This chapter introduces a new paradigm-Optimal Machine Learning (OML)-to transform supply chain planning in the face of global volatility and disruption. Traditional planning methods rely heavily on demand forecasting, which often fails due to data fragmentation, misaligned incentives, and limited responsiveness. The OML framework bypasses forecasting by directly linking granular supply and demand data to planning decisions through a machine learning-enabled decision engine, digital twin simulations, and an end-to-end data architecture. Two Fortune 150 case studies demonstrate OML’s ability to significantly improve service levels and reduce inventory costs. In contrast to conventional ML tools, OML generates interpretable, actionable decisions and supports dynamic scenario analysis to build resilience and agility. OML offers a scalable, adaptable, and transparent solution to address future impactful supply chain challenges, which cannot be predicted, but can be anticipated through development of optimized scenario-specific strategies. The authors caution executives against blind adoption of AI, advocating for a problem-based approach and cross-functional organizational engagement.