This study examines the evolution of supply chain continuity (SCC) and unveils a novel 9R capability framework for manufacturing industries to understand their positioning to improve SCC and, thus, reduce the risks of disruptions caused by internal and external factors to their frms. We follow a comprehensive two-step methodology. First, we apply a machine learning algorithm on 3077 papers, revealing three clusters—risk, disruption and disaster followed by a review unravelling nine critical capabilities (9R capabilities)—Reliability, Resilience, Readiness, Rapidity, Remediation, Reengineering, Relationship, Reinforcement, and Responsibility. Second, the framework was validated by administering a structured survey on a focus group comprising members from 17 distinct manufacturing industries. This organized approach, derived from empirical assessments, ofers actionable insights for organizations aiming to survive and thrive amid disruptions. Three common themes emerge when investigating the evolutionary development of SCC using machine learning tools: risk, disruption, and disaster. Upon classifying the collection of papers into the three themes, it was observed that they could be overcome by leveraging the nine capabilities. It ofers a systematic and comprehensive approach to assessing, managing, and ultimately reducing the risks associated with disruptions in the fow of goods. By employing this novel framework, manufacturing companies can evaluate their SCC performance and proactively safeguard against disruptions despite constantly changing challenges. Also, this helps identify the limitations and counteractions a frm must take to improve its SCC. This study has implications for academia and business, allowing companies to improve their supply chains differently. Potential future work includes refnement of the framework by adding other capabilities (such as Reasoning). Further, longitudinal studies can be extended to the service sector