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XMPro Platform
XMPro Platform
  • What is XMPro?
  • Getting Started
    • Browser Requirements
    • End-To-End Use Case
  • Resources
    • What's New in 4.4
      • What's New in 4.3
      • What's New in 4.2
      • What's New in 4.1.13
      • What's New in 4.1
      • What's New in 4.0
    • Blueprints, Accelerators & Patterns
    • Integrations
    • Sizing Guideline
    • Platform Security
    • Icon Library
    • FAQs
      • Implementation FAQs
      • Configuration FAQs
      • Agent FAQs
      • General FAQs
      • External Content
        • Blogs
          • 2024
            • How to Build Multi-Agent Systems for Industry
            • Why Solving the Problem Doesn’t Solve the Problem: The Importance of Scalable Intelligent Operations
            • Content, Decision, and Hybrid: The Three Pillars of Multi-Agent Systems in Industry
            • Revolutionizing Manufacturing with AI and Generative AI: XMPro’s Intelligent Business Operations Sui
            • The Evolution of Skills: Lessons from Agriculture in the GenAI and MAGS Era
            • Part 1: From Railroads to AI: The Evolution of Game-Changing Utilities
            • Part2: The Future of Work: Harnessing Generative Agents in Manufacturing
            • Bridging Automation and Intelligence: XMPro’s Approach to Industrial Agent Management
            • XMPro APEX: Pioneering AgentOps for Industrial Multi Agent Generative Systems
            • Part 5 – Rules of Engagement: Establishing Governance for Multi-Agent Generative Systems
            • How to Achieve Scalable Predictive Maintenance for Industrial Operations
            • Understanding the Difference Between XMPro AI Assistant and AI Advisor
            • Part 3 – AI at the Core: LLMs and Data Pipelines for Industrial Multi-Agent Generative Systems
            • MAGS: The Killer App for Generative AI in Industrial Applications
            • The Importance of Pump Predictive Maintenance for Operational Efficiency
            • Progressing Through The Decision Intelligence Continuum With XMPro
            • The Value-First Approach to Industrial AI: Why MAGS Implementation Must Start with Business Outcomes
            • New Guide – The Ultimate Guide to Multi-Agent Generative Systems
            • The Ultimate Guide To Predictive Analytics
            • Part 4 – Pioneering Progress | Real-World Applications of Multi-Agent Generative Systems
            • Scaling Multi-Agent Systems with Data Pipelines: Solving Real-World Industrial Challenges
          • 2023
            • How to master Predictive Analytics using Composable Digital Twins
            • Accelerate Your AI Workflow: The 3 Key Business Advantages of XMPro Notebook
            • The Roadmap to Intelligent Digital Twins
            • What is edge computing, and how can digital twins utilize this technology?
            • THE TOP 5 USE CASES FOR COMPOSABLE DIGITAL TWINS IN RENEWABLES + HOW TO SUPERCHARGE RESULTS WITH AI
            • The Technology Behind Predictive Maintenance (PdM) : Hardware & Software
            • The Benefits of Using Digital Twins in Smart Manufacturing
            • XMPro I3C Intelligent Digital Twins Strategy Framework
            • The TOP 5 use cases for composable digital twins in mining – and how to use AI to supercharge result
            • The TOP 5 use cases for Composable Digital Twins in the Oil & Gas industry
            • Why Decision Intelligence with Digital Twins is “kinda like” DCS for Automation and Control
            • XMPro becomes an NVIDIA Cloud-Validated partner
            • From Reactive to Predictive : Introduction to Predictive Maintenance
            • Microsoft Azure Digital Twins : Everything You Need To Know
            • Unlocking Efficiency: The Right Time & Strategy to Launch Your Digital Twin for Enhanced Asset Manag
            • Revolutionize Your Supply Chain: How Digital Twins Can Boost Efficiency and Cut Costs
          • 2022
            • Create a Common Operating Picture of Your Operations with XMPro
            • 7 Trends for Industrial Digital Twins in 2022
            • How to Build a Digital Twin + 60 Use Cases By Industry
            • What are composable digital twins in the metaverse?
          • 2021
            • The Value of a Composable Digital Twin
          • 2020
            • Lean Digital Twin: Part 2
            • Digital Twin: Your Most Productive Remote Worker
            • From the Control Room to the Bedroom
            • Lean Digital Twin: Part 3
          • 2019
            • My Digital Twin: Digital Twin Applications for Real-time Operations (Like Me)
          • 2018
            • XMPro IoT Operational Capability Survey Results 2018
            • What is a Digital Business Platform and Why Should I Care?
            • [Robotic] Process Automation for IoT
            • 3 Patterns of Industrial IoT Use Cases
            • The CXO’s Guide to Digital Transformation – May The Five Forces Be With You
            • Is Security More Important Than Trustworthiness for Industrial IoT?
            • XMPro at bpmNEXT 2018: Watch The Presentation
          • 2017
            • The Top 5 Reasons to Invest in an IIoT Development Platform
            • IoT Business Solutions Start with Big Data & Create Business Outcomes
            • How AI Bots Bring Digital Twins to Life
          • 2016
            • How To Get Started With Industrial IoT
            • How To Overcome The Top 5 Challenges To Industrial IoT Adoption
            • What is an IoT Platform vs. an IoT Business Application Suite?
            • Industrial IoT: How To Get Started with Predictive Maintenance
            • 3 Ways The Internet of Things is Transforming Field Service
            • 7 Types of Industrial IoT Data Sources (And How To Use Them)
          • 2015
            • How Important Are Processes To The Internet Of Things?
            • Understanding the Value of Real Time KPI Management as Your Next Strategic Project
            • 6 Myths About Machine Learning
            • 10 Predictive Analytics Use Cases By Industry
            • What is a “Business Moment” in your business?
            • Does Operational Intelligence Make Business Intelligence Obsolete?
            • How To Reduce Operational Costs by 36% with Predictive Analytics
            • From Many, One – The Nature of Complex Event Processing
            • Herding Cats: What Enterprise Architects need to know about Business Process Management
          • 2014
