The Digital Twin Reality Check: What Actually Drives Value?

🧠 Executive Perspective

Digital Twins Don’t Fail, Expectations Do

Digital twins have crossed the hype threshold.

Most industrial leaders now have one, or are being pitched one. Yet too many walk away asking the same uncomfortable question:

“Why didn’t this deliver the value we expected?”

The issue is rarely the technology.
It is misaligned objectives, unclear ownership, and value definitions that stop at visualization instead of decision impact.

Key Insight:
A digital twin only creates value when it actively changes how people make decisions, before, during, and after operations.

If it doesn’t influence behavior, reduce uncertainty, or improve confidence under pressure, it is just an expensive mirror.

🏗 Engineering Leadership

High Fidelity Alone Does Not Equal High Value

Engineering teams often anchor digital twin value to:

• Model accuracy

• Thermodynamic rigor

• Equipment-level detail

• Design validation use cases

These are important, but insufficient.

Where value actually shows up:

• Faster design alignment across disciplines

• Earlier detection of control strategy weaknesses

• Fewer late-stage logic changes

• Reduced commissioning rework

• Clearer functional intent for operations

Reality Check:
A 95% accurate model that no one trusts or understands delivers less value than an 85% accurate model embedded into real workflows.

Trusted Advisor Note:
Value accelerates when digital twins are treated as engineering communication tools, not just calculation engines.

⚙️ Operations Leadership

If Operators Don’t Use It, It’s Not a Digital Twin

Many “digital twins” never make it past the engineering phase.

Why?

• They don’t reflect real operating pain points

• They aren’t integrated with the actual control system

• They aren’t updated after startup

• They aren’t designed for scenario-based learning

Operators don’t need perfect physics.
They need predictable behavior, realistic alarms, and credible cause-and-effect.

What Actually Drives Operational Value:

• Abnormal situation rehearsal

• Startup and shutdown confidence

• Decision-making under stress

• Consistent response across shifts

• Reduced reliance on tribal knowledge

Trusted Advisor Note:
If your digital twin can’t answer “What should I do next?” during an upset, it won’t survive the operations handover.

🔧 Maintenance & Reliability

A Digital Twin That Doesn’t Teach Failure Is Missing the Point

Maintenance value is often overlooked, or promised later.

Yet maintenance teams benefit immediately when a digital twin is designed correctly:

• Visualizing process impacts of equipment degradation

• Understanding failure propagation

• Practicing isolation and recovery procedures

• Navigating automation layers confidently

• Reducing mean time to diagnose

Reality Check:
Most reliability gains don’t come from predicting failures.
They come from responding better when failures occur.

Trusted Advisor Note:
Design your digital twin to answer, “What breaks next?” and “What happens if I’m wrong?”

📘 Featured Case Study

When a Digital Twin Shifted from “Model” to “Mission-Critical Tool”

A multi-unit process facility invested heavily in a high-fidelity digital twin, yet saw minimal value post-startup.

The Problem:

• Twin used only during design

• No operator scenarios

• No maintenance workflows

• No ownership after handover

The Reset:

Prosera repositioned the twin using a phased value framework.

Phase 1, Design Discovery Revisited

• Revalidated operating philosophy

• Identified top 12 operational risk scenarios

• Captured operator and maintenance decision logic

Phase 2, Simulation Development

• Tuned model behavior to match plant response

• Integrated with the live control system emulator

• Built scenario libraries tied to KPIs

Phase 3, Integration & Lifecycle Enablement

• Embedded training into the LMS

• Assigned operational ownership

• Defined update and governance processes

Results:

• 30% reduction in operator response time

• 25% decrease in nuisance trips

• Faster onboarding of new technicians

• Digital twin used weekly, not annually

Key Shift:
The digital twin became a decision rehearsal platform, not a static asset.

🧰 Toolbox

Digital Twin Value Reality Checklist

Before approving, or rescuing, a digital twin initiative, ask:

✔️ Value Definition

• Is success tied to decisions, not diagrams?

• Are KPIs clearly defined?

✔️ Human Integration

• Are operators involved early?

• Is maintenance included?

• Are instructors empowered?

✔️ Technical Alignment

• Does it reflect real control logic?

• Is behavior tuned to the plant, not theory?

✔️ Lifecycle Ownership

• Who owns it after startup?

• How is it updated?

• How is value measured quarterly?

If you can’t confidently answer at least 9 of 12, value leakage is already happening.

🗣 Community Corner

Subscriber Question:
“Why do so many digital twin projects stall after commissioning?”

Prosera’s Trusted Advisor Answer:
Because ownership ends when engineering ends.
Digital twins create value only when they are operated, not delivered.

➡️ Coming Next Month

Issue #3, From Project to Platform: Making Simulation a Living Asset

We’ll explore how leading organizations transition simulation from a one-time capital project into a long-term operational capability, without runaway costs or complexity.