case study
Transforming fleet operations: How a tailored tool improved efficiency for a shared bike service
Sven Van Hoorebeeck
In the fast-growing world of shared mobility, efficient fleet management is critical to meeting customer expectations and maintaining profitability. For a leading shared bike service provider, this meant finding innovative ways to ensure bikes were always in the right locations, properly maintained, and ready for use. Faced with operational challenges that threatened to slow their growth, the client sought a practical and scalable solution to streamline their processes and optimize costs.
Challenge
As the client expanded into the rapidly growing electric bike segment, they encountered new challenges in managing their operations. Unlike their traditional bikes, e-bikes required timely and recurrent battery swaps. Additionally, rebalancing both e-bikes and non-electric bikes across locations became a focus point to ensure availability to customers and maintain compliance with SLA agreements. The client sought an optimized system to centralize oversight and streamline their fleet management. While they considered AI-powered tools to address these issues, none of the options available on the market met their specific needs—many were overly complex, costly, or unsuitable for their unique operational situation. A tailored, practical solution was needed to address these pain points and improve efficiency across their growing network.
By developing the tool and incorporating learning sessions within the company, we ensured seamless adoption and immediate usability for all operational team members.
Approach
To address these challenges, we developed a tailored solution to streamline their fleet management process. An AI-powered tool was developed and deployed over the span of 7 weeks. While many off-the-shelf AI solutions were overly complex or expensive, we identified a way to use AI selectively to maximize efficiency. A tool from Google Cloud Platform (GCP) was integrated to optimize routing, ensuring service rounds were as efficient as possible. This allowed us to harness AI’s power where it delivered real value—route optimization—while maintaining control over the tool itself, thus keeping operational decisions in the hands of the client.
Through extensive collaboration with the client, we not only developed a practical tool that delivered immediate results while minimizing operational costs but also ensured that the client fully understood and could effectively use it in their daily operations.
The Three-Step Solution:
Status Assessment: The tool evaluates the current situation, assigning each location a status—green, orange, or red—based on parameters to measure the urgency. The parameters are manually adjustable to adapt to specific situations, such as large events.
Optimized Routing: Locations in need of an intervention are grouped together and an optimized service route is created which minimizes travel distances and thus financial costs.
Interactive Map Generation: An interactive map is generated, detailing specific actions required at each location for the external service partners doing the rounds. This map prioritizes locations based on travel efficiency and provides partners with all necessary information to manage fleet operations effectively.
This solution replaced the need for complex, off-the-shelf AI systems by focusing on a straightforward, practical design that directly addressed the client's challenges. Sometimes, simplicity outperforms complexity in achieving impactful results.
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Impact
The implementation of this solution greatly enhanced the client’s fleet operations while keeping costs in check and meeting customer expectations. The tailored approach ensured that the tool functioned perfectly for the operational model of the client, kept knowledge in-house and was aligned with the client’s long-term goals. By avoiding the fees associated with the off-the-shelf tools, it saved the company more than €1000 per month.
Summary
A shared bike provider needed a better way to manage e-bike battery swaps and fleet rebalancing.
Off-the-shelf AI tools were too complex or costly, so a tailored solution was built in 7 weeks.
The tool used AI only where it added value—route optimization—keeping operations simple and efficient.
The solution saved over €1000/month and ensured easy adoption by the team.