Enabling data-driven innovation at Fraport

Challenge

Airports such as Frankfurt Airport (Fraport) face mounting competitive pressure from rapidly growing Middle Eastern hubs and shifting market dynamics driven by the rise of low-cost carriers. At the same time, passenger experience is becoming an increasingly important competitive factor. Fraport sat on vast operational data from multiple business units — but it was siloed, rarely connected, and almost never used systematically for decision-making. Instead, operational processes relied on rules of thumb, leaving significant value locked inside the data.

Approach

Fraport established a Smart Data Lab: a structured four-week analysis sprint in which cross-functional teams of data scientists, business analysts, subject matter experts, and IT specialists worked together to address four specific business challenges. STAT-UP led the analytical work on two of these challenges. To optimize retail revenue, a multivariate model was developed that quantifies the relationship between flight positioning, passenger routes, wait times, and purchasing behavior. For arrival time forecasting, data from radar, weather, and flight operations were combined into a hierarchical model that accounts for airspace movements, ground movements, runway usage, and taxi times.

Impact

The arrival time prediction model showed potential to cut waiting times for ground service providers by up to 60% — directly reducing operating costs and improving service quality. The retail model enabled a more systematic approach to flight positioning decisions and provided a quantitative basis for negotiations with airlines. Furthermore, the project demonstrated that a number of established decision-making rules were not supported by the data. The Smart Data Lab was subsequently established as a permanent unit at Fraport — marking the beginning of a lasting shift towards data-driven decision-making across the organization.