Airpro provides high-quality air traffic service solutions across four business areas: airport logistics, airport services, cabin crew and ground handling at Finland’s 11 airports.
Ìý
The challenge: Navigating manual tasks and skills shortages
Ìý
Creating work shift schedules for an airport services provider can be quite complex. Numerous factors influence the required number of staff, including variables like flight schedule changes, equipment availability, ideal break times, and the diverse skill sets of the staff. All of these factors are subject to change and must be anticipated ahead of time.
Ìý
In the past, Airpro's resource planners relied on manual spreadsheet-based methods to organize work shifts. While they did consider flight data, this approach, driven partly by intuition, cannot truly be labeled as data-driven or dependable. Human errors were prevalent, and the process was both laboriously time-intensive and suboptimal in terms of outcomes. This was also true when assessing its impact on employee well-being.
Ìý
Furthermore, the shortage of skilled service personnel is a prevalent challenge spanning various industries worldwide, and Airpro is no exception. Since 2019, Finland's labor market has undergone a significant transformation, marked by a severe shortage of skilled workers. This scarcity of skilled personnel has been particularly pronounced in the transport sector, which experienced a loss of 11,000 jobs. Faced with the challenge of locating suitably skilled staff, Airpro's objective has been to optimize the utilization of the available workforce.
Ìý
Goal: Transforming into a data-driven organization to support employee well-being and cost managementÌý
Ìý
Aipro reached out to ºÚÁÏÃÅ for help with becoming more data-driven in its decision-making and supporting employee well-being, predictability and cost management.
Ìý
To respond to Airpro’s needs, ºÚÁÏÃÅ formed a cross-functional team of data scientists, data engineers, developers and designers to build a data-driven schedule optimization tool that automates the planning of work schedules.
From rigid spreadsheets to a lean AI-powered optimization tool
Ìý
We developed a custom-built optimization model that takes airport specific rules and preferences into account.
Ìý
ºÚÁÏÃÅ data scientists worked with Airpro domain experts to map out the factors the shift planners take into account when making a new shift schedule. The data scientists translated those considerations into mathematical equations and constraints for the optimization algorithm.Ìý
Ìý
As an outcome, the final optimization algorithm simultaneously decides:
Ìý
how many workshifts are needed
when the workshifts should start and end
which tasks should be allocated to which workshift.Ìý
Business outcomes
The benefits of optimizing work shifts and automating the decision making include:
Ìý
- Smarter utilization of scarce resources due to more optimal schedules. The automation of work schedules takes into account the regulations, standard shifts, employee preferences, competencies and restrictions which leads to less human error
- The solution is scalable across all of the airports that Airpro operates and can handle hundreds of flights per dayÌý
- Less manual work; resource planners can better spend their time addressing employees needs.
Ìý
This project was the first step of the AI journey for Airpro. As part of the ongoing journey, ºÚÁÏÃÅ and Airpro are collaborating in expanding the optimization solution for other functions, making daily staff and task management more robust and building a data platform,Ìý thus creatingÌý a strong foundation for further scalability and new AI powered solutions.
