Microsoft Manufacturing Switzerland

ABB

Customer Service Automation · IT Operations Automation · Real-time Analytics

"For instance, the Genix continuous emission monitoring system can observe and analyze up to 110 different parameters across multiple devices. Based on this data, the system will deliver actionable insights on Genix cloud running on Microsoft Azure.

"For instance, the Genix continuous emission monitoring system can observe and analyze up to 110 different parameters across multiple devices.
ABB customers using Genix Copilot can see up to 35% savings in operations and maintenance, and up to 20% improvement across energy and emission optimization.
By embedding Genix Copilot capabilities with ABB's industrial digital solutions, customers can see up to 35% cost savings in operations and maintenance, boost production efficiency by 30%
While the potential for global transformation in the industrial sector fuels ABB, "this is also giving us a big competitive advantage with our customers," says Ramachandran.

Reported impact

Business outcome

By embedding Genix Copilot capabilities with ABB's industrial digital solutions, customers can see up to 35% cost savings in operations and maintenance, boost production efficiency by 30%

  • Cost Reduction
  • Productivity Improvement

Solution

"For instance, the Genix continuous emission monitoring system can observe and analyze up to 110 different parameters across multiple devices. Based on this data, the system will deliver actionable insights on Genix cloud running on Microsoft Azure. Some statuses triggered might be critical alarms," says David Lincoln, Digital Lead at Measurement and Analytics, ABB. ABB leveraged Microsoft Azure OpenAI Service to integrate generative AI into the Genix platform, powering Genix Copilot. This solution enables users to interact using natural language to obtain answers to critical questions in real time. ABB customers using Genix Copilot can see up to 35% savings in operations and maintenance, and up to 20% improvement across energy and emission optimization. ABB sees about an 80% decrease in service calls with Genix Copilot self-service. ABB, a global leader in electrification and automation, had developed an industrial IoT and AI suite.

Solution architecture

1 components · 1 layer
  1. Compute
    • Azure OpenAI Service Runs the large-language-model features for classification, summarization, retrieval, or conversational responses in the solution.

Architecture clues

  • "For instance, the Genix continuous emission monitoring system can observe and analyze up to 110 different parameters across multiple devices.
  • ABB customers using Genix Copilot can see up to 35% savings in operations and maintenance, and up to 20% improvement across energy and emission optimization.
  • ABB leveraged Microsoft Azure OpenAI Service to integrate generative AI into the Genix platform, powering Genix Copilot.
  • ABB, a global leader in electrification and automation, had developed an industrial IoT and AI suite. This platform, called ABB Ability™ Genix
  • Azure OpenAI Service: Runs the large-language-model features for classification, summarization, retrieval, or conversational responses in the solution.
  • By embedding Genix Copilot capabilities with ABB's industrial digital solutions, customers can see up to 35% cost savings in operations and maintenance

Evidence from the source

"For instance, the Genix continuous emission monitoring system can observe and analyze up to 110 different parameters across multiple devices.
ABB customers using Genix Copilot can see up to 35% savings in operations and maintenance, and up to 20% improvement across energy and emission optimization.
ABB leveraged Microsoft Azure OpenAI Service to integrate generative AI into the Genix platform, powering Genix Copilot. This solution enables users to interact using natural language to obtain answers to critical questi...
By embedding Genix Copilot capabilities with ABB's industrial digital solutions, customers can see up to 35% cost savings in operations and maintenance, boost production efficiency by 30%
Transparency Evidence & methodology 85/100 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 value85/100
  • Problem clarity20/20
    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 specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale5/10
    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
    4 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

    ABB customer story

    First-party
    https://www.microsoft.com/en/customers/story/19773-abb-group-azure
    Source type
    Vendor-published customer story
    Published
    Nov 18, 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 Nov 18, 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.

Related

Similar use cases

See all like this
Microsoft Nov 19, 2024 Lenovo Manufacturing United States As a customer-centric organization, Lenovo was looking for solutions that would enable its support teams to rapidly manage customer inquiries, while streamlining and automating repetitive tasks for its agents. Enhancing the support experience Now customers can interact with Copilot and Lenovo Premier Support customer… Customer Service AutomationReal-time Analytics Dynamics 365 Contact Center · Dynamics 365 Customer Service Microsoft Mar 11, 2024 Schneider Electric Manufacturing France Schneider Electric is a digital partner for sustainability and efficiency, and its vast menu of connected devices, solutions, and services confirms this ambition. It builds objects as ubiquitous as circuit breakers for… Bonnard’s team laid the groundwork for Schneider’s EcoStruxure platform in 2009, well before Internet of Things… IT Operations AutomationReal-time Analytics Azure Machine Learning · Azure OpenAI Service · Microsoft Cloud for Manufacturing Microsoft Feb 27, 2024 Lenovo Manufacturing China Centralization needed to manage global sales growth Lenovo recognized the immense potential to expand its business from hardware sales to a portfolio of smart IoT, Smart Infrastructure, and Smart Verticals through its… Lenovo sales team transforms globally and closes faster with Dynamics 365 Sales Customer Service AutomationReal-time Analytics Dynamics 365 Sales · Microsoft Copilot for Sales · Power BI