## 4 Results
### Results of Machine Learning
#### 4.3.2 Results of Decision Engines and Predictive Analytics  
CFKPIs were analysed comprehensively following the successful implementation of data validation techniques. All five hierarchical levels of the properties introduced in Section 3.2.2 were maintained by each CFKPI. Figure 6 illustrates the average fuel consumption per cycle CFKPI incorporated into the five-level properties structure. The average fuel consumption per cycle belongs to the Logistics enterprise function. The Logistics enterprise function encompasses three business functions, viz., Inbound Logistics, Internal Processes, and Outbound Logistics. These business functions encompass multiple processes each. The Internal Processes business function and two fuel-related processes are demonstrated. The Internal Processes business function encompasses, inter alia, such processes as \"Store Fuel\" and \"Measure Fuel Consumption per Cycle\". The first process (Store Fuel) encompasses the \"Dispense Fuel in the Tank\" and \"Record Dispensed Fuel\" process steps. The first process step uses three resources, viz., \"Fuel Tank\", \"Dispenser Truck\", and \"Dispenser Truck Driver\". The second process step uses two resources, viz., \"Operator\" and \"Industrial Tablet\". The second process (Measure Fuel Consumption per Cycle) encompasses three process steps, viz., \"Read and Record Fuel Consumption\", \"Get and Record Cycle Counts\", and \"Compute Fuel Consumption per Cycle\". The first and second process steps use two resources each, viz., \"Operator\" and \"Industrial Tablet\". The third process step uses two resources, viz., \"Server\" and \"Application Software.\"  
This structure facilitated efficient troubleshooting through the 5 WHYs root cause analysis technique adopted from [50]. A practical scenario is investigating a sudden increase in fuel consumption at the mine, as illustrated in Figure 7. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Bartlett’s Test of Sphericity** & \(t\)-Value (\(t\)) & \(-\)936,049.849 \\ \hline  & Degrees of freedom (\(df\)) & 99 \\ \hline  & Significance (\(p\)-value) & 0.000 \\ \hline
**Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy** & 0.999 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of data validation. Discovering the root cause of a sudden increase in fuel consumption resulted in informed optimization decision-making. A project to prevent the inaccurate recording of fuel consumption was initiated by enhancing TMS to automatically read through sensors and record fuel consumption. This enhancement eliminated human intervention and unnecessary resources, sped up recording processes, and provided accurate recordings of fuel consumption. An inaccurate recording of cycle counts was rectified by enhancing MMS with a geographical positioning system that records the round trips of trucks in real-time. The same 5 WIYJs technique discussed above was applied to find the root cause of exceptional performances (i.e., accurate reporting of fuel consumption) indicated by the average fuel consumption per cycle CFKPI. The root cause of exceptional performance was the automation of process steps. That is, automating the recording of fuel consumptions  
Figure 6: Average fuel consumption per tonne CFKPI. Figure 7: Five WIYJs root cause analysis technique for fuel consumption increase. and cycle counts yielded positive results, and the mine consequently invented a strategy for optimizing business through process automation. Furthermore, discovering the extent to which CFKPI properties influence performance enabled predictive analytics. The average fuel consumption per cycle was predicted by observing the number of cycles made by ore-hauling trucks. Reducing the number of cycles through proper planning (e.g., scheduling of trucks only when there is ore to be hauled and the daily target has not been achieved) reduced the fuel consumption and resulted in significant cost savings over time. The decision engines and predictive analytics were subsequently employed by the mine for all the company KPIs.