Digital twins have become one of the most talked-about ideas in industrial operations, but the term means something different to nearly everyone who uses it. In facilities where downtime is costly and the margin for error is thin, the real question isn’t what a digital twin looks like. It’s what teams can do with the data once it’s all connected.
Abhijeet Jadhav has spent more than 15 years working on that problem. A licensed Professional Engineer (PE) in multi-disciplines, he has worked across semiconductor manufacturing, electric vehicle infrastructure, mining and minerals processing, and other advanced industrial facilities. He also contributes to the profession nationally through PE/FE (Professional Engineering / Fundamental Engineering) exam development and technical peer review. Jadhav spoke with Geo Week News about how digital twins are changing the way mission-critical facilities are designed and operated.
The Age Old Question: What Is a Digital Twin?
Jadhav’s own definition is clear. A digital twin, he explains, is a dynamic, data-connected virtual representation of a mission-critical facility, one that pulls together spatial information, engineering models, real-time operating data, and analytics into a single, continuously updated environment.
What sets it apart is that it doesn’t live in isolation. Data has long operated in separate domains. A digital twin brings this scattered data together. Jadhav frames it with a line that sticks: “GIS tells you where the asset is. Simulation tells you how it may behave. A control system tells you what it is doing. A digital twin brings all three together to tell you what to do next.”
Jadhav recalls his own experience analyzing GIS data across a 32,000-acre mining operation, work that could take weeks. Building and validating simulation models consumed countless engineering hours. A digital twin, he says, can integrate those same resources far more efficiently, turning what used to be weeks of analysis into faster, actionable decisions.
Where Digital Twins Deliver the Most Value
When asked where digital twins make the biggest difference, Jadhav’s answer is immediate: early and continuous. The greatest value comes when a twin is implemented at the design and commissioning stage and then maintained through operation and maintenance. Often, however, the reality is messier than that. The most valuable data, Jadhav says, tends to get lost during handoffs, when a project moves from engineering to construction, from construction to commissioning, from commissioning to operations. Design intent lands in one system, asset information in another, performance data somewhere else entirely, scattered across disconnected documents and platforms.
“The real power of a digital twin is not visualization,” Jadhav says. “It is turning fragmented data into actionable engineering decisions.” It’s a distinction that reframes the conversation. The flashy 3D model isn’t the point. The point is what you can do with the data once it’s connected.
Improving Asset Reliability in Advanced Industrial Environments
In advanced industrial settings the stakes are high and the equipment is complex. In these cases a digital twin improves asset reliability by merging real-time operating data with engineering models to detect abnormal conditions, predict degradation, and optimize performance before problems escalate.
In practice, that means keeping a continuous eye on critical pumps, compressors, process utilities, and material handling systems, watching for efficiency losses, forecasting maintenance needs, and testing operating changes virtually before committing them to the physical facility. It’s the difference between reacting to a failure and anticipating one.
Energy Optimization and Resilience
Energy optimization and resilience are often treated as competing priorities. Digital twins, Jadhav argues, can serve both. By identifying inefficiencies, evaluating operating strategies, and reducing utility consumption, a twin drives energy savings. At the same time, predictive maintenance analysis and failure scenario modeling strengthen resilience, giving teams a view of what could go wrong before it does.
From an owner’s perspective, though, this is more nuanced. The goal is to balance energy and cost savings against uptime, redundancy, safety, and production continuity. Efficiency improvements, no matter how promising, can never come at the expense of mission-critical reliability.
As a certified energy manager who has spent years doing this work by hand, Jadhav offers a perspective that feels less like theory and more like lived experience: “The future of mission-critical facilities is not monitoring what is happening. It is understanding why it is happening, predicting what happens next, and acting before performance gets affected.”
Overcoming Adoption Obstacles
For all their promise, digital twins face real barriers. The data is fragmented, scattered across legacy systems that were never designed to talk to each other. Cybersecurity concerns run deep. In an industry where technology is patented and proprietary, owners are understandably hesitant to share data on online platforms. Inconsistent asset standards plague older facilities that have accumulated decades of disjointed documentation. Then there’s the question of cost, as high implementation expenses paired with an unclear return on investment make it difficult for owners to justify the spend.
Jadhav’s advice for overcoming these obstacles is pragmatic: start small. Identify a high-value use case and run a pilot. Establish strong data governance and interoperability standards from the outset. Build case studies on secure, scalable platforms. Demonstrate measurable value through a focused pilot project, and only then begin expanding across the facility. The key is proving the concept before betting the operation on it.
Jadhav closes with a thought that distills the entire conversation into a single sentence: “A digital twin is not just a model. It is the living connection between engineering intent and real-world performance.” It’s an idea, he notes, that every organization should be taking seriously right now, not as a future possibility, but as a present imperative for building systems that can sustain themselves for the decades ahead.
