big data and analytics for manufacturing and high-tech industries [con8257] gregory sumpter delphi...
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Big Data and Analytics for Manufacturing and High-Tech Industries [CON8257]
Gregory Sumpter
Delphi Electronics & Safety
September 29, 2014
Delphi’s Global Team – at the Center of Technology Innovation
126 manufacturing
sites
15major global
technical centers
19,000engineers
and scientists
$16.5B2013 revenue
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160,000people in
32 countries
$1.7 Bin
Research &Development
Driving Global Innovation – In Close Collaboration with Our Customers
Bascharage, Lux.
Juarez, Mexico Shanghai, China
Auburn Hills, MI
Key Global Technical Centers
São Paulo, Brazil
Krakow, Poland
Bangalore, India
Core Innovations = Future Possibilities
Adjacent Markets
Military/Aerospace
Residential/Commercial Heating and Cooling
Commercial Vehicles
Core Automotive Markets
Electrical/ElectronicArchitecture
Electronics & Safety
Powertrain Systems
Thermal Systems
Aftermarket
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?
Initial Observations
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
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
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
Changes in Business Perspective
“Insanity is doing the same thing over and over again and expecting different results”, Albert Einstein
“If I had asked people what they wanted, they would have said faster horses.”, Henry Ford
"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
"I am looking for a lot of people who have an infinite capacity to not know what can't be done.“, Henry Ford
VOLUME
VARIETY
VELOCITY
Identify specific problem to be solved
Analyze the process steps
Define the problem process
Define inputs and outputs
Find sources of inputs
Identify skillsets needed to obtain dataPrepare a roadmap for the presentation of data
Understand content of the data
Let the data talk to the user
Monitor unknowns
Big Data…
Fast, Effective Response to Warranty Issues is Critical
Optimum timing for WQE/ Delphi Team to detect and address warranty issues: the sooner the better
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
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
Global User
FINANCEDGSSSAPBW
BAANBENSAP GES PBU
=
Search
Discovery
Dashboard Savings
Technology
Warranty Claim
Problem
Tracker
Corporate
Database
Remanufacturing
Database
PHC
PHC - Part
Customer Part –
Delphi Part
Problem Tracker
Component Attributes
Customer - Claim
Claim
Charges
Complaint
Fail Code
Operations
PHC
Delphi Part Num
Delphi Part Num
Customer Part Num
Service Num IN
Part Installed
Site, Seq Num
Delphi Part Num
Vehicle Identification Number
Case Study: System Map for Quick Start Solution
ERP
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
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
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!