Schaeffler unlocks factory insights with Microsoft Cloud for Manufacturing
Schaeffler · Germany
Schaeffler, a global motion technology company with 10,000+ employees in Germany, worked with partner Avanade to pilot data solutions in Microsoft Fabric and a generative AI agent in Azure AI, part of Microsoft Cloud for Manufacturing, to unlock intelligence across diverse IT/OT systems (including ERP, SCM and MES) across a solution designed to scale to over 100 plants. Employees now ask the agent natural-language questions and get near real-time insights such as scrap rates, yields and energy usage; for example, asking what caused downtime on a line yields an instant detailed response instead of engineers spending hours investigating.
Overview
Schaeffler, a global motion technology company with 10,000+ employees in Germany, worked with partner Avanade to pilot data solutions in Microsoft Fabric and a generative AI agent in Azure AI, part of Microsoft Cloud for Manufacturing, to unlock intelligence across diverse IT/OT systems (including ERP, SCM and MES) across a solution designed to scale to over 100 plants. Employees now ask the agent natural-language questions and get near real-time insights such as scrap rates, yields and energy usage; for example, asking what caused downtime on a line yields an instant detailed response instead of engineers spending hours investigating.
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Inspect the highlighted sourceThe challenge
At Schaeffler, data from hundreds of factory machines once took days to decode. The company needed to modernize data management and democratize information access for factory workers across diverse IT/OT systems, including ERP, SCM and MES, in a solution designed to scale to over 100 plants. Previously, engineers might spend hours identifying detailed downtime reasons.
The solution
Schaeffler collaborated with partner Avanade to pilot data solutions in Microsoft Fabric and a generative AI agent in Azure AI, part of Microsoft Cloud for Manufacturing, to extract and normalize factory data. Employees now ask the agent questions in natural language, such as what caused downtime on a production line, and receive actionable insights on metrics like scrap rates, yields and energy usage.
Reported business value
Employees can now immediately search for the reason for downtime and the best way to solve it, instead of spending hours identifying detailed downtime reasons or needing to ask a colleague, making the response instant and detailed rather than something that previously took engineers hours to determine.
Sources
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