Manufacturing is a world of precision, where every second counts. When a machine breaks down or a production line slows, identifying the root cause quickly is the difference between a minor hiccup and a major disruption. That’s where our AI-driven Root Cause Analysis and Insights Generator steps in, powered by Azure’s cutting-edge AI services and Microsoft’s intelligent search capabilities. This solution seamlessly integrates real-time data processing, generative AI, and automated insights to help Schaeffler tackle complex manufacturing challenges with speed and accuracy.

At the core of this system is a robust data ingestion pipeline that brings scattered manufacturing data into a centralized AI-powered search. Machine logs, production data, and maintenance records from PCS and PMO tables are preprocessed, with Azure OpenAI handling translations and error corrections. The processed data is indexed in Azure AI Search, ensuring instant access to critical insights. To take things a step further, Microsoft’s GraphRAG technology helps refine search results by linking relevant data points in a knowledge graph, making troubleshooting more accurate and intuitive. For machine manuals, Azure Document Intelligence extracts structured content, breaking it into larger ten-page chunks for context and smaller two-page chunks for precise retrieval. This means operators no longer have to waste time flipping through thick manuals—they get exactly what they need, when they need it. Similarly, an AI-powered search index is created for manufacturing terminology, allowing users to quickly find definitions and explanations without confusion.

But collecting data is just one piece of the puzzle. The real magic happens in the bot pipeline, where AI transforms raw queries into actionable insights. Every question—whether from an operator diagnosing a machine failure or a team lead analyzing production efficiency—goes through multiple processing layers. Typos are corrected, translations applied, follow-up handled, guardrails enforced, and intent classified using Azure OpenAI’s advanced models. If an operator needs to pinpoint the cause of a breakdown, the system pulls relevant solutions using hybrid search across AI search indexes and GraphRAG, ensuring the most relevant information surfaces. For team leads monitoring production performance, the bot dynamically triggers the right APIs to retrieve benchmarking data, and then extracts key insights using prompt-based techniques. In the case of user manual queries, the bot smartly prioritizes retrieval using the big chunk index first to get a broad context, then refines results using the small chunk index for pinpoint accuracy. If a user asks about a technical term, the AI instantly retrieves the definition, ensuring clarity without delays.