Ore control decisions sit at the narrowest and most value-critical point in the mining value chain, yet grade remains the dominant routing criterion because it is available at operational resolution and cadence. While necessary, grade alone is an incomplete proxy for value. It does not capture rock behaviour, which governs mining response, recovery, throughput, cost and risk, and its continued use contributes to plant instability, reconciliation drift and systematic value erosion between strategic planning intent and operational execution. This paper argues that value-focused ore control requires rock behaviour to be represented explicitly at the point of decision. Building on the Primary–Response Framework and materials science principles, it introduces the Unified Rock Value Framework (URVF), a decision-centred framework that links intrinsic rock characteristics to behavioural response and value across the mine-to-market chain. URVF integrates direct measurement, rock sensing and data-driven modelling to enable behavioural prediction at ore control resolution and to carry those predictions forward into operational, metallurgical and value models that inform routing decisions. The framework addresses a persistent gap in current practice, where sensing, modelling and machine-learning applications often operate as isolated tools that improve local prediction accuracy but remain disconnected from planning workflows, execution and value assessment. URVF provides a consistent behavioural and value structure that aligns spatial modelling, planning, execution and reconciliation across planning horizons. Recent advances in sensing technologies and hybrid geostatistical and machine-learning methods provide the technical enablers, while disciplined implementation focused on decision clarity, data governance, cross-functional capability and trust supports sustained adoption. By reframing ore control from a grade-based classification task to a value-optimisation problem, URVF provides a practical pathway to stabilise operations, improve recovery, narrow reconciliation variance and realise value that is currently left unrealised.