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Achieving Total Quality 2011 Auto Industry Leadership Forum
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Agenda Introduction
Quality Today
Quality Vision
Customer Issues
Solutions
Value
Questions & Discussion
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Quality Today
Marketing
Service Engineering
Manufacturing
Customer
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The Problem Competition & Market Dynamics & Media
Consumers are more informed
Margins are razor thin
How do you stack up?
Buyer’s market
Knowledgeable
Demand satisfaction
Only satisfied customers are profitable
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The Problem Substandard product quality
Compromises customer satisfactions
Negative quality perceptions
Erodes brand image
Damage to brand equity
Become a commodity
“Hot potato” quality
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The Problem Cost?
15% - 40% Cost of goods sold
Scrap, Rework, Returns, Reduced Service Levels, Lost Revenue
5X greater to eliminate in the field versus development or mfg
Made sense to focus on Manufacturing Process
ANDON / Jidoka
Six Sigma
Lean
Kaizan
Problems Persist
Recalls, Engineers spend 80% time integrating data, lack of automation
Must take a broader view of quality
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Customer Driven Quality
Marketing
Service Engineering
Manufacturing
Customer
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Customer Driven Quality Enables entire organization to raise a flag
Fosters collaboration and participation across enterprise
Identifying Issues, Defining Issues, Solving Problems
Results in
Dramatic improvement in return on quality
Improved customers satisfaction
Positive perceived quality
Valuable brand equity
Less customers that experience an issue
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Customer Driven Quality How is the achieved?
Utilize data inside and outside the organization
Both standard and non-standard
Integrating all data sources
Available to all those involved in maintaining quality
Leverage Advanced Analytics to exploit these data
» Automate, Automate, Automate
» “time is money” (find issues sooner)
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Customer Driven Quality
Marketing
Service Engineering
Manufacturing
Customer
• Production
• Assets
Social Media
• Warranty
• Call Center
Continuous
Product
Improvement
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Customer Driven Quality - Examples Quality starts early with product development
Major mobile phone manufacturer
» Short product lifecycle
» 90% component reuse
» Alarm prior to release of new products
Automotive manufacturer
» Longer product lifecycle
» Service, Quality, Product Engineering working together on continuous improvement
» Battery leaks & Association analysis
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Customer Driven Quality - Examples Increased use of machines and automation in process
Great improvements to productivity and quality
Many more subtle ways quality can be compromised
Can only be seen in vast amounts of data
Must apply analytics and automated alerting
» Solve problems fast
» Eliminate scrap, rework, production disruptions
Semiconductor manufacturer
» Yield issues
» Many parameters
» Predictive models, regression, multivariate analysis
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Customer Driven Quality - Examples Major capital assets used to manufacture product
Properly maintained assets will
» Perform longer
» Have a positive impact on quality
» Eliminate maintenance costs
» Maximize availability
Natural gas producer
» Large amounts of capital assets (compressors, pumps)
» Unexpected compressor shut down = 38 days lost
» Gathered data across 400 assets
» Developed predictive models, score new data
» Planned shutdown = 20 day improvement
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Customer Driven Quality - Examples Defect will make it customers
More costly than fixing during development/manufacturing
Customer satisfaction, perceived quality, brand image at stake
Service organization is at the forefront
Properly handled issue can entrench positive brand image
Service must be part of the integrated team
Service data must be leveraged
High-End luxury appliance manufacturer
» Relies entirely on brand image
» Existing process inefficient
» Utilize text analytics and automated early warning
» Detect, define, prioritize improved by 90 days
» 15% reduction in warranty costs
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Customer Driven Quality - Examples What do the iPhone, xBox, and Drymax diapers have in
common?
