## 4 Result and Discussion
### Results of the MTOVE Implementation
The average rate of transportation quality as a combination of quality of service and coal losses is described in Figure 6. The average transportation quality rate was 100%, which was still above the OEE quality benchmark of 99.9% [28]. This indicated that the company was still able to achieve service quality and minimum coal losses. The quality of service and coal loss factors were very close to the desired value. Both values during the three-month periods were 100%, supported by several trip failures and zero accidents. Zero trip failures due to a major breakdown indicated that truck maintenance activities were well performed after the company implemented total productive maintenance practices. Zero accidents also indicated that the safety performance was good, since the control and safety monitoring are very tight in mining operations. Figure 5: Performance rate (PR). Figure 7 shows the average MTOVE calculation result (physical availability \(\times\) use of availability \(\times\) performance efficiency \(\times\) transportation quality) for the three-month period. Values between 12% and 40% were presented with an average of 25%. Maximum MTOVE value appeared during the fourth to seventh week, and this trend was very similar to the trend value of UA. The UA value was also at maximum during the fourth to seventh week, as shown in Figure 4. This indicated that UA affected the MTOVE result. It was also confirmed by the trend graph in Figure 7, where the trends of MTOVE and all factors are combined in one graph. Figure 7 presents a trend-line comparison between the MTOVE and all factors combined. The factors with the lowest averages that affected overall vehicle performances were UA and PR. These two factors were the most influential in decreasing the final MTOVE result. Figure 7 shows the trend line of UA as similar to the trend line of MTOVE; when UA increased, then MTOVE increased, and when UA decreased, then MTOVE decreased. The graph shows that the fluctuation of UA resulted in the fluctuation of MTOVE; this indicated that in this case study UA had an impact on MTOVE. It can be highlighted how the UA factor decreased the performance of the coal mining transportation process. This MTOVE metric laid the foundation for the company to digitize its processes as part of a continuous improvement cycle. MTOVE and all factors contributed to data digitization to encourage faster and more transparent results and a faster decision-making process. The continuous improvement action was fed by information from the metric to eliminate non-added-value activities from the mining process. Through MTOVE we proposed that it was possible to record operational data and measure the process from obtained data for further analysis. MTOVE will help the management team in data-driven decision making and foster improvement for better performance. In addition, MTOVE allows the identification of losses to generate continuous improvement to eliminate the losses that occur in the hauling processes. This research provides a practical contribution to the company that will improve the hauling process with MTOVE implementation. This concept can be applied to measure the performance of the other mining projects within the company and to evaluate the performance of each project by using it as an overall metric for performance comparison. After conducting three months of testing in actual coal-hauling operations, we can provide answers to both initial research questions. MTOVE was able to measure the overall mining transportation effectiveness by consolidating six metrics that were validated through a three-round Delphi survey among experts. This study has demonstrated how selected metrics and the proposed MTOVE metric can measure the effectiveness of the coal-hauling process. The results explained the categorized element with six selected  
Figure 6: Transportation quality rate. metrics of the MTOVE and their influence on the hauling process. This research provided solutions related to the hauling process measurement and structured data documentation.