Achieving Total Quality - SAS · 2011-05-11 · Improved Problem Resolution Pinpointing critical...

transcript

Achieving Total Quality 2011 Auto Industry Leadership Forum

Agenda Introduction

Quality Today

Quality Vision

Customer Issues

Solutions

Questions & Discussion

Quality Today

Marketing

Service Engineering

Manufacturing

Customer

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

The Problem Substandard product quality

Compromises customer satisfactions

Negative quality perceptions

Erodes brand image

Damage to brand equity

Become a commodity

“Hot potato” quality

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

Kaizan

Problems Persist

Recalls, Engineers spend 80% time integrating data, lack of automation

Must take a broader view of quality

Customer Driven Quality

Marketing

Service Engineering

Manufacturing

Customer

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

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)

Customer Driven Quality

Marketing

Service Engineering

Manufacturing

Customer

• Production

• Assets

Social Media

• Warranty

• Call Center

Continuous

Product

Improvement

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

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

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

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

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

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

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

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)

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

SAS® Quality Lifecycle Analysis Overview

Development Yield Ramp Volume Production

Time to Market

Duration

Issue Detection & Resolution

Duration (after)

(before)

Performance Improvement

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:

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.

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.

Click here to access customer success story

SAS® Quality Lifecycle Analysis

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

SAS® Predictive Maintenance Overview

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

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

Text & M&D Data Classification & Clustering

M&D Data, Conditions

Optional:

of service reports

for analysis

to service codes

and conditions

to service code

clusters

Near-Neighbors (Service Code Cluster)

Optional:

of service reports

for analysis

to service codes

and conditions

to service code

clusters

Near-Neighbors (Service Code Cluster)

Conditions/sensor data similar to a known incident or defect

Optional:

of service reports

for analysis

to service codes

and conditions

to service code

clusters

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

Unit F

duction

Issue D

Issue R

Detection

Problem

Definition

SAS Warranty Analysis

Warranty Timeline

Warranty Business Rules & Processes

Warranty

Information

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

Advanced

Analysis Text

Analysis

Emerging

Issues

Dashboard Report

Library

Reporting

& Analysis

Warranty

Information

Claims

Sales Data

Corrective Actions

Customer Surveys

Supplier Audits

Plant Audits

Technician Hotlines

Enterprise Warranty Analysis KPIs Traffic Lights

KPIs Traffic Lights

Solution Overview

Advanced

Analysis Text

Analysis

Emerging

Issues

Dashboard Report

Library

Reporting

& Analysis

Warranty

Information

Claims

Sales Data

Corrective Actions

Customer Surveys

Supplier Audits

Plant Audits

Technician Hotlines

Warranty

Information

Corporate Reports Custom Reports My Reports

Solution Overview

Advanced

Analysis Text

Analysis

Emerging

Issues

Dashboard Report

Library

Reporting

& Analysis

Warranty

Information

Claims

Sales Data

Corrective Actions

Customer Surveys

Supplier Audits

Plant Audits

Technician Hotlines

Production Period Time in Service Time of Claim

Solution Overview

Advanced

Analysis Text

Analysis

Emerging

Issues

Dashboard Report

Library

Reporting

& Analysis

Warranty

Information

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

Advanced

Analysis Text

Analysis

Emerging

Issues

Dashboard Report

Library

Reporting

& Analysis

Warranty

Information

Claims

Sales Data

Corrective Actions

Customer Surveys

Supplier Audits

Plant Audits

Technician Hotlines

Enterprise Warranty Analysis Advanced Text Dynamic Search Clustering Filtering

Solution Overview

Advanced

Analysis Text

Analysis

Emerging

Issues

Dashboard Report

Library

Reporting

& Analysis

Warranty

Information

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

Advanced

Analysis Text

Analysis

Emerging

Issues

Dashboard Report

Library

Reporting

& Analysis

Claims

Sales Data

Corrective Actions

Customer Surveys

Supplier Audits

Plant Audits

Technician Hotlines

Warranty

Information

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

Text Analysis

Multivariate Analysis

“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

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

"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

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

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

warning of issues

-SAS’ problem definition

resolution.

Warranty Analysis Early Warning

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

SAS Warranty Analysis &

Text Miner

- Utilizing SAS text mining to

automatically code warranty

claims.

- SAS’ emerging issues

warning of issues and the

root cause analysis

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

Questions & Discussion

Achieving Total Quality - SAS · 2011-05-11 · Improved Problem Resolution Pinpointing critical...

Documents