One of the most intriguing areas of technology-enabled potential for procurement and

supply chain operations is the “digital twin.” A digital twin is a digital representation, or a

virtual model, of a real system that enables businesses to understand the architecture and

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operation to predict operational performance. It helps enterprises reduce operating costs

and extend the life of machines and other physical assets, while acting as a predictive

“test bed.”

Digital twins serve as evidence that increased computing power and data access can

accelerate the speed companies can achieve without sacrificing effectiveness — in this

case through improved decision support. Being able to create a complete analog of a

real-world situation in the digital domain makes it possible to simulate the total effect of

multiple options in parallel. Once the optimal route is identified, the chosen direction can

be immediately carried out.

It is absolutely critical that the digital twin is fed in real time, and that the input data is

factual rather than being interpreted or assumed. By converging information from the

Internet of Things (IoT), data lakes and other external feeds, companies can reduce the

“leap” between the model and reality. The applicability of the recommendations derived

from working with a digital twin are therefore significantly more reliable than those made

possible by traditional, more artificial modeling.

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