Introduction: You're Making Decisions in the Dark
Imagine you're responsible for a major industrial facility — a gas processing plant, a hydropower station, or a chemical reactor.
Every day, you face critical questions:
- •Should we delay maintenance because a spare part is delayed?
- •Is this drop in output due to equipment degradation or feed variability?
- •How much revenue are we losing right now due to unplanned downtime?
You have data — SCADA systems, maintenance logs, production reports.
But when it's time to decide, you still rely on:
- •Experience
- •Meetings
- •Best guesses
That's not good enough.
The real cost isn't measured in lost barrels or overtime hours.
It's measured in value erosion — the gap between what your asset could produce and what it actually delivers.
And that gap grows wider every time a decision is made without full context.
What Is a Digital Asset? (And What It's Not)
Let's start with what a Digital Asset is not:
- •It is not a dashboard showing live pressure and temperature.
- •It is not a "Digital Twin" that mirrors current conditions.
- •It is not an AI tool that makes predictions without explanation.
An Industrial Digital Asset (IDA) is something deeper:
A computational system that links technical reality to financial performance — so you can see how equipment behavior affects EBITDA, risk, and long-term value.
Think of it as a decision-support engine — one that simulates thousands of scenarios, integrates physics, finance, and operations, and helps you answer:
"What action maximizes business value over time?"
It doesn't replace human judgment.
It enhances it — with clarity, traceability, and confidence.
Why This Matters: The Hidden Cost of Poor Decisions
Most organizations lose value not because of equipment failure — but because of decision fragility.
When interconnectedness, variability, randomness, and uncertainty are ignored, decisions become fragile — and value leaks out.
Examples include:
- •Approving a capital project based on optimistic availability assumptions that don't reflect real-world constraints
- •Scheduling maintenance during peak pricing windows, losing millions in avoidable opportunity cost
- •Believing a vendor's lifecycle cost (LCC) promise — only to discover later that performance doesn't match specifications
These aren't isolated mistakes.
They are symptoms of a larger problem: managing complexity without understanding it.
"The greater risk is not physical failure — it is managing without understanding."
An Industrial Digital Asset fixes that.
How It Works: From Data to Decision
An IDA doesn't just collect data — it structures it into knowledge.
Here's how:
It starts with a model of your asset — built using tools like AspenTech Fidelis.
It adds causal logic: If compressor A fails, how much production is lost? How much revenue?
It incorporates real-world constraints: crew availability, spare parts lead times, market prices.
It runs thousands of simulations to show possible outcomes — not just averages.
It presents results in a way that supports executive decisions.
This is not guesswork.
It is predictive clarity.
The 8-Step Roadmap to Building Your Digital Asset
Now that you understand why an IDA matters, here is how to build one — a proven, structured process used across Latin America's largest industrial operations.
Each step builds on the last, ensuring the final system is not just technically sound, but organizationally owned.
Define the Objective
"Why are we doing this?"
Start by identifying a high-value decision that impacts safety, availability, or profitability.
Example:
"We need to optimize maintenance scheduling for our gas compression train."
Without a clear objective, the effort becomes a technical exercise — not a business enabler.
Diagnose Current Capabilities
"Where are we today?"
Assess four key areas:
- Data:Do we have access to failure histories, maintenance logs, process variables?
- Tools:Are SCADA, CMMS, ERP systems integrated?
- People:Can internal teams build and interpret models?
- Culture:Are leaders willing to make decisions based on integrated models?
This step reveals gaps — and prepares the organization for change.
Build the Base Case Model
"What does 'normal' look like?"
Create a foundational simulation of the asset using reliability modeling tools.
Include:
- •System configuration
- •Failure modes and repair times
- •Maintenance strategies
- •Operational constraints
This model becomes the reference point for all future improvements.
Integrate Value Drivers
"How does performance affect business outcomes?"
Now connect the model to financial impact.
Using PDEL® (Performance Dependency Elucidation Language), map:
- →Equipment degradation → Reduced throughput → Lost revenue
- →Spare part delay → Extended downtime → Contractual penalty
This transforms the model from a technical simulator into a value forecasting engine.
Validate Against Reality
"Does it reflect what actually happens?"
Perform the "Acid Test":
- •Can the model reproduce past events (e.g., unplanned shutdowns)?
- •Do outputs change coherently when inputs vary?
- •Do decision-makers trust its predictions?
Three levels of validation:
- 1.Technical Verification: Does the model behave as intended?
- 2.Functional Validation: Does it represent physical and operational reality?
- 3.Managerial Acceptance: Are leaders willing to act on its outputs?
If it fails, return and refine.
Refine and Iterate
"How can we improve it?"
No model is perfect on the first try.
Update assumptions based on feedback and new data.
Expand scope:
- •Add more subsystems
- •Integrate logistics and crew planning
- •Include energy flows and environmental remediation capacity (via DECA®)
Each iteration increases fidelity and usefulness.
Institutionalize Knowledge
"How do we ensure it lives beyond the project?"
Train internal teams to run, update, and challenge the model — not just consume reports.
Embed the IDA into:
- •Planning cycles
- •Investment reviews
- •Contracting strategies (KVB-C2M®)
- •Performance tracking (Kwvaat)
Deliver structured protocols to ensure continuity.
This is where most efforts fail — Knar succeeds by making capability transfer central to the engagement.
Realize Value On-Line
"How does it guide decisions in real time?"
Connect the IDA to live data streams (SCADA, CMMS, ERP).
Enable:
- •Early warnings of critical degradation
- •Automated recommendations for operational adjustments
- •Risk assessment during maintenance activities
Transform the IDA into the nervous system of the operation — sensing, thinking, anticipating, deciding.
Only at this stage is the full potential realized.
Conclusion: This Is Not a Project — It's a Transformation
An Industrial Digital Asset is not a destination.
It is a journey: a deliberate transformation of industrial knowledge into predictive capability.
At Knar, we don't deliver dashboards.
We co-architect systems where technical truth meets business impact — so clients can navigate complexity with confidence.
"We support you to architect new solutions."