            • Making Business Operations More Intelligent
          • 2013
            • Best Next Action Is The Next Big Thing For Intelligent Operations
            • The learns from two ‘Best in class’ organisations acquiring BPM technology
          • 2012
            • Why Intelligent Business Operations is Mobile, Social and Smart
            • Why Do You Want Intelligent Business Operations?
            • How big of a problem are ‘dark processes’?
            • Operational Risk: When You Stick Your Head In The Sand
            • The Difference Between Event-based And Workflow-based Processes
          • 2011
            • Is mobile BPM now essential to the business?
            • Stretch Socially Dynamic Processes To Fit Your Business
            • Social Listening – Get Control Of The Conversation
            • Operations Management – The Keys To KPIs
            • Benefits of BPM v 1.0
            • How to Prioritise Processes
          • 2010
            • The Business Drivers
            • Preserving Capability and Agility
            • Mobile BPM
        • Use Cases
          • Aging Pipe Predictive Maintenance in Water Utilities
          • Air Quality Monitoring For Agriculture
          • Alarm Management and Triage
          • Asset Condition Monitoring for Surface Processing Plants in the Mining Industry
          • Bogie Health Monitoring in the Rail Industry
          • Boiler Feed Water Pumps
          • CHPP Throughput Loss Monitoring
          • Casting Guidance
          • Conveyor Belt System Monitoring and Optimization in Automotive Manufacturing
          • Cooling Tower Fin Fan Monitoring
          • Cyclone/Slurry Pump Monitoring
          • Demand Planning to Reduce Stockholding in Stores
          • Demin Water Monitoring for Boiler Tube Corrosion
          • EV Battery Assembly Process Optimization for the Car Manufacturing Industry
          • Flood Prediction & Response in Water Utilities
          • Golden Batch For Culture Addition In The Dairy processing Industry.
          • Golden Batch Monitoring
          • Improve First Pass Yield (FPY)
          • Induced Draft (ID) Fan Monitoring
          • Long Conveyor Monitoring
          • Monitor Process Health to Reduce Cash-to-Cash Cycle
          • Monitor Storm Water Reservoirs For Flood Prevention
          • Monitor and Reduce Energy Consumption
          • Oil Well Maintenance Planning
          • Oil Well RTP Monitoring
          • Pipe Scaling Prediction for Roller Cooling
          • Precision Irrigation in Agriculture
          • Predict Heat Exchanger Fouling
          • Predictive Maintenance & Asset Health Monitoring For Haul Trucks In The Mining Industry
          • Predictive Maintenance For Mobile Assets Within The Mining Industry
          • Predictive Maintenance for Robotic Arms in the Automotive Industry
          • Predictive Maintenance for Wind Turbines
          • Pump Health Monitoring in Water Utilities
          • Pumping Station OEE
          • Real-time Balanced Business Scorecard (BBS)
          • Real-time Safety Monitoring
          • Short Term Inventory Planning
          • Strategic Performance & Safety Oversight for Global Mining Operations
          • Wheel and Track Wear Monitoring In The Rail Industry
          • Wind Turbine Performance Optimization
        • Youtube
          • 2024
            • Discover Gen AI Powered Operations With XMPro iBOS
            • Generative AI and Digital Twins in 2024 - XMPro Webinar
            • Go From Reactive To Predictive Operations In Water Utilities With XMPro iDTS
            • How to add Timestamps to Elements in XMPro App Designer
            • How to Build an AI Advisor for Industrial Operations Using XMPro
            • How XMPro Stream Hosts and Collections Enable Scalable, Real-Time Data Processing
            • Mind Blowing AI Agentic Operations For Industry With XMPro MAGS
            • The Ultimate Beginner's Guide To Predictive Analytics Podcast
            • XMPro's Flexible Deployment Options: Flexible Cloud & On-Premise Solutions For Industry
            • XMPro iBOS: The Only AI-Powered Suite for Scalable Intelligent Operations
          • 2023
            • 2023 XMPro Product Roadmap - Webinar
            • An Introduction To Intelligent Digital Twins - Webinar
            • Energy and Utilities Asset Optimisation through Digital Twin technology
            • Explore Model Governance using our MLflow Agent
            • Exploring XMPro Notebook and MLflow for Data Science and Model Governance
            • How Changing Properties For One Block Can Be Applied To All Blocks Within Same Style Group
            • How do I Use A Button To Update a Data Source In XMPro App Designer
            • How Does XMPro Compare To ESBs (Enterprise Service Buses)-
            • How to Configure and Integrity Check in Data Streams
            • How To Create A Widget Within XMPro App Designer
            • How to Create Intelligent Digital Twins Using XMPro AI
            • How to export grid data to Excel In XMPro App Designer
            • How to Revolutionize Your Supply Chain with Digital Twins
            • How To Rotate Text In App Designer
            • How To Update a Data Source Using A Button
            • How To Use & Clone XMPro Demos For Your Own Use
            • How To Use And Build 3rd Party Apps To Extend The Capabilities Of The XMPro App Designer.
            • How to use Avatars and why they are important
            • How to view stream host logs In XMPro Data Stream Designer
            • Logging Provider Support With XMPro
            • Mastering Health Check Endpoints: A Guide to Ensuring Service Uptime and Performance with XMPro
            • Mastering Root Cause Analysis with XMPro: Capture, Value, Impact
            • Microsoft Azure Digital Twins Everything You Need To Know
            • Model Based Predictive Maintenance (PdM) With XMPro
            • Monthly Webinar - Accelerate your digital twin use cases - XMPro Blueprints, Accelerators & Patterns
            • Optimizing Time Series Chart (TSC) Performance
            • Predictive Maintenance & Condition Monitoring - A Hot Seat Q&A Session
            • Predictive Maintenance with XMPro iDTS
            • Smart Facilities Management with Intelligent Digital Twins
            • The Benefits of using Digital Twins in Smart Manufacturing
            • The Four Industrial Revolutions Explained In Under 4 Minutes! #industry4 #smartmanufacturing
            • The Roadmap To Intelligent Digital Twins
