Chapter 6: Quality Management

© Holmes Miller 1999

Quality Specifications Design quality: Inherent value of the product

in the marketplace

Dimensions include:

Performance

Features

Reliability/Durability

Serviceability

Aesthetics

Perceived Quality.

Conformance quality: Degree to which the product or service design specifications are met

Importance of QualityCosts & market share

Market GainsReputationVolumePrice

Lower CostsProductivityRework/ScrapWarranty

ImprovedQuality

IncreasedProfits

Not just the mean is important, but also the varianceNeed to look at the distribution function

The Concept of Consistency:Who is the Better Target Shooter?

Funnel Experiment (Deming) Tampering with a stable system only increases the

production of faulty items and mistakes.

Tampering is taking action based on the belief that a common

cause is a special cause. Improvement of a stable system nearly always means

reduction of variation.

One necessary qualification of anyone in management

is -- -- stop asking people to explain ups and downs

that come from random variation.

Basic Forms of Variation

Assignable variation is caused by factors that can be clearly identified and possibly managed

Common variation is inherent in the production process

Example: A poorly trained employee that creates variation in finished product output.

Example: A poorly trained employee that creates variation in finished product output.

Example: A molding process that always leaves “burrs” or flaws on a molded item.

Example: A molding process that always leaves “burrs” or flaws on a molded item.

Common Cause Variation (low level)

Common Cause Variation (high level)

Assignable Cause Variation

• Need to measure and reduce common cause variation• Identify assignable cause variation as soon as possible

Two Types of Causes for Variation

Taguchi’s View of Variation

IncrementalCost of Variability

High

Zero

LowerSpec

TargetSpec

UpperSpec

Traditional View

IncrementalCost of Variability

High

Zero

LowerSpec

TargetSpec

UpperSpec

Taguchi’s View

Traditional view is that quality within the LS and US is good and that the cost of quality outside this range is constant, where Taguchi views costs as increasing as variability increases, so seek to achieve zero defects and that will truly minimize quality costs.

Traditional view is that quality within the LS and US is good and that the cost of quality outside this range is constant, where Taguchi views costs as increasing as variability increases, so seek to achieve zero defects and that will truly minimize quality costs.

Costs of Quality

External Failure Costs

Appraisal Costs

Prevention Costs

Internal FailureCosts

Costs ofQuality

Six Sigma Quality

A philosophy and set of methods companies use to eliminate defects in their products and processes

Seeks to reduce variation in the processes that lead to product defects

The name, “six sigma” refers to the variation that exists within plus or minus six standard deviations of the process outputs

Process capability measure

3

Upper Specification Limit (USL)

LowerSpecificationLimit (LSL)

X-3sA X-2sA X-1sAX X+1sA

X+2s X+3sA

X-6sBX X+6sB

Process A(with st. dev sA)

Process B(with st. dev sB)

6

LSLUSLC p

x Cp P{defect} ppm

1 0.33 0.317 317,000

2 0.67 0.0455 45,500

3 1.00 0.0027 2,700

4 1.33 0.0001 63

5 1.67 0.0000006 0.6

6 2.00 2x10-9 0.00

The Statistical Meaning of Six Sigma

•Don’t confuse control limits with specification limits: a process can be in control, yet be incapable of meeting customer specs

Six Sigma Quality (Continued)

Six Sigma allows managers to readily describe process performance using a common metric: Defects Per Million Opportunities (DPMO)

1,000,000 x

units of No. x

unit per error for

iesopportunit ofNumber

defects ofNumber

DPMO 1,000,000 x

units of No. x

unit per error for

iesopportunit ofNumber

defects ofNumber

DPMO

Six Sigma Quality (Continued)Example of Defects Per Million

Opportunities (DPMO) calculation. Suppose we observe 200 letters delivered incorrectly to the wrong addresses in a small city during a single day when a total of 200,000 letters were delivered. What is the DPMO in this situation?

Example of Defects Per Million Opportunities (DPMO) calculation. Suppose we observe 200 letters delivered incorrectly to the wrong addresses in a small city during a single day when a total of 200,000 letters were delivered. What is the DPMO in this situation?

000,1 1,000,000 x

200,000 x 1

200DPMO

000,1 1,000,000 x

200,000 x 1

200DPMO

So, for every one million letters delivered this city’s postal managers can expect to have 1,000 letters incorrectly sent to the wrong address.

So, for every one million letters delivered this city’s postal managers can expect to have 1,000 letters incorrectly sent to the wrong address.

Cost of Quality: What might that DPMO mean in terms of over-time employment to correct the errors? Other costs?

Cost of Quality: What might that DPMO mean in terms of over-time employment to correct the errors? Other costs?

Six Sigma Quality: DMAIC Cycle

Define, Measure, Analyze, Improve, and Control (DMAIC)

Developed by General Electric as a means of focusing effort on quality using a methodological approach

Overall focus of the methodology is to understand and achieve what the customer wants

A 6-sigma program seeks to reduce the variation in the processes that lead to these defects

DMAIC consists of five steps….

Six Sigma Quality: DMAIC Cycle (Continued)

1. Define (D)

2. Measure (M)

3. Analyze (A)

4. Improve (I)

5. Control (C)

Customers and their priorities

Process and its performance

Causes of defects

Remove causes of defects

Maintain quality

Example to illustrate the process…We are the maker of this cereal.

Consumer Reports has just published an article that shows that we frequently have less than 15 ounces of cereal in a box.