Each suffered a quality issue made worse by social media
Social media is the newest challenge
Dramatic impact on quality perception and image
Opportunity for manufacturers
Insights never before possible
Both good and bad
Put the “Customer” in Customer Driven Quality
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Customer Driven Quality - Examples P&G Drymax diapers
Networking blogging moms
Baby’s skin burns
P&G initially challenged moms
$8B business segment at risk
Resolution through social media partnering
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Customer Driven Quality - Examples Social media analytics
Detecting Issues
Prioritizing Issues
Calibrating/optimizing internal quality standards
Detect – Facebook Fan Noise
Prioritize – New Product Launch, Crisper Drawers
Optimize
» What is quality but the customers view of your product
» Cosmetic standards
» Fan noise issue passed quality specification
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Customer Driven Quality - Summary Must be an integrated organization
Focus on the customer AND quality
Leverage all available data
Apply automated advanced analytics
Will maintain positive perceptions of products
Will maintain brand image (must valuable asset)
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Alerts Model
Management
Predictive
Modeling
Dashboard Reports Analytical
Workbench
Business Rules and Processes
Products & Hierarchies
Testing
Inspection
Parts Movement
Measurements
Supplier Data
Financial & Organization
Field Failures
Repair & FA
Equipment
Quality Lifecycle Analysis
Information
Store
SAS® Quality Lifecycle Analysis Overview
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Development Yield Ramp Volume Production
25%
70%
90%
Yie
ld
Time
Time to Market
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Development Yield Ramp Volume Production
25%
70%
90%
Yie
ld
Ty Tx
60%
Duration
Time
Issue Detection & Resolution
Ty-a
Duration (after)
(before)
Tx-a
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Development Yield Ramp Volume Production
25%
70%
90%
Yie
ld
92%
Time
Performance Improvement
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Benefits to Customers
Shortened time to market
Establishing control and reduce variation
Determining process capability
Shortening cycle times
Improved Problem Resolution
Pinpointing critical problems quickly
Gaining process understanding.
Performance Improvement
Reducing waste and rework
Improving products and processes
With SAS, manufacturers will
significantly reduce costs during the
entire lifecycle of products:
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Interactive Discovery
Root Cause Analysis
Reporting and Collaboration Inspection
Planning
Monitoring and Alerting
Quality Lifecycle Analysis Operational
Quality Management
SAS ® Quality Lifecycle Excellence: Close the Loop
Document Management
Event Notification
Scheduling Dispatching
Quality Management
Lab Info. Mgmt. Sys.
ERP
SPC
MES
QMS
ERP
SPC
MES
QMS
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Challenge Solution Results
"SAS has directly contributed an ROI of $14 million on Six Sigma projects and an additional $1.5 million on other projects. That’s an impressive result in less than two years, and we have anticipated ways to gain even greater returns in the future."
Ill-Chul Shin,
Manager and “Master Black Belt” at POSCO’s Six Sigma Academy
Make the most of Six Sigma performance strategy to enhance production quality and improve earnings.
The SAS Enterprise Intelligence Platform uncovers new insights into physical processes for significant ROI.
POSCO
Click here to access customer success story
SAS® Quality Lifecycle Analysis
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Copyright © 2010, SAS Institute Inc. All rights reserved.
Business Issue Solution Results
“In one case, we increased the already exceptional reliability of a particular fiber optics technology by more than 70 percent. We could not have reached that level of success without SAS and the collaboration with the SAS developers and the technical support teams."
Dr. José Ramirez,
Industrial Statistician
W.L. Gore & Associates
Enhance product quality and increase yields through process knowledge discovery.
SAS analytics and quality applications help Gore leverage manufacturing information, processes and controls to make intelligent decisions and realize better results.
W.L. Gore
Click here to access customer success story
SAS® Quality Lifecycle Analysis
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Copyright © 2010, SAS Institute Inc. All rights reserved.
SAS® Predictive Maintenance Overview
Model
Management
Predictive
Modeling Alerts Dashboard
Monitoring Reports Analytical
Workbench
Business Rules & Processes
Operations Data
Asset Data (origin,
options, etc.)
Historian Data
SCADA data
Maintenance Records
External data
Financial/Risk Data
Technician/Engineer Data
Inspection Records
Operator Data & Complaints
Predictive Asset Maintenance
Reliability Information
Store
Reliability Information
Store
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Text & M&D Data Classification & Clustering Reliability Information Store
Known Incidents/Defects
Optional:
Text Mining: Prepare text data
of service reports
for analysis
Scoring: Assign M&D data
to service codes
Data Mining: Categorize incidents
and conditions
to service code
clusters
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Copyright © 2010, SAS Institute Inc. All rights reserved.
Text & M&D Data Classification & Clustering
M&D Data, Conditions
Known Incidents/Defects
Optional:
Text Mining: Prepare text data
of service reports
for analysis
Scoring: Assign M&D data
to service codes
Data Mining: Categorize incidents
and conditions
to service code
clusters
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Copyright © 2010, SAS Institute Inc. All rights reserved.
Text & M&D Data Classification & Clustering
M&D Data, Conditions
Known Incidents/Defects
Near-Neighbors (Service Code Cluster)
Optional:
Text Mining: Prepare text data
of service reports
for analysis
Scoring: Assign M&D data
to service codes
Data Mining: Categorize incidents
and conditions
to service code
clusters
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Copyright © 2010, SAS Institute Inc. All rights reserved.
