## 4 Results
### Evolutions of SKPIs  
The DDA model identified the primary drivers of the business by applying ML and AI techniques to the CFKPI dataset for the year 2015, as discussed in previous sections. When the same techniques were applied to the CFKPI datasets for 2016 and 2017, distinct business drivers emerged for each year. To ensure consistency, the same CFKPI names and quantities used in the 2015 dataset were applied to the 2016 and 2017 datasets. The only variation between the three datasets was the actual data values produced by these CFKPIs for each year. This approach guarantees that the model is driven solely by data generated by the mine's systems, rather than changes in the CFKPI names, count, or other factors. Figure 14: SKPIs’ dominance order for the year 2015. Table 4 provides a summary of the key properties for each dataset, including the number of datapoints, \(t\), _df_, _p_-value, KMO, total variance explained, top ten significant CFKPIs, and the resulting number of SKPIs. The DDA model reveals distinct business drivers for each year, from 2015 to 2017, by identifying the most significant CFKPIs and by generating new SKPIs as the business evolves and data change. The evolution of SKPIs over the three-year period provides valuable insights into the shifting priorities of the mine's operations. In 2015, during the start-up phase, logistical variables related to organizing and planning played a dominant role, with financial and integrated planning variables providing supporting functions. In 2016, as the mine expanded its operations and acquired more resources, financial management variables took centre stage, steering the business towards its objectives. Production, maintenance, and integrated planning variables played auxiliary roles during this period. By 2017, as processes matured and resources were utilized to near full capacity, production variables became the primary focus, driving efforts to achieve efficiency, reduced cycle \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Property** & **Year 2015** & **Year 2016** & **Year 2017** \\ \hline Datapoints & 300,000 & 300,000 & 300,000 \\ _t_-value (_t_) & \(-936\),049.849 & \(-976\),670.406 & \(-995\),367.617 \\ Degrees of freedom (_df_) & 99 & 99 & 99 \\ _p_-value & 0.000 & 0.000 & 0.000 \\ KMO & 0.999 & 0.999 & 0.999 \\ Total variance explained & 0.616 & 0.696 & 0.677 \\ Number of new SKPIs (factors) & 11 & 11 & 11 \\  & Average fuel consumption per cycle (0.774) & Ore tonnes moved (0.872) & Tonnes per drill metre (0.795) \\  & Ore tonnes moved (0.730) & Drill rates (0.844) & Ore tonnes moved (0.751) \\  & Tonnes per drill metre (0.723) & Average fuel consumption per cycle (0.826) & D(0.737) \\  & Scheduled maintenance & Tonnes per drill metre (0.819) & Scheduled maintenance \\ Top 10 CFKPIs with highest & adherence (0.717) & Scheduled maintenance & adherence (0.728) \\  & Drill rates (0.714) & adherence (0.813) & Drill rates (0.718) \\ business impact & Failed vs. worn type scraps & Cost vs. budget (0.791) & Average fuel consumption per cycle (0.709) \\  & (0.712) & Depletion vs. plan (0.771) & Failed vs. worn type scraps \\  & Blasted reserve available & Modular vs. FMS (0.759) & (0.705) \\  & (0.703) & \begin{tabular}{l} Blasted reserve available \\ \end{tabular} & \
\begin{tabular}{l} Blasted reserve available \\ \end{tabular} \\  & Depletion vs. plan (0.701) & Failed vs. worn type scraps & Cost vs. budget (0.701) \\  & Modular vs. FMS (0.699) & (0.739) &
\begin{tabular}{l} Dodolve vs. plan (0.660) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Dataset summary for the year 2015 to 2017.