Fuel-efficient flight operations and improved Air Traffic Management (ATM) operations are identified as one of the main pillars in achieving net-zero CO2 emissions by 2050. While considerable research has focused on airspace management and ATM operations, flight operations as managed by airlines have received little attention. Accurate aircraft/tail-specific performance modeling is crucial for identifying savings while existing models such as Euro-Control’s BADA and manufacturers’ book models remain too generic. Additionally, trajectories including optimal routes, altitudes, and airspeeds, must be determined to minimize fuel consumption. Emerging solutions leverage in-flight data connectivity and Machine Learning (ML) methods to provide pilots with real-time decision support. However, quantifying and validating saving potentials present challenges due to unpredictable variables and performance modeling complexities. This thesis aims to address these challenges by developing a tail-specific performance modeling framework using high-fidelity flight data and ML methods. The framework identifies and corrects tail- and flight-specific biases from the flight data, allowing fuel savings to be identified on a per-flight basis in post-flight analysis. The tail-specific performance model shows different Maximum Range Cruise (MRC) speeds than generic values determined by the aircraft. The benefits emerging from these optimal speeds are determined by high-accuracy simulations of different cost index strategies on flight-specific and network-wide levels. Three cost index strategies are evaluated and compared to generic MRC operations. Savings in both fuel and time are observed of 75 kg and 93 s, 96 kg and 111 s, and 127 kg and 107 s, on an average per-flight basis. In conclusion, this research demonstrates the existence and magnitude of fuel and time savings by flying tail-specific cruise speeds compared to generic values determined by the aircraft.