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How Pegasus Airlines Reduced Food Waste and Boosted In-Flight Profitability with AI-Powered Demand Forecasting
Pegasus Airlines set out to improve its in-flight café service by tackling the problems of overloading products, running out of popular items, and relying on manual, error-prone planning.
Together with GTech, Pegasus Airlines developed new AI-based demand forecasting models that could predict exactly which products would be needed on each flight. By bringing together sales history and the impact of special days or calendar events, these models made it possible to load the right products in the right amounts for every journey.
Measurable Impact: Setting a New Standard
Pegasus Airline’s transformation wasn’t just about incremental improvement
it set a new standard for efficiency, sustainability, and profitability in airline catering.
Here’s what true data-driven innovation delivers:
32%
Increase in Per-Passenger Café Revenue
30%
Increase in Per-Passenger Profitability
7%
Reduction in Food Waste Rates
Challenge
Pegasus Airlines relied on legacy AI models to forecast in-flight café product demand, but these tools struggled with sudden changes and operational complexity. As a result, flights often faced overstocking driving up costs and food waste or ran out of popular products, hurting customer satisfaction. The lack of transparent, flexible analytics meant teams couldn’t quickly adapt to real demand, undermining profitability and sustainability targets.
- Outdated models made it hard to accurately predict real-time demand for each flight.
- Overstocking led to unnecessary waste and higher costs, while shortages impacted passenger experience.
- Manual processes slowed decision-making and increased the risk of error.
- Environmental sustainability goals were threatened by excessive food waste.
Solution
To solve these challenges, Pegasus Airlines and GTech launched the Waste Optimization Project—building AI-powered demand forecasting and loading optimization models tailored to the unique dynamics of in-flight service.
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Developed machine learning models to forecast demand using sales history, calendar effects, and flight-specific data.
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Used linear programming algorithms to optimize product loading for each flight.
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Automated decision-making, eliminating manual planning and improving operational speed and accuracy.
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Simulated and tested models on real flight data, delivering results far superior to previous manual approaches.
Turning Data into Real-World Results
Pegasus Airlines transformed its in-flight café service with advanced AI-based demand forecasting and product loading optimization
enabling operational efficiency, higher profitability, and a more sustainable, satisfying passenger experience.
AI-Based Demand Forecasting
Machine learning models were developed using historical sales data and calendar effects to accurately predict product demand for every flight.
Optimized Product Loading
Linear programming algorithms determined the optimal product quantities for each flight, minimizing both overstock and shortages.
Reduced Waste, Greater Sustainability
Waste rates were reduced by 7%, directly supporting environmental sustainability targets through lower food loss.
Increased Revenue and Profitability
Per-passenger café revenue rose by 32%, and profitability by 30%, thanks to smarter demand planning and inventory control.
Consistent Product Availability Onboard
Pegasus ensured that requested items were always available during flights—directly increasing positive customer feedback and overall passenger satisfaction.
Enhanced Customer Satisfaction
Improved planning ensured the right products were always available on board, resulting in higher positive feedback from passengers.