119 Level 1–2 and SIC Level 1–2—and establish the preconditions for the next generation of operational intelligence tools. Figure 4 illustrates this progression within the five-stage digital maturity roadmap contextualizing the Aripuanã case. Figure 4 – Digital Maturity Roadmap: Five-Stage Evolution from Fragmented Data Environments to the Integrated Mining Enterprise (IME). The current positioning of Aripuanã ( ★ Stage 3) is indicated. Framework aligned with Brodny & Tutak (2022) and GMG SIC Maturity Levels (GMG, 2019). Governance mechanisms were established to ensure multidisciplinary ownership and accountability. Data stewards from each technical area (mine planning, geology, geomechanics, topography, ventilation, and infrastructure) were designated to oversee data quality, while a central MDM administrator managed platform configurations and access controls. Workflow design followed a standardized process: identification of pain points (e.g., manual reconciliation or version conflicts), mapping of data flows, implementation of validation gates, and iterative testing with end-users. Automation was prioritized for repetitive tasks, incorporating conditional logic to flag inconsistencies and require multidisciplinary approvals for critical outputs. To evaluate outcomes, a mixed-methods approach was employed. Quantitative metrics were tracked over a baseline period (pre-implementation, 2024-Q4 to 2025-Q1) and a post-implementation window (2025Q2 to 2025-Q3), focusing on cycle times, error rates, and process adherence. Qualitative insights were gathered through user feedback sessions and process audits. Statistical analysis included descriptive statistics (medians and ranges) for time-based metrics, with N values reflecting the number of workflow executions observed (typically 20–50 per workflow type). This methodology ensures that results are grounded in operational data, providing a replicable framework for assessing MDM implementations in underground mining contexts. 4. IMPLEMENTATION (DESWIK MDM) The implementation of Deswik MDM (Mining Data Management) at Aripuanã was executed over a six-month period, from early 2024-Q4 to mid-2025-Q1, involving close collaboration with Deswik's technical team. The process began with a diagnostic phase to map existing data flows and identify fragmentation points, followed by platform configuration, workflow development, and user training. Training sessions were conducted both in-person and remotely, ensuring comprehensive coverage for the 80+ users across six technical disciplines: short-, medium-, and long-term mine planning, geology, geomechanics, topography, ventilation, and mine infrastructure. The timeline emphasized iterative testing and feedback loops to align workflows with operational realities.
RkJQdWJsaXNoZXIy MTM0Mzk2