seven qc tool and more

8/8/2019 Seven QC Tool and More

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By Loh Wan Leng

1

1-Jul-2010, Rev 0


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Quality ControlQuality ControlQuality ControlQuality Control1. What is Quality Control?2. What is Statistical Process Control (SPC)?3. Why use SPC?4. Benefit of SPC5. Type of Statistical Control Chart.6. Type of Variable Control Chart7. Type of Attribute Control Chart8. Which control chart to use?

Quality AssuranceQuality AssuranceQuality AssuranceQuality Assurance1. What is Quality Assurance?2. What is acceptance sampling?3. Why use acceptance sampling?4. How/When to use acceptance sampling?

5. Acceptance sampling explained

Quality System and ToolQuality System and ToolQuality System and ToolQuality System and Tool1. TQM2. ISO 9001:2008

3. Seven Quality Tool2


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What is Quality Control?What is Quality Control?What is Quality Control?What is Quality Control?

Control:Control:Control:Control:

An evaluation to indicate needed corrective responses; the act ofguiding a process in which variability is attributable to a constantsystem of chance causes.

Quality control:Quality control:Quality control:Quality control:The observation techniques and activities used to fulfillrequirements for quality

e.g on-line inspection, use of control-chart

Quote from ASQ websiteQuote from ASQ websiteQuote from ASQ websiteQuote from ASQ website

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What is Statistical Process Control (SPC)?

A method of monitoring processes and process variation. Thepurpose is to identify causes for process variations and resolvethem. Process variables may include rework, scrap, inconsistent rawmaterials, and downtime on equipment.

Invented by Dr. Walter A. Shewhart at Bell Telephone Labs around1920s

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Measures performance of a process

Statistical technique used to ensure process is making product tostandard

Involves collecting, organising & interpreting data

All process are subject to variabilityNatural causes: Random variationsAssignable causes: Correctable problems Machine wear,

unskilled workers, poor material


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SPC in summary:

It is a way of:

presenting data on a chart

determining if your process have a trend

determining if your process are stable

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determining the capability of your process

It is also a way of thinking


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Why use SPC?

Provides a formal method to detect trends.

Provides Credibility and Rigor at minimum cost.

Balances False Alarms and Failures to Detect.

Accepted Industry Standard with long history.

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Control Charts are similar to the circuitry in your homes smokedetector.


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Benefit of SPC

Increases product consistency

Improves product quality

Decreases scrap and rework defects

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ncreases pro uc on ou pu


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Type of Statistical Control Chart.

Variable Control Charts

Deal with items that can be measured . Examples

1) Weight2) Height

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4) Volume

Attribute Control Charts

Control charts that factor in the quality attributes of a process

to determine if the process is performing in or out of control.


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Type of Common Variable Control Chart use in Quality.

X-Bar chart

Deals with a average value in a process.

R chart

Takes into count the range of the values

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x= = = 5.01 cm= x

k50.09

10x= = = 5.01 cm= x

k50.09

10x= = = 5.01 cm= x

k

x

k50.09

10

50.09

10

Example of Variable Control Chart Xbar Chart

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UCL = x+ A2R= 5.01 + (0.58)(0.115) = 5.08

LCL = x- A2R= 5.01 - (0.58)(0.115) = 4.94

=

=

UCL = x+ A2R= 5.01 + (0.58)(0.115) = 5.08

LCL = x- A2R= 5.01 - (0.58)(0.115) = 4.94

=

=

UCL = x+ A2R= 5.01 + (0.58)(0.115) = 5.08

LCL = x- A2R= 5.01 - (0.58)(0.115) = 4.94

=

=

Retrieve factor value A2

wherewhere

xx = average of sample means= average of sample means


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Example of Variable Control Chart Xbar Chart

UCL = 5.08

5.10

5.08

5.06

5.04

UCL = 5.08

5.10

5.08

5.06

5.04

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LCL = 4.94

Mea

Sample number

|

1

|

2

|

3

|

4

|

5

|

6

|

7

|

8

|

9

|

10

.

5.00

4.98

4.96

4.94

4.92

x= 5.01

LCL = 4.94

Mea

Sample number

|

1

|

2

|

3

|

4

|

5

|

6

|

7

|

8

|

9

|

10

.

