Deploying digital twins: 7 challenges businesses can face and how to navigate them

The Promise of Digital Twins: Transforming Systems and Beyond

In today’s fast-paced world, surprises seem to pop up daily. That’s why having tools like digital twins has become increasingly valuable. As Ara Surenian, VP of product management at Plex by Rockwell Automation, explained to ZDNET, “Being able to simulate and mimic your real-world environment lets you make informed decisions based on collected data. It’s incredibly beneficial.”

But what exactly are digital twins? Think of them as software replicas of physical systems, machines, or even ecosystems. By mimicking these entities, they allow us to predict outcomes, optimize performance, and reduce costs—all without touching the real thing. Whether it’s improving industrial machinery, optimizing supply chains, or even testing urban planning scenarios, digital twins hold immense potential.

How Digital Twins Reshape Product Development

Digital twins aren’t just about efficiency; they’re also revolutionizing product development. When paired with extended reality (XR), they create immersive experiences that enhance visualization and collaboration. For instance, manufacturers can simulate assembly lines or engineers can walk through virtual blueprints—all before breaking ground. It’s a game-changer.

However, implementing digital twins isn’t without its challenges. Let’s dive into some of the hurdles and how industry leaders suggest overcoming them.

Challenges and Solutions

1. Complexity

Complexity is often cited as one of the biggest barriers to deploying digital twins. "Companies often aim for perfection instead of settling for 'good enough,'" noted Christine Bush, director of the Robotics Center of Excellence at Schneider Electric. "Start small. Begin with pilot projects to showcase return on investment in controlled settings."

To avoid getting overwhelmed, focus on specific locations rather than entire systems. "Identify areas where data is most accessible and ask what questions you want answered," Surenian advised. "Is it capacity? Inventory? Demand forecasting? Start there."

2. Incomplete Networks

For digital twins to thrive, organizations need robust networking. Thierry Klein, president of Nokia Bell Labs Solutions Research, emphasized, "Connectivity is crucial—not just at the network level but also between humans and machines."

AI can bridge gaps here. An integrated AI model can analyze data, recommend actions, and simulate scenarios, making digital twins smarter over time. "AI transforms digital twins into dynamic tools capable of autonomous optimization," Klein added.

3. Data Velocity

Real-time data processing is critical. Naveen Rao, VP of AI for Databricks, highlighted this point: "If your models aren’t fast enough, alerts might come too late, leading to costly repairs or mistrust among teams."

To address this, companies must invest in high-performance computing and edge analytics to ensure data flows smoothly and swiftly.

4. User Interfaces Not Real-Time Enough

While digital twins excel at simulation, their interfaces sometimes lag behind expectations. XR and VR technologies promise to solve this by offering interactive dashboards that let users explore systems intuitively.

Yet, safety concerns remain paramount. "XR and VR are fantastic tools," said Bush, "but they must be implemented carefully in controlled settings to avoid accidents or distractions."

5. Inconsistent Standards

The absence of universal standards creates silos of data, hindering integration. Shelly Nooner, VP of innovation at Trimble, stressed the need for open, interoperable standards. Organizations like BuildingSmart are paving the way by developing frameworks for industries like construction.

6. Managing Diverse Data Inputs

Digital twins rely heavily on sensor data and IoT devices. Robert Bunger, innovation product owner at Schneider Electric, highlighted the importance of organizing these inputs. "Integrating various data streams while keeping models synchronized is tough," he said.

Machine Learning Operations (MLOps) can help here. By continuously retraining models and ensuring access controls, businesses can maintain accuracy and transparency.

7. Skill Gaps

Finally, the shortage of specialized talent poses a challenge. Ryan Hamze, consultant at ISG, recommended investing in local workforce training. Partnerships with tech firms can also bridge skill gaps.

Conclusion: A Business-Centric Approach

Ultimately, digital twins succeed when they align with business goals. Jason Noel, executive director of emerging technology at EY Consulting, emphasized the importance of designing digital twins that serve non-technical stakeholders too.

"Human-centric digital twins empower decision-makers across departments," Noel stated. "They integrate insights seamlessly into workflows, driving smarter choices and actions."

By focusing on usability and collaboration, digital twins can transform industries and redefine the future of product development.