As an industrial operator, your investment in the OSIsoft PI System has already generated improvements in asset optimization.
Organizing your PI data into a standardized format enables analytics to be run, generating new insights and delivering on your operational KPIs.
Central to this effort is the ability to build an asset model using the PI Asset Framework (AF). The more AFs you build, the more assets you digitize, and the more the organization benefits.
Teams struggle to create AFs. And when an AF is built, it may not be 'fit for purpose' for use by other stakeholders.
Instead, they are left to make sense of a vast array of data by coding in Python, or worse yet, wrangling data in error-prone spreadsheets.
This gap, between creating AFs and enabling analytics, not only limits ROI and potential outcomes/insights - such as moving to a condition-based maintenance strategy (CBM) - but also lengthens time to analytics and drives up cost.
The Element AF Accelerator is an asset data modeling package designed for those who are early in their transformation journey and recognize an important next step is to build and maintain enterprise-ready OSIsoft Asset Frameworks (AFs).
With Element, you can join data from the PI System with other metadata from systems like SAP and Hexagon and export Asset Frameworks back to the PI System.
It offers a powerful suite of cloud-based software and professional services that reduce the burdensome tasks involved in manipulating data to create AFs by up to 90%.
Easily update & sync AF's and Templates.
Integrated IT & OT data easily. Simple to harvest context from P&ID's to speed standardization and mapping.
Build AFs in weeks not months.
Map context to millions of PI tags.
Organize the data once, export many AF hierarchies.
Use software not spreadsheets.
Automatically monitor integrity of AF models.
As an OSIsoft Connected Application partner, we help PI users to organize industrial data into an accessible and useful form. Core to this effort is being able to build and maintain AFs 4x – 10x faster than using traditional approaches, enabling analytics to run on top of PI data.