What Munich Airport actually built
Munich Airport, Germany's second largest hub, has deployed Celonis process mining software to construct a real-time digital twin of its operations, starting with baggage handling. Jan-Henrik Andersson, the airport's chief commercial officer, described the payoff in plain operational terms: Celonis gives the airport the context to see how it actually runs in real time, rather than how planners assumed it would run. That distinction matters more than the technology label. A digital twin built on live process data reflects what is happening on the ground, including the delays and workarounds that never make it into a process diagram.
The detail that should catch a CIO's attention is the timeline. Munich Airport has been a Celonis customer since 2023. This is not a six-month pilot dressed up as a digital twin for a press release. It is the output of two-plus years of process mining discipline applied to a genuinely complex operational environment, one with baggage systems, ground crews, gate assignments, and passenger flow all interacting in real time. The digital twin is a product of that accumulated data maturity, not a separate project bolted on top of it.
Why process mining is the quiet enabler here
Process mining tools like Celonis were originally sold as a way to find inefficiencies in ERP workflows: procurement cycles that took too long, invoices that bounced between approvers, orders that stalled in the queue. What Munich Airport demonstrates is that the same underlying capability, ingesting event logs and reconstructing the actual sequence of what happened, scales up into something closer to a live operational model. The airport did not need to buy a separate digital twin platform. It extended a tool it already trusted for process visibility into a real-time operational layer.
That reuse pattern is the part worth stealing. Enterprises that have already invested in process mining for finance or supply chain operations are sitting on infrastructure that can, with the right data feeds, support the same kind of real-time modeling Munich Airport is doing for baggage. The barrier is rarely the software license. It is whether the organization has the operational data hygiene and event-logging discipline to feed it, which is exactly what two years of Celonis deployment bought this airport.
The peer evidence is starting to stack up
Munich Airport is not operating in isolation. Singapore's Changi Airport has used digital twin technology to cut operational costs by 10 million dollars. Heathrow's AI agent, built on a Salesforce Agentforce deployment that went live in mid-2025, now resolves customer questions 40 percent faster than human staff. Celonis itself is working with Lufthansa and Eurowings on ground workflows and fleet turnaround, and with Airbus on manufacturing operations, which suggests the vendor is treating aviation as a coherent vertical rather than picking off one-off wins.
Florian Schewior, Celonis's managing director for German-speaking Europe, framed Munich's deployment as reinforcing the airport's position among Europe's leading hubs. That is vendor language, but the underlying claim is testable: airports that get real-time operational visibility right are starting to separate from those that don't, measured in hard dollar and speed terms rather than vague efficiency gains. For an industry built on tight connection windows and cascading delays, that separation compounds fast.
What this means outside aviation
Airports are an unusually good proving ground for digital twins because the operational stakes are visible and immediate: a baggage delay cascades into a missed connection within minutes, not weeks. That visibility makes the ROI case easier to sell internally than it is in, say, a distribution center or a claims processing operation where the feedback loop is slower. But the underlying architecture, process mining as the data backbone for a live operational model, transfers cleanly to any enterprise running complex, multi-system workflows on top of an ERP or ITSM backbone.
The practical takeaway for a CIO evaluating this trend is to audit what process mining investment already exists before greenlighting a new digital twin initiative. If finance or supply chain already runs on Celonis, SAP Signavio, or a comparable platform, the incremental cost of extending it into a real-time operational model is far lower than starting fresh. Munich Airport's advantage was not a superior digital twin product. It was two years of process data nobody else in the building had to build from scratch.
The governance question digital twins raise
A live model of operations is only as trustworthy as the event data feeding it, and that creates a governance obligation that airports and enterprises alike tend to underweight. If the digital twin becomes the reference point ground staff and dispatchers act on, data quality failures propagate directly into operational decisions rather than sitting in a dashboard nobody checks. That is a different risk profile than a quarterly process-mining report flagging inefficiencies for a process owner to review at leisure.
CIOs building toward this kind of real-time operational model should treat data lineage and event-log validation as a prerequisite, not an afterthought bolted on after the twin is live. Munich Airport's two-year runway with Celonis likely absorbed a fair amount of that data-quality work before the digital twin ever launched. Enterprises trying to compress that timeline should expect to spend real budget on the unglamorous plumbing before the real-time model earns anyone's trust.
What to budget for before the twin goes live
Vendors selling digital twin capability tend to price the modeling and visualization layer, since that is the demoable part of the product. The costs that actually determine whether a project succeeds sit upstream of that: instrumenting systems that were never built to emit clean event logs, reconciling timestamps across platforms that were never designed to talk to each other, and assigning an owner accountable for data quality once the twin is treated as operationally authoritative. None of that shows up in a vendor's pitch deck, and all of it took Munich Airport roughly two years to work through before this deployment became a case study worth publicizing.
The realistic planning assumption for a CIO chasing a similar outcome is that the process mining license is a small fraction of total cost, and the data engineering work to make that license useful is the larger and slower line item. Budgeting and staffing for that unglamorous phase up front, rather than discovering it mid-project when the twin's outputs don't match reality on the ground, is the difference between a deployment that takes two years like Munich's and one that stalls indefinitely waiting for data nobody planned to clean up.