            • The Technology Behind Predictive Maintenance (PdM) - The Hardware & Software that makes PdM Tick...
            • THE TOP USE CASES FOR COMPOSABLE DIGITAL TWINS IN RENEWABLES
            • Tips on how to use cache in agent configuration and get live updates
            • Webinar - XMPro 4.3 Release Showcase
            • What is a Digital Twin- Why Composable Digital Twins is the Future.
            • What Is Predictive Maintenance- (PdM)
            • What To Do When a Data Source Is Not Showing in Pass Page Parameter
            • XMPro - The World's Only AI - Powered Intelligent Digital Twin Suite
            • XMPro - The World's Only No Code Digital Twin Composition Platform
            • XMPro AI : How It Works
            • XMPro AI End To End Use Case
            • XMPro Auto Scale - Understanding Distributed Caching for Cloud-Native Applications
            • XMPro Promo Video - Dell Validated Design For Manufacturing Edge
          • 2022
            • Aggregate Transformation Agent Example - XMPRO Data Stream Designer
            • App Layout Best Practices for Desktop & Mobile - XMPro Lunch & Learn
            • Broadcast Transformation Agent Example - XMPRO Data Stream Designer
            • Calculated Field Transformation Agent Example - XMPRO Data Stream Designer
            • CRC16 Function Agent Example - XMPRO Data Stream Designer
            • Create a Common Operating Picture of Your Operations with XMPro
            • CSV Context Provider Agent Example - XMPro Data Stream Designer
            • CSV Simulator Agent Example - XMPRO Data Stream Designer
            • CSV Writer Agent Example - XMPRO Data Stream Designer
            • Data Conversion Transformation Agent Example - XMPro Data Stream Designer
            • Digital Twin Strategy To Execution Pyramid - XMPro Webinar
            • Event Printer Action Agent Example - XMPRO Data Stream Designer
            • File Listener Agent Example - XMPRO Data Stream Designer
            • Filter Transformation Agent Example - XMPRO Data Stream Designer
            • Group & Merge Transformation Agent Example - XMPRO Data Stream Designer
            • How To Bind Data To A Chart and Get It Working As Expected - XMPro Lunch & Learn
            • How To Send Data To My App (Including Caching Introduction) - XMPro Lunch & Learn
            • Join Transformation Agent Example - XMPRO Data Stream Designer
            • Min/Max Function Agent Example - XMPRO Data Stream Designer
            • PART 1- How To Manage Complex Operations in Real-time Using Composable Digital Twins
            • PART 3 - How To Manage Complex Operations in Real-time Using Composable Digital Twins
            • PART2 - How To Manage Complex Operations in Real-time Using Composable Digital Twins
            • Pass Through Agent Example - XMPRO Data Stream Designer
            • Pivot Table Transformation Agent Example - Count - XMPRO Data Stream Designer
            • Pivot Table Transformation Agent Example - Sum - XMPRO Data Stream Designer
            • Real-Time Is Real - How To Use Event Intelligence Tools to Manage Complex Operations in Real-time.
            • Row Count Agent Example - XMPRO Data Stream Designer
            • Sort Transformation Agent Example - XMPRO Data Stream Designer
            • Transpose Transformation Agent Example - Columns - XMPRO Data Stream Designer
            • Transpose Transformation Agent Example - Rows - XMPRO Data Stream Designer
            • Trim Name Transformation Agent Example - XMPRO Data Stream Designer
            • Twilio Action Agent Example - XMPRO Data Stream Designer
            • Union Transformation Agent Example - XMPRO Data Stream Designer
            • Variables & Expressions in App Designer - XMPro Lunch & Learn
            • Window Transformation Agent Example - XMPRO Data Stream Designer
            • XML File Reader Action Agent Example - XMPRO Data Stream Designer
          • 2021
            • The Value of a Composable Digital Twin - XMPro Webinar
          • 2020
            • 1. Understanding The Problem - UX Design - XMPRO
            • 1.1 Welcome - XMPRO UI Design Basics
            • 1.2 Introduction To UI Design - XMPRO UI Design Basics
            • 2. Creating User Stories - UX Design - XMPRO
            • 2.1 Responsive Design - XMPRO UI Design Basics
            • 2.2 Grids - XMPRO UI Design Basics
            • 2.3 Visual Hierarchy - XMPRO UI Design Basics
            • 2.4 Wireframes - XMPRO UI Design Basics
            • 3. Creating User Flow Diagrams - UX Design - XMPRO
            • 3.1 Color Palette - XMPRO UI Design Basics
            • 3.2 Typography - XMPRO UI Design Basics
            • 3.3 White Space - XMPRO UI Design Basics
            • 3.4 UI Elements - XMPRO UI Design Basics
            • 4. Plan Your App with Wireframes - UX Design - XMPRO
            • 4.1 Chart Types - XMPRO UI Design Basics
            • 4.2 Chart Styling - XMPRO UI Design Basics
            • 5. Designing for Dynamic Data - UX Design - XMPRO
            • Agents and Their Types - XMPRO Data Stream Designer
            • Data Wrangling: Row Transpose - XMPRO Data Stream Designer
            • Digital Twin: Your Most Productive Remote Worker - XMPRO Webinar
            • End-To-End Real-Time Condition Monitoring Demo - XMPRO Application Development Platform
            • Error Endpoints - XMPRO Data Stream Designer
            • Export and Import Recommendations - XMPRO App Designer
            • How To Add Buttons To Agents - XMPRO Data Stream Designer
            • How To Add EditLists to Agents - XMPRO Data Stream Designer
            • How To Change UI Language - XMPRO Subscription Manager
            • How To Configure a Stream Object - XMPRO Data Stream Designer
            • How To Configure The Aggregate Transformation - XMPRO Data Stream Designer
            • How To Configure The Anomaly Detection Agent - XMPRO Data Stream Designer
            • How To Configure The Azure SQL Action Agent - XMPRO Data Stream Designer
            • How To Configure The Azure SQL Context Provider - XMPRO Data Stream Designer
            • How To Configure The Azure SQL Listener - XMPRO Data Stream Designer
            • How To Configure The Calculated Field Transformation - XMPRO Data Stream Designer
            • How To Configure The CSV Context Provider - XMPRO Data Stream Designer
            • How To Configure The CSV Listener - XMPRO Data Stream Designer
