## 5 Conclusions  
The findings show that even a moderate increase in unplanned dilution, rising from 8% to 15%, can cause a noteworthy decrease in the average stope grade from 32.57% to 30.29%. It directly affects higher CO\({}_{2}\) emissions and increases the operational cost. To be able to understand such results before the implementation of drilling and blasting is the key to successful operations.  
CO\({}_{2}\) emissions rising from 214 t to 228 t also leads to a potential increase in underground ventilation requirements and operational costs. The monitoring and control of dilution are not only critical for optimizing ore recovery and operational performance but also for mitigating environmental impacts and maintaining a sustainable underground mining operation.  
An overall profit reduction of around 5% is one of the key indicators that dilution and loss impact profitability. From this point, mine planners and operations need to take a series of actions that will reduce dilution and loss by investigating drilling, blasting, stope ground support control, bogging practices, and stope designs. D-Loss closes this gap by not only pointing out the physical causes of dilution but also tying them to cost-based indicators, which can help to prioritize stope redesign or operational control and operational strategies.  
D-Loss provides flexibility for working with individual stops while also allowing for multi-store analysis. In addition, the impact of unplanned dilution on stope revenues and CO\({}_{2}\) emissions can be analyzed; accordingly, the number of haul trips expected to increase due to dilution and the amount of carbon dioxide emissions generated as a result can be easily calculated on a per stope basis or for all stopes. In addition, the calculation of stope revenues and costs enables economic analysis and graphs, allowing for quick comparisons of stope profitability. In this context, it offers the user the ability to perform practical calculations for daily, weekly, and monthly planning. The software is fully compatible with all block model types and provides detailed analysis of selected stopes with an average processing time of 40 s. The other key is its significantly lower capital cost compared to other alternatives, making it more accessible to a broader range of users. All outputs of D-Loss were cross-checked using manual Excel calculations and yielded identical results, confirming the accuracy and consistency of the software.  
D-Loss V1.1 version is still under development, and upcoming updates aim to create stope production sequencing algorithms and scheduling features. While designed as a useful tool for both industry and academic applications, D-Loss currently relies on user-provided parameters. A key limitation of the current study is that the work is based on one site case study. Future development will include integration with automated cost databases and mine sequencing algorithms to enhance decision-making automation. In addition, incorporating data inputs, as well as geotechnical and optimization capabilities, could further expand its practical relevance in both industrial and research settings.  
Conceptualization, E.S. and B.U.; Methodology, E.S. and B.U.; Software, E.S.; Validation, E.S.; Writing--original draft, E.S.; Writing--review and editing, E.S. and B.U.; Supervision, B.U. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets used in the current study are available from the corresponding author on reasonable request.  
The authors declare no conflicts of interest.