Quality Management “ It costs a lot to produce a bad product. ” Norman Augustine

Quality Management

“It costs a lot to produce a bad product.”Norman Augustine

Cost of quality

1. Prevention costs

2. Appraisal costs

3. Internal failure costs

4. External failure costs

5. Opportunity costs

What is quality management all about?

Try to manage all aspects of the organization in order to excel in all dimensions that are

important to “customers”

Two aspects of quality: features: more features that meet customer needs

= higher qualityfreedom from trouble: fewer defects = higher

quality

The Quality Gurus – Edward Deming

1900-1993

1986

Quality is “uniformity and dependability”

Focus on SPC and statistical tools

“14 Points” for management

PDCA method

The Quality Gurus – Joseph Juran

1904 - 2008

1951

Quality is “fitness for use”

Pareto PrincipleCost of QualityGeneral

management approach as well as statistics

Defining Quality

The totality of features and characteristics of a product or service that bears on its ability to satisfy stated or implied needs

American Society for Quality

MNBQALeadership: How upper management leads the organization, and how the organization leads within the community. Strategic planning: How the organization establishes and plans to implement strategic directions.Customer and market focus: How the organization builds and maintains strong, lasting relationships with customers. Measurement, analysis, and knowledge management: How the organization uses data to support key processes and manage performance.Human resource focus: How the organization empowers and involves its workforce. Process management: How the organization designs, manages and improves key processes.Business/organizational performance results: How the organization performs in terms of customer satisfaction, finances, human resources, supplier and partner performance, operations, governance and social responsibility, and how the organization compares to its competitors.

What does Total Quality Management encompass?

TQM is a management philosophy:• continuous improvement• leadership development• partnership development

CulturalAlignment

Technical Tools

(Process Analysis, SPC,

QFD)

Customer

Developing quality specifications

Input Process Output

Design Design quality

Dimensions of quality

Conformance quality

Quality Improvement

Traditional

Continuous Improvement

Time

Qua

lity

Continuous improvement philosophy

1. Kaizen: Japanese term for continuous improvement. A step-by-step improvement of business processes.

2. PDCA: Plan-do-check-act as defined by Deming.

Plan Do

Act Check

3. Benchmarking : what do top performers do?

Tools used for continuous improvement

1. Process flowchart


2. Run Chart

Performance

Time


3. Control Charts

Performance Metric

Time


4. Cause and effect diagram (fishbone)

Environment

Machine Man

Method Material


5. Check sheet

Item A B C D E F G

---------------------

√ √ √√ √

√ √

√

√

√ √√ √ √

√√√

√√ √


6. Histogram

Frequency


7. Pareto Analysis

A B C D E F

Freq

uenc

y

Perc

enta

ge

50%

100%

0%

75%

25%102030405060

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 6

Six Sigma Quality

Makes customer

wait

Absent receiving party

Working system of operators

Customer Operator

Fishbone diagram analysis

Absent

Out of office

Not at desk

Lunchtime

Too many phone calls

Absent

Not giving receiving party’s coordinates

Complaining

Leaving a message

Lengthy talk

Does not know organization well

Takes too much time to explain

Does not understand customer

Daily average

Total number

A One operator (partner out of office) 14.3 172

B Receiving party not present 6.1 73

C No one present in the section receiving call 5.1 61

D Section and name of the party not given 1.6 19

E Inquiry about branch office locations 1.3 16

F Other reasons 0.8 10

29.2 351

Reasons why customers have to wait(12-day analysis with check sheet)

Pareto Analysis: reasons why customers have to wait

A B C D E F

Frequency Percentage

0%

49%

71.2%

100

200

300 87.1%

150

250

In general, how can we monitor quality…?

1. Assignable variation: we can assess the cause

2. Common variation: variation that may not be possible to correct (random variation, random noise)

By observingvariation in

output measures!

SPC – suppl ch. 6 Statistical Process Control (SPC)

Control Charts for VariablesThe Central Limit TheoremSetting Mean Chart Limits (x-Charts)Setting Range Chart Limits (R-Charts)Using Mean and Range ChartsControl Charts for AttributesManagerial Issues and Control Charts

Variability is inherent in every processNatural or common causesSpecial or assignable causes

Provides a statistical signal when assignable causes are present

Detect and eliminate assignable causes of variation

Statistical Process Control (SPC)

Samples

To measure the process, we take samples and analyze the sample statistics following these steps

(a) Samples of the product, say five boxes of cereal taken off the filling machine line, vary from each other in weightFr

eque

ncy

Weight

#

## #

##

##

#

# # ## # ##

# # ## # ## # ##

Each of these represents one

sample of five boxes of cereal

Figure S6.1

Samples


(b) After enough samples are taken from a stable process, they form a pattern called a distribution

The solid line represents the distribution

Freq

uenc

y

WeightFigure S6.1

Samples


(c) There are many types of distributions, including the normal (bell-shaped) distribution, but distributions do differ in terms of central tendency (mean), standard deviation or variance, and shape

Weight

Central tendency

Weight

Variation

Weight

Shape

Freq

uenc

y

Figure S6.1

Samples


(d) If only natural causes of variation are present, the output of a process forms a distribution that is stable over time and is predictable Weight

Time

Freq

uenc

y Prediction

Figure S6.1

Samples


(e) If assignable causes are present, the process output is not stable over time and is not predicable

WeightTime

Freq

uenc

y Prediction

????

