The airfreight industry is changing enormously with the digitalization. Coming from a paper based business digital and data-driven processes are gaining importance. Especially for companies with a non-digital core business, implementing a successful Analytics case requires more than just a perfect model. Based on the use case to predict airfreight load factors the session attendees will learn which steps we took from the idea generation to implementation and how we empower our staff to make data-driven decisions in a highly volatile industry.
Target Audience: Project Leader, Decision Makers, Data Scientist
Prerequisites: Basic knowledge and experience in Data Science
Traditionally, the airfreight industry has been a people's and paper dominated business. However, with new technologies available digital and data-driven processes are gaining importance. Additionally, it is a highly volatile and complex business with various parameters to monitor. Implementing a successful Analytics use case requires more than just a perfect model - especially for companies with a non-digital core business.
We developed a model to predict airfreight load factors to support sales and revenue management so that our staff can take faster and more informed data-driven decisions. The session will cover the different steps we took from the idea generation to process integration and will outline data discoveries during the model development as well as important findings regarding a successful use case implementation.
Attend the session to ....
- ... learn how we at Lufthansa Cargo approach Data Science topics
- ... learn how we empower our staff to make data-driven decisions
- ... learn about the importance of a suitable process integration
- ... learn why certain flights are easier to predict than others
- ... get key insights into the aviation industry