Collaborative Governance in AI-Enabled Resilient Cloud Networks: A Case-Based Framework from Ventilator Production

Abstract

Public crises require rapid coordination across institutions, sectors, and digital infrastructures. The COVID-19 pandemic showed that emergency production cannot be treated only as a technical or industrial problem; it is also a governance challenge involving public agencies, healthcare organizations, universities, military units, manufacturers, logistics providers, regulators, and digital platforms. This study develops a case-based conceptual framework for understanding AI-enabled cloud manufacturing as a socio-technical governance mechanism for emergency production networks. Using emergency ventilator production during COVID-19 as an illustrative case, the framework explains how distributed institutional capacity can be transformed into collective crisis-response capability through public-value-oriented demand interpretation, institutional capacity mapping, AI-enabled coordination, accountability structures, traceability, ethical allocation, digital resilience, and fallback governance. The study contributes to humanities and social science by reframing service composition as institutional role allocation and by translating technical resilience concepts into governance categories such as adaptive governance, institutional redundancy, accountability, public trust, and fair allocation. The framework emphasizes that AI-enabled crisis coordination should remain human-supervised, transparent, auditable, and embedded in legitimate public authority. Future research should empirically examine emergency production networks through interviews, comparative case studies, and governance-performance indicators.