Industry 4.0 Resources

Virtualitics Demo Day at the FinTech Innovation Lab
The FinTech Innovation Lab was co-founded by the Partnership Fund for New York City and Accenture in 2010. Our time at the FinTech Lab was invaluable, helping us to hone our product to be a vital tool not only for financial services but for any company’s infrastructure, including transportation, manufacturing, and more.
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When Should Your Company Use Virtualitics VIP?
The traditional data analysis process does not encourage thorough exploration into data patterns and relationships. In fact, it does not matter whether you are a stakeholder, a subject-matter expert, or a data scientist; the traditional approach to data analytics continues to make it difficult to get the maximum value from your data.
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Learn more about using your data efficiently in the transportation industry
Learn more about using data efficiently in the energy sector

Use Case 1:

Virtualitics Predict for Airplane Predictive Maintenance

One company in the transportation industry uses Virtualitics to save $6-15 million in annual maintenance costs. By working with Virtualitics to gather data from unexpected sources, this company was able to:

  • The customized solution yields $6-15M annual savings in estimated maintenance costs.
  • Integration into existing workflow helps drive action early and leads to2-3% decrease in overall machinery downtime.
  • The ground team experienced a 50% reduction in unscheduled maintenance on monitored systems within the first three months.
  • Plus, integration with the existing part inventory system keeps the necessary replacement parts stocked.

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Use Case 2:

Virtualitics Immersive Platform unlocks AI for Industry 4.0 Asset Productivity

Virtualitics pyVIP is our powerful python API that enables flexible integration with tools and systems in your ecosystem. The API enables a Data Scientist to visualize any of their predictive models to enhance model explainability, performance, and overall communication to stakeholders.

  • Anomaly-detection algorithms revealed the root cause of high-temperature operational malfunctions which required excessive servicing, costing more than $500,000 per year in downtime and energy losses.
  • Interactive data exploration in 3D empowered wind farm engineers, analysts, and operators to visualize and identify the key driver for underperformance: the OEM designed a cooling system as the culprit, which was underpowered relative to the wind turbine designer’s recommendations.
  • AI routines highlighted drivers of energy production and identified ideal operational conditions with respect to auxiliary power.

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Man looking at computer with AI-assisted multidimensional data visualizations