7 qc tools

7 MOST IMPORTANT TOOLS

FOR CONTINUAL

IMPROVEMENT

7 QC TOOLS

Confederation of Indian Industry

7 MOST IMPORTANT TOOLS

FOR CONTINUAL

IMPROVEMENT

7 QC TOOLS

Systematic Problem Solvingand Deming Cycle

ACT PLAN

CHECK DO

• Plan :Setup the Objectives and Means• Do :Put the Plan into Practice• Check :Observe the Results and the Process• Act :Standardize if Results are Satisfactory

otherwise Re-plan and Follow the Cycle again

ACT PLAN

CHECK DO

Improvementactivities

DO improvement

PLAN improvement

CHECK ImprovementresultsD C

P A

Standardization

ACT to standardize orreplan

Focus onvital few

Managementstrategy

Initiateimprovement

The PDCA / SDCA Improvement Cycle

Daily work

ACT to improve thestandard or its use

CHECK the workagainst the standard

KNOW the STANDARD

A S

C DDO the workaccording to thestandard

Focus onvital few

Initiateimprovement

Basic Steps of Problem Solving

PLANDEFINITION

OBSERVATION

ANALYSIS

DO

CHECK

ACT

ACTION

CHECK

STANDARDISATION

CONCLUSION

Steps of Problem Solving1. Definition – Identifying and defining the problem2. Observation – Investigating the features of the problem3. Analysis – Finding the root causes4. Actions – Establishing and implementing remedies

(countermeasures)5. Check – Ensuring the effectiveness of remedies6. Standardization – Holding on the gains7. Conclusion – Reviewing the problem solving process and

future plans

1. Definition – Identifying and defining the problem2. Observation – Investigating the features of the problem3. Analysis – Finding the root causes4. Actions – Establishing and implementing remedies

(countermeasures)5. Check – Ensuring the effectiveness of remedies6. Standardization – Holding on the gains7. Conclusion – Reviewing the problem solving process and

future plans

Problem Solving StepsRecognizeProblem

Form qualityimprovement

teams

EvaluateSolution

Ensureperformance

DefineProblem

Continuousimprovement

ACT


EvaluateSolution

AnalyzeProblem

IdentifyPossibleSolutions

DeterminePossibleCauses

DefineProblem

ImplementSolution

PLAN

DO

CHECK

Problem Solving Process

Follow Up

SymptomSymptomRecognitionRecognition

FactFinding

ProblemIdentification


Follow UpIdea

Generation

ProblemIdentification

SolutionDevelopment

PlanImplementation

Benefits of Systematic Problem Solving Process

– It helps avoid jumping to conclusion

– Helps avoid decisions based on opinions andfeeling

– Helps the group in focusing their attention

– It provides a logic for remedies

– Sets a platform to involve all concerned

– Makes implementation simple

– It helps avoid jumping to conclusion

– Helps avoid decisions based on opinions andfeeling

– Helps the group in focusing their attention

– It provides a logic for remedies

– Sets a platform to involve all concerned

– Makes implementation simple

Summary

– QC story and the Basic Seven StepProblem Solving Process.

– Concept of PDCA and its adaptation toproblem solving in specific work situations.

– QC story and the Basic Seven StepProblem Solving Process.

– Concept of PDCA and its adaptation toproblem solving in specific work situations.

Technical Analysis and Statistical Analysis• If you see female Japanese assembly line workers without any technical

background whatsoever making suggestions even engineers haven’t been able to

think of, you will ask what makes it possible for those women to acquire their

technical knowledge. The answer is statistical methods.

• We have two methods of analyzing and eliminating trouble in the manufacturing

shop. One is by technological analysis; the other is by statistical analysis. QC

uses statistical methods to analyze and improve the quality of products.

• If you see female Japanese assembly line workers without any technical

background whatsoever making suggestions even engineers haven’t been able to

think of, you will ask what makes it possible for those women to acquire their

technical knowledge. The answer is statistical methods.

• We have two methods of analyzing and eliminating trouble in the manufacturing

shop. One is by technological analysis; the other is by statistical analysis. QC

uses statistical methods to analyze and improve the quality of products.

Technical Analysis and Statistical AnalysisProblem:

In a color TV factory, a female employee of the Quality Assurance Section found that the

failure rate of TV sets varies though they install same type of TV tuner in all the

models.

Analysis:

She thought that there must be some reason for this difference in failure rate of TV sets.

Therefore, she drew diagrams which showed the relation between the failure rate of

TV sets and the length of the shaft, the temperature of the set, the diameter of the

tuner knob, size of the cabinet and so on.

Conclusion:

At last, she discovered a correlation between the failure rate and the distance from

tuner to speaker; In other words, the failure rate of the turner is quite low when the tuner

is attached far from the speaker. On the other hand, when the tuner is attached near the

speaker, the set doesn’t work well.

Problem:

In a color TV factory, a female employee of the Quality Assurance Section found that the

failure rate of TV sets varies though they install same type of TV tuner in all the

models.

Analysis:

She thought that there must be some reason for this difference in failure rate of TV sets.

Therefore, she drew diagrams which showed the relation between the failure rate of

TV sets and the length of the shaft, the temperature of the set, the diameter of the

tuner knob, size of the cabinet and so on.

Conclusion:

At last, she discovered a correlation between the failure rate and the distance from

tuner to speaker; In other words, the failure rate of the turner is quite low when the tuner

is attached far from the speaker. On the other hand, when the tuner is attached near the

speaker, the set doesn’t work well.

Technical Analysis and Statistical Analysis (Contd.)

• Such a conclusion would be hard to draw through technical analysis alone. But byaccumulating market data we can discover such a phenomenon. We call this the law oflarge numbers.

• Therefore, you may understand that we have two ways to find out the cause ofdefectives, one is by the use of analysis based on technology and the other isthrough statistics.

