## 5 Discussion  
The literature analysis reveals that integrating data-driven and ML models advances remanufacturing systems by enabling real-time monitoring, enhancing quality control, and facilitating dynamic scheduling, thereby supporting circular economy initiatives. Leveraging advanced sensors and connectivity, the IIoT and DPP enable comprehensive data collection and analysis across various stages of the remanufacturing process. Data-driven models derived from IIoT data play a vital role in supporting LCA and closed-loop supply chain management. They provide a thorough evaluation of environmental impacts throughout the product life cycle and aid in making informed decisions to promote sustainability. This capability provides critical insights into operational performance and product life cycle management. It is particularly effective in addressing uncertainties associated with the timing, quality, and quantity of returned parts, which significantly impact inventory control, product design, and production planning for remanufactured products [145]. Advanced ML techniques, such as deep learning and RL, further refine this process by enabling precise defect detection, anomaly management, and dynamic scheduling, thereby addressing uncertainties and improving operational effectiveness. For example, CNNs and YOLO models can be used in automated quality inspection systems to analyze images of remanufactured components, detecting defects with high accuracy, reducing inspection time, and ensuring consistent adherence to quality specifications. Predictive analytics can forecast potential failures, allowing for preemptive interventions that minimize operational disruptions and associated costs. RL optimizes dynamic scheduling and operational strategies to address uncertainties effectively. These advanced techniques not only improve operational effectiveness but also ensure that remanufactured products meet high standards of quality and reliability. From the literature review, it is understood that, despite their advantages, the implementation of data-driven and ML methods in remanufacturing presents several challenges and may require the comprehensive adoption of smart manufacturing technologies. Smart  
Figure 11: The workflow of HRC for disassembly (taken from [100]). manufacturing technologies utilize cutting-edge solutions, such as the IIoT, AI, ML algorithms, advanced sensor networks, and cyber-physical systems, to create digital simulations of production processes, manage computer-controlled equipment, and track and report real-time production data [146]. ML and data-driven models, as part of smart manufacturing techniques, support predictive maintenance, and analytics, enabling more accurate control and optimization of manufacturing processes [147]. Existing studies have discussed smart manufacturing in support of environmental sustainability. [PERSON] et al. reviewed the literature on Industry 4.0, emphasizing its potential to enhance manufacturing sustainability through interconnected, smart technologies. The review examines how internet-connected machines and sensors improve productivity, energy efficiency, and environmental impact by optimizing processes and reducing waste [37]. [PERSON] et al. reviewed recent research on the environmental impacts of industrial activities, focusing on work from the past 10-20 years, organizing their findings around the product life cycle and key topics in environmental impact [148]. Their review also systematically summarizes challenges in design, process improvement, and material efficiency within the framework of a circular economy, all within the context of Industry 4.0 advancements [148]. [PERSON] et al. reviewed the evolution of emerging information and communication technologies to enhance material efficiency and environmental sustainability, adopting a holistic approach that redefines human-nature relations within planetary boundaries [149].