Digital twins have moved beyond factories and wind turbines. A digital twin of a business process — a live, data-fed simulation of how work actually flows — lets organisations test changes in a virtual copy before touching the real operation. This article explains how process twins are reshaping optimisation in operations, finance, and supply chains.
What Does the Current Landscape Look Like?
The concept of the digital twin originated in engineering — NASA's Apollo programme used mirrored systems to simulate spacecraft in the 1960s — and manufacturing adopted it for physical assets in the 2010s. The extension to business processes is more recent and faster-growing. Gartner has projected that by 2027, 40% of large organisations will use digital twins of their organisations — "DTOs" — to simulate processes, test scenarios, and anticipate change, up from a small minority today.
The maturity curve matters for adoption. Early adopters built twins to visualise processes; the current generation builds twins to decide — running what-if scenarios, optimising resource allocation, and stress-testing plans before commitment. This shift from display to decision is what makes the twin a business tool rather than a demonstration, and it is the shift our clients report is hardest to sustain because it requires the twin's outputs to be trusted enough to act on.
The enabler is data. A process twin is only as good as the live data feeding it: event streams from systems of record, operational telemetry, and the same governed data estate that powers analytics. That is why the discipline belongs to the data platform as much as to simulation engineering — and why organisations with strong data foundations are adopting process twins far faster than those without.
What Are the Key Implementation Challenges?
The first challenge is modelling the process accurately. Business processes are messier than physical assets: exceptions, human judgement, and undocumented workarounds mean the "official" process map rarely matches reality. A twin built on the official map will simulate a process that does not exist. The organisations that succeed mine event logs and system telemetry to build the process model from observed behaviour — what actually happens — rather than from intended behaviour.
The second challenge is data fidelity. A twin that simulates with stale or incomplete data produces confident nonsense, and the confidence makes it dangerous. Twins need the same freshness, quality, and lineage guarantees as production analytics — and, in our experience, teams routinely underestimate how much work it takes to keep the twin's inputs current across dozens of source systems.
The third challenge is organisational trust. Process owners are naturally sceptical of a simulation telling them their operation can run 20% faster — until the twin has predicted something that provably happened. Building trust requires the twin to be continuously validated against reality: forecast the next quarter's throughput from current data, then measure how close the forecast was, and publish the results.
The fourth challenge is governance. A twin that recommends changes to real operations needs the same review discipline as any model: who can run scenarios, who approves acting on them, and how are the twin's assumptions documented and revisited? In our experience, enterprises that treat the twin as a governed analytics asset — with owners, versioning, and review — see its recommendations adopted; those that treat it as an experiment see its outputs ignored by the very leaders who commissioned it.
What Would Happen If We Changed the Process?
This is the question a process twin exists to answer, and it is the test of whether the twin is genuinely useful. "What if we reallocated the fulfilment team between orders and exceptions?" "What if we moved approval to the front of the workflow?" "What if a supplier's lead time doubled?" Each question is cheap to ask of a twin and expensive or impossible to test in production — where experiments disrupt real work and real customers.
The discipline that makes this work is scenario discipline: every what-if must be expressed as a change to the model and inputs, run against the same validated twin, and compared against a baseline. Organisations that treat their twin as a live lab in this way report two compounding benefits: decisions get made faster because testing is free, and the organisation builds a library of validated scenarios that becomes institutional memory about how the process behaves.
Which Practical Approaches Actually Work?
Start with a process that is high-volume, measurable, and painful. Order fulfilment, claims handling, onboarding, and procurement are classic candidates: they generate the event data needed to model them, they have clear performance metrics, and small improvements create large savings. Our work with enterprises across Asia-Pacific suggests that teams typically identify a first twin candidate within two weeks when they look for processes with the most manual intervention and the most visible cost.
Build the model from observed behaviour. Mine event logs and system telemetry to reconstruct the real process — including exceptions and workarounds — then validate the model by replaying history: run the twin against past data and compare its simulated outcomes with what actually happened. Once the replay matches reality within tolerance, the twin is trustworthy enough to simulate the future.
Connect the twin to the data platform and to decision workflows. The twin should consume the same governed, lineage-tracked data as your analytics, and its scenario results should flow into the same dashboards and conversations where decisions are made. A twin that lives in a specialist tool nobody opens is an expensive curiosity; a twin that feeds the weekly operating review changes how the organisation plans. Anchor the twin in business KPIs from the start: a twin that simulates cycle time, cost per unit, or SLA attainment is easy to value, while one that simulates abstract process flows is hard to defend. Define the three to five metrics the twin must reproduce accurately — and use those same metrics as the scorecard for every scenario it runs. This keeps the twin honest and keeps executives engaged, because the output speaks the language of the operating review. A pragmatic adoption sequence looks like this:
- Pick one high-volume, measurable process with visible pain
- Reconstruct the real process from event logs, including exceptions
- Validate the twin by replaying history until simulations match reality
- Connect the twin to governed data and decision workflows
- Run scenarios before changes, and measure results after
- Publish forecast-versus-actual scores to build organisational trust
Finally, expand deliberately. Once the first twin has earned trust, extend it in two directions: deeper (more granular sub-processes and constraints) and wider (adjacent processes that share resources with the first). Organisations that follow this path report cycle-time reductions of 15–25% on optimised processes within two years — not because the twin does the work, but because it makes the experimentation that finds the improvements essentially free.
What Is the Fastest Way to Prove Value with a Digital Twin?
The mistake teams make is modelling the entire enterprise before delivering anything. The faster path is to pick one high-friction process, such as order fulfilment or a claims workflow, build a twin of just that process, and run a what-if question the business actually argues about, like the impact of a bottleneck or a staffing change.
