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Solution pathwayReliabilityManufacturing · Mining · Heavy industry

Downtime and Maintenance

Recurring stoppages, unplanned downtime, or manual maintenance checks limit availability and increase cost.

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Section 1

What this problem looks like

  • Recurring unplanned stoppages on key assets
  • Manual rounds for maintenance checks
  • Failure data not captured or analyzed
  • Maintenance is reactive, not planned
Section 2

Common hidden causes

  • Failure history not captured in CMMS
  • Asset criticality is not ranked
  • Sensor data not yet collected
  • Weak data infrastructure
Section 3

Relevant solution pathways

Compare possible pathways side by side. None of these are vendor recommendations — they are starting shapes to help you scope the problem.

Sensors and IoT monitoring

What it is
Adding sensors and connectivity to capture asset condition data.
When it fits
Critical assets known and data infrastructure exists or planned.
What to validate
Asset list, sensor coverage, network availability.
Main risks
Data quality, integration with CMMS.
Match types that may help
IIoT vendor, controls expert.

Predictive maintenance

What it is
Models that flag likely failures before they occur.
When it fits
Failure history available, sensor data collected, model maturity acceptable.
What to validate
Failure history, sensor data, model accuracy expectations.
Main risks
Model maturity, false alarms.
Match types that may help
Predictive maintenance vendor, applied research partner.

Maintenance workflow software

What it is
CMMS or workflow tools that organize planned and reactive maintenance.
When it fits
Maintenance is reactive and undocumented today.
What to validate
Existing CMMS, asset hierarchy, work order process.
Main risks
Adoption, content quality.
Match types that may help
CMMS vendor, maintenance lead.
Section 4

What to validate before vendor conversations

  • Failure history
  • Downtime cost per asset
  • Sensor availability
  • Asset criticality ranking
  • Existing CMMS
Section 5

Common adoption risks

RiskWhy it mattersHow to reduce risk
Data qualityPredictive models depend on clean sensor and failure data.Audit data sources before scoping models.
Model maturityEarly models produce false alarms and erode trust.Set realistic accuracy expectations and feedback loops.
Integration with CMMSInsights need to land in the maintenance workflow.Plan CMMS integration in scope, not after.
Similar anonymized challenges

Anonymized prototype examples of how operational challenges have moved through Innovation Peer review.

Industrial operator

Predictive maintenance

Pathway considered
Sensors + model
Main barrier
Sensor data not yet captured
Lesson learned
Sensor rollout took longer than the model build.
What this means for you
Sequence sensor coverage before promising predictive results.

Anonymized prototype examples.

Section 7

Recommended match types

Vendor / integrator

Solution and integration providers suited to the specific challenge.

Peer operator

An operator who has piloted or deployed a similar pathway.

Independent expert

Domain specialist who can sanity-check the brief before vendor conversations.

Funder / program

Regional or sector innovation programs that may co-fund eligible pilots.

Research partner

Applied research group able to support trials, measurement, or workforce studies.

No introduction is made without your explicit approval.

Next step

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