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Microsoft Manufacturing Germany

Siemens IT

IT Operations Automation

The challenge: Establish a standard for AI models

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Reported impact

Business outcome

[ai:attack] serves both as an enabler for employees in the departments and as a tool for the data analysts. Some 500 employees have already received training in using the platform and in the basics of data analytics, with Siemens IT placing a strong emphasis on IT security and compliance. “Of course we educate our employees about using data and AI responsibly.

  • Compliance
  • Risk Reduction

Solution

The challenge: Establish a standard for AI models Accurate forecasts of operating figures are a boon to any industrial enterprise, enabling them to make precise production estimates and avoid extra costs. Siemens has long used artificial intelligence (AI) to create forecasts and provide targeted support to its departments. In the past, however, the development process ate up a lot of time. AI-driven digital solutions can do so much: They easily read out metadata in PDF documents and use it to make forecasts along time series. They identify seasonalities, such as increased demand for products around Christmastime, thus simplifying production and sales planning. Using automated image analysis, they detect any product damage, making returns management easier.

Solution architecture

1 components · 1 layer
  1. Compute
    • Azure Machine Learning Supports model training, experimentation, and deployment for custom machine learning workloads.

Architecture clues

  • Azure Machine Learning: Supports model training, experimentation, and deployment for custom machine learning workloads.

Evidence from the source

AI-driven digital solutions can do so much: They easily read out metadata in PDF documents and use it to make forecasts along time series. They identify seasonalities, such as increased demand for products around Christmastime, thus simplifying production and sales planning.
Accurate forecasts of operating figures are a boon to any industrial enterprise, enabling them to make precise production estimates and avoid extra costs. Siemens has long used artificial intelligence (AI) to create forecasts and provide targeted support to its departments.
Siemens IT was already using various AI solutions and had already established defined processes for developing them, but they wanted to become even better at this. Previously, when departments had come up with ideas for solutions that could help them speed up business processes, they first had to explain these ideas in a way the data analysts could understand, and convey their specialized domain knowledge.
The challenge: Establish a standard for AI models
[ai:attack] serves both as an enabler for employees in the departments and as a tool for the data analysts. Some 500 employees have already received training in using the platform and in the basics of data analytics, with Siemens IT placing a strong emphasis on IT security and compliance. “Of course we educate our employees about using data and AI responsibly.
Transparency Evidence & methodology 41/100 reference value · 85% 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 value41/100
  • Problem clarity5/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth15/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence85%
  • 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 specificity6/10
    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 IT customer story

    First-party
    https://www.microsoft.com/en/customers/story/1637783244393505156-siemens-azure-machine-learning-en
    Source type
    Vendor-published customer story
    Published
    Jun 13, 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 Jun 13, 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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