AI-based Aircraft Ground Handling application tailored to airline Operation Control Center (OCC) operations teams provides you with insights into excess fuel consumption and CO2 emissions due to ground operation delays and its saving potential through an early delay awareness prediction model. Gain granular insights into daily ground operations delay predictions on a per-flight level and its corresponding CO2 emission savings potential by preventing excess APU runtime and optimizing the aircraft turnaround time, thereby improving planning certainty.
This is where Deloitte and Celonis Combine their Strengths:
Identify delay minimization, fuel-burn, and CO2 emission saving potentials through the application of an early delay awareness machine learning model.<!— htmlmin:ignore —>
Trigger preventive actions before undesired flight delays occur.<!— htmlmin:ignore —>
Investigate ground operations delay root causes and sustainably improve your process accordingly.<!— htmlmin:ignore —>
This view outlines the retrospective savings of fuel and CO2 emissions using evidence data if the predictive machine learning model would have been applied. You can drill down and investigate different dimensions to identify the circumstances in which early delay awareness has the largest business and sustainability impact.
The predictive monitoring performance deep dive view provides you with an overview of the machine learning model’s prediction quality, particularly its prediction classes, regression fit, and recall and precision metrics over time.
This view shows the prediction for each flight and compares it to the actual delay recorded in the event data to calculate the prediction class and CO2 emissions saving potential.
The app includes two root cause analyses to identify weather- and airport-related ground operations delay root causes.
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