Digital Twins as Socio-Technical Decision Systems: A Comparative Systematic Review of Supply Chain Management and Healthcare

Digital Twin (DT) technology is rapidly evolving from a digital representation of physical assets into a decision-support infrastructure that integrates real-time data, analytics, and simulation. However, existing research remains fragmented across application domains, limiting understanding of how DTs enable decision-making under different socio-technical conditions. This study presents a systematic literature review and decision-centric comparative analysis of DT applications in two contrasting decision environments: Supply Chain Management (SCM) and healthcare. Following a Systematic Literature Review (SLR) protocol, 193 peer-reviewed studies (95 SCM and 98 healthcare) were identified, screened, and synthesized across six analytical dimensions: application domains, technological integration, decision-making levels, business value, stakeholder involvement, and implementation challenges. The findings reveal fundamentally different decision logics despite a shared technological foundation based on Internet of Things (IoT), Artificial Intelligence (AI), and cloud computing. SCM-DTs primarily support coordination, optimization, resilience, and organizational scalability, whereas healthcare-DTs emphasize personalization, patient safety, and ethical governance under stringent regulatory constraints. Building on these findings, the study proposes a decision-centric comparative framework and a value–complexity perspective suggesting that value realization is increasingly associated with technological, organizational, and governance complexity as DT ecosystems mature. The paper advances the conceptualization of DTs as socio-technical decision systems and provides practical guidance for researchers, managers, and policymakers seeking responsible and effective digital transformation.