Just as artificial intelligence a few years ago gained traction, digital twin technology has become a widespread management best practice. According to Frank Diana, principal futurist at Tata Consultancy Services, “With the rapid adoption of digital twins, we’re seeing two categories of practical applications arise: use-cases by an industry that solve a very specific challenge and industry-agnostic use-cases which aid in broader strategy and decision making.”

A digital twin can give any organization deeper insights needed before making a game-time decision. For example, simulation modeling of complex programs can be done without implementing limited resources with an unknown outcome which can result in unplanned downtime and loss of revenue. With the availability of real-time data, organizations can mimic realistic environments by using real-world data to accurately predict outcomes. In doing so, organizations can answer pertinent questions such as how will operations be affected? What assets should we test, and when?

One industry that is leading the forefront in digital twin implementation is life sciences. Pharmaceutical companies now have new approaches to medical research and health care. Digital twins can now model and test the delivery of new drugs on simulated human skin versus animal testing.

With the uncertainty and volatility, we see in today’s industries, there will be rapid adoption of this technology by companies to rehearse uncertain futures through simulation. As digital twins become mainstream, it is imperative that the platform is agnostic to the vast amounts of data coming in from disparate systems. Historically siloed data sources must be able to achieve interoperability in order to gain a deeper holistic view of the entire ecosystem. Having this ability will truly unlock the benefits of incorporating a digital twin and reaping the rewards of a more sustainable and efficient operation.

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