## 4 Literature Review
### Life Cycle Management  
Digital technologies, such as the IIoT, digital twins, and cyber-physical systems, are crucial for implementing data-driven and ML models and for optimizing various facets of the remanufacturing, including process quality control, operational efficiency, LCA, and supply chain management. IIoT techniques facilitate data collection, storage, and analysis, and real-time monitoring, thereby enhancing the visibility and management of the remanufacturing process and the entire life cycle [81]. One study found that integrating machine vision systems and IIoT techniques into aerospace remanufacturing significantly enhances the process by enabling intelligent sensing, real-time data acquisition, and advanced monitoring systems, resulting in higher repair yields, reduced human error, and improved operational safety [74]. Adopting data-driven decision-making in remanufacturing reduces costs, optimizes operations, and enhances quality through real-time insights and predictive analytics [77]. Cyber-physical systems and digital twins create real-time, digital replicas of physical processes, aiming to strengthen synchronization, efficiency, and predictive capabilities across the entire remanufacturing system [123]. A proposed control mechanism based on big data analysis, incorporating cyber-physical systems and digital twin techniques, aims to mitigate uncertainty in remanufacturing using real-time perception and predictive optimization [78]. Moreover, a digital twin model enhanced with a neural network and the Bees Algorithm (BA) for real-time data-driven decisions was presented to optimize remanufacturing planning [79]. Figure 5 presents a proposed conceptual framework to integrate data across the various stages of life cycle management [124]. Figure 5: Conceptual framework for integrating big data into product life cycle management (taken from [124]). The framework includes four major components: data sensing and acquisition, data processing and storage, model development and data mining, and big data application for product life cycle management. The data sensing and acquisition phase gathers information from various life cycle stages through product-embedded devices such as Radio Frequency Identification (RFID) tags and smart sensors, enabling real-time tracking of the status of products, materials and machines. The data processing and storage phase handles the collected data using distributed systems such as Hadoop and Storm, allowing for real-time and non-real-time data analysis and storage. The model development and data mining phase creates models to extract knowledge from big data, including both general models (such as classification, clustering and prediction) and specific models tailored to tasks like enhancing product development and optimizing manufacturing processes. The big data application for product life cycle management phase utilizes the analyzed data to support real-time decision-making, enabling efficient production, logistics and maintenance [124]. Figure 6 illustrates a conceptual model from [125] that integrates IIoT concepts into remanufacturing, establishing a framework for real-time information capture and integration, aiming to facilitate the implementation of data-driven production scheduling on the shop floor [125]. Digital technologies have contributed significantly to LCA methodologies and environmental impact evaluations. LCA is an effective method for measuring the environmental impacts of a system throughout its entire life span [126]. The environmental benefits of smart sensors in reducing food loss were assessed using an LCA model, which highlighted the need to manage potential environmental burdens from sensor manufacturing and disposal for overall sustainability [85]. [PERSON] et al. introduced a novel four-layer LCA framework integrating IIoT technology to improve real-time data collection and monitoring, demonstrated through a wind turbine case study [87]. Additionally, [PERSON] et al.