Digital twins allow companies to iteratively simulate and optimize complex multivariable problems, reducing the learning costs associated with experimentation in the physical world. Once the exclusive domain of large corporations, small and medium-sized enterprises (SMEs) can now leverage AI to build advanced digital twins to iteratively simulate and optimize complex multivariable problems. Creating these twins is a five-step process: Set clear business goals. Create a clear flowchart of the twinning process. Identify and structure the data you need. Build a digital model of that flowchart. Then test, implement, and iterate your model.
Strategy implementation is the most risky moment, as this is the point where miscalculations incur tangible costs. One way to reduce that risk is to repeat the process over and over again in a laboratory environment, improving it a little each time. This is the type of thing Toyota’s continuous improvement system is famous for. However, this can be difficult as it requires continuous monitoring of data and making many detailed decisions accordingly.
