# Applications of Digital Twin across Industries: A Review
## 3 Manufacturing  
Although the development of DT started from the Aerospace industry, the industry which is exploring the technology the most is the manufacturing industry. DTs have been described as the key enablers of Industry 4.0 and Smart Manufacturing [56; 57]. Any manufactured product goes through four main phases throughout its life cycle: design, manufacture, operation, and disposal (Figure 3). Smart manufacturers can leverage DTs in all four phases of the product [15; 58]. During the design phase, DT allows the designers to verify their product design virtually, which enables them to test different iterations of the product and choose the best one [56]. Using the real-time data from the products of previous generations, designers get an insight into the features that are working best for the consumers and those which need improvements [21]. This makes the whole process of improving the design easier and faster. An example of this is the car manufacturer Maserati which used DT for optimizing car body aerodynamics using wind tunnel tests virtually, which are elaborate and expensive otherwise [59]. They also optimized the acoustics of the car inside by using the data from a dummy equipped with microphones in the prototype car. [PERSON] et al. [60], taking a bicycle as an example, have proposed a DT-driven product design framework that can be useful for manufacturers to develop DT to aid the product design process. Unlike traditional bike designing methods which are based on the designers' knowledge and experience, the DT of a bicycle constantly collects the data from the physical space, which could be compared, analyzed, and used for designing or redesigning next-generation bicycles. With customer reviews and usage habits, designers can get a better understanding of customer requirements which can be translated into better and improved features. Capturing customer preferences via a DT lets businesses know about the market trends which can be integrated with the customer usage data to see the effects on product performance. This allows businesses to take design-decisions and incorporate them in an informed way, thus  
Figure 3: DT’s applications throughout a product’s lifecycle. making the process of integrating the customer feedback into the product to deliver customized products easier [61, 62]. Moreover, a DT can help designers in the selection of the material for their products. [PERSON] et al. [63] proposed a DT-driven method for the optimal selection of green material and applied it to laptop shells. From designing to waste management, different manufacturing phases were simulated for different materials and, based on their physical properties, cost, and environmental impact, materials were scored to optimize the selection process of materials. Turning raw materials into a finished product is the next step in manufacturing. A DT can be a useful tool at this stage in resource management, production planning, and process control [64]. A DT can help in (i) production planning and control by planning and executing the orders automatically and improving decision support by means of a detailed diagnosis, (ii) maintenance by evaluating and analyzing machine conditions, identifying any changes in the production system and its effects, and taking anticipatory maintenance, and (iii) layout planning by evaluating the production system continuously [11, 65]. By predicting failure, the DT cuts down the downtime as it allows to either schedule maintenance or take preventive measures. [PERSON] [66] et al., in their research, proposed a method to implement DT for a manufacturing plant, which resulted in a significant time reduction in monitoring the machine states, forklift location, asset location, and constraint management process. After the sale of the product, when it is in operation, manufacturers can receive the real-time product operation state via its DT and can develop a maintenance strategy accordingly. According to [PERSON] et al.