• One of QC’s specialties is the use of statistical methods to eliminate trouble in the shop.• In order to find causes for a defect, you don’t need sophisticated technical expertise.

What you need to do is analyze data. And quality control circles have learned to usestatistical tools; this is what makes the circles so successful. Statistical analysis can beused to solve problems not only in manufacturing but also in sales, accounting, personnelmanagement and service.

• Spurred on by this method, QC circles are in demand among various fields includingmanufacturing, construction, financing, restaurants and department stores. The samething may produce varying degrees of results. Companies introducing the method seemto get better results in their work.

• – Source : Hajime Karatsu

• Such a conclusion would be hard to draw through technical analysis alone. But byaccumulating market data we can discover such a phenomenon. We call this the law oflarge numbers.

• Therefore, you may understand that we have two ways to find out the cause ofdefectives, one is by the use of analysis based on technology and the other isthrough statistics.

• One of QC’s specialties is the use of statistical methods to eliminate trouble in the shop.• In order to find causes for a defect, you don’t need sophisticated technical expertise.

What you need to do is analyze data. And quality control circles have learned to usestatistical tools; this is what makes the circles so successful. Statistical analysis can beused to solve problems not only in manufacturing but also in sales, accounting, personnelmanagement and service.

• Spurred on by this method, QC circles are in demand among various fields includingmanufacturing, construction, financing, restaurants and department stores. The samething may produce varying degrees of results. Companies introducing the method seemto get better results in their work.

• – Source : Hajime Karatsu

Quality Improvement: Problem Solving

“Problem solving, the isolation and analysisof a problem and the development of apermanent solution, is an integral part of thequality-improvement process”.– Not hit or miss, but objective and systematic– Not directed at symptoms, but rather at root

causes


“Problem solving, the isolation and analysisof a problem and the development of apermanent solution, is an integral part of thequality-improvement process”.– Not hit or miss, but objective and systematic– Not directed at symptoms, but rather at root

causes

Why do we need the 7 QC tools?

• Developed By Dr. KAORU ISHIKAWA.

• Total Quality Culture is data driven: data are impersonal; opinions

are not.

• Experience is gained quickest by collecting and analyzing data.

• The 7 QC tools provide common methods of analysis to help

problem solving teams operate effectively.

• It helps in taking decisions faster and objectively as factual

approach to decision making.


• Developed By Dr. KAORU ISHIKAWA.

• Total Quality Culture is data driven: data are impersonal; opinions

are not.

• Experience is gained quickest by collecting and analyzing data.

• The 7 QC tools provide common methods of analysis to help

problem solving teams operate effectively.

• It helps in taking decisions faster and objectively as factual

approach to decision making.

Why Statistics• It is more economical to assess a sample of

product and use the result to predict the propertiesof the whole lot.

• It leads to predictions with a high degree ofprecision.


STUDY

• It is more economical to assess a sample ofproduct and use the result to predict the propertiesof the whole lot.

• It leads to predictions with a high degree ofprecision.

Base Population Sample Data

InformationJudgment

Taking actions

Population-Sample Model

STATISTICAL QUALITY CONTROL (SQC)

• When a quality control system uses statisticaltechniques for inspection , testing and analysisto control quality or to conclude whether thequality of product is satisfying the customerneeds or to solve quality problems.

• SQC is systematic as compared to guess-workand avoids personal bias and poor judgments.


• When a quality control system uses statisticaltechniques for inspection , testing and analysisto control quality or to conclude whether thequality of product is satisfying the customerneeds or to solve quality problems.

• SQC is systematic as compared to guess-workand avoids personal bias and poor judgments.

Benefits of Statistical Quality Control(SQC)

• Efficiency: Rapid and efficient inspection at aminimum cost.

• Reduction of scrap: Tells the causes ofexcessive variation in manufacturing and tellsabout potential non-conformance.

• Adherence to Specification: Specifications canbe accurately predicted and control.

• Increases output: By reduction of wastages,effective utilization of Resources.

• Awareness: Creating awareness in organization.


• Efficiency: Rapid and efficient inspection at aminimum cost.

• Reduction of scrap: Tells the causes ofexcessive variation in manufacturing and tellsabout potential non-conformance.

• Adherence to Specification: Specifications canbe accurately predicted and control.

• Increases output: By reduction of wastages,effective utilization of Resources.

• Awareness: Creating awareness in organization.

Types of data:Statistical data can be characterized as

VARIABLE DATA and ATTRIBUTE DATA.

1.1 Variable / Continuous Data:Data which can be measurable and can assume anyvalue over some interval.Examples:- Dimension of a part measured- Temperature in degree centigrade.- Weight in Kg.- Time


Statistical data can be characterized as

VARIABLE DATA and ATTRIBUTE DATA.

1.1 Variable / Continuous Data:Data which can be measurable and can assume anyvalue over some interval.Examples:- Dimension of a part measured- Temperature in degree centigrade.- Weight in Kg.- Time

1.2 Attribute / Discrete Data:Data which can be measurable and can assumeonly certain distinct values (integer values).

Examples:- Defect or not.- Days of the Week / Months of the year.- Performance ranking.- The no. of defective pieces found in a sample.- Cracks in sheets by spots welds etc.


1.2 Attribute / Discrete Data:Data which can be measurable and can assumeonly certain distinct values (integer values).

Examples:- Defect or not.- Days of the Week / Months of the year.- Performance ranking.- The no. of defective pieces found in a sample.- Cracks in sheets by spots welds etc.

BASIC STATISTICAL CONCEPTSVARIATION

Concept of Variation states that no two items will beperfectly identical even if extreme care is taken tomake them identical in some respect.

Variation is fact of Nature and manufacturingprocesses are no exception to this.


