# A Continuous Monitoring and Reallocation Method for Successful Decisions in Change Management
## 4 Experiments and Results
### First Experiment's Results and Analysis  
In order to test the proposed CoMoRe method, we ran the algorithm repeatedly, trying to minimize the possibility that the results were influenced by random values. These random values were used for the initial state of the performance and resistance matrices used during the iterations. Starting with 100 repetitions, the algorithm was also repeated 1.000, 10.000, and 100.000 times. The average performance was calculated for each case. The results of 10.000 repetitions were not significantly different from those of 100.000 repetitions, so there was no need to further increase the number of repetitions. Figure 4 compares the average results of the CoMoRe method with the results of the CCMM based on the classical change curve. Table 6 summarizes the corresponding arithmetic values used to create the curves in Figure 4.  
Figure 4 and Table 6 show that the CoMoRe method achieves a higher total performance than the CCMM method, and the advantages are obvious. The graphical representation and corresponding arithmetic values indicate that the total performance with the CoMoRe method is better at all time intervals of change. Moreover, the drop in total \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Time (\(t\))** & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** \\ \hline \multicolumn{13}{c}{**Human**} \\
**Resources (\(i\))** & & & & & & & & & & & & & & & & \\ \hline
1 & 7 & 4 & 6 & 6 & 10 & 10 & 10 & **7** & **7** & **7** & **7** & **7** & **7** \\
2 & 2 & 2 & 1 & **4** & **4** & **4** & **4** & **4** & **4** & **4** & **4** & **4** & **4** & **4** \\
3 & 9 & 5 & **9** & **9** & **9** & **9** & **9** & **9** & **9** & **9** & **9** & **9** & **9** & **9** \\
4 & 1 & 3 & 10 & 5 & **3** & **3** & **3** & **3** & **3** & **3** & **3** & **3** & **3** & **3** & **3** \\
5 & 5 & 7 & 8 & 10 & 10 & 7 & 7 & 7 & **10** & **10** & **10** & **10** & **10** & **10** \\
6 & 10 & 6 & 4 & 3 & **2** & **2** & **2** & **2** & **2** & **2** & **2** & **2** & **2** & **2** & **2** \\
7 & 6 & 1 & 7 & 7 & 7 & **6** & **6** & **6** & **6** & **6** & **6** & **6** & **6** & **6** \\
8 & 3 & 10 & 5 & **8** & **8** & **8** & **8** & **8** & **8** & **8** & **8** & **8** & **8** & **8** \\
9 & 8 & 9 & 2 & 2 & **5** & **5** & **5** & **5** & **5** & **5** & **5** & **5** & **5** & **5** \\
10 & 4 & 8 & 3 & **1** & **1** & **1** & **1** & **1** & **1** & **1** & **1** & **1** & **1** & **1** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: An example of reallocations during algorithm iterations. performance is much lower. As shown in Figure 3(a)-d, both methods start from the same value, which is the average total performance for 100, 1.000, 10.000, and 100.000 repetitions, respectively. As observed in these figures, the improvement occurs mainly during early time intervals and is maintained until the end of the change period. Another useful observation from the curves is that when using the CoMoRe method, the total performance is always significantly better than that obtained with the classical method; this improvement in performance favors a commitment to change and reduces the likelihood that the change process will be abandoned, which is often observed in companies and organizations when performance drops to significantly low levels. Table 6 shows that in all experiments (100, 1.000, 10.000, and 100.000 repetitions), the average total performance starts (\(t=0\)) at a value of 981 in both methods. From \(t=1\) to \(t=7\), the overall performance for the proposed CoMoRe method begins to be significantly better than that for the CCMM method. At \(t=7\), the lowest total performance has a value of 491 for CoMoRe versus 286 for the CCMM. This is the most critical point in the change process. At the next time step, the performance starts to increase.