Predictive Maintenance

Reduce unplanned downtime and optimize asset maintenance
for better efficiency

Unplanned downtime costs industrial manufacturers approximately $50 billion annually.

We have the solution to this.

A ‘crystal ball’ to predict your asset’s health

Make unplanned downtime due to equipment breakdown a thing of the past. Business owners with high-value assets can be notified about potential breakdowns to better prioritize maintenance works and allocate resources more efficiently. This translates to huge cost and time savings in the long run.

Seamless data collection
through sensors and
cameras

Automated data
analysis

Actionable
Insights

We are the ‘Machine Doctor’

Monitor asset health and detect anomalies in real-time via a user-friendly platform. Receive diagnoses of future asset fatigue, degradation or potential failure from the comfort of your seat. Achieve higher level operational excellence through better strategic planning from now on.

Potential Cost Savings

Average cost of unplanned equipment downtime is $260,000/
hour and lasts around 4 hours

$260.000

$260.000

$260.000

$260.000

$1.04 million

saved for every unplanned
downtime reduced

The Benefits

Reduce unplanned
downtime

Full clarity on machine
capabilities

Extend lifespan of
machinery

Achieve long-term cost
savings from
unnecessary maintenance works

Improve overall
productivity

Avoid operational
stress from sudden
breakdowns

Our 3 Step Solution

Stage 3 - The Forwarder
  • Leverage Machine Learning to accurately predict potential machine failures
  • Use Decision Optimization to prioritize maintenance works of machinery to reduce operational downtime and to better allocate resources
  • Get 100% clarity on machine capabilities
Stage 2 - The Tracker
  • Real-time monitoring of tracked machinery or equipment with proper indicators of actual machinery performance
  • Instant notification if machinery is underperforming
Stage 1 - The Mine
  • Efficient data collection housed in a solid data repository
Stage 1 - The Mine
  • Efficient data collection housed in a solid data repository
Stage 2 - The Tracker
  • Real-time monitoring of tracked machinery or equipment with proper indicators of actual machinery performance
  • Instant notification if machinery is underperforming
Stage 3 - The Forwarder
  • Leverage Machine Learning to accurately predict potential machine failures
  • Use Decision Optimization to prioritize maintenance works of machinery to reduce operational downtime and to better allocate resources
  • Get 100% clarity on machine capabilities

Resources

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