VARIATION

Concept of Variation states that no two items will beperfectly identical even if extreme care is taken tomake them identical in some respect.

Variation is fact of Nature and manufacturingprocesses are no exception to this.

BASIC STATISTICAL CONCEPTS

Measures of Location / Central Tendency:1. Mean = The sum of the values divided by the

number of values.2. Median = The middle value of a data set when

the data is ordered from smallest to largest3. Mode = The value that occurs most frequently

Mean- Mode = 3(Mean- Median)

Measures of Location / Central Tendency:1. Mean = The sum of the values divided by the

number of values.2. Median = The middle value of a data set when

the data is ordered from smallest to largest3. Mode = The value that occurs most frequently

Mean- Mode = 3(Mean- Median)


4. Measures of Spread / Dispersion:The extent to which the data is scattered aboutthe zone of central tendency is known asDispersion or Variation.

Measures of Dispersion or Variability:4.1 Range , R4.2 Variance, V4.3 Standard Deviation,σ or S

4. Measures of Spread / Dispersion:The extent to which the data is scattered aboutthe zone of central tendency is known asDispersion or Variation.

Measures of Dispersion or Variability:4.1 Range , R4.2 Variance, V4.3 Standard Deviation,σ or S

BASIC STATISTICAL CONCEPTSBASIC STATISTICAL CONCEPTS

4.1 Range, R• Range is the simplest of dispersion in a sample. Itis used in the control chart.

• It is the difference between the largest observedvalue and the smallest observed value.

Range, R= XHigh- XLow

4.1 Range, R• Range is the simplest of dispersion in a sample. Itis used in the control chart.

• It is the difference between the largest observedvalue and the smallest observed value.

Range, R= XHigh- XLow


Variability

• Deviation = distance between observations andthe mean (or average)

Observations10

9887

averages 8.4

Deviations10 - 8.4 = 1.69 – 8.4 = 0.6

8 – 8.4 = -0.48 – 8.4 = -0.47 – 8.4 = -1.4

0.0

BASIC STATISTICAL CONCEPTS4.2 Variance, V• Average distance between observations andthe mean squared• Square of the standard deviation.

Observations Deviations Squared DeviationsObservations

10

9

8

8

7

averages 8.4

Deviations

10 - 8.4 = 1.6

9 – 8.4 = 0.6

8 – 8.4 = -0.4

8 – 8.4 = -0.4

7 – 8.4 = -1.4

0.0

Squared Deviations

2.56

0.36

0.16

0.16

1.96

1.0 Variance


4.3 Standard deviation, σ or SSquare root of variance.

Example

Variance Standard Deviation

1.0 1.0

0.24 0.4898979

Symbols and Formulae

Sample Size

Mean

Range

N = The total number of values

X = XN

R = Max - Min


Range

Variance

Standard Deviation

R = Max - Min

V = (X – X)2

N - 1

S = V

Seven Quality Control Tools

Check-sheet

Pareto

Histogram

Control Chart

Cause and Effect diagram

Scatter Plot

Stratification


Check-sheet

Pareto

Histogram

Control Chart

Cause and Effect diagram

Scatter Plot

Stratification

The function of a check sheet is to present

information in an efficient, graphical format.

For problem solving, data is to be captured.

Check list is a tool to capture the parameter.

Check-sheet is a tool to capture data as per

check list.

1. CHECKSHEET


The function of a check sheet is to present

information in an efficient, graphical format.

For problem solving, data is to be captured.

Check list is a tool to capture the parameter.

Check-sheet is a tool to capture data as per

check list.

CHECKSHEET

Shift wiseShift wiseD

efec

t Typ

eD

efec

t Typ

e

Type

Type

--11Ty

peTy

pe--22

ShiftShift--AA ShiftShift--BB ShiftShift--CC ShiftShift--GG


Def

ect T

ype

Def

ect T

ype

Type

Type

--22Ty

peTy

pe--33

Type

Type

--44

CHECKSHEET


18

12

33

TotalSat

2 / 6

III

I

Fri

2 / 5

III

II

IIII

Thu

2 / 4

II

II

Wed

2 / 3

I

IIII

Tue.

2 / 2

II

IIII

Mon.

2 / 1

Dirt in distance sensor

Exterior scratches

Loose screws

To 6 Feb. 1992No. 3 assembly lineProcess name

Miki Tanaka

Form 1 Feb. 1992

Recordedby:

Date

--------

LN1238LN1239LN1240

Measuring instrument

Lot number

100% visual operation

CS20-5D

Measuring method

Date / dayDefect

Product name

18

12

33

TotalSat

2 / 6

III

I

Fri

2 / 5

III

II

IIII

Thu

2 / 4

II

II

Wed

2 / 3

I

IIII

Tue.

2 / 2

II

IIII

Mon.

2 / 1

Dirt in distance sensor

Exterior scratches

Loose screws

To 6 Feb. 1992No. 3 assembly lineProcess name

Miki Tanaka

Form 1 Feb. 1992

Recordedby:

Date

--------

LN1238LN1239LN1240

Measuring instrument

Lot number

100% visual operation

CS20-5D

Measuring method

Date / dayDefect

Product name

• Make boxes for filling in the required items • Decide on methods of stratification. • Fill in data• Indicate items to be checked

Table 1: Check sheet for “defects in camera assembly process”

IIIIIIII

IIIIIIIIIIIIIIIIIIIIIIII IIIIIIII IIIIIIII

IIIIIIII

Check Sheets: To take down data simply and prevent inspection omission


4.5%

91

2

3

3

5

10

5

18

16

I

I

17

I

II

III

20

II

III

I

II

20

II

I

II

I

2037 Percent age defects

18

III

IIII

II

Number inspected

Bonding defect

Operating defect

Gap defect

Part lost

Exterior dirt

Total

Soldering defect

Exterior scratches

4.5%

91

2

3

3

5

10

5

18

16

I

I

17

I

II

III

20

II

III

I

II

20

II

I

II

I

2037 Percent age defects

18

III

IIII

II

Number inspected

Bonding defect

Operating defect

Gap defect

Part lost

Exterior dirt

Total

Soldering defect

Exterior scratches IIIIIIII IIIIIIII

IIIIIIII

• Add totals • Make a scratch of the product to be inspected.• Decide on items to be checked.• Every time a defect occurs, fill in a mark of number a

corresponding location.

