The AI layer can interrogate model results through natural-language queries, surface recurring bottlenecks and sensitivities, recommend new scenarios, explain trade-offs and produce executive-ready insights. This expands the range of scenarios that decision-makers can evaluate beyond what manual analysis would allow. 3.4 Data-driven Minimum Viable Solution (MVS) The outputs from the digital twin and AI layer feed into a data-driven Minimum Viable Solution (MVS) framework. While digital twins make uncertainty testable, MVS makes the capital response disciplined. The framework asks what outcome must be protected, which scope materially protects it, what risk remains if scope changes, and what the cost of additional confidence is. It then classifies each scope element into five decision categories: keep, remove, defer, resize or record. This ensures that capital decisions are traceable, evidence-based and aligned to system-level outcomes rather than individual functional requirements. 4. KEY RESULTS, OUTCOMES AND INSIGHTS 4.1 Case study: maintenance facility project The framework was applied to a maintenance facility project at a large open-cut mining operation. The facility was required to support the long-term operation of the mine’s heavy vehicle fleet. During definition, the capital estimate had increased materially from the original basis, raising the central question: is the proposed scope genuinely required to protect production and fleet availability? Uncertainty around the future fleet profile, maintenance demand and congestion risk was driving scope growth, compounded by limited ability to test end-to-end system behaviour with traditional tools. A system-level digital twin was built and used to test alternatives across bay configurations, maintenance scheduling, labour productivity assumptions and supporting infrastructure requirements. AI-assisted scenario discovery expanded testing beyond manually defined cases, with the analysis framed around identifying a Minimum Viable Solution. Key outcomes included: ● Capital avoided: material reduction in CAPEX, supported by system-level evidence. ● System performance: equivalent performance achieved with reduced scope. ● Governance impact: enabled leadership to challenge scope before lock-in, with confidence. ● Decision framing: shifted the discussion from "designing to be safe" to "designing what the system requires." The complexity of the maintenance system, including interdependencies between fleet scheduling, bay allocation, labour availability and supporting infrastructure, could not be adequately evaluated using traditional analytical tools. The digital twin enabled the project team to test whether proposed redundancy and capacity margins were genuinely required or reflected unresolved uncertainty converted into scope. The result was a more targeted capital solution that
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