As maintenance organizations become more data-driven and focus more on proactive approaches to increase asset reliability and drive down costs, they are challenged by a confusing IoT technologies and complex data requirements. In this session, we will help reduce the learning curve and discuss how you can leverage your existing SAP EAM investment and take advantage of the intelligent features from the SAP Intelligent Asset Management solution to start your predictive maintenance journey:

  • What data do I need for predictive maintenance
  • How should we frame the business problems to drive sustainable value
  • How do we build machine learning models without a lot of data science investment
  • How do we leverage our existing investment in SAP EAM

Key takeaways:

  • What data are needed for predictive maintenance.
  • How to frame business problems to drive sustainable value using predictive maintenance.
  • How to build machine learning models for predictive maintenance without a lot of data science investment.


  • 2:35 – Speaker introduction and agenda overview
  • 4:25 – Why predictive maintenance (PdM)
  • 5:25 – How to start a PdM program and build a business case
  • 21:40 – Integrate PdM to enterprise asset management processes
  • 25:00 – Machine learning basics
  • 33:50 – Machine learning with SAP Predictive Maintenance and Service (PdMS)
  • 39:10 – Leading indicators and failure mode analytics with SAP PdMS
  • 43:10 – Advanced rules and alerts with SAP PdMS
  • 45:20 – Customer project example
  • 47:40 – Q&A


  • Simon Lee, VP of Product Management, SAP Predictive Maintenance and Service

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