2. PARETO CHART

• Vilfredo Pareto (1848-1923), An Italian economist– 20% of the population has 80% of the wealth

• Juran used the term “vital few, trivial many”. Henoted that 20% of the quality problems caused80% of the dollar loss.

• Pareto charts are extremely useful because theycan be used to identify those factors that have thegreatest cumulative effect on the system, and thusscreen out the less significant factors in ananalysis.


• Vilfredo Pareto (1848-1923), An Italian economist– 20% of the population has 80% of the wealth

• Juran used the term “vital few, trivial many”. Henoted that 20% of the quality problems caused80% of the dollar loss.

• Pareto charts are extremely useful because theycan be used to identify those factors that have thegreatest cumulative effect on the system, and thusscreen out the less significant factors in ananalysis.

PARETO CHART• Pareto Diagram allows the user to focus attention

on a few important factors in a process.

• They are created by plotting the cumulativefrequencies of the relative frequency data(event count data), in descending order.

• When this is done, the most essential factors forthe analysis are graphical presentation and in anorderly format.


• Pareto Diagram allows the user to focus attentionon a few important factors in a process.

• They are created by plotting the cumulativefrequencies of the relative frequency data(event count data), in descending order.

• When this is done, the most essential factors forthe analysis are graphical presentation and in anorderly format.

PARETO CHART

Per

cent

from

eac

h ca

use

30

40

50

60

70 (64)


Per

cent

from

eac

h ca

use

Causes of poor quality

0

10

20(10) (6)

(3) (2) (2)

(13)

Example

S. No. Defect No. of Defects

1 Weld Missing 26

2 Dent 11


3 Ovality 21

4 Fitment NG 16

5 Other 6

Example

S. No. Type of Defect No. of Defects1 Weld Missing 26

3 Ovality 21

4 Fitment NG 16

Step 1: Arrange data in decreasing order


4 Fitment NG 16

2 Dent 11

5 Other 6

Step 2 : Summation of individual frequencies.

26+21+16+11+6 = 80

Example

S# Type of Defect No. ofDefects

Cumulativefreq. % of Cumulative freq.

1 Weld Missing 26 26 26/80=33%

3 Ovality 21 47 (26+21)/80=59%

Step 3: Calculate cumulative percentage


4 Fitment NG 16 63 (26+21+16)/80=79%

2 Dent 11 74 (26+21+16+11)/80=93%

5 Other 6 80 (26+21+16+11+6)/80=100%

Total Sum 80

26

21

16

79 93100

60

80

100

20

25

30

Perc

enta

ges

Num

bers

Pareto Chart

Step 4 : Draw Chart


16

11

633

59

0

20

40

60

0

5

10

15

Weld Missing Ovality Fittment NG Dent Other

Perc

enta

ges

Num

bers

EXERCISE


EXERCISE

S.No Defect type No’s

1 Cracks 298

ExerciseMake Pareto Chart for the data given below.

Product : Spanner

Production Per day : 25000


1 Cracks 298

2 Scaling 1266

3 Unfilled – Cavity 435

4 Grinding NG 684

5 Plating NG 1372

Exercise

13721266

65.1

81.992.7

100

60.070.080.090.0100.0

1000

1200

1400

1600

Perc

enta

ge (%

)

No

of D

efec

ts

Pareto Analysis


684

435298

33.8

65.1

0.010.020.030.040.050.060.0

0

200

400

600

800

1000

Plating NG Scaling Grinding NG Unfilled – Cavity Cracks

Perc

enta

ge (%

)

No

of D

efec

ts

3. HISTOGRAM

• A histogram is a graphical summary of variationin a set of data.

• Histogram is a visual tool for presentingvariable data. It organises data to describe theprocess performance.

• The pictorial nature of the histogram enables usto see patterns that are difficult to see in a tableof numbers.


• A histogram is a graphical summary of variationin a set of data.

• Histogram is a visual tool for presentingvariable data. It organises data to describe theprocess performance.

• The pictorial nature of the histogram enables usto see patterns that are difficult to see in a tableof numbers.

Key Concept of Histogram

• Data always have variation

• Variation have pattern

• Patterns can be seen easily when summarized

pictorially.


• Data always have variation

• Variation have pattern

• Patterns can be seen easily when summarized

pictorially.

• Location of mean of the process

• Spread of the process

• Shape of the process

While studying histogramlook for its


• Location of mean of the process

• Spread of the process

• Shape of the process

Calculations for Histogram

535053515050504534849535148493554949504949552534848535050511

XlXs54321


5349514953514910535050535050519544950495049548524850485251517525050505251506514751475150495535053515050504

