Robotic process automation

Project Summary

A large national carrier in the competitive airline industry sought to enhance operational efficiency and cost-effectiveness by leveraging Robotic Process Automation (RPA) to streamline various business units and maximise aircraft utilisation while maintaining high standards and meeting SLA requirements. The client partnered with Avec to identify ‘RPA fit’ processes, establish a Centre of Excellence (CoE), and build a virtual workforce to perform manual, repetitive, and rule-based tasks in capacity load planning using RPA.

The challenge

In the highly competitive airline industry, our client, a large national carrier with a global fleet, sought a technology-based solution to reduce costs across various business units and maximise aircraft utilisation while maintaining standards and meeting SLA requirements. Recognising the potential of Robotic Process Automation (RPA) to address these needs, the client partnered with Avec to leverage RPA for operational efficiency.

The solution

To achieve the client’s objectives, the Avec team conducted a formal assessment to evaluate process complexity and target systems, identifying ‘RPA fit’ processes within the finance and commercial sales business units. The project involved building a virtual workforce to perform manual, repetitive, and rule-based tasks in capacity load planning using RPA. Additionally, a Centre of Excellence (CoE) was established to govern RPA projects and identify further automation opportunities. The processes were mapped end-to-end with input from operating procedures and subject-matter experts, and the chosen processes were configured, tested, and deployed. The project also focused on building client resources’ capabilities in troubleshooting and configuration.

The result

The implemented solution by the Avec team resulted in:

  • Substantial reduction in ongoing operating costs, amounting to approximately half a million dollars per annum.
  • Increased commercial freight revenue of $1million.
  • The planning time per flight was reduced by 50%, leading to improved accuracy and the elimination of errors through machine learning.
  • The cycle time was reduced from 4 days to 1 day, resulting in a reduction of full-time equivalent (FTE) positions, with 3 offshore and 2 onshore roles being affected.
  • The rollout to additional days before flight departure improved planning accuracy.
  • The in-house team was coached on multiple automation initiatives.