Conode for Airlines

Transforming an Airline’s Catering to Reduce Food Waste by >25%

Inaccurate Meal Predictions lead to millions of pounds lost on food waste, increased costs, and undermined sustainability goals.

Can airlines continue to delight customers during their onboard experience, while also saving money?

The Objective

To offer fresh, high-quality meals with weekly menu changes to delight customers and enhance the in-flight experience.

The Problems:

Complex data structures.
Weakly-Linked ID Fields - fragmented data.
Other datasets yet to be taken advantage of.

The status quo of over-uplifting perishable meals is costing catering

How Conode Fixed this problem

Effortlessly built a custom knowledge graph

Import the representative meal orders and meal summary data using simple natural language queries. Conode is completely code-free allowing seamless creation and curation of Knowledge Graphs.

Created Passenger Profile Types

Simplify data complexity to better understand passenger preferences, across specific flight routes that have the largest portions of waste.

Identified consumption patterns

Uncover historic consumption patterns by visualising key data dimensions such as flight routes and embeddings.

Developed a predictive model

Using Conode a new predictive model was designed to optimise catering and ensure the right meals are provided to the right passengers.

Results

In just a single session, Conode empowered the airline to optimise meal planning and reduce food waste by over 25%, achieving:

Substantial Savings: Over £8 million annually in business-class catering alone.
Improved Sustainability: A notable reduction in environmental impact.
Enhanced Customer Satisfaction: Fresh, high-quality meals tailored to passenger needs.

Additional use cases for Airlines

Optimising Meal Uplifts based on Passenger Profiles

Identify flights with excessive meal uplifts and determine if socio- demographic profiles of passengers can predict food preferences, especially for perishable meals that contribute significantly to
food waste.

Having just scratched the surface in this session, we look forward to expanding our analysis by integrating additional data and scaling up our investigation to uncover deeper insights.

Examine Seasonal Demand

Evaluate customer preferences over time, focusing on how demand evolves with seasonality and flight schedules (e.g., morning vs. night flights).

Customer Experience

Map flight data with customer surveys to gain deeper visibility into customer satisfaction and identify opportunities to improve the experience on key routes.