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Microsoft Manufacturing Germany

Siemens Digital Industries Software

Enterprise Knowledge Search · IT Operations Automation

Siemens Digital Industries Software saw the need to enable better cross-functional communication and collaboration for the industrial companies that use its software. Specifically, field and shop floor workers needed to be able to communicate with operations and engineering teams in real-time to better drive innovation and efficiency, but more importantly, rapidly address problems as they arise. The company envisioned an AI-powered collaborative app to help people working across a product lifecycle to close feedback loops faster, solve challenges together, and feel more connected and included as part of the larger organization.

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With a long-standing relationship between Siemens and Microsoft—over 35 years of joint innovation—it was a natural choice for Siemens Digital Industries Software to work with Microsoft and use Microsoft Azure AI to produce an innovative solution for their customers.

Reported impact

Business outcome

The solution is currently in pilot form with a few Siemens Digital Industries Software customers, where Bohman says it’s already helping them better connect teams, report issues, simplify workflows, and collaborate in more inclusive ways across the entire lifecycle of a product, from inception through engineering, design, and manufacturing to service and disposal. “We’re anticipating more problems being reported and faster resolution time, and using Azure AI allows us to do that,” Bohman says.

  • Customer Experience
  • Productivity Improvement

Solution

Siemens Digital Industries Software saw the need to enable better cross-functional communication and collaboration for the industrial companies that use its software. Specifically, field and shop floor workers needed to be able to communicate with operations and engineering teams in real-time to better drive innovation and efficiency, but more importantly, rapidly address problems as they arise. The company envisioned an AI-powered collaborative app to help people working across a product lifecycle to close feedback loops faster, solve challenges together, and feel more connected and included as part of the larger organization.

Data flow

Content from enterprise documents and knowledge sources is ingested into Azure OpenAI Service, Azure Machine Learning, where it is indexed, enriched, or reasoned over before grounded answers are returned to employees through business applications.

Solution architecture

4 components · 3 layers
  1. Compute
    • Azure Machine Learning Supports model training, experimentation, and deployment for custom machine learning workloads.
    • Azure OpenAI Service Provides large language model inference for natural language search, drafting, summarization, and recommendations.
  2. Serving
    • Microsoft Teams Delivers collaborative experiences where users consume analytics, copilots, and workflow notifications.
  3. Orchestration
    • Azure AI Foundry Orchestrates prompts, evaluations, and AI application workflows for enterprise deployments.

Architecture clues

  • Azure AI Foundry: Orchestrates prompts, evaluations, and AI application workflows for enterprise deployments.
  • Azure Machine Learning: Supports model training, experimentation, and deployment for custom machine learning workloads.
  • Azure OpenAI Service: Provides large language model inference for natural language search, drafting, summarization, and recommendations.
  • Microsoft Teams: Delivers collaborative experiences where users consume analytics, copilots, and workflow notifications.
  • With a long-standing relationship between Siemens and Microsoft—over 35 years of joint innovation—it was a natural choice for Siemens Digital Industries Software to work with Microsoft and use Microsoft Azure AI to produ

Evidence from the source

Accelerating groundbreaking transformation for industrial and manufacturing customers is the driving force behind Siemens Digital Industries Software. “We strive to deliver innovation that changes people’s lives,” says Joe Bohman, Executive Vice President of PLM Products at Siemens Digital Industries Software.
Siemens Digital Industries Software saw the need to enable better cross-functional communication and collaboration for the industrial companies that use its software. Specifically, field and shop floor workers needed to be able to communicate with operations and engineering teams in real-time to better drive innovation and efficiency, but more importantly, rapidly address problems as they arise.
Siemens Digital Industries Software saw the need to enable better cross-functional communication and collaboration for the industrial companies that use its software. Specifically, field and shop floor workers needed to be able to communicate with operations and engineering teams in real-time to better drive innovation and efficiency, but more importantly, rapidly address problems as they arise. The company envisioned an AI-powered collaborative app to help people working across a product lifecy
The solution is currently in pilot form with a few Siemens Digital Industries Software customers, where Bohman says it’s already helping them better connect teams, report issues, simplify workflows, and collaborate in more inclusive ways across the entire lifecycle of a product, from inception through engineering, design, and manufacturing to service and disposal. “We’re anticipating more problems being reported and faster resolution time, and using Azure AI allows us to do that,” Bohman says.
“We’re anticipating that our customers will be able to capture more problems and will resolve them faster using Azure AI.”
Transparency Evidence & methodology 70/100 reference value · 87% 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 value70/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • 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 specificity8/10
    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

    Siemens Digital Industries Software customer story

    First-party
    https://www.microsoft.com/en/customers/story/1703499442875711080-siemens-digital-industries-software-microsoft-azure-ai-germany
    Source type
    Vendor-published customer story
    Published
    Nov 15, 2023
    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 Nov 15, 2023 · 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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