            • How To Configure The Data Conversion Transformation - XMPRO Data Stream Designer
            • How To Configure The Edge Analysis Transformation - XMPRO Data Stream Designer
            • How To Configure The Email Action Agent - XMPRO Data Stream Designer
            • How To Configure The Email Listener - XMPRO Data Stream Designer
            • How To Configure The Event Printer Action Agent - XMPRO Data Stream Designer
            • How To Configure The Event Simulator Listener - XMPRO Data Stream Designer
            • How To Configure The FFT Function - XMPRO Data Stream Designer
            • How To Configure The File Listener - XMPRO Data Stream Designer
            • How To Configure The Filter Transformation - XMPRO Data Stream Designer
            • How To Configure The IBM Maximo Action Agent - XMPRO Data Stream Designer
            • How To Configure The IBM Maximo Context Provider - XMPRO Data Stream Designer
            • How To Configure The IBM Maximo Listener - XMPRO Data Stream Designer
            • How To Configure The Join Transformation - XMPRO Data Stream Designer
            • How To Configure The JSON File Reader Context Provider - XMPRO Data Stream Designer
            • How To Configure The MQTT Action Agent - XMPRO Data Stream Designer
            • How To Configure The MQTT Advanced Action Agent - XMPRO Data Stream Designer
            • How To Configure The MQTT Advanced Listener - XMPRO Data Stream Designer
            • How To Configure The MQTT Listener - XMPRO Data Stream Designer
            • How To Configure The Normalize Fields Function - XMPRO Data Stream Designer
            • How To Configure The OSIsoft PI Context Provider - XMPRO Data Stream Designer
            • How To Configure The OSIsoft PI Listener - XMPRO Data Stream Designer
            • How To Configure The Pass Through Transformation - XMPRO Data Stream Designer
            • How To Configure The PMML Agent - XMPRO Data Stream Designer
            • How To Configure The REST API Context Provider - XMPRO Data Stream Designer
            • How To Configure The RScript Agent - XMPRO Data Stream Designer
            • How To Configure The Run Recommendation Agent - XMPRO Data Stream Designer
            • How To Configure The Signal Filter - XMPRO Data Stream Designer
            • How To Configure The SQL Server Action Agent - XMPRO Data Stream Designer
            • How To Configure The SQL Server Context Provider - XMPRO Data Stream Designer
            • How To Configure The SQL Server Listener - XMPRO Data Stream Designer
            • How To Configure The SQL Server Writer Action Agent - XMPRO Data Stream Designer
            • How To Configure The Twilio Action Agent - XMPRO Data Stream Designer
            • How To Configure The Union Transformation - XMPRO Data Stream Designer
            • How To Configure The Unzip Function - XMPRO Data Stream Designer
            • How To Configure The Window Transformation - XMPRO Data Stream Designer
            • How To Create an App - XMPRO App Designer
            • How To Create and Manage Templates - XMPRO App Designer
            • How To Create and Publish a Use Case - XMPRO Data Stream Designer
            • How To Create and Use a Widget - XMPRO App Designer
            • How To Create App Data Connections - XMPRO App Designer
            • How To Create App Pages and Navigation - XMPRO App Designer
            • How To Create Recommendation Rules - XMPRO App Designer
            • How To Create Recurrent Data Streams - XMPRO Data Stream Designer
            • How To Do Integrity Checks - XMPRO Data Stream Designer
            • How To Edit Page Properties - XMPRO App Designer
            • How To Enable Audit Trails - XMPRO App Designer
            • How to Export, Import, and Clone a Data Stream - XMPRO Data Stream Designer
            • How To Export, Import and Clone an App - XMPRO App Designer
            • How to Export and Import an App - XMPRO App Designer
            • How To Find Help for an Agent - XMPRO Data Stream Designer
            • How To Install The XMPRO App Designer
            • How To Maintain and Capture Notes - XMPRO App Designer
            • How To Manage Agents - XMPRO Data Stream Designer
            • How To Manage and Use Server Variables - XMPRO Data Stream Designer
            • How To Manage Buffer Size - XMPRO Data Stream Designer
            • How to Manage Categories - XMPRO App Designer
            • How To Manage Categories - XMPRO Data Stream Designer
            • How To Pass Parameters Between Pages - XMPRO App Designer
            • How To Publish and Share an Application - XMPRO App Designer
            • How To Set Up and Use Charts in Live View - XMPRO Data Stream Designer
            • How To Set Up and Use Gauges in Live View - XMPRO Data Stream Designer
            • How To Share a Data Stream - XMPRO Data Stream Designer
            • How To Share a Use Case - XMPRO Data Stream Designer
            • How To Share an App For Design Collaboration - XMPRO App Designer
            • How To Troubleshoot a Use Case - XMPRO Data Stream Designer
            • How To Upgrade a Stream Object Version - XMPRO Data Stream Designer
            • How To Use App Files - XMPRO App Designer
            • How To Use Application Versions - XMPRO App Designer
            • How To Use Bar Gauge - XMPRO App Designer
            • How To Use Calendar - XMPRO App Designer
            • How To Use Chart Pan, Zoom and Aggregation - XMPRO App Designer
            • How To Use Chart Panes and Axes - XMPRO App Designer
            • How To Use Chart Print and Export- XMPRO App Designer
            • How To Use Charts - XMPRO App Designer Toolbox
            • How To Use Charts: Series - XMPRO App Designer
            • How To Use Collections - XMPRO Data Stream Designer
            • How To Use Content Card - XMPRO App Designer
            • How To Use D3 - XMPRO App Designer
            • How To Use Data Sources - XMPRO App Designer
            • How To Use Embedded Pages - XMPRO App Designer Toolbox
            • How To Use Fieldset and Field - XMPRO App Designer Toolbox
            • How To Use Flex Layout
            • How To Use Form Validation - XMPRO App Designer Toolbox
            • How To Use Input Mappings - XMPRO Data Stream Designer
            • How To Use Linear Gauges - XMPRO App Designer
            • How To Use Live View - XMPRO Data Stream Designer
            • How To Use Lookup - XMPRO App Designer
            • How To Use Maps - XMPRO App Designer
