# Circular Economy Strategy Selection Through a Digital Twin Approach
## 2 Literature Review
Regarding remanufacturing, an efficient disassembly has been demonstrated to reduce the overall costs [29], but it is product-dependent and requires product-related decisions such as the level of disassembly [30]. It is clear that the variability of the CE strategies in each operation determines the profitability of circular operations. High-quality products at the point of return typically require minimal processing, reducing the costs of disassembly, cleaning, and repair. These high-quality returns are more likely to be quickly and efficiently reintegrated into production cycles, leading to faster recovery of value and lower operational expenses. Conversely, products of low quality introduce significant variability into the process, often necessitating extensive operations, higher energy consumption, or complete material breakdown, which can escalate costs and increase lead times. To reduce this variability, simulations have emerged as a useful tool. [PERSON] et al. [31] combined agent-based modeling and system dynamics (SD) to develop a simulation model to aid stakeholders understanding the implications of transitioning from a linear to a circular economy before the real-world implementation. The model was applied to a Mexican food bank and to a confectionary factory [32]. Their results showed the utility of a simulation model in visualizing the mid- and long-term impacts of CE strategies and how these can lead to both economic and environmental benefits if implemented strategically. [PERSON] et al. [33] used discrete event simulations (DESs) and SD to reproduce a remanufacturing process. A DES was used to improve decision-making for remanufacturing electric motors on shop floors, while SD allowed for the exploration of the remanufacturing system behavior over time. The study demonstrated that using simulations based on real sensor data reduced the processing times and associated costs and improved the accuracy in predicting the product quality. [PERSON] et al. [34] introduced a data-driven simulation framework to model remanufacturing operations with high levels of uncertainty, such as varying product returns, conditions, and processes. The framework was applied to a waste electrical and electronic equipment (WEEE) facility that remanufactures items like mobile phones, laptops, and hard drives, where it demonstrated its capability to help face sudden changes in the industrial environment, such as fluctuating staffing levels, process updates, and inventory. [PERSON] et al. [35] discussed combining different simulation models to move towards the concept of \"smart remanufacturing\", a paradigm based on the use of digital technologies (such as Internet of Things or data-driven analysis). The authors focused on evaluating SD, DESs, and agent-based simulations, and concluded that a hybrid simulation model is the best solution for analyzing complex CE environments. Many studies focused on simulating CE production factories to optimize the scheduling of operations considering the quality uncertainty of EoL products [36; 37; 38]. Ref. [36] focused on quality evaluations for recycled automobile engines [37] and the profit optimization for a company that remanufactures laptops and desktops, while Ref. [38] demonstrated that the Monte Carlo simulation-based approach was useful for improving the overall CE system efficiency, without considering a specific application. The life cycle extension of electric vehicle (EV) batteries also represents a hot theme. In this field, simulations have been used for different purposes. In [39], the authors proposed a DES approach to simulate the life cycle of batteries and vehicles separately, enabling an analysis of the supply and demand for remanufactured batteries. The results highlighted that integrating remanufactured batteries into the system extends vehicle lifetimes and reduces the environmental footprint of battery production, demonstrating the effectiveness of the simulation approach.