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XMPro Platform
XMPro Platform
  • What is XMPro?
  • Getting Started
    • Browser Requirements
    • Free Trial
    • 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
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  • Part2: The Future of Work: Harnessing Generative Agents in Manufacturing
  • Part2: The Future of Work – Harnessing Generative Agents in Manufacturing
  • Part2: The Future of Work: Harnessing Generative Agents in Manufacturing
  • Adapting to the Future: The Case for New Work Approaches
  • Driver 1 – Navigating the Shift: Addressing Labor Challenges in Manufacturing and Logistics
  • Driver 2 – Rethinking Efficiency: Addressing the Productivity Plateau in Manufacturing
  • Driver 3 – Addressing Complexity in Today’s Manufacturing Processes
  • The Future of Work: How Generative Agents Can Help
  • The Evolution of Agent Approaches in Work and Task Types
  • What is Multi-Agent Generative Systems (MAGS)?

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Part2: The Future of Work: Harnessing Generative Agents in Manufacturing

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Part2: The Future of Work: Harnessing Generative Agents in Manufacturing

Posted on July 24, 2024 by Pieter van Schalkwyk

Part2: The Future of Work – Harnessing Generative Agents in Manufacturing

Part2: The Future of Work: Harnessing Generative Agents in Manufacturing

Pieter Van Schalkwyk

CEO at XMPRO, Author – Building Industrial Digital Twins, DTC Ambassador, Co-chair for AI Joint Work Group at Digital Twin Consortium

June 26, 2024 – Originallly posted on Linkedin

In part 1 – From Railroads to AI: The Evolution of Game-Changing Utilities, I explained how the development and adoption of Generative AI mirrors the transformative impact of railroads, electricity, and the Internet, and why it is set to become the next game-changing utility driving our future.

Just as we needed machines and appliances to harness the electrical utility, we now need transformative applications to fully leverage the Generative AI Utility.

In part 2, I explore how Generative Agents represent the transformative application for GenAI capabilities, ready to revolutionize how we work.

Unlike current AI Assistants and Copilots that improve efficiency, Generative Agents will bring a step change—a fundamental shift in “how” we work. This transformation will be similar to how electrical appliances replaced manual tools, enhancing productivity, improving society, and elevating the quality of life for everyone.

As we stand on the brink of this shift, it’s important to understand the driving forces behind the need for change, the unique capabilities of Generative Agents, and how they operate. In this article, we will delve into three key focus areas:

  • The compelling reasons why we need to change how we work

  • How Generative Agents are uniquely positioned to facilitate the future of work

  • A deeper dive into the mechanics of Generative Agents and how they function to bring about this transformation.

By exploring these areas, I hope to give you a glimpse of Generative Agents’ immense potential for redefining our work landscape.

Adapting to the Future: The Case for New Work Approaches

The evolving manufacturing landscape demands a significant shift in our work methods. Several key drivers necessitate this change:

1. Skills

The current labor market faces multiple challenges: rising labor costs, shortages of skilled workers, an aging workforce, and declining labor participation rates. These issues create a pressing need for businesses to find alternative solutions to maintain productivity and efficiency. Upskilling and reskilling the workforce to adapt to new technologies like AI, robotics, and advanced data analytics are crucial. Traditional manual labor skills no longer suffice to meet the demands of modern manufacturing.

2. Productivity

Despite technological advancements such as IoT and automation, manufacturing productivity has not significantly improved in recent years. Initial gains from integrating PCs with PLCs, the advent of the internet, and the introduction of communication standards like OPC DA and UA were substantial. However, productivity has plateaued despite further advancements. This stagnation suggests the need for new strategies and transformative approaches to reignite productivity growth and maximize the potential of current technologies.

3. Complexity of Manufacturing Processes and Systems

The increasing complexity of modern manufacturing systems poses significant challenges. Automation, robotics, AI, and IoT integration require advanced technical skills and continuous learning. Effective data management and analysis are essential for process optimization and decision-making. Additionally, achieving interoperability among various systems and technologies, managing global supply chains, and maintaining high-quality control standards demand a shift towards more sophisticated and knowledge-intensive work practices.

Let’s explore each of these in more detail.

