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

Gregory Sumpter

Delphi Electronics & Safety

September 29, 2014

Today’s Agenda

• Introduction to Delphi

• Big Data

• Case Study

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

=

+

Why I am not going to be showing the Endeca Information Discovery Tool!

Disparate data

Time sensitive

Fragmented

Paradigm Shift

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

Diverse and abundant information sources create unstructured data

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

Right Tool for the Job?

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!

Here is My Leaf!

Contact Information

• Speaker: Greg Sumpter

• Email: gregory.k.sumpter@delphi.com

• Phone: 765-451-3309