## 4 Results
### Artificial Intelligence
#### 4.4.2 Significance Levels of CFKPIs to Performance Management  
Communalities were calculated using Equation (7), introduced in Section 3.3.2, to discover the significance level of each CFKPI on the performance management of the mine's operations. The top ten CFKPIs with highest communalities, illustrated in Figure 12, had the most significant impact on the performance management of the mine's operations. Therefore, optimizing the properties of these CFKPIs guaranteed positive performance results for the mine's operations. The average fuel consumption per cycle held a maximum of 0.774 of the total communality. This means that the variation in the average fuel consumption per cycle could have had a maximum of a 77.4% impact on the performance management of the mine's operations, followed by ore tonnes moved (0.730), tonnes per drill metre (0.723), scheduled maintenance adherence (0.717), drill rates (0.714), failed vs. worn tyre scraps (0.712), cost vs. budget (0.711), blasted reserve available (0.703), depletion vs. plan (0.701), and modular vs. FMS (0.699). Each CFKPI was associated, to a certain degree, with all groups of CFKPIs. The proportional variance was calculated, using Equation (7), discussed in Section 3.3.2, to discover the degree to which each CFKPI is associated with each group of CFKPIs. Each CFKPI was assigned to a group that retained the most variance for that particular CFKPI, ensuring that a CFKPI is associated with only one group. Moreover, a cut-off point of 0.4 for factor loadings was applied in order to keep only significant CFKPIs in the respective groups. CFKPIs in the same group were considered to be highly correlated and influential to each other. This means that a change in one of the CFKPIs or its constituents affected the rest of the members in the group. Therefore, managing CFKPIs as groups  
Figure 11: CLD for CFKPIs in the 2 nd group. that strive to achieve respective common goals supplemented the ability to manage these CFKPIs individually. The groups of CFKPIs collectively explained a proportion of the total variance in the whole dataset. This variance proportion represented the collective level of significance or contribution accounted for by these groups of CFKPIs on the performance management of the mine's operations. The analysis results reveal that the eleven groups of CFKPIs collectively explained 61.637% of the variation in the whole dataset. This means that these groups collectively accounted for 61.637% of the impact on the performance management of the mine's operations. Moreover, each group of CFKPIs explained a proportion of the total variance in the CFKPI dataset. This proportion, calculated using Equation (9) of Section 3.3.2, represented the significance or contribution degree accounted for by each group of CFKPIs on the performance management of the mine's operations. The 1 st group explained 8.084% of the variation. This means that the 1 st group of CFKPIs accounted for 8.084% of the impact on the performance management of the operations of the mine. The 2 nd group accounted for 7.086% of the impact, followed by the 3 rd group (7.004/%), 4 th group (6.938%), 5 th group (6.244%), 6 th group (6.128%), 7 th group (4.524/%), 8 th group (4.344%), 9 th group (4.236%), 10 th group (3.820%), and 11 th group (3.230%). These groups of CFKPIs formed a solid foundation for the SKPIs introduced in the next section. Table 3 summarizes the aforementioned individual and collective impact levels of the CFKPI groups on the performance management of the mine's operations. The extraction sums of squared loadings represent eigenvalues greater than one that determine the number of CFKPI groups to be created by the model. The rotated sums of squared loadings represent rotated factor loadings that illustrate the variance explained by each group of CKPIs.