## 4 Analyses of the Applications of Digital Twin Models
### Manufacturing
#### 4.1.1 Application and Adoption of Digital Twin in Manufacturing
The focus is on methods of data collection, storage, transmission, and analysis, with the aim of updating the requirements of intelligent PSS, turning its perspective towards sustainable production. In Ref. [35], a method to evaluate the product life cycle with the aid of DTs was proposed, where real-time data exchange between the virtual and physical components is not necessary. From the point of view of the production stage, real-time monitoring using DTs is presented in several works, including [36; 37; 38; 39], with the last two also referring to the concept \"Digital Thread\" (the information stages and their sequencing that characterize the life cycle of the PSS). Similar information related to the production step was provided in [27] for the scope of manufacturing. Regarding the final phase of the PSS, Ref. [40] presented how the DT can be used to anticipate the faults that may occur in a city, with the aim of avoiding them as much as possible or at least mitigating their impact. In ref. [41], the information collected from the DT during the previous phases of the PSS life cycle was applied to offer recommendations for their improvement or reconditioning. From the perspective of recycling, in [36; 42; 43; 44], the authors present how Digital Twin can be used to ensure that the last phase of the PSS (recycling) is also sustainable. System status monitoring is updated at well-established time intervals or can be performed manually. A complex synthesis of the analysis, applications, and technologies used in the development of DTs in manufacturing was presented in [27]. In the authors' opinion, DT-basedapplications have benefits in terms of both shortening the design and cycle of a product and reducing costs. The study points out the benefits of using DTs in the different stages characteristic of the manufacturing process, namely the stages of design, production, maintenance, and decommissioning. Each of these stages is characterized by a series of activities for which the use of DTs brings benefits, which are presented in this paper. From the manufacturing stage point of view, for example, in [45], it was stated that the information provided by DTs allows designers to form a much more detailed overview of the implementation of the results of the design stage. Verification of the conformity of the specifications can be performed through DT, as presented in [46]. Storing and using the acquired data in real time confers the possibility of knowing the current state of the manufacturing process and the equipment used. As a result, DT can interchange data with the manufacturing environment to evaluate and optimize the process parameters. Regarding asset management (one of the activities of the production stage), DTs help in decision-making, asset planning, configuration, commissioning, reconfiguration, and status monitoring [47]. The Digital Twin Shop floor (DTS) paradigm proposed in [30] supports the fact that the production design is made using Enterprise Information System (EIS) data, both from sensors and simulation results. In ref. [48], a study was conducted that illustrated the concept of a Digital Twin and its application in different industries, such as manufacturing, aerospace, smart cities, AR/VR, and the food industry, based on different customized features, modeling strategies, and methods of use. The authors structured the technical modeling methods into three types: technology-based, DT basic, main, and core technology. The core technologies of DTs examines the perception of physical entities by using sensor-based, IoT, and data transmission technologies, and DT model visualization. The main technologies ensure the accurate prediction of the performance of physical entities and monitor the changes that occur at their level. Advanced technologies, such as big data analysis, edge and cloud computing, machine learning, artificial intelligence, blockchain, mobile devices and technologies, virtual modeling processes, virtual-physical data fusion, data analysis, and simulation, can also be streamlined.