Building an AI tool that can provide a prediction for the meals bought on board for each flight independently and each 'fresh' product depending on destination, flight period, the time of the day, the number of passengers, … - this based on real data from a major airline company Aim is : - Reduce Food Waste - Fuel economy - Increase customer satisfaction - Cargo benefits => Commercial, economic, social and ecological benefits

Reducing the ecological footprint of the Airline Industry

Many of us associate an airplane with holidays: destination chosen, tickets bought, luggage packed and off we go. However, behind the scene of every airline many things must be meticulously organised before we can enjoy that relaxed ‘take off’.
One of the many things in that entire chain is the ability to buy fresh/prepared meals on board. Fresh food gets loaded, sold and at the end of the day the unfortunate waste follows. Tons of fresh meals are thrown in the garbage container, a delicate issue for the Airline Industry. In 2018, airlines generated about 6.7 million tons of cabin waste. (source: The International Air Transport Association)

SAS Viya does the magic

The trick is to be able to predict more accurate the necessary meals on board and that’s where SAS Viya comes in. We have built an AI tool on SAS Viya that uses real airline data to predict the meal requirements of each flight. By reducing food waste and fuel consumption we will obtain a positive environmental impact. This of course has also quite some economic impact.

Business Value

To resume the benefits of such a technology for an airline company would be.
– Reducing food waste,
– Optimize meal organisation
– Reduction of the plane weight
– Weight reduction entails reducing fuel consumption.
– Reducing overall its environmental footprint.
– It brings an innovative technology to a consistently moving industry by given them a modern approach of their business.
– Becoming a 3.0 airline company also helps tackling down some negative fact about their business.
– Finally, the economic impact of such an investment is quite noticeable.

Implementation Plan

1 )Gathering of the data and building a data vault model
2) Data importation and complete dataset
3) Data preparation & cleansing
4 ) Building models & compare for efficiency
5) Improve the model
6) Build in parallel a front end application enabling criteria selection and communicate with the model& database to retrieve data

Used technologies
Effort Needed
30 MD
to be calculated
Travel Industry
Application Type
SAS platform
Tech Stack
SAS Viya
Learning Path