Operationally, the mine employs a multidisciplinary approach to short-term planning, integrating data from geology, geomechanics, topography, ventilation, and infrastructure teams. The planning process relies heavily on software such as Deswik.CAD for sequencing development and stoping activities, with real-time inputs from operational teams regarding block status, cable metering, and geological risk classifications. As described in Cordeiro and Hauschild (2025), the operation has implemented Business Intelligence (BI) tools like Power BI to enhance visual management and decision-making, consolidating multidisciplinary data into interactive dashboards that track production plan adherence, deviations, and losses. This integration supports the identification of bottlenecks in areas such as topographic surveys and operational productivity, enabling faster corrective actions and improved alignment between planning and execution. The complexity of Aripuanã’s operations stems from its interdependent value chain, where decisions in one area—such as geomechanical assessments of excavation stability—directly impact downstream processes like ventilation design and production sequencing. Historical challenges included information fragmentation across systems and teams, leading to inconsistencies in data versions and delayed responses to operational changes. These issues were particularly acute in underground environments, where outdated or misaligned information could compromise safety and productivity. The implementation of Mining Data Management (MDM) at Aripuanã was thus motivated by the need to establish a governed “single source of truth” for operational data, ensuring that all disciplines work with consistent, traceable, and up-to-date information. This context underscores the mine’s suitability as a case study for evaluating MDM’s role in enhancing data governance and operational resilience in complex underground mining settings. 3. METHODS The methodology adopted in this study follows a case study approach, focusing on the implementation and evaluation of Mining Data Management (MDM) at a complex underground mining operation. The research design integrates qualitative and quantitative elements to assess the impact of MDM on data governance, process standardization, and operational performance. Data collection involved direct observation of workflows, analysis of operational logs, and structured interviews with technical teams, complemented by quantitative metrics derived from system records and planning cycles. The conceptual framework is grounded in the principles of data governance and workflow standardization within the mining value chain. Mining Data Management (MDM) is conceptualized as a layered architecture that ensures data consistency, traceability, and real-time availability across disciplines. The architecture comprises four interconnected components: (i) data sources and ingestion mechanisms, which capture inputs from heterogeneous systems (e.g., Deswik.CAD, geological models, and operational spreadsheets); (ii) validation and standardization rules, which enforce data integrity through automated checks and version control; (iii) workflow automation, which structures routine processes and reduces manual intervention; and (iv) Business Intelligence (BI) integration, which enables visualization and monitoring of operational indicators. This architecture aligns with the broader Mining 4.0 paradigm, where data serves as a strategic asset for decision-making, as discussed in Cordeiro and Hauschild (2025).
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