## 4 Literature Review
### Life Cycle Management
developed a new LCA model incorporating blockchain, IIoT, and big data analytics to enhance the efficiency and reliability of LCA, improving data integrity and decision-making [80]. Figure 7 illustrates a multi-level blockchain-based LCA system designed in [80] that connects the manufacturing infrastructures and activities at different stages with a diverse range of applications and users. Moreover, an open-source LCA tool utilizing IIoT to track food quality and assess environmental impacts across multiple stages of the food supply chain was also introduced [86]. Digital technologies have been applied to waste management, playing a critical role in improving product design for remanufacturing. Waste streams of automotive products were analyzed to support product design that facilitates remanufacturing [127] and to determine factors that impede the reuse of parts [128]. [PERSON] et al. introduced WRCloud, a novel service-oriented remanufacturing platform based on cloud manufacturing principles, designed to improve interoperability, intelligence, and adaptability in managing waste electrical and electronic equipment [129]. IIoT is crucial for advancing data acquisition and sharing throughout various closed-loop supply chain stages and remanufacturing processes [130]. AI and blockchain technologies can strengthen supply chain resilience and sustainability by facilitating operations such as just-in-time manufacturing, streamlined automation, and remanufacturing [88]. Additionally, [PERSON] and [PERSON] presented a model for assessing risks in closed-loop supply chains for remanufacturing using neural networks to improve risk assessment accuracy and supply chain management [92]. [PERSON] proposed a novel mathematical model to assist decision-making in reverse logistics for remanufacturing and discussed the impacts of IIoT technology on remanufacturing companies [82]. Innovative approaches in closed-loop supply chain management leverage digital technologies to enhance efficiency and sustainability. For instance, a closed-loop supply chain model utilizing IIoT data has been proposed to optimize the life cycle of products, focusing on EoL recovery processes, including cost and demand for remanufacturing [89]. Similarly, [PERSON] et al. designed a circular supply chain network for handling electric vehicle lithium-ion batteries, leveraging IIoT and big data technologies to address uncertainties and enhance overall management efficiency [90]. The integration of IIoT with a kanban system has enabled real-time monitoring and dynamic scheduling in reverse logistics, improving waste collection and recycling pro cesses [83]. Additionally, [PERSON] et al. investigated trade-in strategies within closed-loop supply chains, optimizing disassembly decisions through simulations integrated with IIoT and blockchain technologies [91]. The use of embedded IIoT devices to evaluate product designs for EoL recovery helps determine the most effective designs for remanufacturing, increasing profitability and reducing waste [84]. The acquisition and collection of cores depend not only on customers and usage history as identical products can be different in quality [131]. A DPP can enhance the acquisition and collection of cores by providing detailed, real-time data on product history and condition, which improves the predictability of recovery processes and differentiates between varying quality levels [132]. [PERSON] et al. introduced a Digital Life Cycle Passport, utilizing a cloud-based platform and the Asset Administration Shell, which enabled comprehensive data sharing across the product life cycle, as described in Figure 8. This was exemplified by an e-waste sorting case study that showcased its potential to automate and optimize sorting decisions [75]. [PERSON] et al. also explored the role of DPPs as a policy tool for supporting a circular economy, emphasizing their capacity to provide critical product-related information to stakeholders throughout the product life cycle [46]. Additionally, [PERSON] et al.