AWS re:Invent 2025 - Modernizing Legacy Systems: Boeing's PLM Cloud Transformation (IND321)

Modernizing Legacy PLM Systems: Boeing's Cloud Transformation on AWS

Challenges with Traditional PLM Architectures

• Performance and Scalability: As product data and analysis solutions expand, with increased 3D model fidelity, business intelligence, metadata, and variation complexity, it becomes more challenging to maintain performance and scale.
• Data Friction: Historical on-premises architectures and legacy integrations lead to data silos and friction, hindering global collaboration.
• Global Collaboration: The need for engineering activities spanning multiple regions and the increasing prevalence of mergers, acquisitions, and partnerships make global collaboration more difficult.

Principles for PLM Modernization on AWS

• Customer Obsession: Understanding customer needs and working backwards to break down complex problems.
• Scalable Solutions: Designing architectures and operations that can scale appropriately with customers.
• Automation and Self-Service: Leveraging tools like CloudFormation and Terraform to ensure consistency, reliability, and efficiency.
• Rapid Iteration: Prioritizing speed and agility over perfection to deliver customer value rapidly.

Boeing's PLM Transformation on AWS

Challenges with On-Premises PLM Infrastructure

• Infrastructure Capacity: Limited by yearly budget and capital acquisition cycles, leading to competition for resources and delays in provisioning.
• Cost Models: Complex chargeback models and inconsistent cost allocation across services.
• Operational Overhead: Significant lag between approval and provisioning, with incentives to hoard resources.

Opportunities with AWS Cloud

• Enterprise Internal Cloud: Boeing worked with AWS to create network-segregated regions in AWS GovCloud, simplifying application access and integration.
• Empowered Teams: Enabling architects to design and operations teams to build without bureaucratic delays.
• Migration Outcomes:
  • Accelerate infrastructure operations (provisioning, decommissioning, upgrades)
  • Avoid bureaucracy and approval cycles
  • Develop AWS proficiency within the team
  • Reduce costs through elastic compute and consumption-based pricing
  • Improve configuration management with infrastructure as code
  • Enable "follow the sun" operations and tech insertion

Technical Approach and Outcomes

Infrastructure as Code and Automation

• Terraform: Used to define and provision consistent, right-sized infrastructure across environments.
• Environment Templates: Standardized baseline configurations for small, medium, and large environments.
• Parallel Deployments: Deploying each service to its own host, enabling parallel installation and reducing deployment time by over 60%.

AWS Services Leveraged

• EC2: API-driven, right-sized instances with user data scripts for bootstrapping.
• Auto Scaling Groups: Enabling automatic scaling driven by application metrics and recovery mechanisms.
• EFS: Replacing legacy network-attached storage with a scalable, API-driven file system.
• RDS: Managed database service providing high availability, automated backups, and simplified administration.

Operational Improvements

• 78% reduction in manual tasks and touch points for a single environment.
• 99% improvement in full environment build time, from up to 30 days to approximately 5 hours.
• Ability to deploy hundreds of environments in parallel, instead of days or weeks for a single build.
• Fewer support tickets and 2 AM incidents, with the team focused on value-added activities.

The Future of PLM: Integrating Generative and Agentic AI

• Generative AI for PLM: Enabling natural language queries to the PLM system, such as material composition, part specifications, and supplier information.
• Agentic AI in Workflows: Integrating AI agents to automate repetitive tasks, check for compatible materials, and provide real-time bill of materials analysis and change recommendations.
• Improved Collaboration and Decision-Making: AI-powered PLM platforms can enhance knowledge discovery, foster cross-team collaboration, and provide relevant contextual information to support informed decision-making.

Key Takeaways

• Boeing's PLM modernization on AWS resulted in significant operational improvements, including a 99% reduction in environment build time and a 78% decrease in manual tasks.
• Leveraging infrastructure as code, standardized environment templates, and AWS services like EC2, EFS, and RDS enabled faster, more consistent, and cost-effective PLM operations.
• The future of PLM involves integrating generative and agentic AI to streamline knowledge discovery, automate repetitive tasks, and enhance collaboration and decision-making.
• AWS services and the AWS approach to modernization, including customer obsession and rapid iteration, were critical to Boeing's successful PLM transformation.