## 3 ML and Data-Driven Models for Remanufacturing  
ML and data-driven models leverage algorithms and statistical techniques to analyze and interpret complex and high-volume data in many fields including manufacturing [36; 37; 38; 39]. In remanufacturing, these technologies offer significant potential to enhance various aspects, such as the automated sorting of a wide range of products, improving asset management, facilitating real-time decision-making, and optimizing the entire product life cycle. These technologies can deliver innovative solutions for sequence optimization, quality control, and predictive analysis throughout remanufacturing processes. In this section, we present a detailed summary of key ML and data-driven methods, including explanations of their potential benefits for remanufacturing. **IIoT**: By connecting industrial machinery and devices to data collection systems, cloud platforms, and the internet, the Industrial Internet of Things (IIoT) supports extensive data acquisition and real-time analysis across the manufacturing ecosystem [31; 40; 41]. For remanufacturing, IIoT facilitates fast and accurate asset tracking and inventory management by providing detailed core histories and spare parts availability. Emerging sensing technologies allow accessibility by installing sensors on the inner structure of machines to better understand the machine's operational statuses [42] and support automation by allowing machines to communicate and coordinate with each other [43], resulting in coherent remanufacturing processes. Figure 3: Articles clustered by topics visualized in the 2D space across the collected 6000 articles. **Traceability**: Traceability systems, such as digital product passports (DPP), track the history, location, and status of products throughout their life cycle [44]. For the purpose of remanufacturing, these tools provide detailed histories of cores, ensuring remanufacters can access all relevant information about previous repairs, modifications, and usage conditions [45]. These data help in assessing the condition of returned EoU products and determining the best remanufacturing approach. The systems also ensure compliance with regulatory standards and build consumer confidence by offering transparency about the origins of materials and processes involved in remanufactured products [46]. **ML models**: Supervised and unsupervised learning and reinforcement learning (RL) technologies can analyze vast amounts of data to make informed decisions, optimize processes, and predict future outcomes [47; 48]. In remanufacturing, these technologies enable predictive analytics to evaluate which parts will need remanufacturing, provide dynamic planning for the timely streamlining of workflows, and support quality assurance to ensure that remanufactured products meet stringent standards [49; 50]. AI can also help in designing and facilitating efficient remanufacturing processes by learning from historical data and continuously improving process quality [42] and equipment healthiness [51]. Key ML models applied in remanufacturing include neural networks, deep learning models, and reinforcement learning algorithms as follows: (i) **Neural Networks and Deep Learning Models**, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, have found significant applications in remanufacturing. CNNs excel in visual inspection tasks, enabling automated defect detection and quality control in remanufactured products [52]. LSTM networks, with their ability to process sequential data, are particularly useful for predicting equipment health and product life cycles, crucial for optimizing maintenance schedules and remanufacturing timing [53; 54]; (ii) **RL models**, including Q-Learning, Deep Q-Network (DQN), and Proximal Policy Optimization (PPO), have emerged as powerful tools for dynamic decision-making in remanufacturing processes.