Munich Airport taps Celonis to cut baggage delays

Munich Airport is turning to process mining specialist Celonis to track baggage handling in real time, using data-driven insights to cut delays and improve resilience at one of Europe’s key aviation hubs.

Data-driven response to baggage disruption

The partnership applies Celonis process intelligence software to Munich Airport’s complex baggage handling operations, where thousands of bags move each hour between check in, sorting systems, aircraft holds and reclaim belts. Publicly available information shows that Munich Airport has been expanding its use of Celonis for broader process digitization projects, and baggage handling is emerging as a priority use case as travel volumes grow.

Recent years have seen highly publicized baggage bottlenecks at major European hubs, including Munich, during peak travel periods. Reports from passengers describe long waits at reclaim and difficulties locating delayed bags, highlighting how quickly operational snags can ripple through ground handling chains. Against this backdrop, more airports are looking at process mining and real time monitoring to identify weak spots earlier and coordinate responses across airport operators, airlines and ground service providers.

In Munich, the volume and interconnected nature of baggage flows make traditional monitoring tools less effective, particularly when irregular operations or staffing shortages occur. By drawing live data from sorting systems, handling agents and airline systems into a single analytical layer, Celonis is designed to reconstruct the actual path each bag takes through the airport and flag deviations that could lead to delays.

How Celonis tracks baggage performance

Celonis belongs to a class of tools known as process mining platforms, which analyze event data generated by operational systems to visualize how work actually flows. In the context of baggage handling, each scan of a bag tag, handover event between handlers, or loading confirmation can be treated as an event in a larger end to end process model.

When applied to Munich Airport’s baggage operations, this approach allows teams to see the current state of the system in near real time. The software can highlight where bags are queuing longer than expected at specific conveyor segments, where handovers between different companies are repeatedly late, or where bags miss intended connections and are rerouted onto later flights. Instead of looking only at averages, analysts can drill down to specific flights, airlines or time windows to understand patterns behind recurring delays.

According to published coverage on Celonis deployments in transport and logistics environments, the platform can also be configured with performance indicators such as average transfer time, number of missed bags per thousand passengers and adherence to service level targets. These indicators enable operations centers to spot emerging problems, prioritize manual interventions and measure the impact of process changes on baggage punctuality.

From incident firefighting to proactive control

Airport baggage operations have traditionally been reactive, with teams responding to delays after passengers arrive at empty belts or airlines receive complaints. Process mining aims to shift this paradigm toward proactive control, where deviations are identified and addressed while baggage is still in motion.

At Munich Airport, this means using Celonis to trigger alerts when certain thresholds are exceeded, such as when a high proportion of transfer bags are at risk of missing tight connections, or when automated systems detect repeated stops on a particular conveyor segment. Operations teams can then reassign staff, adjust routing parameters or coordinate with airlines to hold aircraft briefly while critical bags are rushed to the stand.

Academic case studies on Munich Airport’s use of process mining for turnaround processes indicate that similar analytical techniques have already helped increase transparency around the impact of ground handling, airlines and infrastructure on punctuality. Extending this logic to baggage enables a more holistic view of how aircraft turnaround performance and baggage delivery are intertwined, particularly during disruptions caused by weather or air traffic restrictions.

Reducing costs and improving passenger experience

Beyond operational visibility, reducing baggage delays carries significant financial implications for airlines and ground handlers at Munich Airport. Industry research frequently cites the high cost of delayed and mishandled bags once tracing, compensation, forwarding and customer service are factored in. By identifying structural causes of baggage disruption, such as overloaded connections or inefficient routing rules, Celonis-based analyses can support business cases for targeted investments.

These investments may include additional automation in unloading and sorting, refined staffing plans for peak banks of arrivals, or revised connection policies for high risk routes. Public reporting from Munich Airport already points to pilot projects in automated baggage unloading, indicating a broader modernization drive across the baggage system in which process mining insights can help prioritize where new technologies will deliver the most benefit.

For travelers, the effect of such initiatives is felt in reduced waiting times at carousels and greater predictability after long haul flights. While no technology can completely eliminate baggage issues during extreme disruptions, airports that pair automation with granular process intelligence are generally better positioned to recover quickly and keep passengers informed about the status of their bags.

Part of a wider digital transformation at Munich

Munich Airport has been pursuing an overarching digitalization strategy that encompasses customer experience, internal workflows and technical infrastructure. Documents outlining its sustainability and integrated reporting programs reference the use of Celonis for process optimization, alongside other tools for automation and data analytics. Applying these capabilities to baggage forms part of a broader push to make operations more efficient and resilient.

In practice, process mining projects at airports often start with a limited scope, such as a specific terminal or airline partnership, before scaling up to encompass the full baggage network. As Munich Airport gains experience with Celonis in baggage handling, observers expect the insights to inform cross organizational collaboration with airlines and ground handling companies that share responsibility for on time delivery of bags.

The increased use of process data also aligns with a growing focus on resilience in aviation, where airports are challenged to maintain service quality amid fluctuating demand, staffing constraints and regional disruptions. By giving stakeholders a shared, fact based view of how baggage processes behave under stress, the Munich Airport and Celonis collaboration illustrates how data centric tools are becoming integral to the future of airport operations.