5.00

4.98

4.96

4.94

4.92

x= 5.01x= 5.01


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Example of Variable Control Chart R chart

UCL = 0.243

nge

R= 0.115

0.28

0.24 0.20

0.16

UCL = 0.243

nge

R= 0.115

UCL = 0.243

nge

R= 0.115

0.28

0.24 0.20

0.16

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LCL = 0

R

Sample number

|1

|2

|3

|4

|5

|6

|7

|8

|9

|10

0.12 0.08

0.04

0

LCL = 0

R

Sample number

|1

|2

|3

|4

|5

|6

|7

|8

|9

|10

LCL = 0

R

Sample number

|1

|2

|3

|4

|5

|6

|7

|8

|9

|10

0.12 0.08

0.04

0


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Determining Control Limits for x-bar and R-Charts

2 1.88 0.00 3.27

3 1.02 0.00 2.574 0.73 0.00 2.285 0.58 0.00 2.116 0.48 0.00 2.007 0.42 0.08 1.92

n A2 D3 D4

SAMPLE SIZE FACTOR FOR x-CHART FACTORS FOR R-CHART

2 1.88 0.00 3.27

3 1.02 0.00 2.574 0.73 0.00 2.285 0.58 0.00 2.116 0.48 0.00 2.007 0.42 0.08 1.92

n A2 D3 D4

SAMPLE SIZE FACTOR FOR x-CHART FACTORS FOR R-CHARTn A2 D3 D4

SAMPLE SIZE FACTOR FOR x-CHART FACTORS FOR R-CHARTSAMPLE SIZE FACTOR FOR x-CHART FACTORS FOR R-CHART

Quality Control

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. . .

9 0.44 0.18 1.8210 0.11 0.22 1.7811 0.99 0.26 1.7412 0.77 0.28 1.7213 0.55 0.31 1.6914 0.44 0.33 1.6715 0.22 0.35 1.65

16 0.11 0.36 1.6417 0.00 0.38 1.6218 0.99 0.39 1.6119 0.99 0.40 1.6120 0.88 0.41 1.59

. . .

9 0.44 0.18 1.8210 0.11 0.22 1.7811 0.99 0.26 1.7412 0.77 0.28 1.7213 0.55 0.31 1.6914 0.44 0.33 1.6715 0.22 0.35 1.65

16 0.11 0.36 1.6417 0.00 0.38 1.6218 0.99 0.39 1.6119 0.99 0.40 1.6120 0.88 0.41 1.59

Back to X-bar Back to R Chart


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Type of Common Attribute Control Chart use in Quality.

P chart

A chart of the percent defective in each sample set with

unequal sample sizes.

C chart

A chart of the number of defects per unit in each sample set.

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U chart A chart of the average number of defects in each sample set.


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Example of Attribute Control Chart P chart

A garment factory produces jean. The product should be free ofdefects, meaning no tear, loose thread and etc. Sampling the

batches, quality inspector collected the following data:

NUMBER OFNUMBER OF PROPORTIONPROPORTIONSAMPLESAMPLE DEFECTIVESDEFECTIVES DEFECTIVEDEFECTIVE

NUMBER OFNUMBER OF PROPORTIONPROPORTIONSAMPLESAMPLE DEFECTIVESDEFECTIVES DEFECTIVEDEFECTIVE

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20 samples of 100 pairs of jeans20 samples of 100 pairs of jeans

11 66 .06.0622 00 .00.00

33 44 .04.04

:: :: ::

:: :: ::

2020 1818 .18.18

200200

20 samples of 100 pairs of jeans20 samples of 100 pairs of jeans

11 66 .06.0622 00 .00.00

33 44 .04.04

:: :: ::

:: :: ::

2020 1818 .18.18

200200


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UCL = p+ 3 = 0.10 + 3p(1 - p)

n

0.10(1 - 0.10)

100

= 200 / 20(100) = 0.10total defectives

total sample observations

p =

UCL = p+ 3 = 0.10 + 3p(1 - p)

n

0.10(1 - 0.10)

100

UCL = p+ 3 = 0.10 + 3p(1 - p)

n

0.10(1 - 0.10)

100

= 200 / 20(100) = 0.10total defectives


p = = 200 / 20(100) = 0.10total defectives


p = = 200 / 20(100) = 0.10total defectives


p =

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z = number of standard deviations from process average

p = sample proportion defective; an estimate of process average

= .

