Big Data and Analytics for Manufacturing and High-Tech Industries [ CON8257] - PowerPoint PPT Presentation

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Big Data and Analytics for Manufacturing and High-Tech Industries [ CON8257]

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  1. Big Data and Analytics for Manufacturing and High-Tech Industries [CON8257] Gregory Sumpter Delphi Electronics & Safety September 29, 2014

  2. Today’s Agenda • Introduction to Delphi • Big Data • Case Study

  3. Delphi’s Global Team – at the Center of Technology Innovation 19,000 engineers and scientists $16.5B 2013 revenue . . 126manufacturing sites 15 major global technical centers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . $1.7 B in Research & Development 160,000people in 32 countries .

  4. Driving Global Innovation – In Close Collaboration with Our Customers Juarez, Mexico Key Global Technical Centers    Auburn Hills, MI Krakow, Poland   Bascharage, Lux.   Shanghai, China Bangalore, India São Paulo, Brazil

  5. Electrical/Electronic Architecture Electronics & Safety Residential/Commercial Heating and Cooling Powertrain Systems Military/Aerospace Core Innovations = Future Possibilities Adjacent Markets Core Automotive Markets Commercial Vehicles Thermal Systems Aftermarket

  6. Why I am not going to be showing the Endeca Information Discovery Tool! + =

  7. Paradigm Shift Disparate data Time sensitive Fragmented

  8. Initial Observations What did you see? What are you thinking? Was it what you expected to see? What do you think you know? What can you tell someone about this?

  9. Diverse and abundant information sources create unstructured data

  10. Traditional Data Approach Data appears structured, clear and organized People have knowledge of the data and have time to review People have access to the data People are skilled at gathering and interpreting data Data is as expected

  11. The New Paradigm Increased volume, speed and formats of data Fewer people understand origination of data Reduced time to gather and analyze data People asking more complex questions of the data New generations growing up in Information Age Living in a data-rich environment

  12. Challenges Facing Today’s High Tech Manufacturing We have poor memory We need to do more with fewer people We rely on familiar tools rather than seeking new solutions We seek the path of least resistance We resist change

  13. Right Tool for the Job?

  14. Changes in Business Perspective “Insanity is doing the same thing over and over again and expecting different results”, Albert Einstein "You can't just ask customers what they want and then try to give that to them. By the time you get it built, they'll want something new.“, Steve Jobs “If I had asked people what they wanted, they would have said faster horses.”, Henry Ford "I am looking for a lot of people who have an infinite capacity to not know what can't be done.“, Henry Ford

  15. Big Data… Volume Identify specific problem to be solved Define the problem process Analyze the process steps variety Define inputs and outputs Find sources of inputs Understand content of the data Monitor unknowns velocity Identify skillsets needed to obtain data Prepare a roadmap for the presentation of data Let the data talk to the user

  16. Fast, Effective Response to Warranty Issues is Critical Optimum timing for WQE/ Delphi Team to detect and address warranty issues: the sooner the better

  17. Case Study:Warranty Data Analysis Transformation • 20+ customers (OEM or Tiers) providing warranty data • 20+ Delphi shipping locations • 15,000+ part numbers shipped annually • 100 million parts shipped annually • Customer verbatim data: • Tradition says we can’t use this because it is not structured • We have tried to use it, but it does not fit properly • Ensure we’re listening to customer input

  18. Case Study: Vision for a Warranty Data Solution • Utilize existing commercial technology to access and merge data • No impact to existing database structures or administration • Goal is to be able to address warranty issues before they happen SAP BW SAP BEN BAAN DGSS FINANCE GES PBU Search Discovery Technology Dashboard Savings Global User =

  19. Case Study: System Map for Quick Start Solution ERP PHC Warranty Claim PHC Customer Part Num PHC - Part Customer - Claim Delphi Part Num Vehicle Identification Number Customer Part – Delphi Part Service Num IN Delphi Part Num Claim Problem Tracker Remanufacturing Database Problem Tracker Charges Delphi Part Num Complaint Site, Seq Num Corporate Database Component Attributes Fail Code Operations Part Installed

  20. Case Study: Suggested Steps for Execution • Define clear success criteria relating to specific project • Partner with Oracle to generate Proof of Concept and verify data analysis capability and savings potential • Assuming demonstrated Proof of Concept success, allocate sufficient funds to purchase a Quick Start Solution based on estimate provided • Goal is to realize a payback period of one year or less

  21. Critical Success Factors • Understand the sources of master data • Who is the owner of the source data? • Who has access to this data? • Keep your design team to a minimum size • Too many will slow development • Maintain a fluid decision making process • Think out of the box • Don’t limit thinking to traditional views • Change is inevitable

  22. Lessons Learned • Both the right tool and the right process are critical to success • Do not be afraid to look at data in different ways • Always cross reference your analysis to verify results • Develop standard work for your users • Allow your data model to change!

  23. Here is My Leaf!

  24. Contact Information • Speaker: Greg Sumpter • Email: gregory.k.sumpter@delphi.com • Phone: 765-451-3309