The AI-based Aircraft Ground Handling application tailored for the airline's Operation Control Center (OCC) management provides you with insights into excess CO2 emissions and the corresponding financial impact caused by inefficient ground operations process executions that led to flight delays. Additionally, this app aggregates early ground operations-caused delay criticality predictions of a machine learning model to outline emission and cost-saving potentials leveraging an airline's historical process execution data.
This is where Deloitte and Celonis Combine their Strengths:
Gain insights into your excess CO2 emissions due to ground operations delays and their financial impact.<!— htmlmin:ignore —>
Monitor the ROI of your predictive machine learning model for early delay awareness and prevention, and identify savings potential.<!— htmlmin:ignore —>
Validate the performance of your predictive machine learning model.<!— htmlmin:ignore —>
This view provides an overview of lost value due to ground ops delays in form of excess fuel consumption, CO2 emissions, and cost impacts. The view allows you to drill down and investigate different value loss drivers.
This view outlines the retrospective savings potential of fuel, CO2 emissions, and total costs if the predictive machine learning model would have been applied using evidence data. You can drill down and investigate different dimensions to identify in which scenarios the predictions have large business and sustainability impacts.
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