Driver 1 – Navigating the Shift: Addressing Labor Challenges in Manufacturing and Logistics

The evolving landscape of manufacturing and logistics is driving a significant shift in the required skill sets for frontline workers. Due to labor shortages, industry analysts project that by 2028, there will be more smart robots than frontline workers in these industries. This transition applies to physical and digital robots or agents that will augment the manufacturing workforce.

1. Labor Challenges and Statistics

According to analyst research, labor has become as significant a constraint on operational performance as product availability. The U.S. workforce is expected to grow nearly five times slower than the U.S. GDP over the next decade, with the GDP projected to grow at 2.5% while the overall workforce will grow only 0.5%. Specifically, manufacturing and retail workforces are expected to shrink by 0.1% and 0.4%, respectively.

2. Aging Workforce

By 2030, the U.S. Department of Labor expects that one-quarter of the U.S. workforce will be over 65 years of age. This trend is also evident in Europe and parts of Asia, indicating a global challenge. Manufacturing faces an image problem, with many younger generations perceiving these jobs as outdated, boring, and lacking creativity. This negative perception and the widespread labor shortage make it challenging to attract and recruit new talent to fill the positions left by retirees.

3. Declining Labor Participation Rates

U.S. labor participation rates are projected to decline from 67% in 2000 to 60.4% in 2030. This decline increases the labor shortages faced by various industries. LNS Research found by comparing 2019 vs. 2023 manufacturing employment data that there is a declining workforce tenure: The average tenure of employees in manufacturing has decreased from 20 years in 2019 to only three years in 2023. This means that manufacturers are dealing with a less experienced workforce.

4. Evolving Skill Requirements

The rapid pace of technological change in manufacturing means that the required skills constantly evolve. This makes finding candidates with the right expertise challenging and necessitates continuous upskilling and training efforts.

Retaining these upskilled workers is a further challenge in this competitive labor market. LNS further found in their comparison of 2019 and 2023 labor statistics that there is a high employee turnover: 50% of new employees leave manufacturing roles within the first 90 days. The 90-day retention rate in 2019 was 90%.

This turnover rate further exacerbates the issue of a less experienced workforce and puts additional pressure on manufacturers to improve onboarding and training processes.

Driver 2 – Rethinking Efficiency: Addressing the Productivity Plateau in Manufacturing

Despite significant advancements in IoT and automation, manufacturing productivity has not seen substantial improvements in recent years. According to the U.S. Bureau of Labor Statistics, labor productivity in the manufacturing sector, measured as output per hour for all workers, has plateaued.

I saw this initially in a LinkedIn article by Michael Carroll. If you want to see how the numbers stack up, you can explore the source for yourself here.

https://www.linkedin.com/pulse/looming-copilot-disaster-manufacturing-navigating-complex-carroll-qseye/

Michael marked up the graph with the evolution of manufacturing technologies. It is a great read, and you will find it here.

Over the past several decades, the manufacturing sector has witnessed significant shifts in productivity driven by technological advancements. From the 1990s to the mid-2000s, manufacturing productivity saw substantial gains due to the integration of Programmable Logic Controllers (PLCs) with PCs, the advent of the Internet, and the emergence of the Internet of Things (IoT), which improved communication, data collection, and automation.

Further advancements included manufacturing intelligence combining data analytics with automation, decreasing sensor costs, and the release of OPC Unified Architecture (UA), which enhanced system interoperability. However, despite these continuous technological advancements, productivity growth has plateaued post-2010, indicating diminishing marginal gains or counteracting factors.

1. Reimagining Workflows for Enhanced Productivity

Despite recent innovations, the manufacturing sector has encountered a plateau in productivity growth. This stagnation suggests that the benefits of new technologies are becoming less pronounced or other challenges are counteracting potential gains.

To address this issue, we must rethink how we work. Productivity is measured by worker output per hour, and to significantly improve this, we need to enable workers to produce more with less of their own time and resources.

Reimagining business processes to include Generative Agents that handle repetitive, high-volume tasks provides a pragmatic solution to the productivity problem. New strategies and breakthroughs are essential to break through this plateau and drive future productivity growth.

Driver 3 – Addressing Complexity in Today’s Manufacturing Processes

The increasing complexity of manufacturing systems and tasks poses several challenges that require a shift in how work is approached. Here are the main challenges:

1. Automation and Technology Integration

Modern manufacturing relies heavily on automated systems, robotics, computer-controlled machinery, and advanced technologies like IoT, AI, and data analytics.

