2. PROPOSED FUNCTIONAL FRAMEWORK: FROM CORE TO ADVANCED CAPABILITIES To enable successful digital transformation without disrupting ongoing mining operations, this paper proposes a staged implementation roadmap organized into seven functional categories. The Core Capabilities constitute a Minimum Viable Product (MVP) achievable within 3–6 months, focused on data centralization, basic visualization, and regulatory compliance. The Advanced Capabilities, typically reached within 12–18 months of continuous data maturation, drive predictive intelligence and full interoperability. This staged approach aligns with the maturity progression recommended by ISO 55000 for asset management systems (Tholana and Neingo, 2016) and reflects the practical reality that data quality must be established before analytics can deliver reliable results. The seven functional categories and their staged capabilities are presented in Table 1. Each category maps directly to the key requirements identified in Section 1 and to the relevant clauses of GISTM, ISO 55001, and ISO 27001. Three cross-cutting principles underpin the entire framework. First, Vendor-Agnostic Architecture: all data schemas must be standardized to enable mining operators to switch hardware providers without losing historical data integrity, directly mitigating the well-documented risks of vendor lock-in (Opara-Martins et al., 2016). Second, Single Source of Truth (SSoT): by centralizing geotechnical, hydraulic, and structural data, the platform eliminates information silos and reduces time-to-response during critical geohazard events. Third, Operational Sustainability: streamlining data processing into a unified platform reduces administrative overhead and enhances the longterm monitoring continuity required for TSFs, open-pit slopes, and waste dumps across their full lifecycle. Table 1. Proposed functional framework: Core (MVP) and Advanced capabilities. Functional Category Core Capabilities (MVP) Advanced Capabilities 1. Core Data Management & Inventory Centralized repository unifying static asset lists and historical data into a structured relational database. Digitization of paperbased logs. Bulk upload for CSV/Excel. Manual sensor metadata registration. 4D Digital Twins integrating 3D spatial models (LiDAR/photogrammetry) with temporal sensor data. Automated lifecycle tracking based on operational hours and maintenance events. Real-time streaming ingestion from IoT data loggers. 2. Operational Intelligence & Inspections Digital inspection forms on tablets replacing paper checklists. Static threshold monitoring (min/max alarm values). Basic time-series trending for individual instruments (e.g., piezometric Augmented Reality (AR) overlays of sensor data on field tablets. Computer vision for automatic crack detection from site photographs. Multi-variable correlation (e.g., rainfall vs. pore pressure vs. prism displacement vs. blasting schedules).
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