2013_02_Vortrag_SGS_Symposium_Trends_in_Diagnostics_And_Implications_For_The_Warranty_Business

01.03.2013 © GIGATRONIK, 2012

Trends in Diagnostics and Implications for the Warranty Business


Christian Seiler | GIGATRONIK Stuttgart GmbH

SGS Warranty Managers Symposium | February 28th, 2013

Complexity – Networking – Knowledge

01.03.2013 © GIGATRONIK, 2012



Agenda

1. The GIGATRONIK Group

2. Warranty – Quality - Diagnostics

3. Use Cases of Vehicle Diagnostics

4. Trends in Vehicle Diagnostics

5. Empowering Warranty Management through Diagnostics

6. Conclusion and Outlook

01.03.2013 © GIGATRONIK, 2012



1. The GIGATRONIK Group

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GIGATRONIK Group

Company Overview

GIGATRONIK Group is your specialised

development partner in the fields of electronics,

information technology and consulting.

10 locations in Germany,

Austria and Switzerland

GIGATRONIK Headquarters in Stuttgart

Foundation in 2001

813 employees across all locations

More than 80 customers in the automotive

and automotive supplier industry

01.03.2013 © GIGATRONIK, 2012


Locations

Alpnach

Graz Ingolstadt

Cologne

Munich

Stuttgart

Ulm

Wolfsburg

Böblingen Magdeburg Wolfsburg

Kerns

GIGATRONIK Group

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Number of Employees 2001-2012

28

108

142

185

228

288

347

419 454

515

613

813

0

100

200

300

400

500

600

700

800

900

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

employees

year

GIGATRONIK Group

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Divisions

Information Technology Consulting Electronics

GIGATRONIK Group

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Automotive

Business Segments

System Integration &

Testing

Vehicle Integration

Diagnostics Data

Management

Process & Project

Management

Component Development

© dSpace GmbH

System Architecture &

Electrical Systems

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Diagnostics

Service and Solution Portfolio

Cross-Sectional Technology: Diagnostics

Our professionals combine the expertise in IT and vehicle technology and make this expertise

available to our customers on their road to success.

Requirement Engineering

Diagnostics Knowledge Engineering

Diagnostics Specification and Data Management E/E

IT System Configuration and Operation

Diagnose Knowledge Engineering

Consulting

Diagnostics Engineering

Software Engineering

Technology Processes Strategy

Software and System Engineering

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GIGATRONIK Diagnostics Cooperation Network

Trainings & Workshops

Diagnostics

Consulting Service Portfolio

GIGATRONIK Diagnostics Competence Network

Diagnostics Consulting Services

Diagnostics IT Consulting

Diagnostics Process Consulting

Diagnostics Strategy Consulting

Basement

Innovation Management

Methodology Know-How

Diagnostics Know-How

Tool Know-How

Knowledge Management

Lean Management

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2. Warranty – Quality – Diagnostics

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Warranty – Quality – Diagnostics

Influencing Factors for the Warranty Business

Costs

The main objective of warranty management is to reduce warranty costs. This is directly linked

to quality issues arising from the value-add processes of the product.

Once cost drivers are identified we determine a lever to reduce them.

During the warranty period the OEM has to bear all costs.

Worst case:

Customer: warranty claim.

Dealer: follows the process defined by the OEM and replaces parts.

Supplier: no trouble found.

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Value-Add Processes Linked to Diagnostics The diagnostics value stream runs in parallel with the vehicle development and production

processes.

