## 4 Result and Discussion
### Results of the MTOVE Implementation  
This section describes how MTOVE is applied and measured. The research results are based on the actual case study in a coal-hauling operation utilizing a 12-truck fleet, then the data were collected from the process to be analyzed. The time spent on the data collection was three months. The MTOVE metric for the truck fleet was calculated on a daily basis and each factor was studied separately. Each factor was calculated with the equations in Section 4.3. Figure 4 shows the average rate of all factors per week for the 12-week period for all unit trucks. The average rates of physical availability and use of availability (UA) are described in Figure 4. The PA average value over a three-month period was 95%. This indicated that the trucks were in good condition since the total down-time was only 5% for all trucks. These PA values were still above the OEE availability benchmark value of 90% [28]. This was an achievement owing to the implementation of a basic total productive maintenance (TPM) program. However, the average rate of use availability indicated that the UA average value for the three-month period was 47%. The lowest UA was found in the first three weeks. Losses in these weeks occurred because of rain, which was an external factor and uncontrollable. However, from the fourth to the seventh week, the UA value increased because of less idle time as weather conditions were much better than in the first three weeks. UA increased because of less idle time, thus increasing operation time,  
Figure 4: Average physical availability (PA) and use of availability (UA) rates. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Second Round** & **Third Round** \\ \hline Number of participants (\(n\)) & 12 & 12 \\ CVT & 100\% & 100\% \\ Kappa: 0.75; \(p\)-value: 0.007 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Delphi result for proposed MTOVE. which meant more time for hauling operations and higher truck utilization. The UA results indicated that the greatest loss of time was due to the idle time of the trucks. After further in-depth analysis, we detected that most of the idle time was attributed to rain and slippery roads, since the hauling road was built from original soil that does not resist water during the rainy season. Some of the losses were also due to the absence of a driver, idling the truck. Most of the time, trucks became idle because of weather conditions. The average rate of performance as a combination of productivity and fuel consumption rate is described in Figure 5. This showed that the performance average value for the three-month period was 54%. This value was far below the OEE performance benchmark value of 95% [28]. Performance consisted of productivity and fuel performance, as shown in Figure 5. The average fuel consumption rate was 99%. The fuel performance factor average of 99% was very close to the desired value of 100%, and there were no significant losses for fuel performance. The productivity average value was 54%, which affected overall performance. Further investigation indicated that losses dragged down productivity because of nonproductive time during operation time. Productivity losses occurred because of waiting queues when the operation began since 12 fleet trucks started at the same time in the same loading point. Loading time for one truck was about 7-8 min, and the last truck had a waiting time of 77-88 min. The second problem was excess hauling cycle time because of road conditions. Bad hauling-road-condition quality increased hauling cycle time. Excess cycle time also occurred after rain; because of the conditions; some parts of the hauling road were still not completely dry after rain, which impacted truck speed. The fuel performance on certain occasions provided values above 100% because of variations in hauling cycle time and travel speed.