Modern systems of systems (SoS) operating in high-stakes environments like Distributed Operational System (DOS) are characterized by tightly coupled interactions among human operators, autonomous agents, and heterogeneous technological subsystems. Conventional reliability engineering approaches, which primarily focus on component level failure probabilities and static models are often insufficient for capturing emergent behaviors and nonlinear failure propagation across interconnected sociotechnical systems.

This study proposes a cybernetically informed framework that integrates digital twin technology, the Viable System Model (VSM) and an extended Failure Modes and Effects Criticality Analysis (FMECA) methodology to reconceptualise reliability as a dynamic and emergent system property. Digital twins function as continuously updated virtual representations that synchronise real time data, simulation models, and predictive analytics, enabling recursive observation and anticipatory regulation. Their integration with FMECA supports scenario based reliability analysis, allowing the modelling of cascading failures, coordination disruptions and adaptive system responses.

The findings demonstrate that reliability emerges from system interactions rather than isolated components, advancing the design of adaptive, resilient, and self-regulating systems operating in complex and uncertain environments. Although applicable to systems of systems related contexts, the framework is intentionally generalised to support broader applications across critical infrastructure, healthcare coordination systems, industrial automation, autonomous transportation, emergency response networks and distributed cyber physical systems. Simulation experiments across distributed systems of systems networks demonstrate how local disturbances propagate through interconnected nodes and are mitigated through cybernetic feedback mechanisms. Simulation experiments across distributed systems of systems networks and demonstrate how local disturbances propagate through interconnected nodes and are mitigated through cybernetic feedback mechanisms. Monte Carlo analysis (n = 1000) indicates high robustness, with operational effectiveness (ζ = 0.929 ± 0.021) and system availability (A = 0.98 ± 0.015). Monte Carlo analysis indicates high robustness, with strong operational continuity and system availability across varying disruption scenarios.