L9 Quality Mgmt

8/3/2019 L9 Quality Mgmt

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Total Quality Management What is Quality? Quality Characteristics

Quality Specifications Types of Variation Types of Defects Stages in Inspection

Scope of Inspection Methods /Types of

Inspection Statistical Process Control Quality Control Charts Process Capability Index Why Acceptance Sampling?

Sampling Plans O. C. Curves

Levels of Inspection Costs of Quality

Organization Structure ISO 9002 and Six Sigma Definition of Total

Quality Management Objectives of TQM Principles of TQM Pre requisites for

implementation of TQM Elements of QA System Quality control tools Evaluation of Quality

Control Deptt


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What goes into a buying decision?

Aesthetic Look Fitness for Use Suitability from customers point of view Brand Image

Reliability Durability After sales service and Price


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What is Quality?Quality means:-

Customer SatisfactionFitness for UseConformance to customers well definedneeds, perceptions, use or purpose

Conformance to laid down performancespecifications of the product / serviceConformance to laid down manufacturingspecifications and tolerances of the product

Meeting safety standards equipment, userand othersReliability Durability and

Ease of Repair and service


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Quality

Characteristics

Measurable Non measurable


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Measurable Characteristics Distances: Diameter, Length, Height, Width,

depth Surface finish Voltage Current Weight Pressure

Gloss level Electroplating thickness Impact resistance etc.


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Comparison Between Random & Non Random Variation

RANDOM

Also known as Chance / natural / commonvariation

Inherent in every process Pattern is the cumulative

effect of multiplicity ofminor causes operatingindependently andfollowing the normal lawof errors

Not traceable to anycause and hence beyondthe control ofmanagement

NON RANDOM

Also known as chaotic orerratic variation There is always some

assignable cause Cause can creep in at

any stage of the process Cause of variation is notdistributed Normallyrather Sporadically

Controllable


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What is a defect? A Defect refers to the observed / measured

quality characteristic to be outside the

permissible tolerance limits.A Defective refers to a product which is not

acceptable across a range of characteristics.


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DEFECT

MINOR MAJOR CRITICAL


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DEFECT

NOT SOSERIOUS SERIOUS CRITICAL


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STAGES IN INSPECTION Receiving Inspection Stage

In Process Inspection Stage- Component wise- Sub Assembly wise- Assembly wise- Process wise

Finished Goods Inspection Stage /

Final Inspection Stage Pre Shipment Inspection Stage and Post Shipment Inspection Stage


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Statistical Process Control.. contd.

A process is said to be under control if it is operating in the presence of variation with chance causes and is free from any assignable cause variation.


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UCL

LCL

Samplesover time

1 2 3 4 5 6

Statistical ProcessControl Chart

99.7%LCL UCLX


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Control Charts Control limits are used to evaluate the variations

in quality from sample to sample and aredifferent from specification limits. When the process is under control:

- 2/3 rd of sample points will be near the centre line

- Some of the points will be closer to UCL / LCLlines- The points are located back and forth across thecentre line

- The points are balanced on both sides of thecentre line and - 99.7% points lie within the control limit lines


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UCL

LCL

Samplesover time

1 2 3 4 5 6

UCL

LCL

Samplesover time

1 2 3 4 5 6

UCL

LCL

Samplesover time

1 2 3 4 5 6

Normal Behavior

Possible problem, investigate

Possible problem, investigate

Study of SPC Charts

Steady Trend: Possible problem,investigate


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SPC STEPS

Produce Goods Provide Service

Stop Process

Yes

No

Assign. Causes? Take Sample

Inspect Sample

Find Out Why Create

Control Chart

Start

Eliminate theCause


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Distributionof X

X

Distributionof X

X

UCL

LCL

Case 2:X bar Control chart(when Mean of the population is not known

but X SD of population is known)

Std dev of sample means = Std dev of population / n

Control Limits = X z /2 X

X = ( X / n)


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X bar and R chart Control Limits

