assisted insight generation, into a structured approach for capital decision-making. 3.1 Systems thinking: reframing the capital conversation The first step is to map the capital-risk logic of a project. Rather than evaluating individual scope items in isolation, the approach examines the feedback structures that drive capital growth. This reframes the conversation from traditional questions such as "Do we need this equipment?" or "Can we add more redundancy?" towards systems-level questions: ● What system outcome is the proposed scope intended to protect? ● Which risk is being reduced, and by how much? ● Does redundancy materially improve value chain outcomes? ● What is the minimum viable solution with acceptable risk? The objective shifts from defending scope to understanding which system outcome the scope is intended to protect, enabling a more disciplined conversation about where capital is genuinely required. 3.2 Digital twins: making consequences testable A mining value chain digital twin provides the simulation capability required to evaluate design alternatives under realistic conditions. The digital twin replicates the physical system, including throughput rates, equipment availability, reliability profiles, failure modes, buffer logic, routing constraints and demand scenarios, and enables controlled testing of changes before capital is committed. For each design option, the model produces outputs including constraint location, throughput and value outcomes at different confidence levels (e.g. P10/P50/P90), downside risk exposure and the capital avoided or deferred under each alternative. This creates a safe environment to compare options under realistic operating variability, strengthening engineering judgement with system-level evidence. 3.3 AI-assisted scenario discovery The third layer integrates digital twin simulation with reinforcement learning for guided scenario discovery. The process follows a structured sequence: ● Inputs: Operational and design data define the system baseline and constraints. ● Digital twin simulation: The model replicates the system to enable controlled testing of changes. ● Scenarios tested: Design and process variations are explored across the solution space. ● AI-driven agent: Analyses results to identify variable combinations driving performance shifts. ● Reinforcement learning loop: Iteratively proposes targeted scenarios based on measured outcomes, directing exploration towards high-value regions of the solution space. ● Insights: Quantifies impact and reveals the key drivers of system performance.
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