 168.03 & 189.80 & 331.18 & 197 \\ \hline S09818 & 0.609 & 0.75 & 0.714 & 0.714 & 0.44 & 0.56 & 1.39 & 0.52 \\ \hline S10699 & 0.039 & 0.065 & 0.045 & 0.045 & 0.04 & 0.05 & 0.04 & 0.16 \\ \hline S12125 & 19.763 & 36.32 & 27.52 & 27.52 & 22.44 & 25.12 & 27.74 & 19.50 \\ \hline S12286 & 16.763 & 34.565 & 26.922 & 26.922 & 20.21 & 22.70 & 36.33 & 21.47 \\ \hline S13406 & 0.066 & 0.13 & 0.09 & 0.09 & 0.08 & 0.09 & 0.09 & 0.05 \\ \hline S14210 & 65.7235 & 124,250.9 & 183,854.1 & 183,854.2 & 67,302.0 & 76,021.9 & 381,081.1 & 77,624 \\ \hline \hline \end{tabular}  
* SM: Simple mean method, MA(2): moving average 2-horizon, MA(3): moving average 3-horizon, ES: simple exponential smoothing, LINR: linear regression, HOLT: Holt method, WINT: Holt-Winter method, and GA: genetic algorithm without clustering. Black Color shows best solution among methods. Orange color shows best second solution.  
\end{table} 
Table 5: The success of the methods alone and non-clustered GA comparisons. of this product, we can see that it has sold with a high sales volume. This shows the need to perform optimization estimates on a cluster basis. For GA, it is thought that the forecast performance may be adversely affected as it tries to model a forecast structure that will work well for many products at the same time. For this reason, GA prediction success was measured in the clustered products for the same parameter values by passing to the second scenario as the proposed model CBGA. It is possible to see the estimation performance according to the MSE values of the products in Cluster 1 in Table 6 before and after clustering. According to cluster analysis, Cluster 1 consists of 16 products in Table 6.  
It can be seen that the CBGA MSE accuracy value was better than the GA model for 10 products out of 16 products. Another cluster, cluster 5, consists of six products. Table 7 shows the change in the estimation performances of the products included in cluster 5. Improvements were achieved in all products included in the cluster given in Table 6. There was an improvement of around 10-15% in many products when the CBGA model was used. Improvement of the CBGA can be seen at all clusters with increased product forecasting success. The highest was experienced in Cluster 5. In Figure 8, it is possible to