Case 3 : Population mean & Std. Dev. is unknown

R = ( R i ) / m i = 1m

R = xmax - xmin


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X = x / n

R = ( R i ) / m i = 1m

x = R / d 2

R = (R/d 2 ) d 3

R = xmax - xmin

X = {(xi x ) 2}/(m-1)


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X = x / n

Control Limits X = X 3 X

Control Limits R = R 3 R

R = ( R i ) / m i = 1m

x = R / d 2

R = (R/d 2 ) d 3

R = xmax - xmin

X = {(xi x ) 2}/(m-1)


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Case 3 : Populationmean & Std. Dev. isunknown.short

cut method A2 = 3 / (d 2 n )

D 3 = 1 - 3 d 3 / d 2 and

D 4 = 1 + 3 d 3 / d 2

RA-x=LCL

RA+x=UCL

2

2

LimitsControlChartx

R Chart Control Limits

UCL = D R

LCL = D R

4

3


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n c4 d2 d3

2 0.7979 1.128 0.853

3 0.8862 1.693 0.888

4 0.9213 2.059 0.880

5 0.9400 2.326 0.864

6 0.9515 2.534 0.848

7 0.9594 2.704 0.833

8 0.9650 2.847 0.820

9 0.9693 2.970 0.808

10 0.9727 3.078 0.797

11 0.9754 3.173 0.787

12 0.9776 3.258 0.778

13 0.9794 3.336 0.77014 0.9810 3.407 0.763

15 0.9823 3.472 0.756

Values for c 4 , d 2 , d3 as a function of n aretabulated below


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D3 = 1 - 3 d 3 / d 2 and D4 = 1 + 3 d 3 / d 2

A2 = 3 / (d 2 n )

n A 2 D3 D4

2 1.88 0 3.267

3 1.023 0 2.575

4 0.729 0 2.282

5 0.577 0 2.115

6 0.483 0 2.004

7 0.419 0.076 1.9248 0.373 0.136 1.864

9 0.337 0.184 1.816

10 0.308 0.223 1.777


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How to design X and R chart ? Plot the Mean values on X bar chart and R

values on R chart for the samples. If the values so plotted are within the limits

calculated, the process is under control andthe limits decided could be used for control of

process. If any plotted value does not fall within the

limits decided, delete that observation andrecalculate the values of mean, S.D. and henceUCL and LCL.

Repeat the process till all values are within thelimits of control chart lines.


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How to use X bar and R chart ? X bar and R chart have to be used

simultaneously for the same set ofobservations When X bar chart goes out of limit and R

chart stays put, it indicates the process hasshifted to a new average.

If the change is sudden, the cause may also besudden e.g.

- process picks up dust suddenly.- tool wears out due to hard spots in thematerial being machined and

- use of a new batch of material etc.


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p Chart(also known as proportion defective chart or% defective chart)

c Chart

(also known as count of defects chart)

Attribute Control Charts

h


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P - Chart

p =T o t a l N u m b e r o f D e fe c tiv e s

T o ta l N u m b e r o f O b s e rv a tio n s

n

s)p-(1p

=p

p

p

z-p=LCL

z+p=UCLs

s

Out of a lot of N, take a sample of n, observe thenumber of defectives d in each sample. Repeat for msuch samples. Calculate p , p , s p

Compute control limits:


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2. Calculate the average of the sample proportions

0.036=

1500

55 =p

3. Calculate the standard deviation of thesample proportion

.0188=100

.036)-.036(1=

)p-(1p =p

n

s

Example 1 of Constructing a p -chart:Steps 2&3


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4. Calculate the control limits

3(.0188).036

UCL = 0.0924LCL = -0.0204 (or 0)

p

p

z-p=LCL

z+p=UCL

s

s

Example of Constructing a p -chart:Step 4


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S p = p (1 p ) / n = 0.069UCL = 0.05 + 3*0.069 = 0.05 + 0.207 = 0.257LCL = 0.05 3*0.069 = 0.05 0.207 = 0For np chart: UCL = np + 3 np . (1 p )

= 10*0.05 + 30.5*0.95 = 0.5 + 3*0.69 = 2.57

LCL = 0.5 2.07 = 0

Solution : Estimate of p = 50/ 1000 = 0.05


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C - Chart Used in process control of:

- Typing Errors- QC in textile industry (defects per metre ofcloth)

- Final Inspection of ACs, Refrigerators,Cars, Aero planes, Two wheelers, Batteries,Furniture, Amplifiers etc.