            • How To Use Page Layers - XMPRO App Designer
            • How To Use Pivot Grid - XMPRO App Designer
            • How To Use Polar Charts - XMPRO App Designer
            • How To Use Power BI - XMPRO App Designer
            • How To Use Radio Buttons - XMPRO App Designer Toolbox
            • How To Use Recommendations - XMPRO App Designer Toolbox
            • How To Use Select Box - XMPRO App Designer
            • How To Use Stacked Layouts - XMPRO App Designer Toolbox
            • How To Use Stream Host Local Variables - XMPRO Data Stream Designer
            • How To Use Tabs - XMPRO App Designer Toolbox
            • How To Use Tags - XMPRO App Designer Toolbox
            • How To Use Templated List - XMPRO App Designer
            • How To Use Templates - XMPRO App Designer
            • How To Use Text - XMPRO App Designer Toolbox
            • How To Use Text Area - XMPRO App Designer Toolbox
            • How To Use The Accordion - XMPRO App Designer Toolbox
            • How To Use The Block Styling Manager - XMPRO App Designer
            • How To Use The Box and Data Repeater Box - XMPRO App Designer Toolbox
            • How To Use The Button - XMPRO App Designer Toolbox
            • How To Use The Circular Gauge - XMPRO App Designer Toolbox
            • How To Use The Data Grid - XMPRO App Designer Toolbox
            • How To Use The HTML Editor - XMPRO App Designer Toolbox
            • How To Use The Hyperlink and Box Hyperlink - XMPro App Designer Toolbox
            • How To Use The Image - XMPRO App Designer Toolbox
            • How To Use The Indicator - XMPRO App Designer Toolbox
            • How To Use The Layout Grid - XMPRO App Designer Toolbox
            • How To Use The Number Selector - XMPRO App Designer Toolbox
            • How To Use The Pie Chart - XMPRO App Designer Toolbox
            • How To Use The Range Slider - XMPRO App Designer Toolbox
            • How To Use The Recommendation Chart - XMPRO App Designer Toolbox
            • How To Use The Scroll Box - XMPRO App Designer Toolbox
            • How To Use The Select Box - XMPRO App Designer Toolbox
            • How To Use The Sparkline - XMPRO App Designer Toolbox
            • How To Use The Textbox - XMPRO App Designer Toolbox
            • How To Use Tree Grid - XMPRO App Designer
            • How To Use Tree List - XMPRO App Designer
            • How To Use Unity - XMPRO App Designer Toolbox
            • How To Use Variables - XMPRO App Designer
            • How To Write and Maintain Notes and Business Case - XMPRO Data Stream Designer
            • Interactive 3D Models For Digital Twins - XMPRO Event Intelligence Platform
            • Manage Input Arrow Highlights - XMPRO Data Stream Designer
            • Manage Recommendation Access - XMPRO App Designer
            • Realize Value from End-To-End Condition Monitoring in 6 - 8 Weeks - XMPRO
            • Recommendation Versions - XMPRO App Designer
            • Solution Development Process For Event Intelligence Apps - XMPRO
            • Stream Hosts and How To Install Them - XMPRO Data Stream Designer
            • Use Case Versioning - XMPRO Data Stream Designer
            • XMPRO App Designer Overview - Event Intelligence Applications
            • XMPRO Data Stream Designer - Event Intelligence Applications
            • XMPRO Real-Time Event Intelligence Demo
            • XMPRO Recommendations - Event Intelligence Applications
          • 2019
            • Data Distribution Service: Using DDS in Your IoT Applications
            • My Digital Twin: Digital Twin Applications For Real-Time Operations (Like Me)
            • Setting up a Typical Industrial IoT Use Case with XMPro
            • XMPro Overview & Fin Fan Failure Demo
          • 2016
            • XMPro iBPMS Overview
          • 2013
            • XMPro Best Next Action - 3 Examples for XMPro blog
            • XMPro Case Management Example
            • XMPro Internet of Things Demo
          • 2012
            • Is Agile Business the New Normal
            • The Future of BPM Moving Towards Intelligent Business Operations
            • What industries does XMPro serve-
            • Who is XMPro for-
            • XMPro - The Social Listener - Why You Should Be Listening.wmv
            • XMPro Cool Vendor 2012
            • XMPro iBPMS For SharePoint
            • XMPro iBPMS v6 XMWeb for Intelligent Business Operations
            • XMPro News and Gartner BPM Sydney Summit Discount Offer.mp4
            • XMPro Version 6 - Introducing the Next Generation BPM for Intelligent Business Operations
    • Practice Notes
      • Unified Recommendation Alert Management
      • Performant Landing Pages in Real-Time Monitoring
  • Concepts
    • XMPro AI
      • XMPro Notebook
    • Data Stream
      • Stream Object Configuration
      • Verifying Stream Integrity
      • Running Data Streams
      • Timeline
    • Collection and Stream Host
    • Agent
      • Virtual vs Non-Virtual Agents
    • Application
      • Template
      • Page
      • Block
      • Canvas
      • Page Layers
      • Block Styling
      • Devices
      • Flex
      • Block Properties
      • Data Integration
      • Navigation and Parameters
      • Variables and Expressions
      • App Files
      • Metablocks
    • Recommendation
      • Rule
      • Execution Order
      • Auto Escalate
      • Form
      • Action Requests
      • Notification
      • Recommendation Alert
      • Deleted Items
      • Scoring
    • Connector
    • Landing Pages & Favorites
    • Version
    • Manage Access
    • Category
    • Variable
    • Insights
      • Data Delivery Insights
  • How-To Guides
    • Data Streams
      • Manage Data Streams
      • Manage Collections
      • Use Remote Receivers and Publishers
      • Manage Recurrent Data Streams
      • Use Business Case and Notes
      • Run an Integrity Check
      • Check Data Stream Logs
      • Use Live View
      • Use Stream Metrics
      • Troubleshoot a Data Stream
      • Upgrade a Stream Object Version
      • Setup Input Mappings
      • Use Error Endpoints
      • Use the Timeline
      • Context Menu
    • Application
      • Manage Apps
      • Manage Templates
      • Manage Pages
      • Import an App Page
      • Design Pages for Mobile
      • Navigate Between Pages
      • Pass Parameters Between Pages
      • Page Data
      • Manage Connections
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  5. 2023