This integration requires a workforce with technical skills, problem-solving abilities, and continuous learning capabilities. Traditional manual labor is insufficient to meet these demands.

Think of all the screens in a modern control room or smart factory where an operator must monitor multiple process variables to optimize production in real-time. Or all the data from multiple sensors that a reliability engineer needs to analyze in real-time to reduce downtime as the competitive pressure is on businesses to operate “at the speed of thought,” as Bill Gates predicted in 1999.

2. Data Management and Analysis

The vast amounts of data generated by sensors, machines, and systems demand robust data management practices and advanced analytics capabilities. Extracting actionable insights for process optimization, forecasting, and decision-making is crucial for maintaining a competitive advantage.

3. Integration and Interoperability

Integrating and interoperability between various systems, equipment, and software applications is a significant challenge. Manufacturing operations involve a wide range of modern and legacy systems from different manufacturers with varying data formats and communication protocols.

Additionally, the rapid advancement of technology has created a skill gap, with a shortage of professionals proficient in data analytics, cybersecurity, cloud computing, AI, and robotics. Upskilling and reskilling the existing workforce to adapt to technological changes is crucial.

The Future of Work: How Generative Agents Can Help

A generative agent is an advanced AI entity designed to autonomously recognize patterns, generate predictions, and perform tasks by emulating human reasoning. Unlike traditional computational software, generative agents are trained on extensive data sets to understand context and make informed decisions. They can process large amounts of data, adapt to new information, and optimize workflows, making them valuable for automating complex processes and enhancing productivity across various applications.

Generative Agents are set to revolutionize the future of work by leveraging their unique capabilities. To understand their value, consider Generative AI as a utility, similar to electricity, which powers various applications. Generative Agents are like the appliances and machines that use this “electricity” to create products, improve quality of life, and provide the end-user value.

Here’s how they can help:

1. Reasonable vs. Computational

Human-Like Reasoning

Generative Agents are trained to recognize and predict patterns, allowing them to emulate human reasoning with high accuracy. They can make decisions and provide insights that mirror human thought processes, making them more adaptable and intuitive in dynamic work environments.

Enhanced Predictions

By using “external” computational tools, Generative Agents can enhance the accuracy of their predictions. They combine the strengths of both pattern recognition and computational logic to deliver precise and actionable insights.

2. Speed, Accuracy, and Repeatability

Greater Accuracy

Generative Agents can process vast amounts of data and identify patterns faster than humans. This capability allows them to provide more accurate predictions and recommendations, improving decision-making and efficiency.

Speed

With their ability to process information quickly, Generative Agents can offer real-time solutions and responses, enhancing productivity and reducing workflow delays.

Repeatability

Unlike humans, Generative Agents can perform repetitive tasks with consistent accuracy. This repeatability ensures high-quality outcomes and reduces the risk of errors.

3. Integration with Computational Tools

Hybrid Approach

Generative Agents and computational machines operate in different approaches but can complement each other. While computational AI and applications excel at executing complex mathematical calculations at scale, Generative Agents excel at pattern recognition and predictive reasoning.

Combined Capabilities

Organizations can harness the best of both worlds by integrating Generative Agents with computational tools. It further “grounds” GenAI in physics and the laws of nature. It reduces hallucinations as it can be fact-based but with reasoning capabilities. This hybrid approach enhances productivity and enables more sophisticated real-world analysis and problem-solving.

Generative Agents are not just another form of computational software. Their ability to emulate human reasoning, combined with computational tools, makes them powerful allies in the future of work. They enhance accuracy, speed, and repeatability, providing a hybrid approach that leverages the strengths of both pattern recognition and computational logic. By doing so, Generative Agents significantly improve the productivity and efficiency of our work output.

The Evolution of Agent Approaches in Work and Task Types

As work and task types evolve, so do the approaches to implementing AI and automation in business processes. The progression from simple rule-based systems to sophisticated multi-agent generative systems highlights how different levels of AI and automation capabilities align with varying work objectives.

XMPro Evolution of Agentic Business Processes

Rule-Based Process Automation

Before using AI for workflow planning, automation relied on rule-based process automation, which was extended to Robotic Process Automation (RPA), which performs deterministic workflow tasks. These automations are highly structured, following predefined rules to complete repetitive tasks with consistency and accuracy.