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C - Chart Defects in each sample are counted and

recorded c i No of samples observed = n Average number of defects in a sample c

C = { ci} nSD = cControl Chart Limits = c 3 c

l 3 f d h


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Example 3 of X and R chartAn automatic continuous blending processneeds to be controlled for the acidity ofoutput. 10 samples of 3 readings each weretaken on Monday morning when process wasrunning smoothly.Design a suitable process control chart forSPC by QC department.

Sample 1 2 3 4 5 6 7 8 9 10

Obs15.32 5.28 5.33 5.40 5.31 5.34 5.35 5.27 5.40 5.33

Obs2 5.29 5.41 5.37 5.29 5.40 5.27 5.33 5.38 5.41 5.37

Obs3 5.38 5.40 5.30 5.32 5.39 5.29 5.31 5.36 5.38 5.42


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Solution to Example 3 of X and R chartSample

1 2 3 4 5 6 7 8 9 10

Obs1 5.32 5.28 5.33 5.40 5.31 5.34 5.35 5.27 5.40 5.33Obs2 5.29 5.41 5.37 5.29 5.40 5.27 5.33 5.38 5.41 5.37

Obs3 5.38 5.40 5.30 5.32 5.39 5.29 5.31 5.36 5.38 5.42X = x / n

5.330 5.363 5.333 5.337 5.367 5.300 5.330 5.337 5.396 5.373

R 0.09 0.13 0.07 0.11 0.09 0.07 0.04 0.11 0.03 0.09

x = 53.466 X = 5.347, R = 0.83 R = 0.083 SD of X ={(Xi X)2 (k-1)} = 0.02933UCL = 5.347 + 3*0.02933= 5.347 + 0.088 = 5.435LCL = 5.347 0.088 = 5.259


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X - Chart

5.15

5.2

5.25

5.3

5.35

5.4

5.45

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

X=x/n

Example 3 contd.


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R - Chart

0

0.05

0.1

0.15

0.20.25

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

R

Example 3 contd.


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Alternate Solution to Example 3 of X and R chart

For n = 3, from tables for computing chartlines, values of A2, D3, D4 are read.A2 = 1.023, D3 = 0, D4 = 2.575For X Chart:UCL = 5.347 + 1.023 x 0.083 = 5.432LCL = 5.347 - 1.023 x 0.083 = 5.262For R Chart:UCL = 2.575 x 0.083 = 0.214LCL = 0

l f b d h d


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Example 4 of x-bar and R Charts: Required Data

Sample Obs 1 Obs 2 Obs 3 Obs 4 Obs 51 10.68 10.689 10.776 10.798 10.714

2 10.79 10.86 10.601 10.746 10.7793 10.78 10.667 10.838 10.785 10.7234 10.59 10.727 10.812 10.775 10.735 10.69 10.708 10.79 10.758 10.6716 10.75 10.714 10.738 10.719 10.606

7 10.79 10.713 10.689 10.877 10.6038 10.74 10.779 10.11 10.737 10.759 10.77 10.773 10.641 10.644 10.72510 10.72 10.671 10.708 10.85 10.71211 10.79 10.821 10.764 10.658 10.708

12 10.62 10.802 10.818 10.872 10.72713 10.66 10.822 10.893 10.544 10.7514 10.81 10.749 10.859 10.801 10.70115 10.66 10.681 10.644 10.747 10.728

Example 4 of x-bar and R charts: Step 1. Calculate


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Example 4 of x bar and R charts: Step 1. Calculatesample means, sample ranges, mean of means,and mean of ranges.

