In the dynamic realm of the airline industry, ensuring a just-right and in-time headcount is not just a matter of operational efficiency but a strategic imperative. Airlines are turning to advanced forecasting models, machine learning algorithms, and innovative technologies to calculate crew headcount accurately. This article explores the key components to achieve optimal staffing levels, taking into consideration various factors such as workload, flight time volume, and personnel constraints.
Calculation of Crew Headcount: A Data-Driven Approach
A cornerstone of efficient crew management is the meticulous calculation of headcount based on forecast models and statistical data. Airlines are leveraging machine learning models to forecast workload and flight time volume, factoring in the intricacies of flight structures. These models consider historical data, annual and seasonal schedules, pairing solutions, ground activities, and personnel hiring and termination information.
Moreover, constraints on individual work and flight time limits are integrated into the calculations to ensure compliance with industry regulations and employee well-being. The result is a comprehensive forecast of the required crew size, aligning with the anticipated workload and flight schedules.
«What if» scenarios
Airlines are adopting a proactive approach by considering 'What if' scenarios. This allows for real-time adjustments to calculation parameters such as flight time, working hours, and vacation costs. By simulating various scenarios, airlines can evaluate the impact on crew sizing, enabling them to adapt swiftly to unforeseen changes in operational demands.
Determining and elimination of crew shortage/surplus
Based on accurate crew size forecast airlines proactively determine the shortage/surplus of crews by qualification groups. To prevent a shortage or surplus of personnel, airlines can proactively create a hiring and employee development plan. Additionally, they can ensure the proper distribution of work and rest throughout the year to cover all peak periods.
To assist airlines in proactive management we created Planex crew headcount for accurate forecast and decision making based on machine learning models.