Calculations for Histogram Smallest Value, S= 47

Largest Value, L = 55

Range, R = L-S = 8

No. of cells= 1 + 3.22 log10(50) = 7

Calculated cell width (C.W.)= R / No. of cells=1.14

Rounded off Cell width= 1


Smallest Value, S= 47

Largest Value, L = 55

Range, R = L-S = 8

No. of cells= 1 + 3.22 log10(50) = 7

Calculated cell width (C.W.)= R / No. of cells=1.14

Rounded off Cell width= 1

Calculations for Histogram

Starting value, A= 47

LCB (Lower Class Boundary)= A-(C.W. / 2)= 47-1/2= 46.5

UCB (Upper Class Boundary)= LCB + C.W.= 46.5+1= 47.5


Starting value, A= 47

LCB (Lower Class Boundary)= A-(C.W. / 2)= 47-1/2= 46.5

UCB (Upper Class Boundary)= LCB + C.W.= 46.5+1= 47.5

Plotting Histogram

165050.549.5

104949.548.5

3III4848.547.5

1I4747.546.5

FreqTally MarkTally MarkMid valueUpperLower


15555.554.5

15454.553.5

55353.552.5

25252.551.5

115151.550.5

165050.549.5

2025303540

Histogram


0

5101520

1 2 6 13 10 16 19 17 12 16 20 17 13 5 6 2 1

Types of Histogram

General Type Comb Type Positively Skew Type


General Type Comb Type Positively Skew Type

Left-handPrecipice Type

Plateau Type Twin Peak Type

Isolated PeakType

NORMAL CURVE• If the number of observations are increasedconsiderably, then the no. of cells increases and thewidth of the cell become smaller and smaller.The series of steps that constitutes the top line of thehistogram will then approach a smooth curve. Sucha curve is called Frequency curve.• The frequency curves may be of different shapes.• The most important of these curve as far as SQC isconcerned is the NORMAL CURVE.• It is symmetrical about its mean value and has Bellshape.


• If the number of observations are increasedconsiderably, then the no. of cells increases and thewidth of the cell become smaller and smaller.The series of steps that constitutes the top line of thehistogram will then approach a smooth curve. Sucha curve is called Frequency curve.• The frequency curves may be of different shapes.• The most important of these curve as far as SQC isconcerned is the NORMAL CURVE.• It is symmetrical about its mean value and has Bellshape.

“Normal” bell shaped curve


Manufacturing Outcome: Central Tendency

Falling balls hit these pinsand go either left or right

Ball part way through rowof pins


Ball part way through rowof pins

5. CAUSE AND EFFECT DIAGRAM

• Show the relationships between a problemand its possible causes.

• Developed by Kaoru Ishikawa (1953)• Also known as …

– Fishbone diagrams– Ishikawa diagrams


• Show the relationships between a problemand its possible causes.

• Developed by Kaoru Ishikawa (1953)• Also known as …

– Fishbone diagrams– Ishikawa diagrams

Cause and Effect Diagram• Used to associate multiple possible causes with a

single effect.

• Given a particular effect, the diagram isconstructed to identify and organize possiblecauses for it.

• The primary branch represents the effect (thequality characteristic that is intended to beimproved and controlled) and is typically labelledon the right side of the diagram.


• Used to associate multiple possible causes with asingle effect.

• Given a particular effect, the diagram isconstructed to identify and organize possiblecauses for it.

• The primary branch represents the effect (thequality characteristic that is intended to beimproved and controlled) and is typically labelledon the right side of the diagram.

Cause and Effect Diagram

• Each major branch of the diagram corresponds toa major cause (or class of causes) that directlyrelates to the effect.

• Minor branches correspond to more detailedcausal factors.

• This type of diagram is useful in any analysis, as itillustrates the relationship between cause andeffect in a rational manner.


• Each major branch of the diagram corresponds toa major cause (or class of causes) that directlyrelates to the effect.

• Minor branches correspond to more detailedcausal factors.

• This type of diagram is useful in any analysis, as itillustrates the relationship between cause andeffect in a rational manner.

Cause and Effect skeleton

QualityProblem

Materials Procedures


QualityProblem

EquipmentPeople

QualityProblem

MachinesMeasurement Human

Faulty testing equipment

Incorrect specifications

Improper methods

Poor supervision

Lack of concentration

Inadequate training

Out of adjustment

Tooling problems

Old / worn

Fishbone Diagram


QualityProblem

ProcessEnvironment Materials

Defective from vendor

Not to specifications

Material-handling problems

Deficienciesin productdesign

Ineffective qualitymanagement

Poor processdesign

Inaccuratetemperaturecontrol

Dust andDirt

Fishbone Diagram


Cause and effect diagrams• Advantages

– making the diagram is educational in itself– diagram demonstrates knowledge of problem

solving team– diagram results in active searches for causes– diagram is a guide for data collection


• Advantages– making the diagram is educational in itself– diagram demonstrates knowledge of problem

solving team– diagram results in active searches for causes– diagram is a guide for data collection

Cause and effect diagrams

To construct the skeleton, remember:• For manufacturing - the 4 M’sman, method, machine, material

• For service applicationsequipment, policies, procedures, people


To construct the skeleton, remember:• For manufacturing - the 4 M’sman, method, machine, material

• For service applicationsequipment, policies, procedures, people

Typical causes for non conformance/defects

Machine factors

• Inadequate process capability

• Incorrectly designed tooling

• Worn tools, jigs or dies

• Poor maintenance

• Equipment effected by environmental factors such as heat,

humidity etc.


• Inadequate process capability

• Incorrectly designed tooling

• Worn tools, jigs or dies

• Poor maintenance

• Equipment effected by environmental factors such as heat,

humidity etc.