Sample Obs 1 Obs 2 Obs 3 Obs 4 Obs 5 Avg Range

1 10.68 10.689 10.776 10.798 10.714 10.732 0.1162 10.79 10.86 10.601 10.746 10.779 10.755 0.2593 10.78 10.667 10.838 10.785 10.723 10.759 0.1714 10.59 10.727 10.812 10.775 10.73 10.727 0.2215 10.69 10.708 10.79 10.758 10.671 10.724 0.1196 10.75 10.714 10.738 10.719 10.606 10.705 0.143

7 10.79 10.713 10.689 10.877 10.603 10.735 0.2748 10.74 10.779 10.11 10.737 10.75 10.624 0.6699 10.77 10.773 10.641 10.644 10.725 10.710 0.13210 10.72 10.671 10.708 10.85 10.712 10.732 0.17911 10.79 10.821 10.764 10.658 10.708 10.748 0.16312 10.62 10.802 10.818 10.872 10.727 10.768 0.25013 10.66 10.822 10.893 10.544 10.75 10.733 0.34914 10.81 10.749 10.859 10.801 10.701 10.783 0.15815 10.66 10.681 10.644 10.747 10.728 10.692 0.103

Averages 10.728 0.220400

E l 4 f b d R h S 2


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Example 4 of x-bar and R charts: Step 2.Determine Control Limit Formulas and NecessaryTabled Values

x Chart Control Limits

UCL = x + A R

LCL = x - A R

2

2

R Chart Control Limits

UCL = D R

LCL = D R

4

3

n A2 D3 D4

2 1.88 0 3.267

3 1.023 0 2.575

4 0.729 0 2.2825 0.577 0 2.115

6 0.483 0 2.004

7 0.419 0.076 1.924

8 0.373 0.136 1.8649 0.337 0.184 1.816

10 0.308 0.223 1.777

11 0.29 0.26 1.74

Example 4 of x bar and R charts: Steps 3&4


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Example 4 of x-bar and R charts: Steps 3&4.Calculate x-bar Chart and Plot Values

10.601

10.856

=).58(0.2204-10.728RA-x=LCL=).58(0.2204-10.728RA+x=UCL

2

2

10.550

10.600

10.650

10.700

10.750

10.800

10.850

10.900

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

M e a n s

Sample

UCL

LCL


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Example 5 on X & R - Chart

X = / n

R = ( R i ) / m i = 1m

= R / d 2

R = (R/d 2 ) d 3

1 2 3 4 5 6 7 8 9 101 14.700 14.310 14.673 16.661 13.556 12.422 15.694 15.072 17.206 152 13.722 13.310 14.630 13.388 14.153 16.448 15.323 15.830 16.444 153 15.244 13.153 16.343 15.539 13.478 13.720 14.060 15.862 16.304 154 16.276 14.022 14.915 15.902 14.637 14.346 14.759 14.363 15.113 135 16.198 14.226 14.814 16.919 14.968 15.758 15.132 14.077 15.002 166 16.733 12.882 14.487 14.915 15.028 15.467 15.558 16.111 15.454 147 12.816 14.432 16.972 14.476 14.677 15.875 15.139 13.799 14.974 14

8 14.766 14.596 15.866 15.675 17.195 15.596 14.089 13.441 13.945 149 16.095 15.135 17.376 14.619 13.258 13.628 16.885 15.711 13.225 1410 13.913 14.635 14.345 15.758 14.264 13.884 15.487 15.638 15.828 1

Avg 15.046 14.070 15.442 15.385 14.521 14.714 15.212 14.991 15.350 1Range 3.917 2.253 3.031 3.531 3.937 4.025 2.825 2.670 3.980 2.2

