## 6 Conclusions  
This study stochastically predicted ore production through discrete event simulation, using four different probability density functions for truck travel times, and compared the simulation results for the four cases. The TCT was measured by dividing it based on the four loading points, using the truck tag recognition time record big data obtained through the mine safety management system based on a wireless communication network for an underground limestone mine. The discrete simulation model was designed to stochastically predict ore production in the designated study area. The uniform, triangular, normal, and observed probability distribution obtained using the measured truck driving time were fed into the Monte Carlo simulation to generate the respective TCTs. The generated TCTs were input into a discrete simulation model. The results of the ore production prediction were validated using field data of the morning of 16 October. As a result of the verification, the data on 16 October 2018 included all four models in the distribution of the simulation results. The uniform and triangular distribution models predicted relatively lower ore production than the actual production because the results of P90 and the field data were similar. The normal distribution and observed probability distribution models were similar to the P50 results and the field data. Because the working conditions at the mine site changed regularly, the stochastic simulation model was used in the short term as well as in the long term in various ways. Therefore, we performed a simulation with ten days of valid data for October 2018. Simulated with data for the month of October, the RMSE of the observed probability distribution model was the lowest at 24.9 ton/day. The comparison results prove that the developed observed probability distribution model can predict the rate of total ore production within the range of actual values. To use the observed probability distribution, the truck travel time data must be recorded. Simulation results of higher accuracy can be derived, such as that of the simulation of the observed probability distribution models used, by processing stored data. If the ICT-based mine safety management systems are introduced at various sites, simulations of higher accuracy can be achieved. It was possible to consider time through a Monte Carlo simulation using the data stored in the field without excluding various events occurring in the field. Thus, it is possible to obtain the resulting distribution of various ore productions. Because the stochastic simulation result is displayed as a distribution rather than a single value, various statistical values, such as the maximum and minimum, were confirmed in the field. It will be helpful for managers to use simulation results to set up or modify work plans according to desired production goals or perspectives. Delay time is an important indicator for evaluating the performance of mining operation. This study did not use the delay time as an input or output parameter of the simulator; however, the delay time was considered during the simulation process. In future work, it would be interesting to stochastically estimate the delay time with simulation output to make decisions for maximizing ore production. Conceptualization, Y.C. ; methodology, Y.C., J.B. and D.J. ; software, D.J. ; validation, J.B. and D.J. ; formal analysis, J.B. and D.J. ; investigation, Y.C. ; resources, Y.C. ; data curation, D.J. ; writing--original draft preparation, Y.C., J.B. and D.J. ; writing--review and editing, Y.C. ; visualization, J.B. and D.J. ; supervision, Y.C. ; project administration, Y.C. ; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1A2C1011216). **Informed Consent Statement**: Not applicable. **Data Availability Statement**: Not applicable.