Digital Twin Strategy for Machine Builders

Success as an Original Equipment Manufacturer (OEM), or machine builder, has always hinged on the ability to give customers what they want: reliable equipment that gets the job done. In an era where technology innovations emerge, dominate the news cycle, but don’t always deliver tangible benefits, the digital twin is establishing a track record of real ROI and differentiation for manufacturers across a variety of use cases.

What is a Digital Twin?

Digital twins, virtual representations of physical assets, processes and systems, pair the virtual and physical worlds, enabling informed decision-making through historical data analysis, real-time system monitoring, simulations and emulations. Additionally, this can empower machine builders to drive operational efficiency, innovate faster and enhance their relationships with customers, while also offering benefits to that customer.

The Dual Perspective: Machine Builders and Customers

According to Rockwell’s State of Smart Manufacturing report, machine builders and customers faced similar challenges last year. These included external factors like inflation, rising energy costs, cybersecurity risks and shortages of skilled workers as well as internal factors like deploying and integrating new technology while balancing quality and profitable growth.

Many of these challenges can be addressed through the adoption of digital twin technology. Ultimately, the convergence of machine builder and customer goals fosters mutual benefits, making digital twin technology an essential investment.

Benefit to Machine Builders

For machine builders, digital twins can streamline machine design, testing and maintenance, reducing costs and enhancing productivity. By enabling virtual simulations and real-time data analytics, digital twins accelerate machine development and refinement. Builders can design, test and prove new machine designs digitally before building anything and see how a machine runs and interacts with both people and other machines in a virtual environment without needing a physical prototype. This is especially important for very large systems where it’s just not practical to build a physical prototype. Data-driven insights also enable machine builders to refine products based on real-world applications.

Digital twins can also reduce variation across machine design cycles, ensuring consistency in product performance and reliability. Organizations can avoid last-minute design changes that are not only costly but can cause missed deadlines. By creating a digital twin where builders can see the machine design come together with the control code, they’ll avoid those costly 11th hour surprises during commissioning because controls testing was already done. Builders can have confidence before going to startup.

Another application we’re seeing used by machine builders is the use of the digital twin as a selling tool for sales and marketing. They can be used to demonstrate machinery and show how it can work in a line. The digital demonstration can help ease a customer’s worries about how such a complex system will run. We’ll dive deeper into this model in our next blog.

Benefit to Customers

On the other end of the spectrum for customers, digital twin technology can reduce commissioning time, by up to 40%, and risk in both greenfield and brownfield capacity expansion projects. Virtual commissioning bridges the gap between traditional and virtual development by leveraging digital twin technology. This technology models and emulates an engineering system within a virtual environment, creating a replica of the physical system. This enables the engineering team to test and verify the system virtually, reducing effort and significantly lowering the cost of design changes compared to physical commissioning. To learn more about the benefits of virtual commissioning, please see our previous Viewpoint.

Digital twins provide customers with comprehensive performance data, enabling better decision making. Process simulations can be rapidly conducted using large volumes of historical and/or synthetic data to test variations and identify opportunities for improvement. They can also help prevent equipment failures, reducing downtime and maintenance costs with continuous monitoring.

They can also increase collaboration between machine builders and customers when used as a design tool to create a system. Ultimately, this can improve win rates.

Addressing the shortages of skilled workers, there is also value in the digital twin in terms of training. They support the proper use of machines and ensure they are running to the best of their ability. They also enable workforce training to maintain uptime and ensure manual efforts are not impacting how well the machine runs. For machine operators, digital twins can prepare them for a wider range of incidents than real-world training. Virtual training allows organizations to simulate faults and extreme conditions that they wouldn’t want to re-create physically since these conditions would result in machine damage or even destruction.

Strategy: The Balancing Act

So, now that you've seen the benefits of digital twins, how do you deploy a successful strategy? A well-executed digital twin strategy balances innovation, data security and customer engagement. Success depends on seamless integration, leveraging the right tools, and fostering strong user communities.

Organizations that have used simulation and emulation tools have been able to successfully bridge the gap between design and deployment by performing rapid concept analysis and digital engineering, validation, and optimization to improve engineering efficiency. In addition, engaging subject matter experts and user communities for seamless integration also enhances the effectiveness of digital twin applications.

We’ll dive into each of these later in the series, so keep an eye out for our upcoming articles.

On the flip side, organizations that have struggled with their digital twin strategies often restrict their models to single-use cases that limit scalability and long-term benefits. One common issue we observe is companies creating models for specific purposes—be it material flow modeling, a specific model of a machine, or a multi-physics process model—and then neglecting them. When they need these models one to two years later, they often find them outdated. This highlights the importance of managing models throughout their lifecycle to ensure their relevance and usability. Organizations should develop strategies for creating adaptable models, including ensuring models fit a wide range of use cases. We’ve also seen issues with an overreliance on internal processes and rigid internal frameworks that lead to inefficiencies and missed opportunities for value creation.

Getting Started

To remain competitive, machine builders must embrace digital twin technology. This shift is not optional—it is essential for differentiation, growth and long-term success.

As the digital landscape continues to evolve, future discussions will delve deeper into optimizing digital twin strategies, ensuring sustainable growth, and maximizing value creation. We often have questions from machine builders about how they can implement digital twins with machines that may not be standard in their industry or have a certain level or complexity or nuances. Look for a strategic partner that can help them build the “right” digital offering and provide ongoing support. Building the right technology partnership can propel a machine builder forward in achieving the most business value.

Stay tuned for our next Viewpoint, Interacting with Models: Challenges and Opportunities. We’ll discuss how customers engage with models and adapt them to their needs.