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BMW Group Logo
Industry: Automotive
Location: Germany

From classical infrastructure to Cloud and Edge with SUSE

Highlights

  • Able to deliver more efficient, secure and scalable services to meet diverse customer needs.
  • The use of Harvester and RKE2 allow BMW to develop a central connector system to securely bridge PLCs and production systems with the cloud.
  • Using RKE2, Rancher and Longhorn, BMW was able to deploy LiDAR technology that ultimately enables automated driving of cars.
  • Enhance production efficiency by implementing an indoor GPS-like system to track tools and equipment.

Products

At SUSECON 2024, Andreas Pöschl, senior solutions architect at BMW Group, spoke about how the vehicle and motorcycle manufacturer revolutionizes production with AI and transforms IT with a cloud-first and edge strategy.
 

At-a-Glance

BMW’s cloud-first strategy and SUSE’s Kubernetes ecosystem with Rancher, Harvester, and Longhorn have boosted efficiency, expanding data centers and enabling edge innovations. Clear guidelines ensured effective workload placement, while projects like driverless in-plant maneuvering, indoor GPS for tools, and AI-driven quality control enhanced production.

Introduction

"What we also managed to do when transitioning from classical infrastructure to edge is, we have two data centers in each plant and asked the data center team to add a third. We tried that for 25 years, and it didn’t happen. Then suddenly, they understood—yes, Kubernetes is here, and now we have three data centers."

Andreas Pöschl
Senior Solutions Architect
BMW Group

Takeaways

  • Cloud-first strategy: BMW adopted a cloud-first strategy four years ago to boost efficiency and adaptability, despite initial customer resistance to moving applications from on-premise to public cloud.

  • Kubernetes ecosystem: Utilizing SUSE Rancher, Harvester and Longhorn, BMW modernized its infrastructure, increasing data centers per plant from two to three for improved service delivery.

  • Guidelines for success: Successful edge computing transformation required clear guidelines on workload placement, defining when to keep workloads on-premise due to factors like low latency and data sovereignty. An onboarding process was established to ensure workloads were suitable for cloud or edge environments.

Innovative Edge Computing Solutions:

 

  • Automated driving in-plant project: Deployment of LiDAR technology allows for driverless maneuvering of cars in production plants, utilizing a combination of virtualized environments and bare metal servers with RKE2, Rancher, and Longhorn.

  • Indoor GPS-like system: Implemented to track tools and equipment, improving production efficiency through technologies like RFID sensors to ensure proper torque application during assembly.

  • AIQX project: Employs AI for quality control, verifying assembly steps while addressing privacy concerns through real-time measures and leveraging existing vehicle camera infrastructure to enhance safety monitoring