An Introduction To Intelligent Digital Twins - Webinar

Previous2023 XMPro Product Roadmap - WebinarNextEnergy and Utilities Asset Optimisation through Digital Twin technology

Last updated 11 days ago

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During this informative session, XMPro CEO, Pieter Van Schalkwyk will delve into the concept of Intelligent Digital Twins and discuss how they differ from traditional Digital Twins. This webinar aims to equip professionals across various domains with the knowledge necessary to harness the power of AI, accelerating the adoption and implementation of IDTs in diverse industries.

Don't miss this opportunity to gain valuable insights from an industry expert and stay ahead of the curve in this rapidly evolving field.

Transcript

hi everybody I'm Peter from Skull click

um the CEO of Ericsson Pro and the topic

for today is intelligent digital twins

um I wish I I could take the credit for

coming up with the concept of

intelligent digital twins but we're

really standing on the shoulders of

giants um Dr Michael Greaves who

is also known as the father of digital

twins actually started the concept or in

his work looking at where digital twins

are going

came up with it

concept of industrial of intelligent

digital Twins and you can also find

um a great paper that he where he

explains the whole concept

um at the digital

twin1.org and we'll also make the the um

the link available after the webinar and

I I was fortunate I had the opportunity

to meet with Dr Griggs at one of the

digital twin Consulting member meetings

and we discussed where things are going

with digital Twins and this is a diagram

that he's got in in the piper

um and he explains kind of the evolution

and right now we're seeing quite a lot

of ad hoc so sometimes people refer to

this as digital Shadows where there's

one-way communication so we've got the

physical

items and that updates a a digital

version so I'm creating a static

repository of data and we've seen more

and more digital twin platforms emerging

where there's some replication of data

going around

and and the future is moving towards uh

intelligent digital twins with things

like front running simulations and

everything so

really expensive paper that explains it

all my summary of the paper

um quickly ran through that is we're

moving from a focus on data to focus on

data flow so the difference between a

traditional digital twin where it's just

a passive repository and we kind of

taking data from my physical and just

representing it in in a virtual way to

something which is active and always on

so it continuously monitors the

environment and the the assets itself

where the traditional digital twin is

more of an offline and it writes for the

physical to actuate it so even if you

think of something like airbags there's

a a

um

it's as soon as an event happens on the

physical it updates the digital on the

online one this this continues running

on the side and it monitors scans the

environment and based on that also

creates actuation of of potential

actions that come out of it

with traditional digital twins we give

it a a goal and then we create kpis and

performance measures to see how well

we're doing based on the goals that we

are with that we've set for that

with intelligent digital twins we can

now use more intelligent approaches to

make it more goal-seeking to try and

optimize where we augment what humans

are doing with AI

and smarter digital twins to to support

that

and lastly we have predictive

capabilities so we can predict but it's

not really optimizing so it's not

looking for certain optimal set points

operating points

[Music]