???

???

??????

???

Figure S6.1

Control Charts

Constructed from historical data, the purpose of control charts is to help distinguish between natural variations and variations due to assignable causes

Types of Data

Characteristics that can take any real value

May be in whole or in fractional numbers

Continuous random variables

Variables Attributes Defect-related

characteristics Classify products as

either good or bad or count defects

Categorical or discrete random variables

Process Control

Figure S6.2

Frequency

(weight, length, speed, etc.)Size

Lower control limit Upper control limit

(a) In statistical control and capable of producing within control limits

(b) In statistical control but not capable of producing within control limits

(c) Out of control

Population and Sampling Distributions

Three population distributions

Beta

Normal

Uniform

Distribution of sample means

Standard deviation of the sample means

= x = n

Mean of sample means = x

| | | | | | |

-3x -2x -1x x +1x +2x +3x

99.73% of all xfall within ± 3x

95.45% fall within ± 2x

Figure S6.3

Sampling Distribution

x = m(mean)

Sampling distribution of means

Process distribution of means

Figure S6.4

Control Charts for Variables

For variables that have continuous dimensions Weight, speed, length, strength, etc.

x-charts are to control the central tendency of the process

R-charts are to control the dispersion of the process

These two charts must be used together

Setting Chart Limits

For x-Charts when we know

Upper control limit (UCL) = x + zx

Lower control limit (LCL) = x - zx

where x = mean of the sample means or a target value set for the processz = number of normal standard deviationsx = standard deviation of the sample means

= / n = population standard deviationn = sample size

Setting Control LimitsHour 1

Sample Weight ofNumber Oat Flakes

1 172 133 164 185 176 167 158 179 16

Mean 16.1 = 1

Hour Mean Hour Mean1 16.1 7 15.22 16.8 8 16.43 15.5 9 16.34 16.5 10 14.85 16.5 11 14.26 16.4 12 17.3

n = 9

LCLx = x - zx = 16 - 3(1/3) = 15 ozs

For 99.73% control limits, z = 3

UCLx = x + zx = 16 + 3(1/3) = 17 ozs

17 = UCL

15 = LCL

16 = Mean

Setting Control Limits

Control Chart for sample of 9 boxes

Sample number

| | | | | | | | | | | |1 2 3 4 5 6 7 8 9 10 11 12

Variation due to assignable

causes

Variation due to assignable

causes

Variation due to natural causes

Out of control

Out of control


For x-Charts when we don’t know

Lower control limit (LCL) = x - A2R

Upper control limit (UCL) = x + A2R

where R = average range of the samplesA2 = control chart factor found in Table S6.1 x = mean of the sample means

Control Chart Factors

Table S6.1

Sample Size Mean Factor Upper Range Lower Range n A2 D4 D3

2 1.880 3.268 03 1.023 2.574 04 .729 2.282 05 .577 2.115 06 .483 2.004 07 .419 1.924 0.0768 .373 1.864 0.1369 .337 1.816 0.184

10 .308 1.777 0.22312 .266 1.716 0.284

Setting Control LimitsProcess average x = 16.01 ouncesAverage range R = .25Sample size n = 5


UCLx = x + A2R= 16.01 + (.577)(.25)= 16.01 + .144= 16.154 ounces

Process average x = 16.01 ouncesAverage range R = .25Sample size n = 5

From Table S6.1


UCLx = x + A2R= 16.01 + (.577)(.25)= 16.01 + .144= 16.154 ounces

LCLx = x - A2R= 16.01 - .144= 15.866 ounces

Process average x = 16.01 ouncesAverage range R = .25Sample size n = 5

UCL = 16.154

Mean = 16.01

LCL = 15.866

R – Chart

Type of variables control chart Shows sample ranges over time

Difference between smallest and largest values in sample

Monitors process variability Independent from process mean


For R-Charts

Lower control limit (LCLR) = D3R

Upper control limit (UCLR) = D4R

whereR = average range of the samplesD3 and D4 = control chart factors from Table S6.1


UCLR = D4R= (2.115)(5.3)= 11.2 pounds

LCLR = D3R= (0)(5.3)= 0 pounds

Average range R = 5.3 poundsSample size n = 5From Table S6.1 D4 = 2.115, D3 = 0

UCL = 11.2

Mean = 5.3

LCL = 0

Mean and Range Charts

(a)These sampling distributions result in the charts below

(Sampling mean is shifting upward but range is consistent)

