Digital twins enable businesses to repeatedly simulate and optimize complex multivariable problems, cutting the learning costs that come with experimenting in the physical world. Once the exclusive province of big business, small and medium enterprises (SMEs) can now use AI to advanced digital twins that enable them to repeatedly simulate and optimize complex multivariable problems. Creating these twins is a five-step process that involves: Setting a clear business objective; drawing up a clear flowchart of the process you’re twinning; identifying and structuring the data you’ll need; building the digital model of that flowchart; then testing, implementing, and iterating the model.
A strategy’s execution is its riskiest moment because it is the point when any miscalculations have a tangible cost. One way to reduce that risk is to repeat a process over and over in an experimental setting improving it just a little bit more each time. It’s the type of thing Toyota’s continuous improvement systems are famous for. But this can be difficult as it involves continuously monitoring data and making many micro decisions in response.
