Industrial plants do not usually suffer from a lack of data. In most cases, the information already exists. Engineering teams have P&IDs and design files. Operations teams work with historian and SCADA data. Maintenance teams rely on CMMS or EAM platforms. ERP systems contain asset, procurement, and materials information, while manuals, procedures, inspection records, and other technical documents sit across separate repositories.
The challenge begins when someone needs to understand what is happening with a particular asset. An engineer investigating a pump may need its latest P&ID, vibration history, maintenance records, work orders, spare-parts information, and operating data. All of that information may be available, but finding it often means moving between multiple systems, searching for the same asset several times, and working out whether the information in each system refers to the same equipment.
This is the problem that connected digital twins in manufacturing can help address. The value is not simply in creating a 3D representation of a facility. It is in bringing the information around an asset into context so that operations, maintenance, and engineering teams can work from a more connected view of the plant.
At CoreShift 2026, Acuvate and CADMATIC explored this approach in the session Connected Digital Twins for Intelligent Industrial Operations. The discussion moved beyond digital twin visualization to a more practical question: how can enterprises connect the systems and information they already have so that people can make better operational decisions?
For the full demonstration and discussion, you can Watch CoreShift 2026 On Demand and see how the connected workflow comes together across engineering, operational, and maintenance information.
The Digital Twin Problem Is Really a Context Problem
Most industrial organizations already have a complex technology landscape. Engineering applications hold one part of the asset picture. Maintenance systems hold another. ERP platforms, document repositories, historians, live sensors, and increasingly AI applications all contribute additional information.
Each system has a purpose, and many of them work well within that purpose. The difficulty is that they often operate as separate information environments. A maintenance manager may have access to the CMMS but still need to find the correct P&ID elsewhere. An operator may notice an abnormal condition in live plant data without immediately seeing the maintenance history or relevant technical documentation. An engineer may understand the equipment design but lack the latest operational context.
The CoreShift session described this as a common source of inefficiency. People spend time asking basic questions such as: Where is the document? Is this the latest version? Which system contains the information I need? How does this tag relate to the asset I am looking at? Fragmented information also makes it harder for teams to work from the same understanding of an asset.
A connected digital twin addresses this differently from a conventional dashboard. Rather than merely presenting another collection of metrics, it gives users a way to navigate information through the asset itself. Selecting a pump, valve, compressor, production line, or other component becomes the starting point for accessing the relevant information associated with it.
That is an important shift. The user no longer has to begin by asking which application contains the data. They can begin with the equipment or operational problem they are trying to understand.
A Connected Digital Twin Should Not Replace the Systems You Already Have
A common concern with large digital programs is whether another platform means replacing systems that already perform important operational functions. That was specifically addressed during the CoreShift session.
The connected digital twin is not intended to become the new ERP, CMMS, historian, or engineering system. Those applications remain responsible for their core functions. The twin instead provides a contextual layer across them, making selected information easier to access from a common operational view.
This distinction is especially important for CMMS integration. If a maintenance team uses IBM Maximo or another maintenance management platform, the system should continue to be the system of record for maintenance transactions. A digital twin can show relevant maintenance information against an asset and direct a user to the appropriate asset record when a work order needs to be raised. Once that work order exists, its relevant status or information can also be surfaced back in the twin.
The same principle applies to ERP, document management, engineering systems, and historian platforms. The session demonstrated an architecture in which CADMATIC eShare provides the digital twin experience while Acuvate’s AcuPrism connects the environment with information from ERP, maintenance management, live sensor data, and PI historian sources. The deck similarly positions the twin as a way to bring engineering, maintenance, operations, reliability, safety, and AI information together rather than replace the applications behind them.
For enterprises working through IT OT integration, this is a useful architectural principle. Integration should not be measured only by whether two systems can exchange data. The more meaningful question is whether that information can be connected to the correct operational context and made useful to the person who needs to act on it.
The Asset Becomes the Common Point of Reference
Connecting multiple applications creates another problem: the same piece of equipment may not be represented in exactly the same way across every system.
A pump could have one identifier in an engineering application, another naming convention in the ERP environment, and a slightly different reference in the historian. If those differences are not resolved, simply connecting the systems does not automatically create a connected operational view.
This was one of the questions raised during the live CoreShift Q&A. The approach discussed was to align these naming conventions within the enterprise data and AI layer without forcing every source application to change the way it stores its information. The digital twin can then request the information for a specific piece of equipment while the underlying platform resolves how that asset is represented across the different sources.
