Real-time Predictive Analytics in Manufacturing - Impetus Webinar

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Impetus webcast "Real-time Predictive Analytics in Manufacturing" available at http://lf1.me/hqb/

This Impetus webcast talks about:
• The business value of predictive analytics
• How real-time analytics is enabling ‘intelligent-data’ driven manufacturing
• A Reference Architecture and real world examples based on the experiences of Impetus Big Data architects
• A step-by-step guide for successfully implementing a predictive analytics solution

Published in: Technology, Business

Real-time Predictive Analytics in Manufacturing - Impetus Webinar

  1. 1. Real-Time Predictive Analytics in Manufacturing Vivek A. Ganesan, Principal Architect Yue Cathy Chang, Sr. Director, Business Development Impetus Technologies, Inc.
  2. 2. Big Data in Manufacturing
  3. 3. The Future of Manufacturing INTELLIGENT DATA DRIVEN MANUFACTURING © 2013 Impetus Technologies Recorded webinar is available at http://lf1.me/hqb/ For more Info contact bigdata@impetus.com
  4. 4. Quality of Management Decisions More Data, Better Quality Intuition Amount of Data Analyzed © 2013 Impetus Technologies Recorded webinar is available at http://lf1.me/hqb/ For more Info contact bigdata@impetus.com
  5. 5. Quality of Management Decisions More Data, Better Quality Relevant Data Intuition Amount of Data Analyzed © 2013 Impetus Technologies Recorded webinar is available at http://lf1.me/hqb/ For more Info contact bigdata@impetus.com
  6. 6. Quality of Management Decisions More Data, Better Quality Accurate Relevant Big Data Data Intuition Amount of Data Analyzed © 2013 Impetus Technologies Recorded webinar is available at http://lf1.me/hqb/ For more Info contact bigdata@impetus.com
  7. 7. Quality of Management Decisions More Data, Better Quality Quick? Accurate Relevant Big Data Data Intuition Amount of Data Analyzed © 2013 Impetus Technologies Recorded webinar is available at http://lf1.me/hqb/ For more Info contact bigdata@impetus.com
  8. 8. We Have a Big Data Situation When traditional information systems cannot … Store Process Analyze © 2013 Impetus Technologies Volume Velocity Variety COST TIME Recorded webinar is available at http://lf1.me/hqb/ For more Info contact bigdata@impetus.com
  9. 9. Big Data in Manufacturing Volume • Sensors • Machine data • “Internet of Things” © 2013 Impetus Technologies Recorded webinar is available at http://lf1.me/hqb/ For more Info contact bigdata@impetus.com
  10. 10. Big Data in Manufacturing Volume • Sensors • Machine data • “Internet of Things” © 2013 Impetus Technologies Velocity • Drinking from the fire hose! • Consume or collapse!! • Analyze at the speed of data? Recorded webinar is available at http://lf1.me/hqb/ For more Info contact bigdata@impetus.com
  11. 11. Big Data in Manufacturing Volume • Sensors • Machine data • “Internet of Things” © 2013 Impetus Technologies Velocity • Drinking from the fire hose! • Consume or collapse!! • Analyze at the speed of data? Recorded webinar is available at http://lf1.me/hqb/ For more Info contact bigdata@impetus.com Variety • Dealing with diagnostics • Binary data + log data • Ability to analyze variety of data formats
  12. 12. Where is the Value in Big Data?
  13. 13. Big Data and the Fourth „V‟  The fourth „V‟ is „Value‟  The Value of Big Data in manufacturing is in Analytics  Gartner defines four kinds of Analytics  Descriptive  What? Who? How? Why?  Diagnostic  What if? Why not? Who else?  Predictive  What will happen when?  Prescriptive  What can I do about it? © 2013 Impetus Technologies Recorded webinar is available at http://lf1.me/hqb/ For more Info contact bigdata@impetus.com
  14. 14. Big Data Predictive Analytics The formula is simple: 1. Collect data at every stage of the manufacturing process 2. Store data on a Big Data store • Economical, Accessible, Distributed, and Scalable 3. Process data • Manage the variety and complexity of the data 4. Analyze data • Apply mathematical models to make predictions © 2013 Impetus Technologies Recorded webinar is available at http://lf1.me/hqb/ For more Info contact bigdata@impetus.com
  15. 15. Predictive Analytics Context D A T A Sensors © 2013 Impetus Technologies Control Predictions Instruments I N G E S T Analytics • • • • • • Fix Throttle Alert Adjust Optimize Abort Model • • • • • Represent Learn Predict Iterate Improve  Batch  Historical  Iterative  Real-Time  Immediate Feedback Recorded webinar is available at http://lf1.me/hqb/ For more Info contact bigdata@impetus.com  Instantaneous
  16. 16. Big Data, Big Value  In manufacturing, the greatest value is in :  Real-Time Predictive Analytics  Prescriptive Analytics is possible but depends on :  Good Predictions  Fast Feedback Loop  Real-Time Predictive Analytics is the first step towards :  Intelligent Data-driven Manufacturing © 2013 Impetus Technologies Recorded webinar is available at http://lf1.me/hqb/ For more Info contact bigdata@impetus.com
  17. 17. Manufacturing Analytics Quadrant REAL TIME BATCH  Historical  "What happened"  Hindsight DIAGNOSTIC © 2013 Impetus Technologies PREDICTIVE Recorded webinar is available at http://lf1.me/hqb/ For more Info contact bigdata@impetus.com