Text & M&D Data Classification & Clustering
M&D Data, Conditions
Known Incidents/Defects
Near-Neighbors (Service Code Cluster)
Conditions/sensor data similar to a known incident or defect
Optional:
Text Mining: Prepare text data
of service reports
for analysis
Scoring: Assign M&D data
to service codes
Data Mining: Categorize incidents
and conditions
to service code
clusters
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Copyright © 2010, SAS Institute Inc. All rights reserved.
Challenge Solution Results
Reduced downtimes significantly by
early warning and preventative
maintenance
Improvement of service quality
Improvement of product quality
„The global quality monitoring solution added
true intelligence to our systems. Now, we are
far ahead of our competitors regarding product
quality and customer service“
Martin Masch,
Project Manager MITO(Market
Introduction Tool for Performance
Observation), Roche Diagnostics GmbH
Implement a quality monitoring
system for diagnostics solutions
installed in laboritories around
the world
Reduce downtimes of customer‘s
diagnostic equipment
Calculate worldwide distribution
of the accuracy of measurements
for specific diagnostic tests
SAS® Predictive Analytics
Nightly update of equipment
performance data and
measurement results
Early Warning and automated
alerting of service engineers via
Automated root-cause analysis
of 3,000+ different equipment
alerts
Drill-down capabilities –
geographically and system wise
Manufacturing Roche Diagnostics
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Cla
im A
pp
rov
ed
In S
erv
ice
Unit F
ails
Cla
im S
ub
mit
ted
Issu
e D
efi
ned
Pro
duction
Issue D
ete
cte
d
Issu
e D
ete
cte
d
Issue R
esolv
ed
Fraud
Detection
Issue
Detection
Problem
Definition
SAS Warranty Analysis
Warranty Timeline
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Copyright © 2010, SAS Institute Inc. All rights reserved.
Warranty Business Rules & Processes
Warranty
Information
Store
Customer Call Center
Product Data (origin, options, etc..)
Dealer/Distributor Data
Claims
Sales Data
Corrective Actions
Customer Surveys
Supplier Audits
Plant Audits
Technician Hotlines
Enterprise Warranty Analysis
Solution Overview
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Copyright © 2010, SAS Institute Inc. All rights reserved.
Advanced
Analysis Text
Analysis
Emerging
Issues
Dashboard Report
Library
Reporting
& Analysis
Warranty Business Rules & Processes
Warranty
Information
Store
Customer Call Center
Product Data (origin, options, etc..)
Dealer/Distributor Data
Claims
Sales Data
Corrective Actions
Customer Surveys
Supplier Audits
Plant Audits
Technician Hotlines
Enterprise Warranty Analysis KPIs Traffic Lights
KPIs Traffic Lights
Solution Overview
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Copyright © 2010, SAS Institute Inc. All rights reserved.
Advanced
Analysis Text
Analysis
Emerging
Issues
Dashboard Report
Library
Reporting
& Analysis
Warranty Business Rules & Processes
Warranty
Information
Store
Customer Call Center
Product Data (origin, options, etc..)
Dealer/Distributor Data
Claims
Sales Data
Corrective Actions
Customer Surveys
Supplier Audits
Plant Audits
Technician Hotlines
Enterprise Warranty Analysis
Warranty
Information
Store
Corporate Reports Custom Reports My Reports
Solution Overview
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Copyright © 2010, SAS Institute Inc. All rights reserved.
Advanced
Analysis Text
Analysis
Emerging
Issues
Dashboard Report
Library
Reporting
& Analysis
Warranty Business Rules & Processes
Warranty
Information
Store
Customer Call Center
Product Data (origin, options, etc..)
Dealer/Distributor Data
Claims
Sales Data
Corrective Actions
Customer Surveys
Supplier Audits
Plant Audits
Technician Hotlines
Enterprise Warranty Analysis
Production Period Time in Service Time of Claim
Solution Overview
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Copyright © 2010, SAS Institute Inc. All rights reserved.
Advanced
Analysis Text
Analysis
Emerging
Issues
Dashboard Report
Library
Reporting
& Analysis
Warranty Business Rules & Processes
Warranty
Information
Store
Customer Call Center
Product Data (origin, options, etc..)
Dealer/Distributor Data
Claims
Sales Data
Corrective Actions
Customer Surveys
Supplier Audits
Plant Audits
Technician Hotlines
Enterprise Warranty Analysis 12 Out-of-the-box Warranty-focused Analyses,
drillable, highly interactive
Solution Overview
39
Copyright © 2010, SAS Institute Inc. All rights reserved.