LCL = 0.010

LCL = p- 3 = 0.10 - 3p(1 - p)

n

0.10(1 - 0.10)

100

= .= .

LCL = 0.010

LCL = p- 3 = 0.10 - 3p(1 - p)

n

0.10(1 - 0.10)

100

LCL = 0.010

LCL = p- 3 = 0.10 - 3p(1 - p)

n

0.10(1 - 0.10)

100LCL = p- 3 = 0.10 - 3

p(1 - p)

n

0.10(1 - 0.10)

100


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0.120.12

0.140.14

0.160.16

0.180.18

0.200.20

efective

efective

UCL = 0.190

0.120.12

0.140.14

0.160.16

0.180.18

0.200.20

efective

efective

UCL = 0.190

0.120.12

0.140.14

0.160.16

0.180.18

0.200.20

efective

efective

UCL = 0.190

19

0.020.02

0.040.04

0.060.06

0.080.08

0.100.10

Proportion

Proportion

Sample numberSample number

22 44 66 88 1010 1212 1414 1616 1818 2020

LCL = 0.010

p = 0.10

0.020.02

0.040.04

0.060.06

0.080.08

0.100.10

Proportion

Proportion


22 44 66 88 1010 1212 1414 1616 1818 2020

LCL = 0.010

p = 0.10

0.020.02

0.040.04

0.060.06

0.080.08

0.100.10

Proportion

Proportion


22 44 66 88 1010 1212 1414 1616 1818 202022 44 66 88 1010 1212 1414 1616 1818 2020

LCL = 0.010

p = 0.10


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Example of Attribute Control Chart C chartA new hotel had completed. The hotel room need to beinspected before ready for opening. The quality inspectorcollected the following data:

Number of defects in 15 sample roomsNumber of defects in 15 sample rooms

NUMBEROF

Number of defects in 15 sample roomsNumber of defects in 15 sample rooms

NUMBEROF

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1 121 12

2 82 8

3 163 16

: :: :

: :: :15 1515 15

190190

cc= = 12.67= = 12.67190190

1515

UCLUCL == cc++ zzcc= 12.67 + 3 12.67= 12.67 + 3 12.67= 23.35= 23.35

LCLLCL == cc++ zzcc= 12.67= 12.67 -- 3 12.673 12.67= 1.99= 1.99

1 121 12

2 82 8

3 163 16

: :: :

: :: :15 1515 15

190190

cc= = 12.67= = 12.67190190

1515cc= = 12.67= = 12.67190190

1515cc= = 12.67= = 12.67190190

1515190190

1515

UCLUCL == cc++ zzcc= 12.67 + 3 12.67= 12.67 + 3 12.67= 23.35= 23.35

UCLUCL == cc++ zzcc= 12.67 + 3 12.67= 12.67 + 3 12.67= 23.35= 23.35

LCLLCL == cc++ zzcc= 12.67= 12.67 -- 3 12.673 12.67= 1.99= 1.99

where

c= number of defects per sample


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1818

2121

2424

efects

efects

UCL = 23.35

c= 12.671818

2121

2424

efects

efects

UCL = 23.35

c= 12.671818

2121

2424

efects

efects

UCL = 23.35

c= 12.67

Example of Attribute Control Chart C chart

21

33

66

99

1212

Number

of

Number

of


22 44 66 88 1010 1212 1414 1616

LCL = 1.9933

66

99

1212

Number

of

Number

of


22 44 66 88 1010 1212 1414 1616

LCL = 1.9933

66

99

1212

Number

of

Number

of


22 44 66 88 1010 1212 1414 161622 44 66 88 1010 1212 1414 1616

LCL = 1.99


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Example of Attribute Control Chart U chartA transcription company wants to assess its accuracy. Ananalyst samples transcribed documents and counts the numberof errors. Because the number of pages in each document

differs, comparing the number of errors across documents ismisleading. Instead, the analyst compares the average errorsper page.

Number of defects in 27 documentsNumber of defects in 27 documents

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NO. OFNO. OF NO OFNO OF AVE. ERRORAVE. ERRORPAGESPAGES DEFECTIVESDEFECTIVES PER PAGEPER PAGE

3232

2626

4949..

..

__

901901

77

66

99..

..

__

219219

.21.21

.23.23

.19.19..

..

..