The information flow in reverse direction is equally important to create value:

It delivers quality measures based on existing diagnostics knowledge

It delivers new knowledge on as yet unknown fault scenarios

Warranty – Quality – Diagnostics

Diagnostics Value Stream Forward

Diagnostics Value Stream Reversed

Development and Production Value Stream

OEM

Supplier Development Production After Sales Dealership Network

Customer

Quality of the

Product

Quality of the

Service Processes

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3. Use Cases in Vehicle Diagnostics

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Use Cases in Vehicle Diagnostics

Overview

Development

Production

Product

After Sales Service

delivered

rele

ased

Inspection of correct

assembly

Inspection of correct system

configuration during vehicle

development

Configuration,

commissioning and

vehicle hand-over

Fault finding, maintenance

and monitoring

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4. Trends in Vehicle Diagnostics

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F-22 Raptor jet fighter

1.7 million lines of SW code

Boeing 787 Dreamliner

6.5 million lines of SW code

Mercedes-Benz S-Class W221

approx. 100 million lines of SW code

Source: http://spectrum.ieee.org/green-tech/advanced-cars/this-car-runs-on-code/0

Roughly 33% is

devoted to

diagnostics!

Trends in Vehicle Diagnostics

Complexity in Comparison

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The Importance of Standards

Development

Creation of Diagnostics Specification

Application Development

HW App

D P A

D Data- base

Runtime System Modules

+

Risk reduction:

by frontloading the validation of diagnostic components

by using off-the-shelf components and products

Long-term availability and support of diagnostic content as data and systems are separated.

Business aspects:

no customer lock-in effect

reduced investment costs by economies of scale

commodity: no customer visibility of low-level components

Production


HW App

After Sales


HW App

P

A

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The Importance of Standards

The Next Big Thing … Diagnostics over IP (DoIP)

Source: C. Saalfeld, Daimler AG, Fachkongress CTI Forum Automotive Diagnostic Systems 2009)

01.03.2013 © GIGATRONIK, 2012



Connected Vehicles – Connected Diagnostics

Vehicle Communication Infrastructure

Dealership Customer

Relationship Manager

(Remote) Workshop

Technician

Helpdesk

Staff (Remote)

Cloud

OEM Backoffice

Expert

Customer

Apps Server-based

Content Analytics

eMobility Service

Platform (e.g. Hubject)

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4. Empowering Warranty Management through Diagnostics

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Empowering Warranty Management through Diagnostics

Warranty Cost Drivers Risk Assessment

Warranty Costs

Driving Factors Impeding Factors

Development speed

New technologies

Increased complexity

Application of standards

Managing diagnostics value streams

Big data analytics

01.03.2013 © GIGATRONIK, 2012



Application of Standards

OTX Runtime Kernel

MCD Kernel

MCD-1D PDU-API, ISO 22900 Part 2

Vehicle Communication Interface

OTX

ODX

OTX

ISO

13209

MCD

-2D

(O

DX)

ISO

22901

Part

1

MCD-3D API, ISO 22900 Part 3

MCD-1D PDU-API, ISO 22900 Part 2

ISO 14229 (UDS)

MCD-3D API, ISO 22900 Part 3

Service Application

Development Application

Production Application

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Managing Diagnostics Value Streams


Diagnostics Value Stream Forward

Development and Production Value Stream

OEM

Supplier Development Production After Sales Dealership Network

Customer

Product Quality

Service Process Quality

Diagnostics Value Stream Reversed

Using big data analytics brings faster results, once data is instantly available

Analysis

Improvements in the diagnostic process reduce NTF and improve FTF rates

Improvements in product quality reduce system failures

01.03.2013 © GIGATRONIK, 2012



Big Data Analytics

Big data analytics is the process to examine

large amounts of data of different types in

order to uncover hidden patterns, unknown

correlations and other useful information.

Relevant data for vehicle diagnostics can

mainly be grouped in two clusters:

Unstructured content: mainly phrases and

sentences generated by people, e.g. service

employees’ transcripts of customer

complaints, hotline employees’ notes on

probable root-causes …

Structured content: data generated by the

vehicle, e.g. DTCs, sensor data,

configuration data, timestamps and logs …

Dependent on the data characteristics,

different analytic methods have to be applied

to support the respective use case.

