## 5 Conclusions  
This research addressed the critical imperative for modern manufacturers to simultaneously improve operational efficiency and environmental sustainability, moving beyond traditional, often siloed, improvement efforts. We presented and validated a comprehensive, cloud-based, data-driven optimization framework that uniquely integrates real-time Process Mining with dynamic Life Cycle Assessment (LCA) and advanced Multi-Objective Optimization (MOO) techniques, specifically NSGA-II and TOPSIS. The application of this framework to a complex, real-world tube manufacturing facility processing 391,127 events across nine active stations provided significant insight and demonstrated its practical effectiveness. Through the integrated analysis of live operational event logs and environmental sensor data, the framework successfully:  
* Identified and quantified five critical bottleneck stations (202-205) within the production flow, accounting for extended operational durations exceeding 9900 h each and cycle times ranging from 10.6 to 13.4 min per event. * Dynamically assessed key environmental performance indicators, revealing baseline metrics of 4015 kWh daily energy consumption, 2007.5 kg CO\({}_{2}\)/day emissions, and 146.5 kg/day waste generation across the manufacturing system. * Explicitly mapped the inherent trade-offs between maximizing operational efficiency (87.2-95.2%) and minimizing environmental impact (1715-2007 kg CO\({}_{2}\)/day) through an optimal Pareto front of 18 solutions. * Generated optimized operational configurations that offer quantifiable improvements, with the optimal balanced solution (P9) achieving an efficiency of 92.3% and 1758.2 kg CO\({}_{2}\)/day emissions representing a 5.1% efficiency gain and a 12.4% reduction in emissions from baseline. * Provided a structured decision support mechanism (TOPSIS) that ranked solutions based on varying organizational priorities, demonstrating the adaptability of the framework through sensitivity analysis across efficiency-focused, sustainability-focused, and balanced scenarios. The primary contribution of this work lies in the development and successful empirical validation of this holistic and dynamic integration methodology. By processing 32,257 hourly sensor measurements across 123 parameters, the framework bridges a significant gap in the literature and practice by combining the diagnostic power of real-time Process Mining with the evaluative capabilities of dynamic LCA. The system successfully identified process anomalies (1547 total) and their correlation with downtimes (r = 0.426), and fed this unified intelligence into a robust MOO engine that generated actionable optimization strategies. The findings underscore substantial potential for manufacturers to achieve synergistic gains. The framework identified specific improvement opportunities, including redistributing workload from bottleneck stations, implementing predictive maintenance based on anomaly patterns, and reducing rework loops (205 \(\rightarrow\) 204 transition) by 50%. These optimizations demonstrate how informed, data-driven decision making can simultaneously improve resource utilization, reduce cycle times from 960 to 564 s, and minimize environmental impact. While validated in tube manufacturing, the proposed framework is designed to be adaptable. Its principles can be applied to other discrete or batch-based manufacturing sectors, such as pharmaceuticals, electronics assembly, or food and beverage packaging. The core methodology of integrating Process Mining, dynamic LCA, and MOO remains the same; only the specific operational KPIs, sensor data streams, and LCA impact models would need to be customized for the target industry. This demonstrates the framework's potential as a general-purpose tool for sustainable manufacturing optimization. Several limitations remain, pointing to avenues for future research.