identifying automation potential in your production process
Many small and medium-sized enterprises recognize the potential of automation but often struggle to get started—not because of the technology. Why? Getting started is often challenging. Processes have evolved over time, data is not available in a structured format, and there is no clear assessment of which areas can be automated cost-effectively. This white paper shows you how to get started in a structured way, from process analysis to a turnkey system. A comprehensive real-world example from final assembly in the rail infrastructure sector illustrates the implementation.
What You’ll Find in This White Paper
You’ll gain a solid foundation for better identifying and evaluating automation potential in your production and for planning the next steps.
- Clarity on how to get started: Two starting points—with and without a concrete idea—and how to proceed in a structured manner in both cases.
- A proven process model: Six steps from defining objectives to supporting the launch, with clear decision points.
- Real-world example: How Vossloh Rail Services Deutschland GmbH fully automated a manual assembly process, including the challenges, implementation, and results.
- Economic Context: Which factors determine profitability
- Transferable Approaches: Which types of processes can be automated across industries and where your company can start.
- Outlook on Physical AI: How AI systems will transform manufacturing automation in the coming years and what groundwork you can lay for this today.
How Vossloh Successfully Introduced the First Robot at the Veddel Plant
Vossloh AG is a global leader in the field of rail infrastructure. In the final assembly department of its subsidiary, Vossloh Rail Services Deutschland GmbH, grinding wheels for high-speed grinding trains had previously been assembled manually. This was a repetitive process. Batch variations were not consistently detected.
Together with EDAG, Vossloh automated this process: An industrial robot now handles retrieval, assembly, and quality inspection. AI-powered image processing detects the position of the components in real time, even when they are delivered in an unstructured manner.
“With EDAG’s support, we were able to successfully automate a manual assembly process. We now work more efficiently, have more consistent process quality, and—for the first time—can ensure 100-percent quality inspection. At the same time, this was our first step toward systematically integrating robotics into our manufacturing operations at the Veddel site.”
(Sergej Lemke, Quality and Production Engineer, Vossloh Rail Services Deutschland GmbH)
Download the white paper for free here!