## 3 Integrating DT in CPS
### Smart Manufacturing
Since smart manufacturing can apply to many different levels of technologies in manufacturing systems, it is important to consider the level of adoption for technologies in Industry 4.0 scenarios. For example, [PERSON] et al. [80] analyzed different levels of adoption in several different manufacturing domains and concluded that the implementation of Industry 4.0 technologies is interrelated and provides potential benefits when more systems are integrated with smart manufacturing processes. Additionally, the utilization of both front-end technologies and base technologies could provide more complete integration between physical systems and virtual representations that can better handle the challenges of requirement complexity and decision support systems. With the support from the communication layer and techniques to enable data aggregation, DT implementation can help with the following scenarios in a smart manufacturing system. The first scenario is the detection of fault tolerance and device health, which can help smart manufacturing systems to avoid outages by predicting device failures and rerouting workflows in the production process. In particular, cloud-based frameworks that are designed to handle large amounts of data utilize embedded sensor systems in manufacturing devices in order to monitor system health, leading to the creation of predictive models for avoidance procedures [83]. In the example of a robotic gripper in a manufacturing production line, [PERSON] et al. [82] investigated anomaly detection in the gripper closing speed for detecting via pressure sensor to detect faults and degradation that could lead to failure and considered utilizing the result to reroute production traffic to another assembly line. This type of fault detection compares recent samples to historical data and builds a model that determines when a device is likely to fail in a production environment, enabling the automated scheduling of preventative maintenance. The second scenario is supporting supply chain management, which involves the integration of DT systems and the communication of real-time data between suppliers and distribution centers. The tracking of products can be integrated into the supplier systems to inform the manufacturer system and can be integrated with the customer-facing deliverable to accurately determine product delivery timelines and end-to-end supply chain logistics. The tracking of goods required for a customer deliverable can be remotely monitored and integrated between supplier and manufacturer systems to determine the availability of goods required to produce a product [84]. In the event that a good required for a custom deliverable is not available, the decision support system can determine whether there is a more suitable supplier for the product and whether it would be cost-efficient to utilize a different supplier. By generating a pricing and timeline model for the availability of goods, the manufacturer can determine whether the product is worth producing and provide an accurate cost analysis to the customer. Note that one of the emerging challenges of manufacturing systems is the ability to create highly customized products in a manner that is efficient. Manufacturing in general is often focused on the mass production of a good or product in order to decrease the cost and effort required per unit. This type of mass production is often associated with processes that are easily automated to speed up the production time. With the shifting paradigm of manufacturing trending towards more customized deliverables [85], product variety becomes a factor when designing production systems that will maintain efficiency while still meeting diverse customer requirements. Integration with DT systems can allow for the application of ML/DL algorithms and modeling technologies to determine whether a custom deliverable requirement is feasible in a given manufacturing system. Manufacturing systems need to be both flexible and efficient in order to be useful for custom deliverable objectives [86].