Airline catering operations generate millions in disposal costs each year, yet waste management practices remain largely linear—relying on landfill and incineration with limited visibility into root causes. With more than 700 million passengers served annually across 200+ catering units and food waste reaching roughly 20% of total production—75% driven by overproduction and expired stock—the industry faces mounting financial and environmental pressure as global air traffic continues to grow.
In collaboration with a leading global airline catering company, MIT CTL applied system dynamics modeling and machine learning across 72 catering units to map waste flows, identify systemic patterns, and evaluate eight waste management solutions. The results demonstrate 91.1% modeling accuracy and show that shifting from landfill to alternatives like biogas processing can reduce emissions by up to 99%, while delivering $31,000–$50,000 in annual savings per large facility. The framework enables scalable, facility-specific circular strategies grounded in data rather than guesswork.
Scaling Circular Supply Chains
An MIT CTL project in airline catering
The Challenge
In the airline catering industry, food waste generation is accelerating and waste management practices remain outdated. Airline catering companies generate millions in waste disposal costs while operating primarily through the traditional methods of landfill and incineration. Catering operations lack visibility into where waste originates, why it's generated, and which solutions offer the best combination of cost efficiency and environmental benefit. With air passenger traffic projected to double over the next two decades, this gap prevents the industry from scaling circular supply chain practices globally.
Key Insights
- 16+ metric tons of organic waste generated monthly at largest units*
- 20% of all food used in airline catering operations is wasted†
- 75% of food waste driven by overproduction and expired stock*
- 700+ million passengers served annually across 200+ catering units in 60+ countries*
*Internal company data
†"Food Waste In An Airline Caterer's Production Kitchen," Ross, 2014
The Research
MIT CTL’s Emerging Market Economies Logistics Lab, directed by Dr. Chris Mejía Argueta, worked with a leading global airline catering company to bridge the gap between recognizing food waste as a problem and implementing scaled solutions. The research applied system dynamics modeling combined with machine learning to map waste streams with precision, analyzing 72 catering units across multiple regions and identifying operational patterns that enable global scaling.
By clustering catering kitchen operations using machine learning, they identified that waste is not randomly distributed but follows systemic patterns driven by production volume, segregation practices, and disposal methods. The analysis revealed that waste generation is highly responsive to targeted interventions on segregation rates and collection timing.
Meet the Waste Optimization Framework
Using system dynamics simulation and machine learning, the framework enables airline catering operations to:
- Model waste flows to capture production, segregation, and disposal across entire systems
- Identify optimal waste segregation strategies that simultaneously minimize costs and environmental impact
- Compare eight different waste management solutions with quantified financial and environmental outcomes
- Scale recommendations globally across facility types using operational clustering aligned with local cost structures and capabilities
The Impact
For supply chain companies, the implications are significant:
- Achieve significant cost savings (potential $31,000–$50,000 annually per large facility) while reducing CO2 emissions by up to 99%
- Select facility-specific waste solutions tailored to regional costs, regulations, and constraints: biogas, internal composting, food banks, or animal feed
- Transition from linear disposal models to circular solutions validated through rigorous data analysis and system dynamics modeling
Model results:
- 91.1% accuracy in modeling waste generation and disposal across complete operational systems
- 99% reduction in emissions possible by shifting from landfill (1.70 tons CO2e/ton) to biogas processing (0.010 tons CO2e/ton)
- 45% optimal waste segregation rate for minimum combined cost and environmental impact
