Across the industrial world, one misconception continues to shape investment decisions: Digital Twins are for greenfield plants.

New builds. Clean data. Modern systems. But the reality is the opposite.

Brownfields with legacy systems, scattered archives, outdated drawings, and decades of undocumented changes pay the highest price for poor data integrity. And every year, these plants lose millions without realizing the true source of their inefficiencies.

McKinsey highlights that poor data quality and fragmented information environments force engineers and technical teams to spend a significant portion of their time on non-value-added activities, such as searching for, reconciling, and validating information.

The firm further notes that organizations that address data quality and governance challenges can eliminate millions of dollars in avoidable costs while unlocking substantial operational and analytics value.

For ITCAN Solutions, this is more than research; it reflects what clients witness daily inside operating facilities.

“The myth is that greenfields get the most benefit from Digital Twins.

But brownfields are the ones bleeding money because of unreliable data,” says Ahmad Farshoukh, General Manager at ITCAN Solutions.

“Fixing data integrity isn’t modernization it’s financial recovery. It’s stopping losses that have been happening for 20 or 30 years.”

Where Brownfields Lose Money: The Hidden Cost of Bad Data

In brownfield environments, information lives everywhere server folders, archives, cabinets, vendor PDFs, outdated P&IDs, Excel sheets, and the memories of people who may retire

tomorrow.

When tags don’t match, or there is discrepancies between documentation and on site real conditions, operators pay for it through:

• Wrong procurement orders

• Repeated site mobilizations

• Extended shutdown durations

• Safety risks due to incorrect documentation

• Slow decision-making cycles

• Higher maintenance and troubleshooting times

At one brownfield site assessed by ITCAN, over 40% of the drawings had discrepancies with the field, causing recurrent delays and rework that cost hundreds of thousands every year.

“Every incorrect tag or attribute triggers a chain reaction, delays, extra cost, and sometimes even unsafe conditions.”

says Farshoukh.

“A Digital Twin is not about digitizing the past. It is about restoring trust in the data that runs the plant.”

In almost every brownfield facility ITCAN has assessed, the same challenges appear, regardless of the industry:

1. Documentation Is Fragmented Across Multiple Sources

Engineering records exist in PDF archives, scanned drawings, spreadsheets, DMS folders, contractor drives, and legacy ERP fields often with conflicting versions.

2. Different Contractors = Different Engineering Standards

Each expansion or upgrade introduced a new format, new templates, or new tagging rules, leaving plants with inconsistent documentation across decades.

3. As-Built Conditions Mismatches with Engineering Records

Hundreds of plant modifications accumulate without being fully documented, creating major discrepancies between design and reality.

4. Engineers Waste Hours searching and verifying Information On-Site

When trust in documentation drops, field verifications become routine delaying maintenance, planning, and troubleshooting.

5. AI, Automation, and Advanced Systems Cannot Function Properly

Without structured engineering data, even the most sophisticated solutions misinterpret assets or fail to scale.

So what does the scope actually include, and how do we ensure that every piece of data stored in the Digital Twin is accurate, validated, and trusted?It begins with a bottom-up engineering methodology, not with software.

We don’t simply upload old documents into a system; we rebuild the truth by reconstructing the real as-built environment, validating every asset, and synchronizing all disciplines.

Below is the end-to-end approach that transforms decades of scattered brownfield documentation into a unified Digital Twin ecosystem.

Every brownfield carries a different history, level of documentation maturity, and operational complexity. This means there is no one-size-fits-all starting point when building a Digital Twin. Some environments require beginning with laser scanning, others need immediate engineering validation, and some start with documentation restructuring or datasheet consolidation.

But regardless of where the journey begins, the outcome is always the same:

a single, trusted source of truth built from the ground up.

The following steps represent the general end-to-end methodology we apply across projects, adapted to fit the technical reality, maturity, and goals of each facility.

1. Laser Scanning & 3D Modeling:

The foundation of accuracy begins with capturing the plant exactly as it exists today.

ITCAN performs high-resolution laser scanning across the facility, generating a precise point- cloud model of every structure, piece of equipment, line, and instrument.

This point cloud is then transformed into a fully modeled, intelligent 3D environment, where each asset can later be tagged and connected to engineering information.

Together, the laser scan + 3D model allow:

• Accurate spatial measurements

• Remote walk-throughs and engineering assessments

• Early shutdown and modification planning

• Reduced site mobilizations

• Identification of undocumented or historical changes

2. Multi-Disciplinary Validation & As-Built Corrections

Engineering validation, the step where we cross-check what is documented with what actually exists in the field. This is where decades of discrepancies surface and where the Digital Twin begins to reflect true operational reality.

Mechanical teams review the P&IDs line by line, comparing every valve, nozzle, line number, and piece of equipment against the real installation. They correct sizes, orientations, flow

directions, and connectivity to ensure that the diagrams reflect on site conditions.

Instrumentation specialists follow the same principle: transmitter ranges, signal types, logic references, naming, and missing datasheet information are all checked, corrected, and aligned. This removes inconsistencies that often lead to misinterpretation or operational errors.

Electrical engineers validating E&ICs, updating panel and cabinet information, revising device ratings, and correcting wiring labels. In many cases, missing diagrams are recreated entirely so that every electrical asset is fully traceable and properly documented.

3. Intelligent Engineering Documentation

Brownfields often have thousands of legacy documents in different formats. The transformation includes:

• Converting P&IDs into intelligent, structured diagrams

• Standardizing mechanical and instrumentation datasheets

• Rebuilding missing or inconsistent electrical diagrams

• Establishing unified tag structures and attribute formats

• Replacing outdated templates with modern, cross-disciplinary standards

This ensures that every asset is represented consistently across engineering domains.

4. Asset Information Management: Creating the Single Source of Truth

All validated and intelligent data is then integrated into a unified, tag-centric digital environment containing:

• 1D data (attributes, specifications, datasheets)

• 2D drawings (P&IDs, E&IC diagrams, layouts)

• 3D models

• Manuals, certificates, and vendor documents

• Links to existing operational systems

This is now the visualization layer where everything comes together.

Engineers can: click a tag, view its datasheet, drawing, and 3D location, trace its connections and go to its P&ID, navigate across disciplines, access validated documents in seconds, and most importantly: they can trust the data.

Once engineering data is integrated and accurate, brownfields see measurable improvements:

Faster turnaround & shutdown planning: 3D scans and validated documentation eliminate unnecessary delays.

Major reduction in rework: Correct drawings reduce procurement errors and engineering misalignment.

Increased safety & operational reliability: Teams act on verified information not assumptions.

Faster maintenance response: Accurate asset data allows technicians to diagnose issues immediately.

A clean, AI-ready dataset: Structured data allows AI to finally generate reliable, not misleading, insights.

Conclusion:

Truth is simple: brownfield plants don’t lose money because of technology gaps, they lose it because decisions are made on unreliable information. Every drawing mismatch, every

undocumented modification, every missing datasheet quietly accumulates into real operational losses. Digital Twins stop that leakage. They give engineers back their time, give management real visibility, and give the plant a dataset it can finally trust to run efficiently, safely, and

competitively.

As Ahmad Farshoukh puts it:

“A Digital Twin is not a software upgrade, it’s an operational reset.

When you fix the truth of your data, you fix the economics of your entire plant. The plants that win in the next decade aren’t the ones that automate first,

but the ones that trust their information enough to automate safely.”

If your facility is still making decisions based on unverified or incomplete data, now is the time to change that.

Learn more: www.itcansolutions.com