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Microsoft Travel and Hospitality Denmark

Molslinjen

Real-time Analytics · Sales Automation

Molslinjen is Denmark’s largest domestic ferry service and one of the world’s largest fast ferry companies. The company wanted to revamp its operations with dynamic pricing and demand prediction.

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The Azure Databricks solution has reduced fuel emissions, improved customer satisfaction, and brought in millions of additional revenue.
The earnings come from a combination of cost savings, increased revenue, and a 3% reduction in fuel consumption and emissions in 2023, which roughly totaled to 5000 tons of CO2.
This powerful AI toolbox has brought in an additional 15-20 million DKK ($2.6-3.2M) per year.
With over 15 million annual guests and a fleet of 21 ships, Molslinjen is Denmark’s largest domestic ferry service and, thanks to its catamaran ferries, one of the world’s largest fast ferry companies.

Reported impact

Business outcome

This powerful AI toolbox has brought in an additional 15-20 million DKK ($2.6-3.2M) per year. And it all started with just one route and Azure Databricks. Azure Databricks was the foundation of this entire transformation,” says Cloetta Kristiansen.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

Molslinjen built this solution on Azure Databricks, Azure Service Fabric, Azure Functions, Power BI. Molslinjen is Denmark’s largest domestic ferry service and one of the world’s largest fast ferry companies. The company wanted to revamp its operations with dynamic pricing and demand prediction. Azure Databricks processes, transforms, and engineers large data sets for analytics and machine learning. Azure Service Fabric runs and coordinates microservices that power the application. Azure Functions executes event-driven business logic inside automated workflows. Power BI publishes dashboards and self-service analytics to business users. With the assistance of AI company Halfspace, Molslinjen created an AI analytics toolbox via Azure Databricks that provides data-driven innovation and modernized services. The Azure Databricks solution has reduced fuel emissions, improved customer satisfaction, and brought in millions of additional revenue.

Data flow

Operational data is collected 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. Compute
    • Azure Databricks Processes, transforms, and engineers large data sets for analytics and machine learning.
    • Azure Functions Executes event-driven business logic inside automated workflows.
  2. Serving
    • Power BI Publishes dashboards and self-service analytics to business users.
  3. Orchestration
    • Azure Service Fabric Runs and coordinates microservices that power the application.

Architecture clues

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

Evidence from the source

Molslinjen is Denmark’s largest domestic ferry service and one of the world’s largest fast ferry companies. The company wanted to revamp its operations with dynamic pricing and demand prediction.
The Azure Databricks solution has reduced fuel emissions, improved customer satisfaction, and brought in millions of additional revenue.
This powerful AI toolbox has brought in an additional 15-20 million DKK ($2.6-3.2M) per year. And it all started with just one route and Azure Databricks.
With over 15 million annual guests and a fleet of 21 ships, Molslinjen is Denmark’s largest domestic ferry service and, thanks to its catamaran ferries, one of the world’s largest fast ferry companies.
With the assistance of AI company Halfspace, Molslinjen created an AI analytics toolbox via Azure Databricks that provides data-driven innovation and modernized services.
Transparency Evidence & methodology 91/100 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 value91/100
  • 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 specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 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

    Molslinjen customer story

    First-party
    https://www.microsoft.com/en/customers/story/18735-molslinjen-azure-service-fabric
    Source type
    Vendor-published customer story
    Published
    Sep 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 Sep 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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