Predictive Analytics for Manufacturing

In today’s fast paced market, manufacturing downtime and the release of substandard products can quickly damage your reputation and bottom line. Presidion helps asset-intensive organizations keep manufacturing processes, infrastructure and equipment running efficiently to maximise utilisation and performance and minimize costly, unscheduled downtime that can disrupt production, service and delivery.

Our Solutions

Predictive maintenance differs considerably from the traditional approaches to determining when to service or replace equipment. Waiting until a component fails incurs lost production time and revenue. In-person inspections are expensive and purely based only on the inspector’s best guess. Following the manufacturer’s recommended maintenance schedule often results in replacing parts unnecessarily.

Benefits of Predicitive Maintenance include:meters-compressed

  • Predict asset failure based on usage and wear characteristics
  • Estimate and extend component life
  • Increase return on assets, productivity and lifetime
  • Optimize maintenance, inventory, and resource schedules
  • Optimise assets for better availability, utilisation and performance
  • Prevent Downtime
  • Enhance operations and supply chain processes

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Case Study

Israel Electric Corporation moves towards smarter maintenance, using sophisticated data models to predict and preempt turbine failures at its power plants.

  • Estimated to reduce costs by up to 20 percent.
  • Provides early warning of certain types of failure up to 30 hours before they occur.

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White Paper

Discover the specific benefits predictive maintenance solutions provide asset-intensive organizations.

  • Maximize asset productivity
  • Optimize asset-associated processes

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In-depth root cause analysis identifies the key factors of substandard batches so adjustments can be made to save the batch.quality-compressed

  • Reduce Recalls / Warranty claims
  • Reduce time to identify issues
  • Enhance operations and supply chain processes

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Case Study

Automotive manufacturer increases productivity for cylinder-head production by 25 percent.

  • 50 percent reduction in the time taken to ramp up the process to Daimler’s target levels.
  • Enables rapid adjustments by monitoring the process in near real time.

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Case Study

IBM Bromont gains huge ROI through smarter quality management. Predictive analytics enables instant root-cause analysis and supports better investment decisions.

  • For one specific operation, 97 percent of fault patterns can now be identified automatically.
  • Predicted 150% ROI in year one.

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White Paper

Maximize productivity and operational performance.

  • Anticipate asset maintenance and product quality problems.
  • Reduce unscheduled asset downtime.
  • Spend less time solving production machinery and field asset problems.
  • Improve asset productivity and process quality.
  • Monitor how assets are performing in real-time and predict what will happen next.
  • Identify poor quality issues earlier than traditional quality control techniques.

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Related Resources

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Is my data good enough for Predictive Maintenance?



Presidion’s Data Readiness Assessment (DRA) helps clients to achieve an accurate understanding of where they are in relation to data held to address their maintenance objectives.

  • “We have already made huge progress in improving root cause identification, optimizing operating conditions and reducing operational costs in specific processes.”

    Eric Paradis
    IBM Bromont
  • "Analytics gives us a method of assessing impact of factors that we may not be able to measure directly – so we can do more with the data we already have, instead of making big investments in hightech equipment."

    Matthieu Lirette-Gelinas
    IBM Bromont
  • "Our turbines have an alarm built-in by the manufacturer which is triggered 30 minutes before a major failure, but with our data we can predict such an event 30 hours before it happens."

    Erez Daly
    Israel Electric Corporation (IEC)

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