0 . 5 2 7 5

0 . 5 2 7 5 0 . 5 2 7 5

0 . 5 2 7 5

2 7

2 72 7

2 7

0 . 2 4 3

0 . 2 4 3 0 . 2 4 3

0 . 2 4 3

3

33

30 . 2 4 3

0 . 2 4 3 0 . 2 4 3

0 . 2 4 3

n

nn

n

u

uu

u

3

33

3u

uu

uU C L

U C LU C L

U C L

=+=

+=

0

00

0O R

O RO R

O R0 . 0 4 1 6

0 . 0 4 1 6 0 . 0 4 1 6

0 . 0 4 1 6

2 7

2 72 7

2 7

0 . 2 4 3

0 . 2 4 3 0 . 2 4 3

0 . 2 4 3

3

33

30 . 2 4 3

0 . 2 4 3 0 . 2 4 3

0 . 2 4 3

n

nn

n

u

uu

u

3

33

3u

uu

uL C L

L C LL C L

L C L

==

=

0 . 2 4 3

0 . 2 4 3 0 . 2 4 3

0 . 2 4 3

9 0 1

9 0 19 0 1

9 0 1

2 1 9

2 1 9 2 1 9

2 1 9

n

nn

n

c

cc

c

u

uu

u ==

=


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Quality Control

Example of Attribute Control Chart U chart

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What is acceptance sampling?

Acceptance Sampling

A form of inspection that is used to determine whether ornot goods are coherent with a set standard of quality.

A Statistical quality control technique, where a random

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samp e s a en rom a o , an upon e resu s o e

sample taken the lot will either be rejected or accepted.

To determine the quality level of an incoming shipment or,at the end production.

To ensure that the quality level is within the level that hasbeen predetermined


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Quality Assurance

What is acceptance sampling?

The decision to accept or reject the shipment is based on the

following set standards:

Lot size = N Sample size = n Acceptance number = c

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e ec ve ems =

If d c, reject lot


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How/When use acceptance sampling?

Determine how many units, n, to sample from an lot.

Determine maximum number of defective items, d, that can befound before the lot is rejected.

Determine the acceptance level to know the acceptance

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num er, c.

If d c, reject lot.


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At any point in production.

The output of one stage is the input of the next.

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Quality Assurance


Sampling at the Process stage Can help adjust the process and reduce the amount of poor

quality in production.

Helps to determine the source of bad production andenables return for reprocessing before any further costsmay be incurred.

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Quality Assurance

Acceptance sampling explained

Acceptable Quality levels(AQL) -Number of defect percentage allowed in a lot which can still beconsidered accepted(Type I error).

Lot Tolerance Percent Defective(LTPD) -Amount of defects that will come with a lot of goods(Type IIerror).

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Sampling Plan -Forms after n and c values have been found.

Producers risk -Risk associated with a lot of acceptable quality rejected.

Consumers risk -Receive shipment, assume good quality, actually bad quality.


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Alpha (())

Type I error(producers risk).

Beta ()() -Type II error(consumers risk)

Quality Assurance

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N -

Sample size taken for your sampling plan.

C -Where rejections would occur when defects exceeded thispercent


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Attributes(go no-go) -Defectives-product acceptability across rangeDefects-number of defects per unit

Variable(continuous) -Usually measured by mean and standard deviation

Quality Assurance

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RememberYou are not measuring the quality of the lot, but, you are tosentence the lot to either reject or accept it


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Quality Assurance


ANSI/ASQC Z1.4. Sampling Procedures and Tables forInspection by Attributes(Replaced MIL-STD-105E, Sampling Procedures and Tables forInspection by Attributes )

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Quality Assurance


ANSI/ASQC Z1.4. Sampling Procedures and Tables forInspection by Attributes

Acceptance sampling system for inspection by attributes. It isindexed in terms of acceptance quality level (AQL).

Aim - to induce a supplier to maintain a process average at

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eas as goo as e spec e accep ance , w s a e

same time providing an upper limit for the risk to the consumerof accepting the occasional poor lot.

Applicable to end products, raw materials, operations,maintenance operations, administrative procedures.


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Quality Assurance



Normal inspection -Use of a sampling plan with acceptance criteria devised tosecure the producer a high probability of acceptance when theprocess average of the lot is better than the AQL. Used when

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ere s no reason o suspec e process

average differs from an acceptable level.

Tightened inspection Use of a sampling plan with an acceptance criteria that istighter than that for the corresponding plan for normalinspection. Invoked when the inspection results of consecutivelots indicate that the process average might be poorer than theAQL.


