Time travel with the digital twin

Consider a production engineer discovering that production throughput has fallen short of expectations. Traditionally, diagnosing such issues requires combing through vast amounts of live production data, a daunting task that can take hours, or even days.

Eventually, the engineer uncovers the source of the disruption — a machine encountered a motor error over the weekend. The resulting downtime led to a production shortfall, jeopardizing quarterly targets.

Precious time is now focused on analyzing the problem to prevent it happening in the future — time that could have been dedicated to process optimization or energy efficiency projects.

As industry accelerates, companies need accurate diagnostics and the ability to develop and implement solutions as quickly as possible. However, traditional troubleshooting methods are reactive and time-consuming, making it difficult to implement proactive solutions.

Now, companies can use the comprehensive digital twin to transform how engineers analyze, optimize and predict product and production outcomes.

Companies can expand their digital twin over time by integrating additional datasets, tailoring their models to specific needs as their experience grows.

As industries become more data-driven, leveraging a digital twin will allow manufacturers to gain deeper insights into production mechanics, resource allocation, and operational efficiency.

Due to these capabilities, the comprehensive digital twin can act as a sort of "time machine." It can replay and analyze the past, reflect the present and predict future states.

Initially, it enables the optimization of product and production system before investing in physical assets, reducing the need for physical prototypes and helps to avoid expensive failures.

During the operational phase, the comprehensive digital twin generates tremendous value by running "what-if" scenarios to predict performance and behavior.

It lays the foundation for making quick and confident decisions before taking action in the real world.

Fast-forwarding and rewinding the digital twin replay, the operator can analyze key events — such as the start of cutting with a new tool — and compare performance across different spindles.

Suppose one spindle experiences significantly higher torque levels during the cutting operation, indicating an anomaly in the workpiece properties.

By identifying this issue early, engineers can make informed decisions on corrective actions, reducing downtime and improving machine reliability.

Additionally, historical data analysis through the digital twin provides manufacturers with invaluable insights into recurring issues.

If a particular component frequently fails due to overheating, engineers can design alternative configurations, modify cooling processes, or incorporate predictive maintenance alerts to mitigate future risks.

Beyond diagnostics, the digital twin offers unparalleled predictive capabilities. It can predict future states to make the right decisions at the right time.

By leveraging simulation, engineers can forecast future machine performance, evaluate potential process modifications and mitigate risks before they materialize.

For instance, electric vehicle manufacturers use simulation to refine battery designs, assessing expected range, thermal performance and packaging efficiency.

Engineers can rapidly test design iterations, selecting the most optimal configuration without the constraints of physical prototyping.

In another case, wind turbine manufacturers use digital twin models to simulate operational conditions across various geographic locations. These simulations provide detailed insights into aerodynamic performance, energy output fluctuations, and the impact of environmental variables such as wind speed and temperature.

By forecasting turbine efficiency under diverse conditions, manufacturers can optimize blade designs and maintenance schedules to maximize energy generation.

The digital win supports a dynamic, data-driven approach to product development and production management.

By empowering engineers with real-time insights, historical analysis, and predictive simulations, the digital twin reduces costly downtime and enhances overall production agility.

Through closed-loop optimization, companies can continuously refine product offerings and production processes using real-world performance data.

In the evolving industrial landscape, the comprehensive digital twin enables engineers to engage in a form of time travel — using insights from the past, present, and future to drive efficiency, sustainability and competitiveness in an increasingly complex industrial world.

June 2025