Jabil's implementation of GenAI technologies, particularly Amazon Q, represents a comprehensive approach to transforming manufacturing and supply chain operations at scale. This case study demonstrates how a major manufacturing company approaches LLMOps with a focus on governance, value creation, and practical implementation. ## Company Background and Challenge Jabil is the second-largest contract manufacturing company globally, operating across 100 locations in 35 countries. They manage relationships with 400 customers and 38,000 suppliers, processing $25 billion in global supply chain transactions. The company faces significant challenges in managing vast amounts of documentation, multilingual operations, and complex supply chain decisions. ## LLMOps Strategy and Governance The company established a sophisticated governance structure for their AI initiatives: * Created a Data and AI Council with senior leadership (VP/SVP level) representation from each function * Developed comprehensive AI and data policies defining roles, responsibilities, and usage guidelines * Established a dedicated GenAI council as a subset of the main AI council * Implemented strict value-creation requirements for all AI projects * Created three categories of AI initiatives: advanced machine learning, computer vision, and GenAI projects ## Technical Implementation Jabil's GenAI implementation, centered around Amazon Q, spans three major use cases: ### Shop Floor Operations (Ask Me How) * Deployed a multilingual knowledge assistant for operators * Integrated machine documentation and error code solutions * Implemented automatic translation capabilities * Built a system for capturing and sharing operational knowledge across facilities * Created reference linking to source documentation for verification ### Procurement Intelligence Platform (PIP) * Integrated Amazon Q with existing procurement systems * Implemented real-time market intelligence analysis * Developed capability to analyze demand signals and supply trends * Created predictive insights for commodity purchasing * Built systems to analyze supply chain risks and alternatives ### Supply Chain Management (V-command) * Deployed virtual command and control system * Integrated multiple data sources for comprehensive supply chain visibility * Implemented landing cost optimization * Created risk management and alternative supplier identification capabilities * Built sales enablement tools for supply chain services ## Data and Infrastructure Considerations Jabil emphasized the importance of solid digital foundations: * Established clear data ownership and governance structures * Created semantic layers for data interpretation * Implemented API strategy for system integration * Focused on data security and privacy * Built scalable infrastructure to support global operations ## Change Management and Adoption The company took a comprehensive approach to ensure successful adoption: * Implemented AI literacy programs through Workday platform * Conducted education sessions for management and board members * Created new roles including "value tracker" to monitor ROI * Established cross-functional collaboration frameworks * Made strategic decisions to use vendor solutions over internal development ## Results and Impact The implementation has delivered several key benefits: * Reduced manufacturing downtime through proactive problem-solving * Improved operator efficiency with multilingual support * Enhanced procurement decisions with market intelligence * Accelerated sales cycles for supply chain services * Created new revenue opportunities through data-driven insights ## Lessons Learned and Best Practices Key takeaways from Jabil's implementation include: * Importance of clear governance structures and policies * Need for cross-functional ownership of data and AI initiatives * Value of focusing on practical use cases with clear ROI * Benefits of vendor solutions for core GenAI capabilities * Significance of change management and user education ## Future Direction Jabil continues to evolve their GenAI implementation with focus on: * Expanding knowledge capture and sharing capabilities * Developing more sophisticated automation workflows * Enhancing predictive capabilities in manufacturing and supply chain * Building more advanced multilingual support * Creating closer integration between different GenAI systems The case study demonstrates how a large manufacturing organization can successfully implement GenAI technologies by focusing on clear use cases, strong governance, and practical value creation. Their approach to balancing innovation with responsible AI usage provides valuable insights for other organizations embarking on similar transformations.