Munich Airport taps Celonis to cut baggage delays

Munich Airport is expanding its use of Celonis process mining technology to monitor baggage handling in near real time, part of a broader push to curb delays and keep flights running on schedule during periods of heavy traffic.

Digital process intelligence moves into the baggage hall

Munich Airport has been working with Munich based process mining specialist Celonis for several years as part of a wider digital transformation of ground operations. Early projects focused on the aircraft turnaround process, using data from refueling, catering, cleaning, passenger boarding and baggage loading to understand how delays build up on the apron.

According to publicly available information from Celonis and academic case studies, this work has now evolved into a broader program that pulls operational data from multiple airport and airline systems into one analytical layer. For baggage, that includes information from check in, conveyor systems, baggage sortation, ground handling teams and flight schedules, allowing planners to see how every step in the chain affects on time performance.

By treating each flight as a digital "case" with time stamped activities, the Celonis software reconstructs the actual path a bag takes through the airport. This makes it possible to see where luggage frequently arrives late at the aircraft, where handovers between teams are slow, or where equipment breakdowns translate directly into missed connections for passengers and their bags.

Reports indicate that this consolidated view is being used to support operational decision making at Munich during daily peaks and special traffic events. The same dataset also underpins longer term planning, helping the airport and its airline partners identify recurring structural bottlenecks in the baggage system.

From turnaround analytics to baggage delay prevention

Initial joint work between Munich Airport, Lufthansa and Celonis focused on improving turnaround reliability, with process mining revealing how delays in catering, cleaning or baggage unloading could cascade into late departures. As these turnaround models matured, attention increasingly shifted to the baggage stream itself, which has become a key focus area across the airline industry following high profile disruption in recent years.

Industry coverage of Celonis projects with Lufthansa describes how process mining is applied to lost and delayed baggage cases, combining data from check in systems, baggage handling equipment, message brokers and flight operations. The same technical approach is being extended at Munich to regular baggage flows, allowing teams to distinguish between one off incidents and patterns that systematically put bags at risk of missing a flight.

Public case material on Munich Airport’s digital initiatives highlights that the airport uses near real time data to track critical milestones during a turnaround. For baggage, these milestones include the time bags leave the check in area, enter the sorting hall, are loaded into containers or carts, and are finally scanned at the aircraft. When deviations from expected timelines occur, alerts can be triggered and staff can be redeployed to protect tight connections.

The aim is to move from reacting to delayed baggage after the fact to preventing delays before they impact passengers. By continuously comparing actual performance with target process models, the Celonis platform can flag when an individual flight’s baggage flow is trending toward a risk threshold, long before that risk materializes as a missed bag.

Quantifying delay drivers and operational savings

While detailed performance figures for Munich’s baggage operation are not published for every season, related case studies around the airport’s use of process mining show measurable gains in on time performance. In earlier turnaround work that also covered baggage unloading and loading, analysis of tens of thousands of flight movements helped identify specific activities that consistently ran late and where small procedural changes could recover minutes per flight.

Process mining allows planners to attribute delay minutes to specific causes rather than treating all disruptions as generic “operational issues.” For baggage, this can highlight problems such as overloaded conveyors at certain times of day, late arrival of transfer bags from particular inbound flights, or slow handoffs between handling teams at busy gates. Each delay driver can then be quantified in terms of how many flights and passengers it affects across a season.

Reports on Celonis deployments in aviation indicate that this kind of granular insight has helped airlines and airports eliminate thousands of hours of accumulated delays. Although these figures often cover the end to end turnaround process, baggage logistics are a central component of the gains, given their tight integration with aircraft departure readiness.

At Munich, the combination of process mining and operational know how is being used to prioritize improvement initiatives with the greatest effect on punctuality and baggage reliability. Investments in staffing, equipment or procedural changes can be evaluated against hard data on delay reduction, rather than assumptions or isolated incident reports.

Handling post pandemic peaks and structural pressure

Munich Airport, like many European hubs, experienced dramatic swings in passenger volumes during and after the pandemic. Traffic collapsed in 2020 before rebounding sharply, creating intense pressure on baggage systems and ground handling resources during peak waves of recovery and during large events such as Oktoberfest.

Academic research into Munich’s operations notes that such volatility has increased the value of data driven tools that can adapt quickly to changing patterns of demand. Process mining offers a way to test different staffing levels, handling strategies or baggage system configurations against historical data, helping the airport prepare for peak days when misrouted or delayed bags are most likely.

Recent traveler reports continue to describe long waits at baggage claim in Munich during busy periods, reflecting the complexity of coordinating airlines, ground handlers and infrastructure. Against that backdrop, the expansion of Celonis based monitoring represents an effort to give operators more precise visibility into where capacity is strained and which parts of the baggage chain need reinforcement.

By using process intelligence to smooth baggage flows, Munich aims to protect its position as a major European transfer hub. Reliable baggage delivery is a decisive factor for connecting passengers choosing between competing routings, particularly on long haul journeys where a missed bag can disrupt an entire trip.

Broader implications for travelers and the industry

For travelers passing through Munich, the use of Celonis in the baggage hall remains invisible. The system works behind the scenes, drawing on scan events, time stamps and operational logs to provide airport and airline control centers with a constantly updated view of how luggage is flowing toward each flight.

Industry observers note that Munich’s collaboration with Celonis is being watched by other hubs looking to modernize legacy baggage handling systems. Many airports still rely on fragmented monitoring tools and manual reports, which can make it difficult to spot systemic issues that lead to recurring baggage delays, especially during irregular operations.

If Munich’s approach continues to show reductions in delay minutes and mishandled bags, it could accelerate wider adoption of process mining in airport logistics worldwide. Similar methods are already being trialed for security checkpoints and passenger processing, pointing toward a future in which more parts of the airport are managed through real time data and predictive analytics.

For now, the partnership between Munich Airport and Celonis illustrates how combining operational data with process intelligence can turn a traditionally opaque area of airport operations into one that is measurable and manageable, with tangible effects on baggage reliability and the overall travel experience.