Crew roster scheduling is a challenge to manage:
The airline must calculate crew roster payment details taking into account their bi-weekly pay schedule, as well as other factors such as number of years served, overall flying time, shift length, time zones travelled, government regulations, civil aviation authorities mandates, and shifts worked over holidays and weekends. All these criteria must be matched against the assigned PayScale and the designation of the flying officer, which is complicated to capture, manage, and store and typically runs over several pages and columns. From month to month, nearly 35% of crew roster payment details must be adjusted for accurate payments to be made.
Automation Improves Roster Management and Accuracy
With analytical and mobile technologies available in the market today, it is possible for airline authorities to modernize and streamline the trip coverage details. Jiffy.ai’s automation platform that includes AI-augmented intelligent document processing, real-time machine learning and natural language processing capabilities helped save time, reduce administrative costs, and improve accuracy.
Intelligent document processing for better roster payment management:
Jiffy.ai successfully automated an end-to-end roster payment management process that involved nearly 250,000 documents for just a single travel leg between two destinations. Since the roster consists of many datapoints and runs into reams of sheets, an intelligent document processing method was critical to implement.
Through our intuitive user interface for RPA bot design, the business user defined the fields, the bots were deployed, data was successfully extracted, and the bots continued to self-learn in the background without interrupting the business user and ensured the process moved seamlessly.
With just a 6-member team, a 5-day audit period and nearly 14,000 audits per month to be reviewed, Jiffy.ai downloads the trip data, extracts all the necessary trip information and stores it in a JDI interface for matching and reconciliation. Our automation solution now handles nearly 35% of all exceptions, provides 95% data extraction accuracy and reduces operational costs by 50-60%.
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