stoping reconciliation, drilled reserves) are precisely the data streams required for intra-shift SIC control. Analogous operations support this progression: at OZ Minerals' Prominent Hill copper mine, Deswik MDM SiteView was integrated into the mine control room, enabling controllers to conduct pre-shift briefings from a single governed environment (Deswik, 2020). From Data Management to Decision Management. The outcomes extend beyond operational efficiency gains. We recognize that MDM alone does not constitute the terminal objective of digital transformation in underground mining. Rather, we position MDM as a foundational capability enabling the transition toward SIC and, ultimately, toward an Integrated Mining Enterprise (IME) aligned with Mining 4.0 principles. Brodny and Tutak (2022) demonstrate that organizations progressing beyond DDDM Level 3 achieve statistically significant improvements in return on assets and asset utilization. Our implementation at Aripuanã places the operation at DDDM Level 3–4, with clear pathways toward predictive and autonomous decision architectures. Cacciuttolo et al. (2025) demonstrate that Digital Twin frameworks for underground mining require precisely the structured, validated data environment that MDM establishes. Our MDM deployment functions as the foundational 'data layer' of a future Digital Twin architecture. Near-Term Strategic Implications. This progression toward decision management creates three concrete implications for Aripuanã: First, accelerated real-time decision cycles: as workflow execution times decrease, supervisors respond to deviations within the same shift rather than deferring cycles. Second, closed-loop planning and execution: integration of MDM-governed data with short-term scheduling enables continuous plan-versus-actual reconciliation. Third, a foundation for predictive analytics: audit trails and historical workflow records in Deswik MDM constitute training datasets for machine learning models targeting equipment performance prediction, ground condition assessment, and production forecasting (Qi, 2020). Operational visibility was enhanced through BI integration, with dashboards providing real-time monitoring of planning adherence, deviations, and losses, supporting faster bottleneck identification and improved cross-departmental collaboration. Limitations and Implications. Data quality at source remained a challenge, with legacy inputs requiring manual cleansing. User adoption varied by discipline, necessitating ongoing training. The full impact on long-term metrics requires observation beyond six months. Compared to traditional spreadsheet-based approaches, Deswik MDM demonstrated superior scalability and traceability, though it demanded upfront investment in governance and training. These results underscore MDM's dual role as a performance enabler and safety mechanism, positioning Aripuanã for progression toward an Integrated Mining Enterprise aligned with Mining 4.0 principles. 6. CONCLUSIONS This study demonstrates that Mining Data Management (MDM), when implemented as a governed platform for operational data, serves as a critical enabler for digital transformation in underground mining. By establishing a single source of truth, enforcing standardization, and automating workflows, Deswik MDM addressed persistent challenges of information fragmentation and interdepartmental inconsistencies at Aripuanã, yielding measurable reductions in cycle times (up to 90% in select processes) and enhanced operational visibility through BI integration. The implementation provides a replicable model for other operations, emphasizing the importance of multidisciplinary governance, iterative training, and phased rollouts over six
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