There are numerous use cases for a digital twin in a manufacturing enterprise. Depending on what is being modelled, the applications can vary. In a process factory that manufactures chemicals, the entire plant and its processes can be modelled and replicated to create a digital twin. Whereas in a rail engine yard, the product itself (the rail engine) can be modelled. But depending on what is being modelled, various benefits can be derived out of a digital twin.
Load balancing and throughput maximization is a very common use case for a digital twin. A digitally modelled manufacturing plant can be simulated for various inputs to increase throughput. The outcomes can then be used to deliver maximum output from the plant. This has several measurable monetary benefits like increased production, yield per square feet, asset utilization, energy efficiency, and TAKT time.
A digital twin can also be used to define safe operating limits for a manufacturing plant. The digital replica can be simulated to various yielding points to identify the minimum viable safe operating conditions. This can be used to define safety procedures and policies. The direct monetary benefit of this can be reduced downtime due to lesser workplace incidents, and reduced cost of insurance. It can also drive other non-monetary benefits like improved employee safety, better working conditions, higher levels of employee satisfaction and higher levels of employee retention.
Simulation of a real-world entity in a digital environment can also help identify causes of issues in quality. This can be preemptively addressed in the production environment thereby improving quality, reducing waste, and reducing the cost of quality. Digital twins can also help in reducing uncertainty involved in a production line. This can contribute to business continuity and risk mitigation.