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Quality Assurance



AQL values shall not exceed 10% nonconforming.

When the quality level is expressed as number ofnonconformities per 100 items, AQL values up to 1000

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noncon orm es per ems may e use .

Sampling: Sample selection must be drawn from the lot by simple

random sampling.

When double or random sampling is to be used, eachsubsequent sample shall be selected from the remainder ofthe same lot.


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Quality Assurance



4 special inspection levels - S1, S2, S3, S4.

3 general inspection levels - I, II, III.

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pec a nspec on eve s use w en samp e s ze mus e ep

small and larger sampling risks can be tolerated.

Level II will be used unless another inspection level is specified.

Level I is used when less discrimination is required, Level IIIwhen greater discrimination is required.


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Quality Assurance



Sample Size Code Letter ChartGeneral Inspection Levels

Lot or batch size I II III2 8 A A B

9 15 A B C

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16 25

26 50 C D E51 90 C E F

91 150 D F G

151 280 E G H

281 500 F H J

501 1,200 G J K

1,201 3,200 H K L

3,201 10,000 J L M

10,001 35,000 K M N

35,001 150,000 L N P

150,001 500,000 M P Q

500,001 and Over N Q R


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Quality Assurance



Single Sampling Plan

Acceptable quality levels ( norm al inspection )

S ample 0.010 0.015 0.025 0.040 0.065 0.10 0.15 0.25 0.40 0.65 1.0 1.5 2.5 4.0 6.5 10 15

Size

Co de S am ple Ac Rc Ac Rc Ac Rc Ac Rc Ac Rc Ac Rc Ac Rc Ac Rc Ac Rc Ac Rc Ac Rc Ac Rc Ac Rc Ac Rc Ac Rc Ac Rc Ac Rc

Acceptable Quality Level Chart

45

Letter S ize

A 2 0 1

B 3 0 1 1 2

C 5 0 1 1 2 2 3

D 8 0 1 1 2 2 3 3 4

E 13 0 1 1 2 2 3 3 4 5 6

F 20 0 1 1 2 2 3 3 4 5 6 7 8

G 32 0 1 1 2 2 3 3 4 5 6 7 8 10 11

H 50 0 1 1 2 2 3 3 4 5 6 7 8 10 11 14 15

J 80 0 1 1 2 2 3 3 4 5 6 7 8 10 11 14 15 21 22

K 125 0 1 1 2 2 3 3 4 5 6 7 8 10 11 14 15 21 22

L 200 0 1 1 2 2 3 3 4 5 6 7 8 10 11 14 15 21 22

M 315 0 1 1 2 2 3 3 4 5 6 7 8 10 11 14 15 21 22

N 500 0 1 1 2 2 3 3 4 5 6 7 8 10 11 14 15 21 22

P 800 0 1 1 2 2 3 3 4 5 6 7 8 10 11 14 15 21 22

Q 1,250 0 1 1 2 2 3 3 4 5 6 7 8 10 11 14 15 21 22

R 2,000 1 2 2 3 3 4 5 6 7 8 10 11 14 15 21 22

li


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Quality Assurance



Double Sampling Plans

Normal Inspection

46

Acc Re Acc Re Acc Re Acc Re Acc Re

G First 20 20 0 2 0 3 1 3 2 5 3 6

G Second 20 40 1 2 3 4 4 5 6 7 9 10

H First 32 32 0 3 1 3 2 5 3 6 5 9

H Second 32 64 3 4 4 5 6 7 9 10 12 13

J First 50 50 1 3 2 5 3 6 5 9 7 11

J Second 50 100 4 5 6 7 9 10 12 13 18 19

Code

letterSample

Sample

Size

Cumu.

Samples

AQL

1.5 2.5 4 6.5 10

Q li A


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Quality Assurance



ExampleA lot containing 210 units is to be inspected using GeneralInspection Level II to a 2.5% AQL. Single sampling is to beused.

47

Find the lot sizeThe sample size code is G

The sampling plan for sample size code letter G is32 and we accept the lot if 2 or lower not acceptable itemsare found. We reject the lot if 3 or more not acceptable

items are found.