This makes asset tag management an important part of the digital twin conversation. Industrial facilities can have thousands of assets and more than 10,000 technical drawings, covering P&IDs, electrical systems, instrumentation, safety, cabling, and other engineering disciplines. The session highlighted how incomplete tag registries, disconnected spreadsheets, and engineering information that is not aligned with EDMS environments can make even basic asset discovery difficult.
A connected digital twin therefore depends on more than visualization. It needs reliable asset identity and enough contextual information to know that the vibration reading, work order, engineering drawing, and ERP record being shown all belong to the same real-world asset.
You Do Not Need a Perfect 3D Model to Get Started
Digital twins are often associated with highly detailed 3D models, which can make the starting point appear more complex than it needs to be. The CoreShift demonstration showed a more flexible approach.
Where a suitable 3D design model exists, it can provide an intuitive way to navigate the facility. Where one does not exist, organizations can use laser scans, photogrammetry, meshes, or other virtual-site representations. More importantly, the user does not necessarily need a 3D environment at all for every workflow.
P&IDs can also act as a navigation interface. In the demonstration, users could open a P&ID, select an asset from the drawing, and immediately see information related to that equipment. That makes the concept of an intelligent P&ID particularly relevant for brownfield plants, where engineering drawings may be far more readily available than complete and current 3D models.
The session also showed why digitizing engineering information matters. P&ID data and tag information can be extracted and structured so that drawings become more than static PDFs. DiagramIQ was presented within the wider architecture as a way of scanning engineering drawings, extracting tag information, digitizing P&IDs, and making that engineering context more accessible.
The practical lesson is that digital twin initiatives do not have to wait for a perfect representation of the entire facility. Organizations can work with the engineering information they already have and progressively improve the visual and data context over time.
What a Connected Maintenance Workflow Actually Looks Like
The most useful part of the CoreShift session was a simple maintenance scenario because it showed how the different pieces work together in practice.
Imagine an operator walking through a plant who notices that a pump sounds unusual. Instead of immediately calling another department or opening several systems, the operator can identify the equipment and open its digital context. Live sensor information can then help determine whether something requires further attention.
Perhaps the vibration level looks high. Perhaps discharge pressure is also outside its normal range. Vibration condition monitoring on its own provides an important signal, but it becomes much more useful when the reading is associated with the equipment, its history, technical information, and maintenance context.
The operator does not need to become the maintenance expert. In the demonstration, the issue could be assigned to the maintenance team directly within the digital twin environment while retaining its connection to the affected pump. The maintenance manager could then open that same asset and look deeper into vibration history, pressure, bearing temperature, drawings, documents, and other relevant information.
If the investigation shows that maintenance is required, the workflow moves into the maintenance management system. The work order is created there because the CMMS remains the appropriate system of record. Once created, relevant work-order information can be visible again within the digital twin so that the operator and other stakeholders can understand the status without repeatedly moving between applications.
The workflow is straightforward: an abnormal condition is detected, the asset is investigated in context, the issue is assigned, maintenance analyzes the available information, a work order is raised in the appropriate system, and the status becomes visible to the people who need it.
This is what makes a connected digital twin operationally meaningful. The value is not that every data source has been moved into one application. The value is that the context remains connected as responsibility moves from one role to another.
Different Teams Need Different Views of the Same Asset
Another important point from the session was that a connected environment should not expose every piece of information to every user.
An operator investigating a developing condition needs a different view from a maintenance manager preparing a repair. A reliability engineer may be more interested in historical trends and equipment risk. A project manager may care about design information, engineering changes, and project status. A technician preparing to execute work may need the relevant procedure, P&ID, isolation information, and work-order details.
The digital twin can provide different views while keeping the asset itself as the common point of reference. The session showed how role-specific environments can present only the information that is useful to that user while still drawing from the same connected information landscape.
This reduces the risk of creating yet another overloaded dashboard. The objective is not to display all available information. It is to surface the right context for the decision being made.
The Industrial Data Platform Is What Makes the Twin More Intelligent
The visual layer of a digital twin is only one part of the architecture. Behind it, organizations need a way to bring operational, engineering, enterprise, and other data together.
