Addressing downtime root causes at the supplier level with AI-powered data foundation and analytics delivered in one week
Production operations at the OEM were being disrupted by late-arriving or missing components from suppliers—parts that frequently caused unplanned downtime. Despite detailed shift logs, the OEM and its suppliers lacked unified, near real-time visibility into which components were repeatedly disrupting the line.
Suppliers relied on end-of-shift spreadsheets, pivot tables, and manual patchwork reporting, making it nearly impossible to spot patterns or act quickly. This lack of systemized visibility eroded OEE and added avoidable cost and coordination overhead across the supply chain.
Beye.ai deployed its GenBI platform in under a week, creating a unified, AI-ready data fabric that transformed supplier spreadsheets and logs into a structured database. A semantic model codified production KPIs, including part-level inventory, lead times, and downtime impacts. Conversational analytics agents let teams ask natural language questions like, "Which parts caused downtime more than once this month?" or "Which suppliers are missing 3-day rolling inventory targets?"
Messy end of shift spreadsheets are automatically landed in Fabric where event driven ELT cleanses validates and stores them in a single AI light database ready for analysis.
We worked with the production team to codify OEE availability and inventory measures so our AI agents understand business context and deliver consistent answers.
Production managers can ask natural language questions about performance and inventory levels and receive instant charts and alerts saving time and accelerating decisions.
AI agents provide business context and unified visibility across the supply chain, enabling proactive identification of disruption patterns.
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