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.

Time­stamps:

• 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