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Microsoft Manufacturing Germany

Schaeffler

Data Lake / Data Warehouse Modernization · Predictive Maintenance · Real-time Analytics

Schaeffler needed to modernize data management and unlock intelligence across diverse IT/OT systems, using generative AI. This enables advanced factory reporting and troubleshooting to boost uptime, quality, productivity, and yields.

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Now, they can simply ask the agent, “What caused the downtime on Line 3 yesterday?” and receive an instant, detailed response.
We needed a solution that has the potential to scale to over 100 plants,” says Stefan Soutschek, Vice President, Operations Digitalization & IT Delivery Hub at Schaeffler.

Reported impact

Business outcome

This is vital in complex, fast-moving, and continuously changing environments. It helps manufacturers make superior products faster with less waste, driving cost and carbon reduction co-benefits.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

Schaeffler built this solution on Azure AI, Microsoft Fabric, Microsoft Cloud for Manufacturing. Schaeffler needed to modernize data management and unlock intelligence across diverse IT/OT systems, using generative AI. This enables advanced factory reporting and troubleshooting to boost uptime, quality, productivity, and yields. Azure AI provides managed AI services used to automate classification, recommendations, or decision support. Microsoft Fabric unifies lake, warehouse, and analytics workloads on a governed data foundation. Microsoft Cloud for Manufacturing provides an industry cloud foundation tying manufacturing data, AI, and business applications together. The global manufacturing leader implemented data solutions in Microsoft Fabric and an agent in Azure AI, part of Microsoft Cloud for Manufacturing. Schaeffler collaborated with Avanade to pilot data solutions in Microsoft Fabric and an agent in Azure AI , part of Microsoft Cloud for Manufacturing.

Data flow

Operational data is collected from line-of-business systems, unified in Microsoft Fabric so business teams can act on a shared set of insights.

Solution architecture

3 components · 3 layers
  1. Compute
    • Azure AI Provides managed AI services used to automate classification, recommendations, or decision support.
  2. Storage
    • Microsoft Fabric Unifies lake, warehouse, and analytics workloads on a governed data foundation.
  3. Orchestration
    • Microsoft Cloud for Manufacturing Provides an industry cloud foundation tying manufacturing data, AI, and business applications together.

Architecture clues

  • The solution is built around a governed cloud data foundation for shared analytics.

Evidence from the source

Now, they can simply ask the agent, “What caused the downtime on Line 3 yesterday?” and receive an instant, detailed response.
Schaeffler needed to modernize data management and unlock intelligence across diverse IT/OT systems, using generative AI.
The global manufacturing leader implemented data solutions in Microsoft Fabric and an agent in Azure AI, part of Microsoft Cloud for Manufacturing.
This is vital in complex, fast-moving, and continuously changing environments. It helps manufacturers make superior products faster with less waste, driving cost and carbon reduction co-benefits.
We needed a solution that has the potential to scale to over 100 plants,” says Stefan Soutschek, Vice President, Operations Digitalization & IT Delivery Hub at Schaeffler.
Transparency Evidence & methodology 85/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value85/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Microsoft

    Schaeffler customer story

    First-party
    https://www.microsoft.com/en/customers/story/20385-schaeffler-microsoft-fabric
    Source type
    Vendor-published customer story
    Published
    Dec 16, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Dec 16, 2024 · Microsoft · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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