(Slide 44)

(Slide 45)


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Q lit A


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Quality Assurance


MIL-STD-1235C, Single and Multi-Level Continuous SamplingProcedures and Tables for Inspection by Attributes

The Continuous Sampling Plan CSP-1 is for application tocontinuous production meaning that each unit is inspectedas it is produced.

49

pp ca on o s samp ng me o s s or pro uc ow ng

in assembly-line fashion.

The sampling plan also requires a homogeneous productionsystem.

Q alit Ass ance


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Quality Assurance



All units must be made in accordance with the samespecification under stable conditions of production (instatistical control).

50

ny n errup on n e pro uc on process, e e c ange

of material source, change of tooling, or stopping ofproduction, is assumed to terminate a production runhomogeneous conditions.

Quality Assurance


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Quality Assurance



The operating procedure of the CSP-1 plan as stated by Dodgeis as follows :

a) At the outset , inspect 100% of the units consecutively as

51

pro uce an con nue suc nspec on un un s n

succession are found clear of defects.

b) When i units in succession are found clear of defects,discontinue 100% inspection, and inspect only a fraction fof the units, selecting individual units one at a time fromthe flow of product, in such a manner as to ensure an

unbiased sample.

Quality Assurance


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Quality Assurance



c) If a sample unit is found defective, revert immediately to a100% inspection of succeeding units and continue until again iunits in succession are clear of defects, as in step (a).

52

orrec or rep ace w goo un s, a e ec ve un s oun .

Thus, the CSP-1 plans are characterised by two parameters iand f.

The i and fvalue can be find in the standard.

Quality Assurance


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Quality Assurance



Inspection 100%

53

i consecutive

acceptable

Inspect fractionf of

the unit

Defect found

in sample

Yes

Yes

No

No

Quality Assurance


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Quality Assurance



The condition that must exist before the sampling plans may beused are: Moving product.

54

mp e space, equ pmen an manpower a or near e s e

of inspection to permit rapid 100% inspection whenrequired.

Relatively easy and quick inspection.

A process which is producing, or is capable of producing

material whose quality is stable.

The inspection is non-destructive.

Quality Assurance


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Quality Assurance



Current practice in Times Printers as follow:

1. Continuous inspection 100% of first 36 bundles.

55

. no e ec s are oun , ran om y samp e un e ou o

bundles.

3. Whenever a defect is found, correct the flaw and repeatstep 1.

Quality System & Tools


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Total Quality Management (or TQM)

TQM is a business philosophy - a way of doing business coinedby W. Edwards Deming. It describes ways to managing

people and business processes to ensure complete customersatisfaction at every stage.

TQM is often associated with the phrase - "doing the right

56

ngs r g , rs me . s rev s on no e summar ses e ma n

features of TQM.

TQM recognises that all businesses require "processes" thatenable customer requirements to be met. TQM focuses on theways in which these processes can be managed - with two keyobjectives:

1. 100% customer satisfaction

2. Zero defects

Q lit S t & T l


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Quality System & ToolsTotal Quality Management (or TQM)

A core concept in implementing TQM is Demings 14 points, aset of management practices to help companies increase theirquality and productivity:

1. Create constancy of purpose for improving products andservices.

57

. op e new p osop y.

3. Cease dependence on inspection to achieve quality.

4. End the practice of awarding business on price alone;instead, minimize total cost by working with a singlesupplier.

5. Improve constantly and forever every process for planning,production and service.

6. Institute training on the job.



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7. Adopt and institute leadership.

8. Drive out fear.

9. Break down barriers between staff areas.

10. Eliminate slogans, exhortations and targets for the workforce.

58

11. Eliminate numerical quotas for the workforce and numericalgoals for management.

12. Remove barriers that rob people of pride of workmanship, andeliminate the annual rating or merit system.

13. Institute a vigorous program of education and self-improvementfor everyone.

14. Put everybody in the company to work accomplishing thetransformation.



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Main Principles of TQM

The main principles that underlie TQM are summarised below:

Prevention Prevention is better than cure. In the long run, it ischeaper to stop products defects than trying to find them

Zero defects: The ultimate aim is no (zero) defects - or

59

excep ona y ow e ec eve s a pro uc or serv ce s

complicated

Getting things right first time: Better not to produce at allthan produce something defective

Quality involves everyone: Quality is not just the concern of

the production or operations department - it involves everyone,including marketing, finance and human resources



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Main Principles of TQM (Contn)