  18. 18. Manufacturing Analytics Quadrant REAL TIME BATCH  Near-term  "What is happening"  Insight  Historical  "What happened"  Hindsight DIAGNOSTIC © 2013 Impetus Technologies PREDICTIVE Recorded webinar is available at http://lf1.me/hqb/ For more Info contact bigdata@impetus.com
  19. 19. Manufacturing Analytics Quadrant REAL TIME BATCH  Near-term  "What is happening"  Insight  Historical  "What happened"  Hindsight  Inferential  "What may happen"  Foresight DIAGNOSTIC © 2013 Impetus Technologies PREDICTIVE Recorded webinar is available at http://lf1.me/hqb/ For more Info contact bigdata@impetus.com
  20. 20. Manufacturing Analytics Quadrant REAL TIME BATCH  Near-term  "What is happening"  Insight  Influential  "Make it happen"  Intelligent  Historical  "What happened"  Hindsight  Inferential  "What may happen"  Foresight DIAGNOSTIC © 2013 Impetus Technologies PREDICTIVE Recorded webinar is available at http://lf1.me/hqb/ For more Info contact bigdata@impetus.com
  21. 21. Opportunities and Challenges Business Opportunities • Preventive Maintenance • Real-time/near real-time actionable response • Improve Productivity/Margins • Reduce wastes, improve efficiency • Improve Yield • High Ingestion Rates • Sensor/tool data with subsecond ingestion requirements • Millions of writes per second • Complex Log Formats • Semi-structured data • Huge Amount of Data • Supply Chain • Optimize supply chain © 2013 Impetus Technologies Technical Challenges • TB/PB of data storage for deeper analytics Recorded webinar is available at http://lf1.me/hqb/ For more Info contact bigdata@impetus.com
  22. 22. Let's Get Real!
  23. 23. Real-Time Predictive Architecture Machine Data © 2013 Impetus Technologies NoSQL + Search Recorded webinar is available at http://lf1.me/hqb/ For more Info contact bigdata@impetus.com
  24. 24. Real-Time Predictive Architecture Machine Data © 2013 Impetus Technologies NoSQL + Search Recorded webinar is available at http://lf1.me/hqb/ For more Info contact bigdata@impetus.com
  25. 25. Input Data: Raw Logs • Meta-data about the prospective product line is created at a factory site. – E.g., number of sensors emitting log files or readings. • Various log files are generated: – Containing Text. – Containing specific Sensor readings, continuous as well as binary values. – At each time-step, a specific pass/fail. © 2013 Impetus Technologies Recorded webinar is available at http://lf1.me/hqb/ For more Info contact bigdata@impetus.com
  26. 26. Input Data: Parsing This data is parsed into a matrix representation • Columns representing sensor logs • Rows representing the time In our dataset: • 590 attributes, approximately every minute Machine 1, sensor 1-120 – Missing Data from logs – Failure : success :: 1 : 15 • 50,000 time-steps, i.e., 29.5 million values (“parsed”, not raw) in a month © 2013 Impetus Technologies Time • Also has labels: {+1,-1} for failure/success from for each time-step Machine 12, sensor 1-92
  27. 27. Interactive Visualization Misalignment © 2013 Impetus Technologies Time: 12:00 Yield: 85% Failure < 1%
  28. 28. Interactive Visualization Machine 4, Tool 3, 4. Input from Machine 2. Misalignment © 2013 Impetus Technologies Time: 12:01 Yield: 86% Failure < 2%
  29. 29. Interactive Visualization Misalignment © 2013 Impetus Technologies Time: 12:02 Yield: 81% Failure < 3%
  30. 30. Interactive Visualization Alert: Machine 2, Tool 3, 12, 14 are not nominal Misalignment 30% 70% © 2013 Impetus Technologies Time: 12:03
  31. 31. Interactive Visualization Alert: Machine 2, Tool 3, 12, 14 are not nominal Misalignment 90% © 2013 Impetus Technologies Time: 12:04
  32. 32. Interactive Visualization Alert: Machine 8 and machine 4 are failing. Cause: Machine 2 has voltage imbalance Misalignment © 2013 Impetus Technologies Time: 12:05
  33. 33. Interactive Visualization Alert: Machine 8 and machine 4 are failing. Cause: Machine 2 has voltage imbalance 90% Misalignment 80% © 2013 Impetus Technologies Time: 12:05
  34. 34. Interactive Visualization Alert: Machine 8 and machine 4 are failing. Cause: Machine 2 has voltage imbalance 90% Misalignment 80% © 2013 Impetus Technologies Time: 12:05
  35. 35. Key Takeaways • • • • Measure and Collect Everything Process, Diagnose, and Predict Get Real with Real-Time Generate Actionable Intelligence © 2013 Impetus Technologies Recorded webinar is available at http://lf1.me/hqb/ For more Info contact bigdata@impetus.com
  36. 36. Talk to us about Impetus Solutions and Services for Manufacturing Big Data Assessment Objectives & Strategy Model Solution Modeling BUSINESS PROCESS MANAGEMENT Analyze & Optimize Solution Analysis People, Process, Technology Impact Business Analytics and Data Science Solution Architecture, POC and Production planning Technology strategy, Use Case development & Validation bigdata@impetus.com Big Data Platform Implementation © 2013 Impetus Technologies bigdata.impetus.com Recorded webinar is available at http://lf1.me/hqb/ For more Info contact bigdata@impetus.com Operations and Visualization
  37. 37. Thank You bigdata@impetus.com bigdata.impetus.com Recorded version available at http://lf1.me/hqb/ © 2013 Impetus Technologies

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