R-chart(R-chart does not detect change in mean)

UCL

LCL

Figure S6.5

x-chart(x-chart detects shift in central tendency)

UCL

LCL

Mean and Range Charts

R-chart(R-chart detects increase in dispersion)

UCL

LCL

Figure S6.5

(b)These sampling distributions result in the charts below

(Sampling mean is constant but dispersion is increasing)

x-chart(x-chart does not detect the increase in dispersion)

UCL

LCL

Automated Control Charts

Control Charts for Attributes

For variables that are categoricalGood/bad, yes/no, acceptable/unacceptable

Measurement is typically counting defectives

Charts may measurePercent defective (p-chart)Number of defects (c-chart)

Control Limits for p-Charts

Population will be a binomial distribution, but applying the Central Limit Theorem allows us to

assume a normal distribution for the sample statistics

UCLp = p + zp^

LCLp = p - zp^

where p = mean fraction defective in the samplez = number of standard deviationsp = standard deviation of the sampling distributionn = sample size

^

p(1 - p)np =^

p-Chart for Data EntrySample Number Fraction Sample Number FractionNumber of Errors Defective Number of Errors Defective

1 6 .06 11 6 .062 5 .05 12 1 .013 0 .00 13 8 .084 1 .01 14 7 .075 4 .04 15 5 .056 2 .02 16 4 .047 5 .05 17 11 .118 3 .03 18 3 .039 3 .03 19 0 .00

10 2 .02 20 4 .04Total = 80

(.04)(1 - .04)100p = = .02^p = = .0480

(100)(20)

.11 –

.10 –

.09 –

.08 –

.07 –

.06 –

.05 –

.04 –

.03 –

.02 –

.01 –

.00 –

Sample number

Frac

tion

defe

ctiv

e

| | | | | | | | | |

2 4 6 8 10 12 14 16 18 20

p-Chart for Data EntryUCLp = p + zp = .04 + 3(.02) = .10^

LCLp = p - zp = .04 - 3(.02) = 0^

UCLp = 0.10

LCLp = 0.00

p = 0.04

.11 –

.10 –

.09 –

.08 –

.07 –

.06 –

.05 –

.04 –

.03 –

.02 –

.01 –

.00 –

Sample number

Frac

tion

defe

ctiv

e

| | | | | | | | | |

2 4 6 8 10 12 14 16 18 20

UCLp = p + zp = .04 + 3(.02) = .10^

LCLp = p - zp = .04 - 3(.02) = 0^

UCLp = 0.10

LCLp = 0.00

p = 0.04

p-Chart for Data Entry

Possible assignable causes present

Control Limits for c-Charts

Population will be a Poisson distribution, but applying the Central Limit Theorem allows us to

assume a normal distribution for the sample statistics

where c = mean number defective in the sample

UCLc = c + 3 c LCLc = c - 3 c

c-Chart for Cab Company

c = 54 complaints/9 days = 6 complaints/day

|1

|2

|3

|4

|5

|6

|7

|8

|9

Day

Num

ber d

efec

tive 14 –

12 –10 –8 –6 –4 –2 –0 –

UCLc = c + 3 c= 6 + 3 6= 13.35

LCLc = c - 3 c= 3 - 3 6= 0

UCLc = 13.35

LCLc = 0

c = 6

Patterns in Control Charts

Normal behavior. Process is “in control.”

Upper control limit

Target

Lower control limit

Figure S6.7

Upper control limit

Target

Lower control limit


One plot out above (or below). Investigate for cause. Process is “out of control.”

Figure S6.7

Upper control limit

Target

Lower control limit


Trends in either direction, 5 plots. Investigate for cause of progressive change.Figure S6.7

Upper control limit

Target

Lower control limit


Two plots very near lower (or upper) control. Investigate for cause.Figure S6.7

Upper control limit

Target

Lower control limit


Run of 5 above (or below) central line. Investigate for cause. Figure S6.7

Upper control limit

Target

Lower control limit


Erratic behavior. Investigate.

Figure S6.7

Which Control Chart to Use

Using an x-chart and R-chart:Observations are variablesCollect 20 - 25 samples of n = 4, or n = 5, or

more, each from a stable process and compute the mean for the x-chart and range for the R-chart

Track samples of n observations each

Variables Data


Using the p-chart:Observations are attributes that can be

categorized in two states We deal with fraction, proportion, or percent

defectivesHave several samples, each with many

observations

Attribute Data


Using a c-Chart:Observations are attributes whose defects per

unit of output can be countedThe number counted is often a small part of

the possible occurrencesDefects such as number of blemishes on a

desk, number of typos in a page of text, flaws in a bolt of cloth

Attribute Data

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Quality Management “ It costs a lot to produce a bad product. ” Norman Augustine

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