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IBM Commercial and Professional Services Sweden

Quant

Predictive Maintenance

Quant wanted to deliver more advanced predictive-maintenance services to industrial clients, but fragmented asset data, legacy equipment monitoring, and separate IoT tooling limited its ability to manage assets efficiently at global scale.

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1 central platform for asset management
2,000 data streams from IoT sensors
40 global client sites live
more than 105,000 assets across 40 client sites

Reported impact

Business outcome

Quant gained a scalable platform for higher-value maintenance services, better asset visibility, lower infrastructure overhead, and stronger support for sustainability and uptime goals across its global client base.

  • Cost Reduction
  • Productivity Improvement
  • Scalability

Solution

Quant, AFRY, and IBM modernized the company's maintenance platform around IBM Maximo Application Suite so client asset data, health monitoring, and technician workflows could run from one environment. IBM delivers the suite as a managed service, giving Quant an integrated platform to manage and analyze asset information across global client sites. For modern equipment, OEM sensors feed operating data into the platform; for legacy equipment, Quant adds IoT devices and plans to move that monitoring into IBM Maximo Health so those readings can be analyzed alongside work histories and asset context. Maximo Mobile extends the system to technicians in the field, while Maximo Scheduler replaces spreadsheet-based planning with rules-driven scheduling and work-order management. The design lets Quant consolidate maintenance data, reduce separate infrastructure, and scale predictive and condition-based maintenance across tens of thousands of assets.

Data flow

Sensor streams from OEM-connected assets and IoT devices added to legacy equipment flow into Maximo Application Suite and Maximo Health, where the data is analyzed alongside asset histories; planners then coordinate work in Maximo Scheduler and technicians consume the resulting tasks and operational context through Maximo Mobile.

Solution architecture

4 components · 3 layers
  1. Compute
    • IBM Maximo Health Analyzes IoT and asset-condition data so Quant can detect issues earlier and move toward predictive maintenance.
  2. Serving
    • IBM Maximo Application Suite Provides the integrated managed-service platform for asset management, analytics, and global rollout across client sites.
    • Maximo Mobile Gives technicians field access to operational data and work information while they service client equipment.
  3. Orchestration
    • Maximo Scheduler Automates scheduling and work-order planning that had previously been coordinated with spreadsheets.

Architecture clues

  • IBM provides Maximo Application Suite as a managed service for Quant's rollout.
  • Legacy equipment is instrumented with IoT devices so it can join the same monitoring fabric as modern sensor-equipped assets.
  • Quant plans to decommission its separate IoT management infrastructure by moving that capability into Maximo Health.

Evidence from the source

"2,000 data streams from IoT sensors"
"At the moment, we run our own IoT management platform."
"By moving this capability to IBM Maximo Health ... we'll also be able to decommission our existing IoT management infrastructure"
"Maximo Application Suite would enable us to unify our asset management data on a single, integrated platform"
"already using the solution to manage more than 105,000 assets across 40 client sites around the world"
Transparency Evidence & methodology 71/100 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 value71/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    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 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. IBM

    Quant customer story

    First-party
    https://www.ibm.com/case-studies/quant
    Source type
    Vendor-published customer story
    Published
    Apr 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

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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