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Microsoft Manufacturing Sweden

Epiroc

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

With its headquarters in Sweden, Epiroc is a leading manufacturer of rock excavation equipment. The company sources premium steel and then processes it at locations across the globe to produce drilling tools and equipment for the mining and construction industries. Epiroc also provides round-the-clock equipment service and technical expertise for its customers.

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Malmberg notes that the company has reduced customer rejections and product returns by 30 percent, saving time and money.
The company was able to get it up and running within 60 hours.
The entire process took just 60 hours.
This created inefficiencies and redundancies, and it sometimes impacted quality—resulting in customer equipment returns.

Reported impact

Business outcome

Jerns agrees, saying the speed in which Epiroc was able to implement the solution and see noticeable improvement in the heat treatment process catalyzed his team to look at other areas of application. Currently we have eleven analytical teams with use cases running in the Epiroc worldwide ESML AI Factory and utilizing Azure Machine Learning—with more planned in the coming year,” says Jerns.

  • Compliance
  • Cost Reduction
  • Customer Experience
  • Productivity Improvement

Solution

Epiroc built this solution on Azure Machine Learning, Azure Data Factory, Azure Databricks, Power BI. With its headquarters in Sweden, Epiroc is a leading manufacturer of rock excavation equipment. Azure Machine Learning supports model training, evaluation, and deployment for machine learning workloads. Azure Data Factory moves and orchestrates data from source systems into the analytics platform. Azure Databricks processes, transforms, and engineers large data sets for analytics and machine learning. Power BI publishes dashboards and self-service analytics to business users. Because Epiroc had already implemented Microsoft Azure across the organization, it was a natural choice to use Azure Machine Learning Azure Data Factory, and Azure Databricks to create a solution that could deliver the p And, we knew Microsoft already had policies and processes in place to help support our sustainability objectives,” says Camilla Goldbeck-Lowe, Vice President Sustainability at Epiroc.

Data flow

Operational data is ingested through Azure Data Factory from line-of-business systems, engineered and transformed in Azure Databricks before Power BI dashboards surface insights to business users.

Solution architecture

4 components · 3 layers
  1. Ingest
    • Azure Data Factory Moves and orchestrates data from source systems into the analytics platform.
  2. Compute
    • Azure Machine Learning Supports model training, evaluation, and deployment for machine learning workloads.
    • Azure Databricks Processes, transforms, and engineers large data sets for analytics and machine learning.
  3. Serving
    • Power BI Publishes dashboards and self-service analytics to business users.

Architecture clues

  • The platform uses a modular data mesh design across domains.

Evidence from the source

Because Epiroc had already implemented Microsoft Azure across the organization, it was a natural choice to use Azure Machine Learning Azure Data Factory, and Azure Databricks to create a solution that could deliver the p
Jerns agrees, saying the speed in which Epiroc was able to implement the solution and see noticeable improvement in the heat treatment process catalyzed his team to look at other areas of application.
The company was able to get it up and running within 60 hours.
This created inefficiencies and redundancies, and it sometimes impacted quality—resulting in customer equipment returns.
With its headquarters in Sweden, Epiroc is a leading manufacturer of rock excavation equipment.
Transparency Evidence & methodology 95/100 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 value95/100
  • 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 impact25/25
    3 metrics 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

    Epiroc customer story

    First-party
    https://www.microsoft.com/en/customers/story/1653030140221000726-epiroc-manufacturing-azure-machine-learning
    Source type
    Vendor-published customer story
    Published
    Sep 1, 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 Sep 1, 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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