Material factors

• Use of untested materials

• Mix-up of materials

• Substandard material accepted on concession because of non-

availability of correct material

• Inconsistency in specifications on the part of vendors


• Use of untested materials

• Mix-up of materials

• Substandard material accepted on concession because of non-

availability of correct material

• Inconsistency in specifications on the part of vendors


Men factors

• Incorrect knowledge of setting up machines

• Careless operator and inadequate supervision

• Undue rush by the operator to achieve quality targets

• Lack of understanding of drawing instructions relating to a process

• Operator does not possess requisite skill for operating machines


• Incorrect knowledge of setting up machines

• Careless operator and inadequate supervision

• Undue rush by the operator to achieve quality targets

• Lack of understanding of drawing instructions relating to a process

• Operator does not possess requisite skill for operating machines


Method factors

• Inadequate process controls

• Non availability of proper test equipments

• Test equipment out of calibration

• Vague inspection/ testing instructions

• Inspectors do not possess the necessary skill


• Inadequate process controls

• Non availability of proper test equipments

• Test equipment out of calibration

• Vague inspection/ testing instructions

• Inspectors do not possess the necessary skill

PAPER / BOTTOM PANELFITMENT

EXCESSIVESEEPAGE

LOW SHOT WEIGHT TAPE MISSING

SIDE WALL TEMP

VOIDS

MENMETHOD

Cause & Effect diagram

Example


CHEMICALTEMPIMPROPER POL/CP MIXING

POURING HOLEMISMATCH

DAMAGED PESHEETS.

VOIDS

MATERIAL

CHEMICALPRESSUREIMPROPER P/I RATIO

M/C ALARMS

MACHINE

Cause & Effect diagram

Exercise


6. SCATTER PLOT

• Scatter diagrams are graphical tools that attemptto show the influence that one variable has onanother.

• A scatter diagram shows the relationship betweenindependent variable (cause) and dependentvariable (effect).


• Scatter diagrams are graphical tools that attemptto show the influence that one variable has onanother.

• A scatter diagram shows the relationship betweenindependent variable (cause) and dependentvariable (effect).

Characteristics of Independent Variable• It should be measurable on a continuous

scale.

• It should have a logical relationship with thedependent variable.

• Changes in level of independent variableshould cause changes in level of dependentvariable.


• It should be measurable on a continuousscale.

• It should have a logical relationship with thedependent variable.

• Changes in level of independent variableshould cause changes in level of dependentvariable.

Typical Relationship We Normally Liketo Study

Independent Variable Dependent Variable

• Moisture contents Elongation of thread

• Wax purity Hardness of lipstick

• Roller Pressure Paper thickness

• Charge weight Range of bullet

• Number of users Response time


Independent Variable Dependent Variable

• Moisture contents Elongation of thread

• Wax purity Hardness of lipstick

• Roller Pressure Paper thickness

• Charge weight Range of bullet

• Number of users Response time

Leng

th o

f bar

Typical RelationshipY


Pull Speed

Leng

th o

f bar

X

Sta

min

aTypical

RelationshipY


Life (Age)

Sta

min

a

X

Table - Humidity Vs VoltageVoltageHumidity %

V1 V2 V3 V4 V5102030405060708090100

40464549515454575960

43434345475152555758

41464348505151545657

42464449515255585958

40444346495353585758


VoltageHumidity %V1 V2 V3 V4 V5

102030405060708090100

40464549515454575960

43434345475152555758

41464348505151545657

42464449515255585958

40444346495353585758

Scatter Plot60

4045

50

55

Volta

ge


10 5020 6030 70 80 90 100

40

Humidity

Volta

ge

35

40

7. STRATIFICATION• It is the process of segregating or regrouping the

data on the basis of certain characteristics ( e.g.machine wise, operator wise etc.) for identifyingthe influence factors (i.e. identifying contributorycauses to the problems being handled)

• Data on Customer Complaints may besegregated by

a) Nature of Complaints: defective products,Delayed delivery etc.

b) Department Responsible: Production, Design,Quality etc.


• It is the process of segregating or regrouping thedata on the basis of certain characteristics ( e.g.machine wise, operator wise etc.) for identifyingthe influence factors (i.e. identifying contributorycauses to the problems being handled)

• Data on Customer Complaints may besegregated by

a) Nature of Complaints: defective products,Delayed delivery etc.

b) Department Responsible: Production, Design,Quality etc.

Benefits of Stratification• Separate Data into groups.

• Draw meaningful and correct inferences from

the data.

• Diagnose and Localize problems i.e. establish

clear relationship between cause and effect.

• Identify the influencing factors, thereby making it

easier to solve the problems.


• Separate Data into groups.

• Draw meaningful and correct inferences from

the data.

• Diagnose and Localize problems i.e. establish

clear relationship between cause and effect.

• Identify the influencing factors, thereby making it

easier to solve the problems.

1025

1550

51015202530

A B C D

% D

efec

tives

Suppliers

% Defectives Stratified supplierwise


Suppliers

1030 25 15 20

010203040

% A

ccid

ents

% Accidents Stratified Shopwise


Shops

• Fundamental tool of statistical process control.

• It indicates the stability of the process.

• It helps determine whether a process is in controlor if a special cause exists to change the processmean or variance.

Control Chart


• Fundamental tool of statistical process control.

• It indicates the stability of the process.

• It helps determine whether a process is in controlor if a special cause exists to change the processmean or variance.

In all production processes, we need to monitor the extent

to which our products meet specifications.

In the most general terms, there are two "enemies" of

product quality:

• deviations from target specifications, and

• excessive variability around target specifications.

Control Chart

Purpose


In all production processes, we need to monitor the extent

to which our products meet specifications.

In the most general terms, there are two "enemies" of

product quality:

• deviations from target specifications, and

• excessive variability around target specifications.

Why Control Chart ?

To find out,

• Any change in location of process average ?

• Any change in the spread of the process ?

• Any change in shape?

• To identify if special cause variation exists.


To find out,

• Any change in location of process average ?

• Any change in the spread of the process ?

• Any change in shape?

• To identify if special cause variation exists.

Significance of Control Chart ?

The quality of a product manufactured in a process isinevitably accompanied by dispersion. Various Causes ofsuch dispersion exist and they can be classified into thefollowing two types.

1. Chance / Random cause:

Dispersion by chance is natural and unavoidable i.e.inevitably occurs in a process, even if the operation iscarried out using standardized raw materials and methods.

It is impossible to avoid the Random cause variation.

Significance of Control Chart ?

