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The Future of Smart Fleet Planning

Planning the right vehicles at the right place and right time is one of the key topics in the mobility industry. As a global premium service provider and innovation leader in the mobility sector, SIXT pursues a consistent digitalization strategy that encompasses both its product portfolio and sales channels as well as its operational business processes. New data-driven approaches and data science based solutions incl. use of AI provide enormous potential for the comprehensive optimization of fleet planning process.

#SustainableFleetPlanning #FleetOptimization #DataDrivenFleet

  • ‚úÖ Challenge completed
  • ūüŹĀ Winner
    Congratulations to A.S. & Eol Robotics!
  • ūüŹÜ  Rewards
    Prize pool of EUR 5,000 + funding and collaboration programs + joint paper
  • ūüĆé Scope

    International - open to participants from all over the world

Important details
Winning Teams
About Sixt

This challenge is part of the AI & Data Science Innovation program. You can find an overview of the program and the other challenges here.

Smart Fleet Planning 

Increasing urbanization, city mobility, and the need for action to make the best use of resources associated with climate change confront the entire mobility industry with a range of challenges that will transform existing business models and operational processes.

As one of the world's leading providers of mobility services, SIXT is driving this transformation.

The current mobility sector covers a wide variety of operations, interactions, and services, where planning and forecasting processes can be taken to a new level with emerging technologies. Planning, procurement, and control of vehicles stretch across different channels, systems, operations, and departments. New data-driven approaches and data science based solutions including the use of AI provide enormous potential for the strategic fleet planning process.


Therefore, the central question of this challenge is: 

  • How does an optimal fleet planning powered by AI-driven and other data science based solutions look like?

The challenge is built on the following steps, which are relevant to tackle the overall mission:


The mobility space faces rapidly changing influencing factors and market demands. Therefore, a sound understanding of upcoming long-term developments and trends is crucial. The task here is to identify forecasting methods to calculate potential long-term (1-2 year) demand predictions including possibilities of all assessing uncertainties like e.g., macroeconomic trends, OEM supply situation etc.

  • Which forecasting methods will facilitate long-term potential/unconstrained demand predictions including uncertainty assessment for an optimized strategic fleet planning?


Various identified factors influence the actually realized demand like e.g. price, customer purpose etc. For understanding which unconstrained demand could be realized, e.g. at a certain price level, it is essential to understand demand elasticity and influencing factors. The key task of this step is to derive appropriate approaches on sufficient granular elasticity models.

  • Which factors influence demand and to what extent?
  • What are useful approaches to measure demand elasticity?


The final objective of the Challenge is to discover how unconstrained demand and demand elasticity interplay and how an optimal fleet on certain business objectives can be derived from this.

The aim is to develop an implementable algorithmic/AI solution to take SIXT¬īs fleet planning systems to the next step.

  • What algorithms and AI approaches deliver a comprehensive solution for an optimal fleet planning system?


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