BLUEPRINT FOR BIG DATA SUCCESS:

Increase Useful Life of Assets

With complex operational systems, the cost of maintenance greatly increases. Strategic organizations aim to increase the useful life of all assets to keep the operational chain uninterrupted while driving down costs.

Get More Usability Out of Each Asset

“Useful life of an asset” is defined by four main factors:

  • Ability to operate the asset cost efficiently
  • Low asset rate of failure
  • Acceptable quality of output
  • Economical maintainability

The three phases of failure that plague assets are early asset failure, constant random failure which happens during the useful life period, and wear out failure toward the end of life. To drive down system failure, organizations aim to minimize downtime and increase asset longevity.

With Pentaho’s end-to-end data integration and analytics capabilities, you can increase mean time between failures (MTBF) by predicting and preventing outages due to “random failures” and using predictive/prescriptive maintenance to reduce wear and tear.

Quickly Build IoT Data Pipelines

Increasing the useful life of an asset is a function of increasing MTBF by predicting and reducing random failures and reducing the wear and tear phase of the asset’s life through predictive/prescriptive maintenance.

Pentaho provides the ability to quickly build IoT data pipelines from edge to outcome and to ingest, process, blend, prepare, apply machine learning and take action. Pentaho empowers you to:

  • Access varied data sources – batch and streaming
  • Architect for predicting asset failure and/or quality issues before they happen
  • Enable use of in time maintenance rather than schedule maintenance
  • Avoid costly downtime and reduce maintenance costs
  • Increase the useful life of the asset

The ROI of Extending the Useful Life of an Asset

When organizations extend the useful life of an asset, they maximize investments made in the assets, which for heavy industrial organizations is often the largest capital expense. Another strategy for extending the life of an asset is to use predictive maintenance which eliminates the need for costly scheduled maintenance and reduces the cost associated with downtime due to outages.  Equally important, the organization becomes more reliable, and delivers better quality products and services to their customers. Fundamentally, this puzzle can be solved using IoT analytics and big data integration.

  • Machine Learning Orchestration empowers data teams to engineer new features, creating smarter systems that predict and reduce random failures and identify wear and tear.  
  • Embedding Analytics provides analytic capabilities directly inside applications enabling decision makers the ability to assess the insight they need at the point of impact, and ultimately drive outcomes.

How Companies Extend the Life of Assets

Increase reliability and reduce cost by extending the life of your assets.

By leveraging the Internet of Things (IoT), sensor data is blended with corporate data assets to prevent and predict downtime outages. Your organization will save significant time and money, be more reliable and cost effective, and provide better service delivery.

  • Predictive and preventive maintenance apply machine learning models to reduce random failures and identify system vulnerabilities to ensure greater outcomes that improve performance, reduce costs and increase efficiencies.
  • Prescriptive maintenance iterates on algorithms, making the system more sensitive to evolving issues with increasing accuracy, continually improving outcomes over time.