Exercise: What should we do? Define -- What is the critical-to-quality

characteristic? Measure -- How would we measure to evaluate

the extent of the problem? Analyze -- How can we improve the capability of

our cereal box filling process? Improve -- How good is good enough?

Motorola’s “Six Sigma” Control -- Statistical Process Control (SPC)

Analytical Tools for Six Sigma and

Continuous Improvement: Flow Chart No, Continue…

Material Received

from Supplier

Inspect Material for

DefectsDefects found?

Return to Supplier for Credit

Yes

Can be used to find quality problems

Can be used to find quality problems

Analytical Tools for Six Sigma and Continuous Improvement: Run Chart

Can be used to identify when equipment or processes are not behaving according to specifications

Can be used to identify when equipment or processes are not behaving according to specifications

0.440.460.480.5

0.520.540.560.58

1 2 3 4 5 6 7 8 9 10 11 12Time (Hours)

Dia

mete

r

Analytical Tools for Six Sigma and Continuous Improvement: Pareto

Analysis

Can be used to find when 80% of the problems may be attributed to 20% of thecauses

Can be used to find when 80% of the problems may be attributed to 20% of thecauses

Assy.Instruct.

Frequ

ency

Design Purch. Training

80%

Analytical Tools for Six Sigma and Continuous Improvement:

Checksheet

Billing Errors

Wrong Account

Wrong Amount

A/R Errors

Wrong Account

Wrong Amount

Monday

Can be used to keep track of defects or used to make sure people collect data in a correct manner

Can be used to keep track of defects or used to make sure people collect data in a correct manner

Analytical Tools for Six Sigma and Continuous Improvement: Histogram

Nu

mb

er

of

Lots

Data RangesDefects

in lot0 1 2 3 4

Can be used to identify the frequency of quality defect occurrence and display quality performance

Can be used to identify the frequency of quality defect occurrence and display quality performance

Analytical Tools: Cause and Effect Diagrams (Fishbone Diagrams)

Materials

MachinesSpecifications /information

People

Vise positionset incorrectly

Machine toolcoordinates set incorrectly

Vice position shiftedduring production

Part clampingsurfaces corrupted

Part incorrectlypositioned in clamp

Clamping force toohigh or too low

Cutting tool worn

Dimensions incorrectlyspecified in drawing

Dimension incorrectly coded In machine tool program

Material too soft

Extrusion stock undersized

Extrusion dieundersized

Extrusion ratetoo high

Extrusion temperaturetoo high

Error in measuring height

Steer support height deviates from specification

Can be used to systematically track backwards to find a possible cause of a quality problem (or effect)

Types of Statistical Sampling Attribute (Go or no-go information)

Defectives refers to the acceptability of product across a range of characteristics.

Defects refers to the number of defects per unit which may be higher than the number of defectives.

p-chart application

Variable (Continuous) Usually measured by the mean and the

standard deviation. X-bar and R chart applications

Analytical Tools for Six Sigma and Continuous Improvement: Control

Charts

Can be used to monitor ongoing production process quality and quality conformance to stated standards of quality

Can be used to monitor ongoing production process quality and quality conformance to stated standards of quality

970

980

990

1000

1010

1020

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

LCL

UCL

Statistical Process Control (SPC) Charts

UCL

LCL

Samples over time

1 2 3 4 5 6

UCL

LCL

Samples over time

1 2 3 4 5 6

UCL

LCL

Samples over time

1 2 3 4 5 6

Normal BehaviorNormal Behavior

Possible problem, investigate

Possible problem, investigate

Possible problem, investigate

Possible problem, investigate

Process Capability

Process limits

Specification limits

How do the limits relate to one another?

Process Capability Index, Cpk

3

X-UTLor

3

LTLXmin=C pk

Shifts in Process Mean

Capability Index shows how well parts being produced fit into design limit specifications.

Capability Index shows how well parts being produced fit into design limit specifications.

As a production process produces items small shifts in equipment or systems can cause differences in production performance from differing samples.

As a production process produces items small shifts in equipment or systems can cause differences in production performance from differing samples.

The Cereal Box Example We are the maker of this cereal. Consumer reports

has just published an article that shows that we frequently have less than 15 ounces of cereal in a box. We would like to have 16 ounces in each box.

Let’s assume that the government says that we must be within ± 5 percent of the weight advertised on the box.

Upper Tolerance Limit = 16 + .05(16) = 16.8 ounces Lower Tolerance Limit = 16 – .05(16) = 15.2 ounces We go out and buy 1,000 boxes of cereal and find that

they weight an average of 15.875 ounces with a standard deviation of .529 ounces.

Cereal Box Process Capability Specification or

Tolerance Limits Upper Spec = 16.8 oz Lower Spec = 15.2 oz

Observed Weight Mean = 15.875 oz Std Dev = .529 oz

3

;3

XUTLLTLXMinC pk

)529(.3

875.158.16;

)529(.3

2.15875.15MinC pk

5829.;4253.MinC pk

4253.pkC

What does a Cpk of .4253 mean? This is a process that will produce a relatively high number of defects. Many companies look for a Cpk of 1.3 or better… 6-Sigma company wants 2.0!

Basic Forms of Statistical Sampling for Quality Control

Acceptance Sampling is sampling to accept or reject the immediate lot of product at hand

Statistical Process Control is sampling to determine if the process is within acceptable limits