SamplesObservation

d2 = 3.078, d 3 = 0.797R = ( R i ) / m i = 1

m

Sol tion to E ample 5 on X & R Chart


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Solution to Example 5 on X & R - Chart

X = x / n

Mean X = 149.594 10 = 14.959Mean Range R = 32.434 10 = 3.243

R = (3.243 3.078) * 0.797 = 0.84 X = (3.243 3.078)/ 10 = 1.0534/3.1623

= 0.333For X chart, limits are 14.959 1 = 15.959 and 13.959For R chart, limits are 3.243 2.520 = 5.763 and 0.723

R = x . d 3 = (R/d2 ) . d3

I = m

R = ( R i ) / mI = 1


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14.959

15.959

3.959

UCL

MCL

LCL X Chart


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3.243

5.763

0.723

UCL

MCL

LCL

R - CHART

E l 6 f h t


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Example 6 for p - chart A visual inspection for paint quality of Gear

changing lever was carried out last week. For eachsample, 30 levers were inspected. Number of leversfound defective in each sample were recorded asgiven in the table.

This week 3 samples, each of 30 levers were takenon Monday, Tuesday and Wednesday. Number ofdefectives found are 6, 8 and 10 respectively.Comment.

Sample No 1 2 3 4 5 6 7 8 9 10

d 5 4 4 5 7 4 5 6 4 5Sample No 11 12 13 14 15 16 17 18 19 20

d 5 7 4 5 4 5 5 7 6 4

E l 6 f h t


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Example 6 for p - chart

11 12 13 14 15 16 17 18 19 20 21

5 7 4 5 4 5 5 7 6 4 11 0.167 0.233 0.133 0.167 0.133 0.167 0.167 0.233 0.200 0.133 0.373

Sample No 1 2 3 4 5 6 7 8 9 10

d 5 4 4 5 7 4 5 6 4 5

p 0.167 0.133 0.133 0.167 0.233 0.133 0.167 0.200 0.133 0.167


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Example 6 for p - chart

p =T o t a l N u m b e r o f D e fe c t iv e s

T o t a l N u m b e r o f O b s e rv a t io n s p (for last week) = [ d] n . m = [ p] m

= 101 600 = 0.1683 Sp = p (1 p ) / n = 0.0683 UCL = 0.1683 + 3 * 0.0683 = 0.1683 + 0.2049= 0.3732 LCL = 0.1683 - 3 * 0.0683 = 0.1683 - 0.2049

= - .0366 = 0Limits of chart are: 0, 0.1683, 0.3732

On plotting these three values on the control chart, we observethat though these observations lie within the limits, their trendindicate that problem is likely to brew in the near future.


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p - chart

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

p b

a

r

p

Example 7 for c chart


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Example 7 for c - chart

During inspection of 20 air crafts, the numberof missing rivets observed c were as givenbelow in the table.

Set up a suitable process control chart forquality control.

AircraftNo

1 2 3 4 5 6 7 8 9 10

c i 15 11 10 14 18 15 7 13 10 13AircraftNo

11 12 13 14 15 16 17 18 19 20

c i 20 22 12 14 16 8 17 16 15 14


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Solution to Example 7 for c - chart

C = { ci} n = 280 20 = 14SD = c = 3.742 Control Chart Limits = c 3 c

= 14 3 * 3.742 = 14 11.23= 25.23 and 2.77

Since all observations lie within UCL &LCL, these form the basis for thecontrol chart.


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Solution to Example 7 for c - chart

C Chart

0

5

10

15

20

25

1 3 5 7 9 11 13 15 17 19

Sample Number

C Series1

Modified Example for c chart


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Modified Example for c - chart

During inspection of 20 air crafts, the numberof missing rivets observed c were as givenbelow in the table.

Set up a suitable process control chart forquality control.

