As industries face increasing pressure from climate change and demographic shifts, traditional performance metrics like Overall Equipment Effectiveness (OEE) prove insufficient for addressing long-term sustainability. While OEE effectively measures productivity, it does not integrate environmental, social, and economic considerations that are crucial for sustainable operations. To bridge this gap, we propose the Overall Sustainable Equipment Effectiveness (OSEE) framework, which extends conventional performance evaluation by incorporating sustainability dimensions. The framework is developed through a comprehensive literature analysis and refined through expert validation to ensure applicability in real-world industrial settings. To navigate the complex interdependencies of sustainability factors, we employ causal AI methods, specifically Dynamic Bayesian Networks (DBN) and Knowledge Graphs (KG). These tools enable a structured representation of cause-effect relationships within sustainability contexts while facilitating quantitative optimization of their impacts on operational efficiency. By integrating AI-driven insights, the OSEE framework provides decision-makers with a systematic approach to balancing efficiency and sustainability. This research contributes to the broader twin transition, linking digital transformation with sustainability efforts to create resilient and future-oriented industrial systems.