The quality of a product manufactured in a process isinevitably accompanied by dispersion. Various Causes ofsuch dispersion exist and they can be classified into thefollowing two types.

1. Chance / Random cause:

Dispersion by chance is natural and unavoidable i.e.inevitably occurs in a process, even if the operation iscarried out using standardized raw materials and methods.

It is impossible to avoid the Random cause variation.

Control Chart2. Assignable / Special cause:

Dispersion from an assignable cause is unusual andmeaningful in that it is avoidable and cannot beoverlooked.Example: Neglecting various standards or application ofimproper standards.

In order to control a process it is necessary to eliminateassignable causes and take action to prevent theirrecurrence, while tolerating dispersion by chance /random cause.

2. Assignable / Special cause:

Dispersion from an assignable cause is unusual andmeaningful in that it is avoidable and cannot beoverlooked.Example: Neglecting various standards or application ofimproper standards.

In order to control a process it is necessary to eliminateassignable causes and take action to prevent theirrecurrence, while tolerating dispersion by chance /random cause.

A control chart was first used in 1924 by W.A. Shewhart,who belonged to the Bell Telephone Laboratories, with aview to classifying an abnormal process by distinguishingvariations due to chance from those due to assignablecauses.

Example of Control Chart

Upper controlLimit

Central line

Lower controlLimit

Control chart for controlled state

To make a control chart it is necessary to classify theprocess by type of raw materials, machine and line andfurther to classify these data into small groups such as timeor shift.

There are various types of control chart, according to thecharacteristic values or purpose. However, in any type ofcontrol chart the control limits are calculated by the formula :(average value) ± 3 x (standard deviation)Therefore such a chart is called a three sigma control chart.

Control chart for uncontrolled state

To make a control chart it is necessary to classify theprocess by type of raw materials, machine and line andfurther to classify these data into small groups such as timeor shift.

There are various types of control chart, according to thecharacteristic values or purpose. However, in any type ofcontrol chart the control limits are calculated by the formula :(average value) ± 3 x (standard deviation)Therefore such a chart is called a three sigma control chart.

• The bounds of the control chart are marked byupper and lower control limits that are calculatedby applying statistical formulas to data from theprocess.

• Data points that fall outside these boundsrepresent variations due to special causes, whichcan typically be found and eliminated.

• On the other hand, improvements in commoncause variation require fundamental changes in theprocess.


• The bounds of the control chart are marked byupper and lower control limits that are calculatedby applying statistical formulas to data from theprocess.

• Data points that fall outside these boundsrepresent variations due to special causes, whichcan typically be found and eliminated.

• On the other hand, improvements in commoncause variation require fundamental changes in theprocess.

18

15

21

24

Num

ber o

f def

ects

UCL = 23.35

x = 12.67

Control Chart


12

6

3

9

2 4 6 8 10 12 14 16Sample number

Num

ber o

f def

ects

LCL = 1.99

x = 12.67

Control LimitsUpper Control Limit

Target

3 x sd of means


1 2 3 4 5 6 7Sample Number

Lower Control Limit

Control Charts

Variables Attributes

Types of Control Charts


p Chartnp ChartC Chartu Chart

Variables Attributes

– R Chart– s Chart

XX

o Defect prevention andprocess improvement

o More expensive toconstruct and maintain

o Can tell reasons forprocess behavior

o Smaller n (1 to 10)needed

o Defect detectiono Cheaper to construct

and maintaino Cannot tell cause of

defecto Need large n (>100)o A screening device to

initiate variables controlcharting

Variable Control Charts Attribute Control Charts


o Defect prevention andprocess improvement

o More expensive toconstruct and maintain

o Can tell reasons forprocess behavior

o Smaller n (1 to 10)needed

o Defect detectiono Cheaper to construct

and maintaino Cannot tell cause of

defecto Need large n (>100)o A screening device to

initiate variables controlcharting

DATA TYPE

Start

SampleSize, n

MeasurableVariables Data

X-bar MRChart

n = 1 Defectivesor

Defects?

CountableAttributes Data

Constant‘n’

Yes

Defects

No

Selecting a Control Chart


X-bar MRChart

Range orS.D

X-bar -R Chart

Range,if n<10 Constant

‘n’Yes

np or pChart

Constant‘n’

c (or) uChart

Yes

Defectives

No

pChart

X-bar -s Chart

S.D,if n>10

uChart

No

n > 1

Types of Charts

Variable control charts are :-

• X bar – R Chart

• Run Chart

Attribute control Charts are :-

• P Chart

• C chart

• U Chart




• Run Chart

Attribute control Charts are :-

• P Chart

• C chart

• U Chart

Types of Charts



• Run Chart




• Run Chart

X bar – R Chart

•Shows both the mean value ( X ), and the range ( R ).

•The Xbar portion shows any changes in the mean value of

the process, while the R portion shows any changes in the

dispersion of the process.

•This chart is particularly useful in that it shows changes in

mean value and dispersion of the process at the same time,

making it a very effective method for checking abnormalities

within the process


•Shows both the mean value ( X ), and the range ( R ).

•The Xbar portion shows any changes in the mean value of

the process, while the R portion shows any changes in the

dispersion of the process.

