Industry 5.0-enabled sustainable transformation: a multi-objective and probabilistic influence framework for industrial strategy optimization toward achieving sustainable development goals

Sustainable transformation of industries in line with the United Nations Sustainable Development Goals (SDGs) remains a major challenge, particularly in emerging economies. Existing frameworks often provide qualitative insights but lack robust mechanisms for uncertainty modeling, strategic adaptability, and cross-sector scalability. This paper proposes I5-OPTIMA, an Industry 5.0-enabled hybrid framework that integrates Bayesian network-driven influence analysis (BN-IA), evolutionary game theory-based industrial strategy modeling (EGT-ISM), and K-means +  + clustering to support data-driven decision-making for sustainable industrial development. Empirical validation was conducted using multi-sectoral Indian industry datasets, supplemented by cross-country evidence from Germany, Japan, USA, China, and EU. Results show that I5-OPTIMA achieves superior performance compared to existing frameworks, with clustering accuracy of 0.85, Nash equilibrium deviation of 0.15, and log-likelihood convergence of 198, alongside a 22% carbon footprint reduction. Furthermore, the framework demonstrates enhanced adaptability to small and medium-sized enterprises (SMEs), achieving faster convergence and lower runtime without compromising accuracy. Policy recommendations are provided in the form of staged incentive frameworks, international best-practice integration, and sector-specific interventions. The study highlights I5-OPTIMA’s novelty in combining probabilistic reasoning, strategic modeling, and empirical validation, offering a globally scalable pathway for Industry 5.0-driven sustainable transformation of industries.