## 4 Results
### Artificial Intelligence
#### 4.4.1 Results of Data Patterns and Correlations  
Exploratory FA techniques were applied on 300,000 datapoints of CFKPIs to develop an inductive research approach. These techniques discovered, through the calculation of eigenvalues based on Equation (5) of Section 3.3.1, eleven groups of CFKPs that shared significant common patterns and correlations. Significant common patterns and correlations were determined by observing eigenvalues greater than one. The calculated eigenvalues were (1) 52.895, (2) 1.5856, (3) 1.495, (4) 1.405, (5) 1.332, (6) 1.269, (7) 1.247, (8) 1.189, (9) 1.153, (10) 1.109, and (11) 1.096. Therefore, eleven groups of CFKPIs were created using the orthogonal rotation method (varimax) in order to produce a rotated factor loading for each CFKPI. The rotated factor loadings were calculated using Equation (6) of Section 3.3.1. CFKPIs in each group were significantly correlated and interdependent on each other. The correlations and interdependencies of CFKPIs in the 1 st group are illustrated by the Interrelationship Diagram (ID) in Figure 8. An ID reveals cause-and-effect relationships that are not easily recognizable [52]. This ID reveals CFKPIs that were drivers (D) and the ones that were outputs (O). Inputs from the driving CFKPIs are denoted by arrows labelled with a \"D\", and outputs from output CFKPIs are denoted by arrows labelled with an \"O\". Each CFKPI can be influenced directly or indirectly by one or more driving or output CFKPIs, and the same CFKPI can be a direct or an indirect driver of other CFKPIs. For example, the truck speed (D) directly drove (influenced) the average fuel consumption per cycle (O) and fuel consumption per tonne hauled (O), thus indirectly driving fuel bowser receipts and issues (O). The average fuel consumption per cycle (O) and fuel consumption per tonne hauled (O) were also direct drivers of the amount of fuel received and issued by the bowser (O). That is, a decrease in fuel consumption reduced the amount of fuel dispensed (issued) from the bowsers and consequently decreased the amount of fuel to be replenished (received) in the bowsers. The increase in fuel consumption resulted in more fuel dispensed from and replenished in the bowsers. The correlations and interdependencies of CFKPIs in the 1 st group revealed by this ID are further clarified by the Causal Loop Diagram (CLD) in Figure 9. A CLD reveals variables that have direct influence on other variables, that is, variables that trigger occurrences of other variables [53]. The success of the mine's cost-saving business strategy was underpinned by, amongst other factors, the ability of the Logistics enterprise function to plan accurately. The ability of the Logistics enterprise function to plan accurately was underpinned by the relationships and interdependencies of the CFKPIs in one or more groups. The drill rates determined the time it took before a drill failed when in operation. The life of the drill rod was determined by the time it took before the rod failed when in operation. The number of broken reserves was determined by the drill rates. Broken reserves determined the available blasted inventory. The blasted inventory resulted in the number of tonnes to be excavated, loaded, and hauled. This resulted in the fuel consumption per tonne hauled. The amount of fuel consumption per tonne hauled was also influenced by the speed of trucks during hauling. The speed of trucks also influenced the rate at which tyres failed or got worn out and the average fuel consumption per cycle. The failed and worn-out tyres had a direct impact on the cost of the tyre failure. The fuel consumption per ton and blasted inventory enabled a comparison between mining production (Modular) and fuel consumption (FMS). The  
Figure 8: Correlations of CFKPIs in the 1 st group. fuel consumption per cycle and fuel consumption pet tonnes hauled enabled a comparison between the bowser receipts and issues. The fuel consumption per tonne hauled directly influenced the average fuel consumption per cycle.