I've been working with the philosophy behind digital twins since before most of the industry had a name for it. Digital Twin Services is where 25+ years of that thinking and the hard-won lessons from real implementations, become available to your organisation.
Every digital twin engagement falls into one of three areas, and most span all three. I've found that organisations fail at digital twins not because the technology isn't there, but because the strategy, governance and data discipline haven't been established first.
This is where most people get it wrong. A digital twin isn't just a 3D model, however detailed. It isn't a database, however well-structured. And it certainly isn't cheap. Anyone selling you a quick digital twin at a modest price is selling you something else.
A digital twin is a philosophy, a methodology of policies, processes and discipline applied to a facility to ensure every operational and maintainable asset has an accurate digital counterpart. That counterpart is kept up to date as the real asset changes. Not just at project handover. Continuously.
I've been working from this definition since my early days in process plant engineering. The technology available to build and maintain a digital twin has changed enormously since then. The philosophy hasn't.
The formal definition I work from: A Digital Twin is a philosophy, a methodology of policies and processes implemented on a facility to ensure an accurate replication in a digital form of the operational and maintainable assets.
Three things I hear routinely that miss the point entirely.
A 3D model is a valuable tool and often part of a digital twin implementation. But without the data, governance and operational update cycle, it's a snapshot, not a twin. The model has to be maintained and connected to the live asset.
A well-structured asset database is a building block, not the finished article. A digital twin integrates multiple data sources, 3D geometry, maintenance records, sensor feeds, documentation, into a coherent, connected whole.
Done properly, a digital twin requires investment in strategy, standards, systems and sustained data quality. The return is substantial, but the organisations that try to shortcut the foundations end up rebuilding from scratch. I've seen it happen.
This is the question I ask every client who tells me they're already building their digital twin. A digital twin is dynamic — it only truly exists once the facility is in operations and the information is being continuously updated against the real world.
The construction and commissioning phases are where the data foundation is built, and that work matters enormously. Get it right there and you arrive at operations with a solid starting point. Get it wrong and you spend years trying to retrofit data quality onto an asset that's already running.
The key principle: a digital twin is dynamic — accurate information being updated continuously. It only comes alive in the operations phase of a facility. Everything before that is preparation.
Three delivery areas, often running in parallel. All of them start with understanding your current state before prescribing anything.
Strategic definition of what a digital twin means for your organisation, what achieving it looks like, and how you'll govern the data quality that makes it real. This is where most projects either get it right or plant the seeds of future problems.
The written standards, processes and workflows that make a digital twin sustainable beyond the initial implementation. If the discipline isn't documented and the team isn't trained, the quality degrades within months.
The technical implementation: systems selection, data formats, validation tools and bespoke migration solutions. The goal is accurate, accessible data in a format your organisation can actually maintain and use.
These are the outcomes I've seen clients achieve, not marketing claims.
Know a component is approaching failure before it fails. Condition monitoring connected to asset history changes maintenance from reactive to planned.
The information lives in the system, not in people's heads. When someone leaves, the knowledge stays. This is underestimated until the wrong person walks out the door.
A maintained twin reflects what's actually installed and operating, not what was originally commissioned. That difference matters enormously for maintenance, modification and safety.
Know exactly where every instance of a part number is installed across your facility. Essential for recalls, safety investigations and bulk replacements.
Operational planning based on what's actually there, not what the original design drawings show. The two are rarely the same after a few years of operations.
Stop holding stock you don't need. Start holding what you do. The twin tells you what's installed, how old it is, and what the failure patterns look like.
Start the next modification or expansion project from accurate as-operated data rather than 20-year-old as-built drawings. The time saved at project start pays for significant twin maintenance costs.
Where IoT sensors feed data back to the twin in real time, you get a continuously updated view of plant health, not a quarterly snapshot.
Darbury was an early pioneer in digital twin technology, with experience predating the current widespread adoption. Since 2000, the practice has been the trusted technology partner for major blue chip companies.
Digital Twin Services — Darbury Ltd · Est. 2000
A selection of articles I've written on digital twin philosophy, methodology and practice.
Digital Twin in the Building Industry
The building sector is where digital twin conversations often start and frequently stall. This article looks at where the building industry genuinely is versus where the marketing says it should be.
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Digital Twin vs Digital Records
A distinction I make a lot in practice: the difference between having good digital records of your assets and actually having a digital twin. They're not the same thing, and confusing them leads to expensive dead ends.
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Digital Twins — Asset Management Principles
The asset management principles that sit behind a well-implemented digital twin. How ISO 55000 thinking and digital twin methodology align and where organisations try to skip the foundations and regret it.
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How Much Does Data Truly Cost?
The cost of data isn't what you pay to collect it, it's what you pay when you don't have it, when it's wrong, or when it's locked somewhere nobody can access it. This article puts some practical numbers around the real cost of poor data.
Read on LinkedIn →
Start the ConversationWhether you're at the beginning and need to understand what's involved, halfway through and running into problems, or further along and looking to raise the standard, I'm happy to have a straight conversation about it. No sales pitch, no jargon. Just an honest assessment of where you are and what would actually help.