Munich Airport Taps Celonis to Cut Baggage Delays

Munich Airport is expanding its use of process mining specialist Celonis to monitor baggage handling in near real time, aiming to pinpoint the causes of baggage delays and strengthen the hub’s performance during busy travel periods.

Data-Driven Push to Improve Baggage Reliability

Munich Airport has been working with Celonis for several years as part of a broader digitalization strategy, using the company’s process mining software to analyze operational workflows. Publicly available corporate reports indicate that Celonis has been integrated into airport-wide initiatives focused on process optimization and automation, creating a foundation for more targeted applications in ground handling and baggage operations.

Process mining software reconstructs actual workflows from event data generated by operational systems. For an airport, that can include time stamps from check-in, baggage sorting, loading, unloading, and transfer processes. By mapping how baggage really moves through the system, rather than how procedures are designed on paper, Munich Airport can identify process variants linked to late bags, bottlenecks in sorting halls, or recurring delays at specific transfer points.

The airport’s most recent digital transformation and sustainability disclosures describe Celonis as a core tool for process optimization, highlighting its role in efforts to modernize the working environment and make data-driven decisions. That positioning suggests that baggage handling, traditionally one of the most complex and failure-prone parts of hub operations, is a prime candidate for deeper analysis and continuous monitoring.

Industry case studies on process mining in aviation, including work with German carriers and other airport stakeholders, show that similar techniques have already been used to improve turnaround processes and on-time performance. Munich Airport’s collaboration with Celonis builds on this experience and extends it into the baggage domain, where delays directly affect passenger satisfaction and airline costs.

Focusing on Transfer Hubs and Peak-Load Stress Points

Munich serves as a major European transfer hub, with a high share of connecting passengers whose bags must be moved rapidly between incoming and outgoing flights. Published research referencing Munich Airport and Celonis notes that process mining has been applied to turnaround processes, revealing where buffers and handover points are most sensitive to delay. Applying the same analytics to baggage workflows allows the airport to focus on steps where misrouted or late bags are most likely to occur, such as tight minimum connection times and high-volume transfer corridors.

Reports from recent travel seasons have repeatedly highlighted operational stress at major European hubs, including queues at check-in, labor shortages, and backlogs of unclaimed bags. In that context, Munich Airport’s use of Celonis provides a way to quantify how disruptions propagate through baggage systems. For example, event data can show how late-arriving flights affect outbound loading, how often bags miss their intended flight, and how quickly recovery processes restore normal service.

By drilling into these stress points, Celonis dashboards can surface patterns that are not obvious in day-to-day operations. Recurrent slowdowns on certain belts, repeated manual interventions for specific routes, or consistent delays when multiple widebody aircraft arrive in close succession can be flagged and ranked by impact. This makes it possible for the airport and its ground handling partners to prioritize targeted investments, schedule additional staff for defined peaks, or adjust connection buffers where data shows that risk is highest.

Industry analyses of process mining in air transport suggest that improvements in punctuality and baggage reliability often come from many small corrections rather than a single major change. Munich Airport’s data-driven approach is aligned with this pattern, using Celonis to support a series of incremental adjustments that collectively reduce the risk of baggage delays.

From Historical Insight to Near Real-Time Control

Early deployments of process mining often focus on retrospective analysis, using historical data to understand how processes behave. According to academic work involving Munich Airport and Celonis, such analyses have already delivered greater transparency into airport processes and revealed previously hidden process variants. In baggage handling, this type of analysis can reveal where procedures deviate from standards, where handovers between systems or teams are inconsistent, and where manual workarounds create additional risk.

As data integration matures, airports are increasingly moving from retrospective dashboards to near real-time monitoring. Celonis platforms can ingest data continuously, enabling operations teams to track key indicators such as bag transfer times, load completion moments, and backlog levels during the operating day. For a hub like Munich, that capability allows earlier detection of developing problems, such as a sudden spike in late bags for a particular destination or an abnormal queue in the sorting system.

Publicly available information on Munich Airport’s digitalization roadmap describes a push toward broader automation and greater use of advanced analytics across the campus. In that framework, Celonis serves as a layer that links raw operational data to decision-making. When configured for near real-time visibility, it can trigger alerts for supervisors, support scenario planning during irregular operations, and help coordinate responses between airlines, ground handlers, and airport units involved in baggage services.

Industry observers note that this type of control-tower view can be especially valuable during seasonal peaks, severe weather events, or airspace disruptions, when baggage systems are at greatest risk of overload. The combination of historical models and live monitoring increases the likelihood that emerging issues are mitigated before they result in significant numbers of delayed bags.

Aligning Baggage Analytics With Broader Automation Efforts

Munich Airport’s focus on baggage delays using Celonis is part of a wider modernization program that also includes investments in automated baggage unloading and upgraded ground support technology. Integrated reporting for 2024 highlights a pilot of automated container unloading equipment in the satellite terminal area, indicating that the airport is testing new hardware solutions alongside data-intensive software tools.

Bringing these strands together, process mining insights from Celonis can help determine where automation yields the most benefit. If data shows that particular aircraft stands, baggage halls, or transfer corridors consistently suffer from congestion or manual handling bottlenecks, those locations may be prioritized for new unloading systems or layout changes. Similarly, analytics on dwell times and handling errors can inform how future baggage infrastructure is planned and phased.

According to published coverage on airport digitalization, there is growing interest in combining process mining with simulation and predictive models to test changes before they are implemented on the ground. In the baggage context, this could allow Munich Airport and its partners to model how new equipment, staff rosters, or schedule patterns might affect delay risk, helping to avoid costly trial-and-error in live operations.

The use of Celonis also aligns with Munich Airport’s stated goals around resource efficiency and employee workload. By reducing avoidable rework, misrouted bags, and crisis-driven manual interventions, process improvements can support more stable operations for ground staff, while also improving reliability for passengers and airlines.

Implications for Passengers and the Wider Industry

For travelers passing through Munich, the use of Celonis to track and reduce baggage delays is largely invisible, taking place behind the scenes in control rooms and data platforms. Nevertheless, its effects can be felt through more predictable transfer experiences, shorter waits at baggage claim, and more reliable delivery of delayed bags in the event of disruptions.

Industry watchers suggest that data-driven operations of this kind are likely to become standard at large hubs, particularly where passenger volumes and airline connectivity make traditional manual oversight insufficient. Munich Airport’s work with Celonis places it among a group of early adopters using process mining as a central tool for managing complex ground operations.

Other airports and airlines are monitoring these developments as they weigh their own investments in process analytics. Case studies featuring Celonis in aviation settings, including at German carriers and airport operators, point to measurable gains in punctuality and resilience when process mining is embedded in day-to-day management.

As global air travel continues to recover and seasonal peaks become more pronounced, the pressure on baggage systems is unlikely to diminish. Munich Airport’s decision to lean on Celonis for deeper visibility into baggage handling reflects a broader industry shift toward using granular operational data to address long-standing pain points, with the goal of turning baggage reliability into a competitive advantage.