quality management -...

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 quality freedom 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 Principle

�Cost of Quality

�General management approach as well as statistics

History: how did we get here…

• Deming and Juran outlined the principles of Quality Management.

• Tai-ichi Ohno applies them in Toyota Motors Corp.

• Japan has its National Quality Award (1951).

• U.S. and European firms begin to implement Quality Management programs (1980’s).

• U.S. establishes the Malcolm Baldridge National Quality Award (1987).

• Today, quality is an imperative for any business.

What does Total Quality Management encompass?

TQM is a management philosophy:

• continuous improvement

• leadership development

• partnership development

Cultural Alignment

Technical Tools

(Process Analysis, SPC,

QFD)

Customer

Developing quality specifications

Input Process Output

Design Design quality

Dimensions of quality

Conformance quality

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

σ6±

Six Sigma Quality

Six Sigma Roadmap (DMAIC) Next Project Define

Customers, Value, Problem Statement

Scope, Timeline, Team

Primary/Secondary & OpEx Metrics

Current Value Stream Map

Voice Of Customer (QFD) Measure

Assess specification / Demand

Measurement Capability (Gage R&R)

Correct the measurement system

Process map, Spaghetti, Time obs.

Measure OVs & IVs / Queues

Analyze (and fix the obvious) Root Cause (Pareto, C&E, brainstorm)

Find all KPOVs & KPIVs

FMEA, DOE, critical Xs, VA/NVA

Graphical Analysis, ANOVA

Future Value Stream Map

Improve Optimize KPOVs & test the KPIVs

Redesign process, set pacemaker

5S, Cell design, MRS

Visual controls

Value Stream Plan

Control Document process (WIs, Std Work)

Mistake proof, TT sheet, CI List

Analyze change in metrics

Value Stream Review

Prepare final report

Validate

Project $

Validate

Project $

Validate

Project $

Validate

Project $

Celebrate

Project $

Six Sigma Organization

Quality Improvement

Traditional

Time

Qualit

y

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

Per

cent

age

50%

100%

0%

75%

25% 10 20

30

40

50

60

Summary of Tools

1. Process flow chart

2. Run diagram

3. Control charts

4. Fishbone

5. Check sheet

6. Histogram

7. Pareto analysis

Case: shortening telephone waiting time…

• A bank is employing a call answering service

• The main goal in terms of quality is “zero waiting time” - customers get a bad impression - company vision to be friendly and easy access • The question is how to analyze the situation and improve quality

The current process

Custome

r B

Operator Custome

r A

Receiving

Party

How can we reduce waiting time?

Makes

custome

r 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

Ideas for improvement

1. Taking lunches on three different shifts

2. Ask all employees to leave messages when leaving desks

3. Compiling a directory where next to personnel’s name appears her/his title

Results of implementing the recommendations

A B C D E F


100%

0%

49%

71.2%

100

200

300 87.1%

100%

B C A D E F


0%

100

200

300

Before… …After

Improvement

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 observing variation in

output measures!

Statistical Process Control (SPC)

Every output measure has a target value and a level of “acceptable” variation (upper and lower tolerance limits)

SPC uses samples from output measures to estimate the mean and the variation (standard deviation)

Example

We want beer bottles to be filled with 12 FL OZ ± 0.05 FL OZ

Question:

How do we define the output measures?

In order to measure variation we need…

The average (mean) of the observations:

∑=

=N

i

ixN

X1

1

The standard deviation of the observations:

N

XxN

i

i∑=

−

= 1

2)(σ

Average & Variation example

Number of pepperoni’s per pizza: 25, 25, 26, 25, 23, 24, 25, 27

Average:

Standard Deviation:

Number of pepperoni’s per pizza: 25, 22, 28, 30, 27, 20, 25, 23

Average:

Standard Deviation:

Which pizza would you rather have?

When is a product good enough?

Incremental Cost of Variability

High

Zero

Lower Tolerance

Target Spec

Upper Tolerance

Traditional View

The “Goalpost” Mentality

a.k.a Upper/Lower Design Limits

(UDL, LDL) Upper/Lower Spec Limits

(USL, LSL)

Upper/Lower Tolerance Limits (UTL, LTL)

But are all ‘good’ products equal?

Incremental Cost of Variability

High

Zero

Lower Spec

Target Spec

Upper Spec

Taguchi’s View

“Quality Loss Function”

(QLF)

LESS VARIABILITY implies BETTER PERFORMANCE !

Capability Index (Cpk)

It shows how well the performance measure fits the design specification based on a given

tolerance level

A process is kσ capable if

LTLkXUTLkX ≥−≤+ σσ and

1and1 ≥−−

≤σσ k

LTLX

k

XUTL

Capability Index (Cpk)

Cpk < 1 means process is not capable at the kσ level

Cpk >= 1 means process is capable at the kσ level

−−

=σσ k

XUTL

k

LTLXC pk ,min

Another way of writing this is to calculate the capability index:

Accuracy and Consistency

We say that a process is accurate if its mean is close to the target T. We say that a process is consistent if its standard deviation is low.

