Digital twin use-case prioritisation for sustainability in smart manufacturing: A multi-criteria decision analytics approach

Manufacturers face growing pressure to invest in digital twins for sustainability and supply chain resilience. Yet, no systematic framework exists to guide the prioritisation of use cases amid competing objectives and practical resource constraints. Digital twins, which integrate real-time sensing, simulation, big data analytics, and artificial intelligence, are among the frontier technologies reshaping industrial production, and their prioritisation is a core socio-economic planning problem of the Industry 4.0 transition. This study addresses this gap by developing an interview-informed multi-criteria decision analysis (MCDA) that prioritises seven recurring digital twin use cases based on sustainability and operational benefits, as well as implementation and scaling burdens. The decision model scores sustainability benefits and feasibility burdens directly. At the same time, implications for supply chain resilience are interpreted ex post through the comparative positioning of the use cases rather than being scored as a separate family of criteria. Drawing on 50 semi-structured expert interviews conducted across 27 countries, the analysis combines qualitative content analysis with a pipeline comprising TOPSIS, VIKOR, and Stochastic Multicriteria Acceptability Analysis (SMAA). Criteria weights derived from interview salience reveal that data readiness, legacy integration, and interoperability effort dominate the prioritisation. The energy and facilities twin ranks first in TOPSIS. In contrast, the supply chain and scenario twin emerge as the VIKOR compromise solution and the most robust first-rank option under interview-centred weight uncertainty (85.6% probability). Sensitivity analysis shows that product passport and circularity twins rise to the top when sustainability benefits are privileged over feasibility burdens. The results yield a prioritisation framework for digital twin investment applicable to manufacturers, industry consortia, and policymakers designing Industry 4.0 roadmaps. Prescriptive implications address both private-sector technology prioritisation and staged implementation, and public policy on digital infrastructure, interoperability standards, and sustainability regulation, with attention to emerging economies.