maintenance intervals those kind of

things where going forward doing things

like front running

simulation we are able to speed up and

that's what we mean by manipulate time

we can take real-time data we can take

contextual data we can take historical

data and speed that all up and then

based on that anticipate what is going

to happen and see what are the better

responses for those and great example of

front running simulation is what happens

in Formula One car Rising

and example is a partner with Dell it's

this is not built on XM Pro but this is

work that Dell has done with McLaren

around things like front running

simulation so this is one of the fastest

Edge devices out there

um it goes uh 200 miles an hour or 360

km 360 uh

kilometers per hour

and at that it generates about a hundred

thousand data points per second so a lot

of information that you can put together

and based on that determine fuel

strategies and a whole bunch of other

things during race time so ability to

speed up so take the data run it faster

than real time to speed it up and then

decide and

um on on on certain actions so this is

the McLaren example and this also

great work done by Amazon AWS and others

on on on on similar things so things

like again tracking information on the

vehicles they can track information

combining all of that you can create

multiple use cases and I think that's

one of the the other elements of digital

twins that's that's quite uh that's not

often understood it's not about one

application or use case that I'm trying

to do I can now take that data and I can

actually facilitate a multiple number of

different things that I can do so

multiple use cases in this instance of

battle Focus pitch strategy Striker

Performance Tire performance exit speeds

a whole bunch of different front running

simulations that I can do in a specific

interesting one is undercut thread so

um great video on the AWS website as

well

um but with undercut thread it's

actually deciding when to bring your car

in and also looking at what are your

competitors potentially doing so are

they looking at undercutting you by

bringing in a vehicle by bringing in a

race car at a certain window of

opportunity now in order to do that you

need to meet you need to measure the

real-time speed of all your other

competitors look at their way by using

computer vision and a whole bunch of

things die away

um trying to figure out what their fuel

consumption is take all of that and run

it in your strategy so these are very

sophisticated and advanced examples of

front running simulation but we're

starting to see that move into the

industrial space and other areas where

we are trying to do this so

from a digital twin perspective how we

make decisions is changing as you saw in

those very sophisticated examples we can

bring a whole lot of information from

multiple different places

and that's really the essence of

decision intelligence

where we've got external information

we've got internal information and

traditionally what we've done with kind

of the more static digital Twins or or

conventional digital twins is really

decision support

so there's a business process there's a

human in that Loop and we are now

um trying to give them some decision

support and traditionally it's been

dashboards and business intelligence

condition monitoring basic predictions

as well

um so again the mind of of decision

support we're seeing more and more

decision augmentation where we can bring

in smarts from Ai and other tools

so um we still have the human in the

loop that make the decision but that

human is now the the decision process is

augmented with

um information that can be processed at

a speed that humans can't do it the

volume of information that that can be

processed again is done at a speed that

we can't do as as humans but

at the end the decision Still Remains

with us so this is where front running

simulations prescriptive recommendations

um

is is coming in and this provides us the

opportunity to create a closed loop a

feedback loop and learn from that and

kind of improve our decision making as

well as improve the models that we have

and this is kind of where a lot of um

focus is at the moment in terms of

moving towards more intelligent digital

twins

but there is a future where we also look

at decision automation so once we have a

high level of confidence on in some of

these decisions or or some of these

models we can actually let the machine

make the decision through business

process Automation and create this

distributed intelligence system

where we can maintain the rules and the

models and everything centrally and it

it it it provides us the opportunity to

get to algorithmic business where again

a certain number of these of of these

um

business processes that can be done in

an automated advice so if you think

about fly by wire which you can do with

aircraft

you might be able to operate by wire in

a certain envelope those decisions can

all be made by machines so that's

the future that we see in terms of where

this is all heading distributed

intelligence systems and now if you

combine that with the control

environments if you look at a

distributed control systems and you add

distributed intelligence systems my

personal views that's probably the

future of what operations will look like

now what does this mean for intelligent

digital Twins and I refer back to the

digital twin Consortium

um

definition of what a digital twin is

it's a virtual representation of

entities uh of real world entities and

processes to synchronized at a certain

frequency frequency and fidelity

interested in being on it's synchronized

and it helps us with decision making and

taking effective action again it's about

decision making it uses all sorts of

data and it is motivated by outcomes and

it's focused around use cases again that

example that I showed with the front

running simulation a whole bunch of

different

use cases

um that are being facilitated for that

and we implement it in it requires the

main knowledge and implemented through

this so

at a traditional digital twin has got

um that synchronization Sometimes some

of the information may go back

but it's really passive it's offline

um in the sense that it's waiting for

the thing on the on the left hand side

the physical twin something to happen

and that will then update that's not um

and it's called Givens I've got kpis and

things that I'm trying to to measure and

yeah we can predict but it's not focused

on optimization

going forward with intelligent digital

twins we see that decision intelligence

structure that I showed a little bit

earlier make its way into kind of

operating on the side so

we've got expert knowledge business

rules all the math and physics models

that we have and quite a few of those

are already being used in the uh more

decision support type digital twins that

we have we're now starting to see convey

what is regarded as conventional AI so

regression models and all of those but

then also the new generative AI that

we've seen lightly and large language

models that are making its way in

and some of the more sophisticated

techniques like deep learning neural

networks and those so that in

intelligence making is it's why

into digital Twins and then providing

the opportunity to Market goal seeking

and learning and doing this front

running simulations now the question

that we get is how do how do we make

this happen how do we do this because in

order in order to do this we need this

thing that intelligence that run on the

side almost and we go from where in the

previous one we had information at the

bottom there we now have prescriptions

and that is synchronized at a different

rate so it's not just the information

we've seen in back we actually send a

prescription in terms of what to do

um back now this could be augmented or

potentially automated as well but we

have to have this mechanism on the side

that continuously runs so now going from

data this requires a data flow where it

continuously run on the site and

interact with that

so in order to do this we came up with a

framework

as organizations are considering how do

we move to intelligent digital twins

well first of all it needs to be

integrated and it needs to be based on

standards models and have that

bi-directional integration that we that

I mentioned it needs to have

intelligence and we'll go into each of

these in a little bit more detail but

needs to be executable so it needs to be

able to run in real time we need some

way to make it Innovative and explore

experimentation and doing those

simulations but we also need to provide

an environment where we can bring in

the the the help from from the digital

Twin Side and augment so that we can

learn from that

and lastly we need to migrate

interactive so

um this is about the visualization so

how do we how do we provide

recommendations how do we make it in a

generative multi-experience user

interface

because all of this is becoming the

foundation for the industrial metaverse

whatever the metaverse looks like when

it comes out in order to do this at

scale we also need to do this on the

composable kind of

um

framework where we can reuse components

almost like the Lego blocks that kids

build

um toys with you can actually reuse

components and and and have a plugable

composable uh price for this

so if we briefly look at the integrated

side of things

um standards-based apis for these

capabilities that we package together a

model driven approach and bi-directional

in order to do this again this is about

data flow so example that we're showing

here this is our data stream designer is

being able to create standard

Integrations

to the apis of different systems then