I have a problem with …

Hotline

Vehicle

Analyzing Systems

Unstructured

Content

So maybe it could be …

Structured

Content

ECU DTC Mileage …

Engine 28891 27136km …

…

Customer

I tested and found out that …

Workshop Technician

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Big Data Analytics Process

Data

Data

Data

Data

Data Storage

Data Acquisition

Visuali-zation

Real-time Analytics

Batch Analytics

.

. .

.

Many different data

sources need to be

integrated

Data based analysis is

not enough - the goal is

data automation

01.03.2013 © GIGATRONIK, 2012



Big Data Analytics Example

Powered by:

One entry in a dataset of 1.3 million diagnostic

datasets: an interesting pattern is found in a

few seconds with in-memory analytics:

The DTC’s value is below 1000, …. … while Customer Impact is “9.0” Batch Analytics

Data Inconsistency Pattern Test

Statistics show that a DTC below

1000 and Customer Impact in the

range 0-5 is much more likely …

… maybe we have found

a needle in the haystack

01.03.2013 © GIGATRONIK, 2012


PREDIC-TION

MODULES

DATA MINING

MODULES

HYPO-THESIS TESTS

MULTI-VARIANT ANALYSIS

CORRE-LATION,

OUT-LINERS

AD-HOC ANALYSIS, PATTERN

Key U

sers

Beg

inn

er A

naly

sts

Maturity of Big Data Analytics Methods

Prediction

Detect +

Understand

Exp

erts

D

ata

Min

ers


Big Data Analytics Maturity Model

Powered by:

01.03.2013 © GIGATRONIK, 2012



Opportunities for Warranty Processes

More analyses per time unit

Enables early warnings

More precise root causes

Machine pattern learning

Identify “no errors found”

Identify root causes

Projection based on big data

Yield rate projection

Spare part dispatching

Shorter latency/elapsed time of above

processes, leads to

Cost reduction

Higher customer satisfaction

01.03.2013 © GIGATRONIK, 2012



Conclusion and Outlook

Increasing complexity will be a permanent phenomenon.

One major approach is the application of standards for data and system components.

Future vehicles will be “connected”. Diagnostic systems will have to manage this complexity.

Vehicles will increasingly become “networked devices”. The instant access to vehicle data is a major opportunity for remote services and just-in-time data analytics, which can be applied to multiple use.

One use case is reducing warranty costs for the OEMs by improving the quality of the service processes with the help of big data analytics.

… There are a lot of things to do. Let’s get started!

01.03.2013 © GIGATRONIK, 2012


Come and join the Vehicle Diagnostics

Professionals Community on XING!

www.xing.com/net/fahrzeugdiagnose


XING Group Vehicle Diagnostics (German)

01.03.2013 © GIGATRONIK, 2012


Christian Seiler Manager Business Development Diagnostic Consulting GIGATRONIK Stuttgart GmbH A company within the GIGATRONIK Group Hortensienweg 21 D-70374 Stuttgart Germany Phone: +49 711 84 96 09-431 Fax: +49 711 84 96 09-99 Mobile: +49 162 26 548 09 E-Mail: [email protected] Homepage: www.gigatronik.com Customer Portal: www.gigatronik.com/gruppe/kundenportal

Login: diagnose_consulting Password: gigatronik_584

Discussion welcome!

mailto:[email protected]

http://www.gigatronik.com/

http://www.gigatronik.com/gruppe/kundenportal

01.03.2013 © GIGATRONIK, 2012


1985 1990 1995 2000 2005 2010

1985 944

3 6

11

22

27

58

54

1989 928

1989 Boxster (986)

2002 Cayenne (E1)

2005 911 (997)

2009 Panamera

2010 Cayenne (E2)

Number of ECUs with diagnostic capabilities

in recent Porsche vehicles

based on: Falschebner et. al. Diagnostics in mechatronical vehicle systems, Dresden 2011

Background Information

Increasing Complexity of Vehicle Functions

01.03.2013 © GIGATRONIK, 2012


1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

1967 VW introduces

first on-board computer (electronic fuel injection)

