April Anderson is President of Twinify Technologies.

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Staying ahead in business requires leaders to make smart, quick decisions. That’s a trait digital twins excel at by delivering intelligent forecasts for complex outcomes. They’re like a crystal ball that can give companies a sneak peek into the future of their assets and operations.

What Are Digital Twins?

You know what a simulation is. Now imagine running one for every component of your business, with each constantly iterating based on new information. Digital twins can provide a virtual copy of everything: machines, buildings and even whole factories. This copy, which is where the name “digital twin” comes from, isn’t just a static snapshot. It’s a living model that updates and changes according to real-world conditions. The enhanced level of insight it can provide can be a game-changer for companies with a lot of physical assets or complex processes.

Simulating The Physical To Transform Decision-Making

Digital twins work by capturing data from sensors on a company’s real-world assets. They can deliver 3D models of physical infrastructure, real-time and historical data on performance and conditions, and software logic that simulates behaviors and interactions. Companies can essentially mirror and simulate parts of the physical world using data they already generate, allowing them to test decisions in the digital world before implementing them across their business.

This allows for a transformational depth of simulation that can forecast performance across a huge array of scenarios. Organizations can identify opportunities, quickly resolve issues and be proactive instead of reactive to situational changes. Rather than relying on assumptions and guesswork, executives can use digital twin forecasting to make data-driven decisions with less risk.

For example, an energy firm could simulate the effects of increased demand on its generation and distribution infrastructure over the next five to 10 years. Its digital twin model would incorporate weather, usage patterns, plant degradation and more to predict stress points. The firm could then optimize its capital investment plans to ensure consistently reliable supply and safeguard against issues that might come up—problems that might be unforeseeable given a less rigorous evaluation process.

Driving The Bottom Line Through Enhanced Operations

Early digital twin adopters are already seeing the financial upside. “A Digital Twin Case Study on Automotive Production Line” from the National Center for Biotechnology Information (NCBI) found that using digital twins in automotive production resulted in a “6.01% increase in the commercial production line efficiency and an 87.56% gain for downtime.”

Beyond cost efficiency, digital twins can directly drive revenue growth through enhanced operations. For a transportation company, a dynamic model of the supply network could reveal opportunities to streamline material flows. This in turn could enable faster throughput and tighter delivery windows for customers.

Capitalizing On The Data Explosion

Digital twin technology has become increasingly viable and high-utility over the past few years thanks to the explosion of industrial data now available. Companies often generate a vast amount of real-world operational data without knowing its true value or how to benefit from it. But this data can be used to extrapolate insights that power business decision-making years in advance. And digital twins continuously learn and improve their predictions from real-world data to yield even greater business insights over time.

Digital twins also provide a broad range of opportunities for improved efficiency across a business. The technology offers insights into how processes are working and where improvements can be made. This means less waste and more profits. It’s not just about fixing problems—it’s about finding new opportunities to grow.

Leadership Needs To Overcome The Challenges To Realize The Potential

Although digital twins show immense promise, organizations need strong leadership to pursue the vision and address barriers. Executives must foster a culture of data sharing and analytical decision-making, or internal resistance can undermine the initiative. Naturally, leaders will also need to align key stakeholders within IT and business units.

Some employees who already use supervisory control and data acquisition (SCADA) systems might not understand the added value that digital twins provide. Given the negative press that some AI-driven products have generated, skepticism about how digital twins are going to help people in their jobs—and not replace them—is totally understandable. Buy-in can be easier to gain if initial implementation focuses on addressing a long-standing pain point or data gap in the organization—a limitation that’s broadly recognized. Small but effective gains can win the day.

This is why it’s crucial not to bite off more than you can chew when it comes to implementation. Some digital twin systems can take years to start yielding useful results. By starting with one aspect of your operation, you can demonstrate a rapid return on value by upgrading from stale reports to useful, real-time dashboards. That will help you facilitate the change management needed to scale up and expand digital twins across your organization.

The Future Of Digital Twins

In the future, digital twins could help design smarter products, create more efficient factories and even predict market trends. They’re not just a tool for today—they’re shaping the future of business.

According to a recent report, 29% of global manufacturing companies have already partly or fully implemented a digital twin strategy, and the market has a 30% projected CAGR between 2023 and 2027. In short, digital twins are going to be a part of the industrial future. As companies in particularly data-heavy industries look to harness the power of that data to make smarter decisions, digital twins offer a new type of operational insight.

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