AircraftNo

1 2 3 4 5 6 7 8 9 10

c i 15 11 10 14 18 05 27 13 10 13AircraftNo

11 12 13 14 15 16 17 18 19 20

c i 20 22 12 14 26 8 7 06 15 14


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Solution to Modified Example for c - chart

C = { ci} n = 280 20 = 14SD = c = 3.742 Control Chart Limits = c 3 c

= 14 3 * 3.742 = 14 11.23= 25.23 and 2.77

Since all observations do not lie withinUCL & LCL, we delete two observationsand recalculate limits.



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c - chart

0

5

10

15

20

25

30

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

sample no.

ci

l d f d l f


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Solution to Modified Example forc chart.. contd.

Revised C = { ci} n = 227 18 = 12.61SD = c = 3.55 Control Chart Limits = c 3 c

= 12.61 3 * 3.55 = 12.61 10.65= 23.26 and 1.96

Since all observations lie within UCL &LCL, these form the basis for thecontrol chart.

Example for c - chart


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Example for c - chart

c - chart

0

5

10

15

20

25

30

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

sample no.

ci


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Basic Forms of Statistical Samplingfor Quality Control

Statistical Process Control is samplingto determine if the process is within

acceptable limits Acceptance Sampling is sampling to

accept or reject the immediate lot of

product at hand


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Why Acceptance Sampling?

To determine quality level

To ensure quality is within desired level

Ad t f A t S li


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Advantages of Acceptance SamplingOver all Economy due to:

Less handling damage Fewer inspectors Entire lot rejection (motivation for

improvement)

Highly desirable applicability to destructivetesting situations.

Acceptance Sampling (Continued)


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Acceptance Sampling (Continued)

Disadvantages Risks of accepting bad lots and rejecting

good lots Added planning and documentation Sample provides less information than

100-percent inspection


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SamplingPlans

SingleSampling

Plan

DoubleSampling

Plan

MultipleSampling

Plan

Single Sampling Plan


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Single Sampling Plan

A Single Sampling Plan is one whichdecision to accept or reject a lot istaken based upon the quality level of asingle sample drawn from the lot.

Sample size = n ,Maximum number of acceptabledefective items in the sample = c ,Criteria for rejection = c + 1

Double sampling plan


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Take a sample of size n1 and inspectfor the number of defectives

No. of defectives is

less than or equal toc1

Acceptthe lot

Take a second sample of size n2Does the number of defectives in

the combined sample of size n1 +n2 exceed c2 ?

Rejectthe lot

No. of defectives in thesample is between c1 and c2

No. of defectives

is greater thanc2

no yes


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Operating Characteristic Curve


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p g

n = 99

c = 4

AQL LTPD

00.1

0.20.30.40.50.60.70.80.9

1

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

Percent defective

P r o

b a

b i l i t y o

f a c c e p

t a n c e

=.10(consumers risk)

= .05 (producers risk)

The OCC brings the concepts of producers risk, consumersrisk, sample size, and maximum defects allowed together

The shapeor slope of the curve isdependenton aparticularcombinationof the four

parameters

Example: Acceptance Sampling


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ProblemZypercom, a manufacturer of video interfaces,purchases printed wiring boards from an outsidevender, Procard. Procard has set an acceptablequality level of 1% and accepts a 5% risk of rejecting

lots at or below this level. Zypercom considers lotswith 3% defectives to be unacceptable and will assumea 10% risk of accepting a defective lot.

Develop a sampling plan for Zypercom and determinea rule to be followed by the receiving inspectionpersonnel.

Example: Step 1 What is given and


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Example: Step 1. What is given andwhat is not?

In this problem, AQL is given to be 0.01 and LTPDis given to be 0.03. We are also given an alpha of0.05 and a beta of 0.10.

What you need to determine is your samplingplan i.e. c and n.

E ample: Step 2 Determine c


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Example: Step 2. Determine c First divide LTPD by AQL.