1980 GM introduces proprietary

assembly line diagnostic link including blinky codes in the instrument cluster

1991 OBD-I in California, US

1996 OBD-II in the US

2003 EOBD diesel in Europe

2001 EOBD gasoline in Europe

2015 WWH-OBD

1986 Daimler introduces first digital

diagnostic system with blinky codes

1990 Daimler introduces first

diagnostic system for CAN network


The Evolution of Vehicle Diagnostics

01.03.2013 © GIGATRONIK, 2012


The Vehicle as Distributed System


f1

f2

f4 f3

Fault Fault

Condition Failure

Root Cause Effect Consequence

• Accepted or expected root cause for a fault condition

• Incident which should be avoided or tolerated

• A fault condition often has more than one

root cause

• Fault conditions occur, when a system

changes to a state which is not intented

by specification

• Fault condition = part of a system state

which is responsible for a failure

• A failure can be the root cause for more faults

• If a fault occurs in a system it can lead to a partial or total

break down - e.g. a broken gear can lead to a transmission

failure

• Failure = deviation of the actual performance from the

required performance in the specification.

X X X

X

01.03.2013 © GIGATRONIK, 2012


Medical diagnosis refers both to the process of attempting to determine or

identify a possible disease or disorder (diagnosis can also be termed (medical)

diagnostic procedure) […] From the point of view of statistics the diagnostic

procedure involves classification tests. (Wikipedia)

Vehicle diagnostics means to identify unknown states by checking the system‘s

behaviour. Knowledge about causal links is required. Diagnostic systems support the

vehicle diagnosis by identifiying correlation between faults, symptoms and root

causes.

Identification of Symptom(s)

The Diagnostic Process


Consequence Root Cause

Effect

Classification strategy

Inspection Plan

Fault Root Cause

Repair

01.03.2013 © GIGATRONIK, 2012


Vehicle Diagnostics is More Than Just the Process


Vehicle diagnostics include all functions which are used to activate, maintain,

monitor, find and repair faults of a vehicle.

Preconditions a vehicle has to fulfill:

• It consists of electronic components (ECUs with CPUs)

• There are interfaces to exchange data with these electronic components

Objectives for the development of vehicle diagnostic systems:

1. Fault localisation and identification of root causes (support of the diagnostic

process)

2. Data exchange of all relevant operating data between a vehicle and all adjacent

systems

3. Connect different knowledge and information sources to improve the operation

relevant processes with the help of information technology.

01.03.2013 © GIGATRONIK, 2012


Service

OEM Dealership

Network

Problem

Solution

Customer

Customer

Relationship

Manager

Technology

Authoring Process

Backoffice Experts

System Control

Backend Knowledge

Base Experimental

Knowledge

Diagnostic Systems &

Enbedded Knowledge

Rules

Structure


Manage the Value Streams in After Sales Diagnostics

Technician

Structured Content (Diagnostics Data,

Configuration, Logs etc.)

01.03.2013 © GIGATRONIK, 2012


Backend Knowledge

Base

Diagnostic

System

Tutoring Expert System

Information Retrieval

Supportive Guidance

Substancial Feedback

Rules

Structure

Experimental

Knowledge

User learns

System „learns“ Technician

with special talent


User-Centred Application Design Process

01.03.2013 © GIGATRONIK, 2012


Different ways possible: Direct feedback e.g. via support hotlines

Social media plattforms

Content analytics on both on unstructured data (e.g. IBM Watson) and structured data (e.g. Synop Analyser)

Diagnostic System Authoring Process

OEM

Users and Expert Communities


Connected Knowledge: Use All Sources

Experimental

Knowledge

„Voice of the Customer“

Vehicle

ECU DTC …

Engine 28891 …

…

Unstructured Content

Structured Content

Analyzing Systems

2013_02_Vortrag_SGS_Symposium_Trends_in_Diagnostics_And_Implications_For_The_Warranty_Business

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