Advanced
Analysis Text
Analysis
Emerging
Issues
Dashboard Report
Library
Reporting
& Analysis
Warranty Business Rules & Processes
Warranty
Information
Store
Customer Call Center
Product Data (origin, options, etc..)
Dealer/Distributor Data
Claims
Sales Data
Corrective Actions
Customer Surveys
Supplier Audits
Plant Audits
Technician Hotlines
Enterprise Warranty Analysis Advanced Text Dynamic Search Clustering Filtering
Solution Overview
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Copyright © 2010, SAS Institute Inc. All rights reserved.
Advanced
Analysis Text
Analysis
Emerging
Issues
Dashboard Report
Library
Reporting
& Analysis
Warranty Business Rules & Processes
Warranty
Information
Store
Customer Call Center
Product Data (origin, options, etc..)
Dealer/Distributor Data
Claims
Sales Data
Corrective Actions
Customer Surveys
Supplier Audits
Plant Audits
Technician Hotlines
Enterprise Warranty Analysis Advanced Ad Hoc Data Mining Text Mining
Create Custom Models, Analytic Tasks, and Reports
Claim Fraud Detection
Environmental Influences
Associated Claims
Predict Failures
Refine Codes
Solution Overview
41
Copyright © 2010, SAS Institute Inc. All rights reserved.
Advanced
Analysis Text
Analysis
Emerging
Issues
Dashboard Report
Library
Reporting
& Analysis
Warranty Business Rules & Processes
Customer Call Center
Product Data (origin, options, etc..)
Dealer/Distributor Data
Claims
Sales Data
Corrective Actions
Customer Surveys
Supplier Audits
Plant Audits
Technician Hotlines
Enterprise Warranty Analysis
Warranty
Information
Store
Solution Overview
®
Failure Relationship Analysis
Links indicate relationships among codes.
The table includes information on the
strength of the links, as well as the cost.
®
Failure Relationship Analysis
Each code is linked to itself and shows how often the same code occurs on the
same unit.
® Slide Title
• Notes
• Notes
• Notes
Text Analysis
®
Multivariate Analysis
®
Multivariate Analysis
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“We are making constant progress and after six months, we
are still discovering new ways of improving our warranty
analysis process”
Mr. Nanxiang Gao,
Field Performance Engineer
Challenge Solution Results
Issue identification cycle
time was lengthy
Problem solving cycle time
was too long
Data volumes were
growing quickly and
competition was getting
more fierce
Shanghai GM has reduced
issue identification and
definition time by 70%,
saving more than 4 months.
Warranty costs have been
reduced by 34%
Shanghai GM saved over $2M
USD in the first six months of
using SAS Warranty Analysis.
SAS Warranty Analysis
-SAS’ emerging issues
component provides early
warning of issues
-SAS’ problem definition
capabilities accelerate issue
resolution.
Warranty Analysis Early Warning
48
Copyright © 2010, SAS Institute Inc. All rights reserved.
"SAS built us a comprehensive solution and offered us six
more techniques they knew had worked for other
manufacturers."
John Kerr ,
General Manager of Quality
& Operational Excellence
Challenge Solution Results
Issue identification cycle
time was lengthy
Need to reduce problem
solving time to focus
attention on innovation
and design
Terabytes of data in SAP
R/3 and ServiceBench
needed to be brought
together for analysis.
Whirlpool has reduced the
time required to detect and
resolve issues by up to 90
days.
SAS is an integral part of
Whirlpool’s plan to cut the
cost of quality in half
SAS Warranty Analysis &
Text Miner
-SAS’ emerging issues
component provides early
warning of issues
-SAS’ problem definition
capabilities accelerate issue
resolution.
Warranty Analysis Early Warning
49
Copyright © 2010, SAS Institute Inc. All rights reserved.
Challenge Solution Results
Issue identification cycle
time was lengthy
Manually processed each
text based service order,
introducing time and
variation to the warranty
process.
Multiple different tools for
root cause analysis
Automated text coding using
text mining has freed up
resources for analysis and
removed coding errors.
Sub-Zero has reduced issue
identification and definition
time by more than 3 months.
Reduced warranty costs by
14%
SAS Warranty Analysis &
Text Miner
- Utilizing SAS text mining to
automatically code warranty
claims.
- SAS’ emerging issues
component provides early
warning of issues and the
root cause analysis
capabilities accelerate issue
resolution.
“We can detect and resolve issues much quicker – before a
large number of products ever reach customers' homes.”
David Bien,
Corporate Director of Reliability
Warranty Analysis Claim Coding & Early Warning
Copyright © 2010 SAS Institute Inc. All rights reserved.
Questions & Discussion