The CoreShift architecture positioned the industrial data platform as this connecting layer. AcuPrism was discussed as the environment where different data sources can be brought together, data-quality issues can be addressed, AI models can be developed and run, and information can be prepared for ontology services.
This becomes particularly important as organizations move beyond simply displaying information toward Industrial AI. A maintenance engineer may initially analyze vibration or temperature trends manually. Over time, analytics and machine learning could help identify risk or surface a potential issue earlier.
The session deliberately made an important qualification here: organizations do not need to begin with predictive maintenance. Different plants are at different stages of digital maturity, and advanced analytics may initially make sense only for the most critical assets.
That keeps the digital twin strategy grounded in business value rather than technology ambition.
Start With One Problem and Expand From There
Perhaps the strongest practical recommendation from the session was to avoid treating the digital twin as an all-or-nothing transformation.
A manufacturer does not need to connect every plant, every production line, every data source, and every use case before creating value. The session recommended starting with what a minimum viable project: identify an important problem, connect only the information required to address it, demonstrate value, and then expand.
That starting point could be one production line. It could be a group of critical pumps. It could be improving access to engineering information for maintenance teams. Another organization might begin with a specific reliability or turnaround-planning challenge.
This approach also changes the question enterprises should ask when assessing digital twins in manufacturing. Instead of asking, “How do we create a complete digital twin of our facility?”, start with, “Which operational problem would become easier to solve if the people involved could see the right information around the asset in one context?”
That question naturally leads to a more manageable scope.
From a Digital Model to an Operational Decision Layer
The long-term opportunity for connected digital twins is not better visualization alone. It is better decision-making across the asset lifecycle.
When engineering drawings, operational signals, maintenance information, enterprise data, and AI insights can all be related back to the same asset, the digital twin becomes a practical interface between the physical plant and the information needed to operate it.
A maintenance team can understand an emerging issue without spending time hunting across applications. An operator can see whether action is already underway. An engineer can navigate from the plant model or P&ID to the information needed for the equipment in question. Project and operational teams can work from more consistent information rather than separate interpretations of the same facility.
This is also where the combination of digital twins, industrial data platforms, ontologies, and Industrial AI becomes more relevant. Each is useful independently, but their value increases when they are connected around real business and operational workflows.
The goal is not to create another destination where users have to search for information. It is to reduce the distance between seeing a problem, understanding its context, and making the right decision.
For organizations evaluating where to begin, the most useful starting point is therefore not the technology itself. Identify the operational decision that is currently slowed down by fragmented information. Determine which assets, systems, documents, and data are needed to improve it. Connect that context first, prove the value, and expand from there.
That is how a digital twin moves from being a model of the plant to becoming part of how the plant actually operates.
Talk to Our Experts to explore how a focused connected digital twin initiative can be built around the systems, engineering information, and operational priorities you already have.
Connected Digital Twins for Intelligent Industrial Operations - FAQs
A connected digital twin brings engineering, operational, maintenance, and enterprise information together around physical assets. Instead of acting only as a 3D model, it gives users contextual access to data from systems such as ERP, CMMS, historians, P&IDs, documents, and live operational sources.
Digital twins help operations teams access relevant asset information in one context instead of searching across multiple systems. This can support faster issue investigation, maintenance planning, asset monitoring, and better coordination between operations, engineering, and maintenance teams.
Yes. A connected digital twin can work alongside existing systems rather than replacing them. CMMS, ERP, historians, and engineering platforms can remain systems of record while the digital twin surfaces the relevant information around each asset.
No. Manufacturers can start with the engineering information they already have, including P&IDs and other technical drawings. Where needed, 3D models, laser scans, or other visual representations can be added later as the digital twin evolves.
Digitized P&IDs can become an interactive way to navigate plant information. Users can select an asset or tag from the drawing and access related engineering, maintenance, and operational information instead of treating the P&ID as a static document.
By connecting live sensor data, historian information, asset context, technical documents, and maintenance records, digital twins can give teams a clearer view of equipment condition. This can support use cases such as vibration condition monitoring, predictive maintenance, and faster investigation of abnormal equipment behavior.
An industrial data platform brings data from different operational and enterprise sources together, addresses data quality and contextualization, and prepares that information for analytics, AI, ontology services, and digital twin experiences.
Start with a focused business problem rather than trying to digitize the entire plant at once. A small MVP can target one production line, asset group, or operational challenge, demonstrate value, and then expand to additional systems and use cases over time.