being able to create a model now the

benefit of this is I can apply this to

um uh 100 wind turbines or a thousand

when turbines is exactly the same model

so in terms of the data model that

supports it and potentially the digital

twin model also model driven but in

terms of of creating The Logical data

flow structure making that model driven

and at the bottom right

it's not just about in uh have receiving

information one way but also sending

information back and potentially

changing set points um based on

recommendations that could come from Ai

and some of the other elements

so that's the the from integration

perspective making it more intelligent

where we now adding this capability

first of all in terms of those three

elements we need to make it executable

we need to provide an environment to to

to to for um to create these AI elements

and then we need to augment the user

experience with that and again different

audiences which this applies to so if I

look at that executable how do I bring

it into the data flow traditionally we

would take real-time data we would have

a model and

um so we've got the streaming agent that

with that with um

bring in real-time data we've got a

configuration of what the fly looks like

and that just gives us a result

with executable AI

um we can create a training model and

I'll briefly Show an example we'll

create a training model I'm using the in

this instance our XM Pro AI notebook

deploy that model into into an ml Ops

environment because again we need to now

look at how do we do this at scale if

I've got tens of thousands of models how

do I do the model management and as part

of the digital twin management as well

and then bring that in through bring

that model in through again an agent

that's got the intelligence bringing

live data on that can now run on that

training model and again in our in our

now code in environment you can

configure all of this and that will give

you the prediction and simulation

so what does it look like when you

actually do it

um what you can see here is a very basic

Way Reading uh tanks type and you'll see

where the yellow perform machine

learning analysis that actually calls a

beer quality model that sits in uh

uh the through um ml flow where where

which is the data repository for the

model so that's how we make the digital

twins

executable in the data streams that we

have the next part is

um

to be able to bring in an environment

where we can make it more Innovative so

we've embedded jupyter notebooks but

we've added some functionality to that

so you can wire it up into our data

streams but you can also integrate it

with things like giant GPT and others to

help you in the process

so again aimed at

on the one hand in analyzing the data

but also building models

um for things like front running

simulations and some of the others in

this instance I've got chat GPT

and um I I can ask it to tags to create

a code for me which is the next part

over here to create the guide for me to

represent this data

in this in a certain way so having the

the

um the request

um

having a request here through GPT

how do I visualize this data it then

writes the python code for me and it I

hope if I run that and it gives me the

visualization here's another example of

of embedding

um

or augmenting

um the the user experience in this

instance there's a copilot so we ran

this and based on the recommendations at

the top we can see that there is

potentially a impeller problem but I can

also see my discharge pressure is not

what it should be

so I can ask you know what are the top

five root causes for centrifugal pump if

there's a loss in discharge pressure and

you can see what it came up with if we

have time on briefly jump into and

showing you what that

is um towards the international fact let

me briefly let me quickly do that

um

okay

so

this is the Jupiter notebook with the

um

the

foreign

I can run through this it will generate

data for me

um

and

this is

machine generated data and now I ask

Chad GPT

to actually create the um

let me move down a little bit so I'm now

going to ask Jack GPT to create the code

for me

to visualize this data

and that's done that and now it's

created based on the code that it's

created for me in Python automatically

and also created the visualization

um and again this goes into more

actually putting this into into an email

slope on the the other example that I

briefly mentioned so

um

on the well Refinery operations

and I just go to that pump

we just got an issue

and you can see the normal information

that I have I've got all the contextual

information but I also want to ask it a

question now

um I'll just one of the top five root

causes and this thing interrogates using

um functionality of chat GPT to do that

so that's ways that we can bring

intelligence to it lastly on the

interactive side

to make it more accessible

in terms of providing recommendations

again uh

the intelligent digital twins have have

the opportunity to not just

um

tell you what is wrong but also give a

recommendation on on what you should be

doing triage instructions and some

additional context around what happened

in the past this gives you the

opportunity to also close the loop I can

create work orders and and things from

here and then be able to track that as

well to see how effective we are at

making certain decisions in terms of

creating a collaborative environment as

you can type as the previous example

which I briefly showed you

you can create very collaborative

environments and user experiences where

you now bring that intelligence

um into a front end that that users and

can use

and as I mentioned going forward you

know this will form the foundation of

the industrial nativist now the Brew the

Brewing example that you see here is is

actually an example that we built out

for the Dow validated

solution for the manufacturing Edge side

of things but this is what we see going

forward in terms of creating a meta

versus in a very interactive environment

where you know it doesn't matter what

your user experience is whether it's AR

VR desktops mobiles

um the intelligence is portable across

all of those

environments lastly and

the composable side

um

we came up with the the

um

positioning XM Pros that can composable

platform and

um

bringing in data from all the the the

underlying systems to be able to build

authorize various different use cases

and for that we have a whole bunch of

of

um modules that that support that so

in order to do that the basis for all of

that is is

um

capabilities and inside digital

Consortium we were instrumental in

creating the the

um capabilities periodic table these are

all capabilities that we see at a really

high level and that is used for digital

twins if you're wanting more information

on the digital Consortium website you

can download

the the whole

capabilities framework we also have a

webinar that we've done in the past

which you can have a look at where we go

into the capabilities and composability

and side for that so so here's our view

in terms of what the future looks like

oh sorry oh so the the the the

components that you need in order to

create intelligent digital twins which

we see as the future of where digital

twins are going we've created we've run

all that all into what we call

intelligent digital twin Suite example

idps consists of a number of modules and

and

happy to share more information on that

just lastly we also see that this way in

terms of where we are going and how

we're doing this at that line in future

may disappear and that intelligence all

being built into the

um digital twins of the future so with

that um there's a few minutes left any

questions that that everyone's got that

I can address

great presentation you do have a few

questions that came out there

um take it from the top uh do you one of

the questions that I that that we get is

you know do you supply the AI models

it's

it is the reason why we created that

environment so inside

um the XM Pro AI notebook

um you can create your own models there

are libraries of existing models

um there are Auto ml there's a whole

um uh kind of Continuum of opportunity

to use pre-made libraries of of models

we don't specifically do do models but

um there's a large library of of

simulation models of

all the standard

um traditional IR models as well as as

you saw being able to bring in

generative Ai and others pretty easily

how do you get started with this

um we're happy to run you through kind

of the the kind of three-step process

um on on how to get started with these

digital twins but hopefully that's been

been helpful in giving you an

understanding of you know what we see as

the future and what digital twin what

what intelligent digital twins

look like going forward

any additional questions

I have one question that was sent to me

around

um so yeah again this is I think the the

the way that we

um

that we look at how to how to create

this look at it it needs to be

integrated that needs to be intelligent

needs to be interactive all on a

composable approach and then in terms of

um the question is really do are they

starters or examples we have a

blueprints that we are creating they are

they are started

examples available in GitHub so we're

really easy to export into your

environment

um and then play with that we do have a

freemium option so if you go on a

website you can download premium option

um of us of our software and also some

of these libraries and examples that we

have

over that um thank you very much I

really appreciate you watching this and

we'll see you uh on the future webinar

we will send out the recording of this

um after the event thank you