X

Example 1: Capability Index (Cpk)

X = 10 and σ = 0.5

LTL = 9 UTL = 11

667.05.03

1011or

5.03910

min =

×

−

×

−=pkC

UTL LTL X


X = 9.5 and σ = 0.5

LTL = 9 UTL = 11

UTL LTL X


X = 10 and σ = 2

LTL = 9 UTL = 11

UTL LTL X

Example

Consider the capability of a process that puts pressurized grease in an aerosol can. The design specs call for an average of 60 pounds per square inch (psi) of pressure in each can with an upper tolerance limit of 65psi and a lower tolerance limit of 55psi. A sample is taken from production and it is found that the cans average 61psi with a standard deviation of 2psi.

1. Is the process capable at the 3σ level? 2. What is the probability of producing a defect?

Solution

LTL = 55 UTL = 65 σ = 2 61=X

6667.0)6667.0,1min()6

6165,

6

5561min(

)3

,3

min(

==−−

=

−−=

pk

pk

C

XUTLLTLXC

σσ

No, the process is not capable at the 3σ level.

Solution

P(defect) = P(X<55) + P(X>65) =P(X<55) + 1 – P(X<65) =P(Z<(55-61)/2) + 1 – P(Z<(65-61)/2) =P(Z<-3) + 1 – P(Z<2) =G(-3)+1-G(2) =0.00135 + 1 – 0.97725 (from standard normal table)

= 0.0241

2.4% of the cans are defective.

Example (contd)

Suppose another process has a sample mean of 60.5 and a standard deviation of 3. Which process is more accurate? This one. Which process is more consistent? The other one.

Control Charts

Control charts tell you when a process measure is exhibiting abnormal behavior.

Upper Control Limit

Central Line

Lower Control Limit

Two Types of Control Charts

• X/R Chart

This is a plot of averages and ranges over time (used for performance measures that are variables)

• p Chart

This is a plot of proportions over time (used for performance measures that are yes/no attributes)

When should we use p charts?

1. When decisions are simple “yes” or “no” by inspection

2. When the sample sizes are large enough (>50)

Sample (day) Items Defective Percentage

1 200 10 0.050

2 200 8 0.040

3 200 9 0.045

4 200 13 0.065

5 200 15 0.075

6 200 25 0.125

7 200 16 0.080

Statistical Process Control with p Charts


Let’s assume that we take t samples of size n …

size) (samplesamples) ofnumber (defects"" ofnumber total×

=p

n

pps p

)1( −=

p

p

zspLCL

zspUCL

−=

+=

066.0151

200680

==×

=p

017.0200

)066.01(066.0=

−=ps

015.0 017.03 066.0

117.0 017.03 066.0

=×−=

=×+=

LCL

UCL


LCL = 0.015

UCL = 0.117

p = 0.066


When should we use X/R charts?

1. It is not possible to label “good” or “bad”

2. If we have relatively smaller sample sizes (<20)

Statistical Process Control with X/R Charts

Take t samples of size n (sample size should be 5 or more)

∑=

=n

i

ixn

X1

1

}{min }{max ii xxR −=

R is the range between the highest and the lowest for each sample


X is the mean for each sample

∑=

=t

j

jXt

X1

1

∑=

=t

j

jRt

R1

1


X is the average of the averages.

R is the average of the ranges

RAXLCL

RAXUCL

X

X

2

2

−=

+=

define the upper and lower control limits…

RDLCL

RDUCL

R

R

3

4

=

=


Read A2, D3, D4 from Table TN 8.7

Example: SPC for bottle filling…

Sample Observation (xi) Average Range (R)

1 11.90 11.92 12.09 11.91 12.01

2 12.03 12.03 11.92 11.97 12.07

3 11.92 12.02 11.93 12.01 12.07

4 11.96 12.06 12.00 11.91 11.98

5 11.95 12.10 12.03 12.07 12.00

6 11.99 11.98 11.94 12.06 12.06

7 12.00 12.04 11.92 12.00 12.07

8 12.02 12.06 11.94 12.07 12.00

9 12.01 12.06 11.94 11.91 11.94

10 11.92 12.05 11.92 12.09 12.07

Example: SPC for bottle filling…

Sample Observation (xi) Average Range (R)

1 11.90 11.92 12.09 11.91 12.01 11.97 0.19

2 12.03 12.03 11.92 11.97 12.07 12.00 0.15

3 11.92 12.02 11.93 12.01 12.07 11.99 0.15

4 11.96 12.06 12.00 11.91 11.98 11.98 0.15

5 11.95 12.10 12.03 12.07 12.00 12.03 0.15

6 11.99 11.98 11.94 12.06 12.06 12.01 0.12

7 12.00 12.04 11.92 12.00 12.07 12.01 0.15

8 12.02 12.06 11.94 12.07 12.00 12.02 0.13

9 12.01 12.06 11.94 11.91 11.94 11.97 0.15

10 11.92 12.05 11.92 12.09 12.07 12.01 0.17

Calculate the average and the range for each sample…

Then…

00.12=X

is the average of the averages

15.0=R

is the average of the ranges

Finally…

91.1115.058.000.12

09.1215.058.000.12

=×−=

=×+=

X

X

LCL

UCL

Calculate the upper and lower control limits

015.00

22.115.011.2

=×=

=×=

R

R

LCL

UCL

LCL = 11.90

UCL = 12.10

The X Chart

X = 12.00

The R Chart

LCL = 0.00

R = 0.15

UCL = 0.32

The X/R Chart

LCL

UCL

X

LCL

R

UCL

What can you conclude?

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