Continuous improvement: Businesses should always be

looking for ways to improve processes to help quality

Employee involvement: Those involved in production andoperations have a vital role to play in spotting improvement

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oppor un es or qua y an n en y ng qua y pro ems



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TQM Wheel

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Q y y

ISO 9001 : 2008

ISO 9000 is a family of standards for quality managementsystems. ISO 9000 is maintained by ISO, the InternationalOrganization for Standardization and is administered by

accreditation and certification bodies. The rules are updated, asthe requirements motivate changes over time. Some of therequirements in ISO 9001:2008 (which is one of the standardsin the ISO 9000 family) include

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a set of procedures that cover all key processes in the business; monitoring processes to ensure they are effective; keeping adequate records; checking output for defects, with appropriate and corrective

action where necessary; regularly reviewing individual processes and the quality system

itself for effectiveness; and facilitating continual improvement


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Q y y

ISO 9001 : 2008

ISO 9001:2000, the requirement standard, includes thefollowing main sections:

Quality Management System

Management Responsibility

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Resource Management

Product Realization

Measurement Analysis and Improvement



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Q y y

Seven Basic Tool of Quality

The Seven Basic Tools of Quality is a designation given to afixed set of graphical techniques identified as being most helpfulin troubleshooting issues related to quality.

They are called basic because they are suitable for people withlittle formal training in statistics and because they can be usedto solve the vast majority of quality-related issues.

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The tools are:

1. The cause-and-effect (Ishikawa diagram or fishbonechart): Identifies many possible causes for an effect orproblem and sorts ideas into useful categories.

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. e c ec s ee : s ruc ure , prepare orm or co ec ngand analyzing data; a generic tool that can be adapted for awide variety of purposes.

3. The control chart: Graphs used to study how a processchanges over time.

4. The histogram: The most commonly used graph for showingfrequency distributions, or how often each different value in aset of data occurs.


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1. The cause-and-effect (Ishikawa diagram or fishbonechart):

ResponsivenessAppearance

time

ersonnel

equipment

ResponsivenessAppearance

time

ersonnel

equipment

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Poor Service

Attention Reliability

courtesy

facility

One on one

servicedependability

accuracy Poor Service

Attention Reliability

courtesy

facility

One on one

servicedependability

accuracy



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Constructing a Cause and Effect Diagram

First, clearly identify and define the problem or effect for whichthe causes must be identified. Place the problem or effect atthe right or the head of the diagram.

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en y a e roa areas o e pro em.

Write in all the detailed possible causes in each of the broadareas.

Each cause identified should be looked upon for further morespecific causes.

View the diagram and evaluate the main causes.

Set goals and take action on the main causes.



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2. The check sheet:

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Simplifies data collection or analysis.

Arrange data automatically so that they can be used easily atlater stage.


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3. The control chart:

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Helps in reducing the process variability Performance monitoring over time Out-of-control and a particular trend is immediately known Helps in deciding the quality of out coming samples of a lot



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Steps Used in Developing Process Control Charts:

1. Identify critical operations in the process where inspectionmight be needed.

2. Identify critical product characteristics.

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3. Determine whether the critical product characteristic is avariable or an attribute.

4. Select the appropriate process control chart.

5. Establish the control limits and use the chart to monitor and

improve.

6. Update the limits.



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4. The histogram:

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r e d

Histogram

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50

60

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r e d

Histogram

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Creating a Histogram

1. Collect data and sort it into categories.

2. Then label the data as the independent set or the dependentset.

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3. The characteristic you grouped the data by would be theindependent variable.

4. The frequency of that set would be the dependent variable.

5. Each mark on either axis should be in equal increments.

6. For each category, find the related frequency and make thehorizontal marks to show that frequency.



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5. The Pareto chart:

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Constructing a Pareto Chart:

1. First, information must be selected based on types or

classifications of defects that occur as a result of a process.

2. The data must be collected and classified into categories.

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. en a s ogram or requency c ar s cons ruc e s ow ng enumber of occurrences.



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6. The scatter diagram:

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Stratification (alternately flow chart or run chart):

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Creating a Flow Chart:

1. First, familiarize the participants with the flow chart symbols.

2. Draw the process flow chart and fill it out in detail about each

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e emen .

3. Analyze the flow chart. Determine which steps add value andwhich dont in the process of simplifying the work.



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Stratification (alternately flow chart or run chart):

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seven qc tool and more

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