AI Assistants and Co-Pilots

With advancements in AI, the introduction of AI Assistants and Co-Pilots marked a significant shift. Utilizing large language models (LLMs), these systems can handle freestyle chat and more dynamic interactions, providing support and enhancing productivity by assisting with routine tasks and decision-making processes.

Task-Based Agentic Workflow

As business needs grow more complex, the evolution towards task-based agentic workflows continues. Single generative agents are capable of handling specific tasks autonomously. These generative operator agents can focus on goal-based single objectives, such as optimizing production processes or managing energy resources.

Supervisory Control of Agentic Work Teams

For more complex scenarios, supervised agentic work teams are used. Directed generative agents operated under a supervisory structure, where a central agent coordinated the efforts of multiple agents working towards deterministic group goals. This setup allows for better management of interdependent tasks and improved efficiency in achieving collective objectives.

Multi-Agent Generative Systems (MAGS)

The most advanced approach is the deployment of Multi-Agent Generative Systems (MAGS). These systems consist of numerous autonomous agents collaborating within a shared framework to achieve autonomous goal-seeking. MAGS can handle distributed intelligence systems, managing intricate processes like production, energy management, ESG (Environmental, Social, and Governance) compliance, and supply chain operations. Each agent has a defined role, and they work together to deconstruct and solve complex problems, mimicking real-world team dynamics.

Tailoring Solutions to Business Process Requirements

It’s important to note that not all business processes require the complexity of complete multi-agent systems. Depending on the specific needs and objectives, organizations can implement varying levels of AI and automation:

  • For Routine and Structured Tasks: Rule-based RPA and AI Assistants are often sufficient.

  • For Goal-Based Objectives: Single generative agents can effectively manage individual tasks.

  • For Collaborative Workflows: Directed generative agents with supervisory control provide an efficient solution.

  • For Highly Complex and Distributed Processes: MAGS offers the necessary capabilities to handle large-scale, integrated operations.

The evolution from rule-based automation to sophisticated multi-agent generative systems reflects the increasing complexity and diversity of modern business processes. By choosing the appropriate level of AI and automation, organizations can optimize their operations and enhance productivity across various tasks and objectives.

What is Multi-Agent Generative Systems (MAGS)?

Multi-agent generative Systems (MAGS) represent an advanced integration of computational software agents and large language models (LLMs) designed to simulate and optimize complex industrial processes and interactions. MAGS leverages generative AI to create dynamic, adaptive systems that enhance productivity, efficiency, and decision-making across various operational aspects.

Key Characteristics:

  1. Integration of LLMs and Agent Technology: MAGS combines the reasoning capabilities of LLMs with the interactive and adaptive nature of software agents.

  2. Complex Environment Simulation: They can simulate intricate environments where multiple agents interact, make decisions, and influence each other.

  3. Emergent Behavior: Through agent interactions, MAGS generate emergent behaviors that may not be predictable from individual agent characteristics alone.

  4. Memory and Reflection: Agents within MAGS can record observations, reflect on past experiences, and use this information to inform future actions.

  5. Adaptive Decision Making: Agents can create and modify plans to achieve goals, adapting to changing circumstances in their environment.

  6. Multi-modal Capabilities: MAGS are evolving to incorporate multiple modalities, including text, image, audio, and video, enhancing their ability to interact with and understand complex environments.

As Multi-Agent Generative Systems (MAGS) evolve, they promise to provide unprecedented insights into complex systems, enabling more accurate predictions, better decision-making, and innovative problem-solving approaches across multiple industries and scientific disciplines.

Think of MAGS as the appliance in the electricity utility analogy from the start of this article. MAGS will not simply replace current tasks but expand their use by unlocking new opportunities.

Like the seafarers who discovered new lands despite the maps warning “There Be Dragons,” venturing into this new territory brings immense potential to enhance the quality of life for everyone.

In Part 3, “AI at the Core: LLMs and Data Pipelines for Industrial Multi-Agent Generative Systems”, we will explore specific examples of MAGS applications with XMPro and practical use cases.

If this excites you and you want to be part of the next evolution in manufacturing, please reach out.

We are opening limited pilot opportunities for innovative, agile leaders in the industry.