Because the twin is a living model, stakeholders can test decisions safely before committing capital. The first win is usually a debate that gets settled with evidence instead of opinion, which builds the political capital to expand. Keep the model honest by feeding it real operational data and by clearly marking where it is a simplification.
Value compounds as more processes join the twin and interactions between them become visible. What began as a single optimisation tool matures into an operational simulation environment where leaders rehearse change, quantify risk, and make moves with far greater confidence than intuition alone would allow.
How Do You Keep a Digital Twin Trustworthy?
A twin is only as good as its feed. The fastest way to lose trust is to let the model drift from reality while nobody notices, so instrument it with validation, reconcile its predictions against actual outcomes, and flag divergence before anyone makes a bad call on stale assumptions.
Treat the twin as a hypothesis machine, not an oracle. Every recommendation should state its assumptions and its confidence, and humans should retain the right to override with explanation. When the twin is honest about its limits, people use it wisely; when it pretends to certainty, they either ignore it or, worse, believe it blindly. The discipline of humility is what makes the model durable.
Which Processes Are Best Suited to a Digital Twin?
The best candidates share traits: they are repeated, they have measurable inputs and outputs, and mistakes are expensive enough to justify the modelling effort. Examples include supply-chain flow, claims handling, loan origination, and manufacturing scheduling. In each, a twin can rehearse decisions before committing real resources, and the savings from avoided errors fund the build.
Poor candidates are one-off, creative, or poorly instrumented processes, where the cost of modelling exceeds the benefit and the data to feed the twin does not exist. Forcing a twin onto such a process produces an expensive mimicry of chaos. The discipline is to start where the payoff is obvious and the data is already flowing.
A useful test is the rehearsal question: can you ask the twin what happens if we change X, and get an answer better than a meeting? If yes, model it. If the answer is vague or the inputs are guesses, wait until the process is instrumented. The twins that deliver are the ones built on real operational data and aimed at real, repeated decisions, not the ones commissioned to look innovative.
What Skills Build a Digital Twin Program?
A twin program needs modellers who can represent a process faithfully, data engineers who keep the feed honest, and domain experts who validate that the simulation reflects reality. The rare and valuable skill is the translator who sits between them, turning a business question into a model specification and a model output back into a decision.
Equally, the program needs sponsors who will act on results, because a twin that produces insights nobody uses is just an expensive simulation. Invest in the human system, the skills and the mandate, as seriously as the technical one. The deployments that endure are the ones where a clear owner acts on the twin's recommendations and is accountable for the outcome.
Which Processes Are Best Suited to a Digital Twin?
Not every process rewards the effort of building a twin. The best candidates share three traits: they are repeated often enough that small improvements compound, they generate clean event data as a by-product of normal operation, and their outcomes are sensitive to sequence and timing rather than a single dominant variable. Order fulfilment, claims handling, and factory scheduling fit this profile; a one-off strategic project does not.
The selection test is simple to state and hard to skip: if you cannot measure the process today, you cannot validate a twin of it tomorrow. Start where instrumentation already exists, build the model, and prove it predicts before you trust it to prescribe. A twin that merely describes what happened is a dashboard with extra steps; the value arrives when it can answer "what happens if we change this" with enough accuracy that a manager acts on it. Choose processes that meet that bar and the digital twin earns its keep quickly; choose poorly and it becomes an expensive science project nobody consults.
What Skills Build a Digital Twin Program?
A digital twin program lives or dies on a small mix of skills that most organisations do not have in one place. The first is process modelling — the ability to translate a messy real workflow into a representation the machine can simulate, which is closer to journalism than to coding. The second is data engineering, to keep the twin fed with timely, trustworthy event data, because a twin starved of fresh inputs silently drifts from reality.
The third skill is the rarest: someone who can translate the simulation's output into a decision a manager will actually make. Without that bridge, the twin produces beautiful charts that change nothing. Build the program around a small cross-functional cell — a process owner, an engineer, and a business translator — rather than handing it to a single department. That cell becomes the institutional memory of how the twin is trusted and used, and it is what lets the organisation tell the difference between a model it should act on and a model it should keep questioning.
Frequently Asked Questions
A live model of a process, built from operational data, that lets you test decisions safely, rehearse changes, and see the likely impact before committing real resources.
Repeated, measurable, high-cost processes such as supply chains, claims handling, and manufacturing scheduling, where mistakes are expensive enough to justify the modelling effort.
By feeding it real operational data, validating its predictions against actual outcomes, and stating assumptions and confidence so humans can override with explanation.
Model one high-friction process, run a real what-if question the business argues about, and use the evidence to settle the debate, building capital to expand.
What Are the Key Takeaways?
- Build process models from observed behaviour — event logs and telemetry, not official maps
- Validate the twin by replaying history before trusting any future simulation
- Use the twin as a scenario lab for what-if questions that are too risky to test live
- Connect the twin to governed data and the workflows where decisions are made
- Publish forecast-versus-actual scores to earn and keep organisational trust
What Bottom-Line Actions Should You Take?
Digital twins turn process optimisation from a series of risky experiments into a continuous, low-cost discipline of simulation and validation. The enterprises that build twins from observed behaviour, validate them against reality, and embed them in decision workflows will out-optimise competitors who still change processes by committee and hindsight.
The opportunity is not limited to heavy industry. Banks simulate onboarding and credit-decision flows, insurers simulate claims journeys, and retailers simulate fulfilment networks — any process with measurable steps and data is a candidate. The organisations that adopt process twins early are building a simulation capability that will only become more valuable as AI makes scenario testing cheaper.
At Beehive Strategy, we help enterprises build process twins on top of their existing data estate — event-driven models, historical validation, and scenario workflows that connect directly to how the organisation plans. For operations leaders, the promise is simple: before you change the process, see what would happen if you did.
