The case study demonstrates that this approach can deliver material capital savings while maintaining equivalent system performance, providing leadership with the confidence to challenge scope before lock-in. Capital that is no longer absorbed by over-scoped projects becomes available to advance the next development, accelerating the pace at which new supply reaches the market. The enterprise opportunity is to move from one-off project analysis to a repeatable decision capability embedded across the capital portfolio. A six-step pathway is proposed: ● Screen the portfolio to identify projects where capital intensity, uncertainty and system complexity intersect. ● Map the capital-risk logic using systems thinking to identify where risk is being converted into scope. ● Apply fit-for-purpose digital twins to test the key uncertainty behind the capital decision. ● Accelerate insights with AI to interrogate results, recommend scenarios and translate findings into decision narratives. ● Apply data-driven MVS to determine what to keep, resize, defer or remove based on system evidence. ● Embed and learn by using outputs in stage gates and capital reviews, then reusing patterns across subsequent projects. For the broader industry, this represents an opportunity to challenge the status quo of how capital is allocated. The tools now exist to test system-level consequences of design decisions with a level of rigour and speed that was not previously practical. Organisations that embed this capability can make more confident, evidence-based investment decisions, building trust with investors and society by demonstrating that every dollar of capital is directed where it creates the most value. In doing so, the industry moves closer to delivering the minerals the world urgently needs, with the discipline and responsibility that the moment demands. REFERENCES Sterman, J. D. (2000). Business dynamics: Systems thinking and modeling for a complex world. Boston, MA: McGraw-Hill. Merrow, E. W. (2011). Industrial megaprojects: Concepts, strategies, and practices for success. Hoboken, NJ: John Wiley & Sons. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). Cambridge, MA: MIT Press. Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In F. J. Kahlen, S. Flumerfelt, & A. Alves (Eds.), Transdisciplinary perspectives on complex systems (pp. 85–113). Cham, Switzerland: Springer.
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