LTPD

AQL

=.03

.01

= 3

Then find the value for c by selecting the value from thetable in LTPD/AQLcolumn that is equal to or just greaterthan the ratio above. Also read the value n. AQL

Exhibit TN 7.10

c LTPD/AQL n AQL c LTPD/AQL n AQL

0 44.890 0.052 5 3.549 2.6131 10.946 0.355 6 3.206 3.2862 6.509 0.818 7 2.957 3.9813 4.890 1.366 8 2.768 4.6954 4.057 1.970 9 2.618 5.426

So, c = 6.

Example: Step 3 Determine Sample Size


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Example: Step 3. Determine Sample Size

c = 6, from Tablen (AQL) = 3.286, from TableAQL = .01, given in problem

Sampling Plan: Take a random sample of 329 units from a lot.Reject the lot if more than 6 units are defective.

Now given the information below, compute the sample

size in units to generate your sampling plan

n(AQL)/(AQL) = 3.286/.01 = 328.6, or 329 (always round up)


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Quality Costs - Categories

Control Costs Failure Costs

Prevention Appraisal Internal External

Quality Costs


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Quality Costs Categories


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Quality Costs - Categories Cost of Appraisal

Cost of inspectors and supervision Quality Audits Q C lab expenses

Incoming Inspection costs In-process inspection costs Finished goods and Pre-shipment

inspection costs Inspection at Vendor s end etc.



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Quality Costs - Categories Cost of Internal Failure

Cost of rejections Cost of reworking Scrap / rework of defectives from vendors

Cost of investigating into causes ofrejections / recalls / market returns

Cost of Trouble shooting

Warranty claims including cost ofreplacements / re-working



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Quality Costs - Categories Cost of External Failure

Transit Damages / pilferage Attending to Customer Complaints Warranty claims including cost of

replacements / re-working Replacement Inventories Loss of customer goodwill . etc.

Total Quality Management Defined


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Total Quality Management Defined

Total quality management may be defined as managing the entire organization so as to excel in all processes and aspects of products and services that are important to the customer.

Total Quality Management comprises of a setof all those activities, which are required to beperformed in order to provide total range ofproducts / services to the customer atcompetitive price to achieve the objective oftotal customer satisfaction .

Total Quality Management contd.


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Total Quality Management contd. Total Quality Management is a continuous

performance and not a programme Total Quality Management is an intensive and

long term effort at the creation andmaintenance of high standards of total quality of the product. This requires investing in peoplein accordance with HRM principles andestablishing quality control systems.

Total Quality Management is not simply amatter of demanding ever increasing qualitytarget levels and also can not be achieved onlythrough the application of quality control tools /techniques, but it is an attitude of mind whichleads to appropriate behaviour and actions withcommitment at every level to a zero defect

product.


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Principles of TQM


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Principles of TQM Prime Responsibility lies with the Top

Management:- Organization Structure- Product Design

- Manufacturing Process Planning- Quality of Inputs &- Control Systems

Quality must be customer focused No inspection department contd.

Principles of TQM contd


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Principles of TQM contd. Quality can t be inspected into a

product rather it should be producedright first time and each time.

Monitoring of quality, identifyingproblems if any & taking correctiveaction without any delay.

Focus on Continuous improvement and Organization culture Positive attitude

of mind, commitment at all levels

Elements of a Quality Assurance System


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Q y y

Understand

customer needs Translate them to

meaningfulmeasures for theoperating system

Mechanisms foridentifying

qualityproblems

Tools & techniquesfor the employees For tracking

problems to theirroot causes

Identifyingcorrective measures

Methods forpreventing

recurrence of problems

Documentation of all quality

relatedinitiatives for

continuouslearning &

improvement

Employeeinvolvement for

continuous focuson quality

improvement

QualityAssurance

System

QualityCertifications

& Benchmarkingexercises

TopManagementCommitment

to Quality

External Benchmarking Steps


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External Benchmarking Steps

1. Identify those processes needingimprovement

2. Identify a firm that is the world leader inperforming the process

3. Contact the managers of that company andmake a personal visit to interview managersand workers

4. Analyze data

L9 Quality Mgmt

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Transcript of L9 Quality Mgmt