The Art of Increasing Profitability with Predictive Maintenance

With operating budgets on the decline, new asset acquisitions are impractical at best. Costly equipment breakdowns further aggravate the situation, and leading enterprises are finding themselves in a quagmire, frustrated with extensive repair hours and customer dissatisfaction.

Attend this webcast and learn how to use data to overcome the unexpected maintenance challenges organizations routinely encounter – before they occur. Discover how to predict asset failure, reduce costly downtime and extend the life of equipment using data science for predictive maintenance.

This webcast covers techniques and best practices, including:

  • What is predictive maintenance?
  • The benefits of using predictive maintenance
  • How you can implement predictive maintenance
  • Common algorithm, data and deployment challenges
  • Integrating data from SAP HANA and OSIsoft’s PI System
  • A case study on predicting ID fan failure

Predictive maintenance can be your best tool in overcoming maintenance challenges and extending the life of your organization’s equipment. View this webcast to learn how to increase efficiency and reduce equipment failure expenditures with predictive maintenance!



Feroze Arif
Director of Analytics & Innovation
Auritas, LLC

Feroze Arif has 25+ years of experience with expertise spanning Data Analytics, SAP Master Data Management, Data Quality & Governance, as well as SAP Data Migration. He is passionate about using data science for business and regularly uses Python R, and other technologies to build machine learning and predictive analytics solutions.

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