•This chart is particularly useful in that it shows changes in

mean value and dispersion of the process at the same time,

making it a very effective method for checking abnormalities

within the process

Formula’s Used

X bar – R Chart

Center Line = X

X bar Chart


Center Line = R

R Chart

X bar – R Chart

SNO. 1 2 3 4 5 6 7 8 9 10

1 65.84 65.88 65.86 65.86 65.9 65.84 65.88 65.88 65.92 65.92

2 65.88 65.86 65.88 65.88 65.84 65.9 65.9 65.9 65.9 65.88

3 65.82 65.9 65.86 65.88 65.86 65.86 65.9 65.86 65.92 65.88

4 65.9 65.84 65.84 65.84 65.88 65.88 65.86 65.82 65.88 65.86

5 65.88 65.86 65.9 65.86 65.86 65.86 65.88 65.84 65.88 65.84

Average X 65.864 65.868 65.868 65.864 65.868 65.868 65.884 65.86 65.9 65.876

Range 0.08 0.06 0.06 0.04 0.06 0.06 0.04 0.08 0.04 0.08

Example

SPECIFIC: 65.8±0.2

PART NAME: SPACER FRAME CROSS MEMBER


SNO. 1 2 3 4 5 6 7 8 9 10

1 65.84 65.88 65.86 65.86 65.9 65.84 65.88 65.88 65.92 65.92

2 65.88 65.86 65.88 65.88 65.84 65.9 65.9 65.9 65.9 65.88

3 65.82 65.9 65.86 65.88 65.86 65.86 65.9 65.86 65.92 65.88

4 65.9 65.84 65.84 65.84 65.88 65.88 65.86 65.82 65.88 65.86

5 65.88 65.86 65.9 65.86 65.86 65.86 65.88 65.84 65.88 65.84

Average X 65.864 65.868 65.868 65.864 65.868 65.868 65.884 65.86 65.9 65.876

Range 0.08 0.06 0.06 0.04 0.06 0.06 0.04 0.08 0.04 0.08

(x)

X bar – R Chart

X = Average (Average X) = Average X

=658.72/10 = 65.872

R = Average (Range)

=0.6/10 = 0.06


R = Average (Range)

=0.6/10 = 0.06

X bar – R Chart

Center Line = X

For X bar ChartUCL = 65.872 + 0.590x0.06

= 65.907

X =65.872

LCL = 65.872 - 0.590x0.06= 65.836


Center Line = R

R Chart

LCL = 65.872 - 0.590x0.06= 65.836

UCL = 0.06x2.110 = 0.1266

LCL = 0.06x0 = 0

R = 0.06

Size of Sub-group X-Chart R Chart R Chart R Chart

n A2 D3 D4 d22 1.880 - 3.267 1.128

Coefficients for X bar R Charts


2 1.880 - 3.267 1.1283 1.023 - 2.575 1.6934 0.729 - 2.282 2.0595 0.590 - 2.110 2.3266 0.483 - 2.004 2.534

65.88

65.89

65.9

65.91

65.92

X ba

r

UCL = 65.905

X = 65.872

X bar Chart


65.83

65.84

65.85

65.86

65.87

0 1 2 3 4 5 6 7 8 9 10

X ba

r

LCL = 65.836

X = 65.872

UCL = 0.126

R = 0.06

R Chart

0.08

0.1

0.12

0.14

Ran

ge


LCL = 0

R = 0.06

0

0.02

0.04

0.06

0.08

0 1 2 3 4 5 6 7 8 9 10

Ran

ge

X bar – R ChartExercise

PART NAME: PIPE COMP. STRG. HEAD-KTEA

SPECIFIC: 8+0 /-0.2

SNO. 1 2 3 4 5 6 7 8 9 10

1 7.9 7.98 8 8.1 7.85 7.84 7.91 7.98 7.85 7.84

2 7.84 7.84 7.84 7.94 7.97 7.94 7.94 7.98 7.84 7.94

3 7.96 7.94 7.88 7.82 7.88 8.1 7.85 7.84 7.91 7.98

4 7.84 7.84 7.94 7.97 7.94 7.84 7.91 7.98 7.85 7.84

5 7.94 7.97 7.94 7.94 7.98 8.1 7.85 7.84 7.91 7.98


SNO. 1 2 3 4 5 6 7 8 9 10

1 7.9 7.98 8 8.1 7.85 7.84 7.91 7.98 7.85 7.84

2 7.84 7.84 7.84 7.94 7.97 7.94 7.94 7.98 7.84 7.94

3 7.96 7.94 7.88 7.82 7.88 8.1 7.85 7.84 7.91 7.98

4 7.84 7.84 7.94 7.97 7.94 7.84 7.91 7.98 7.85 7.84

5 7.94 7.97 7.94 7.94 7.98 8.1 7.85 7.84 7.91 7.98

Attribute Charts:

• p Chart Fraction Defective

• c Chart No. of Defects in a fixed sized Product

• u Chart No. of Defects in a varying sized product

Attribute Charts:

• p Chart Fraction Defective

• c Chart No. of Defects in a fixed sized Product

• u Chart No. of Defects in a varying sized product

p Chart

Control charts dealing with the proportion or fraction

of defective product are called p chart (for proportion).

Control charts dealing with the proportion or fraction

of defective product are called p chart (for proportion).

p ChartFormula’s Used

‘p’ is the fraction defective in a lot or population

‘n’ is the number of lot

p ChartExample

S.No. Date Total QuantityProduced

Defective Qty.

1 1st Jan 07 990 87

2 2nd Jan 07 1000 93

3 3rd Jan 07 1110 1893 3rd Jan 07 1110 189

4 4th Jan 07 980 126

5 5th Jan 07 1000 109

6 6th Jan 07 1100 102

7 8th Jan 07 910 145

8 9th Jan 07 1080 90

9 10th Jan 07 985 81

p ChartStep 1: Calculate Fraction Defective

S.No Date Total QuantityProduced

Defective Qty. Fraction Defective

1 1st Jan 07 990 87 87/990 =0.088

2 2nd Jan 07 1000 93 93/1000 =0.093

3 3rd Jan 07 1110 189 189/1110 =0.1703 3rd Jan 07 1110 189 189/1110 =0.170

4 4th Jan 07 980 126 126/980 =0.129

5 5th Jan 07 1000 109 109/1000 =0.109

6 6th Jan 07 1100 102 102/1100 =0.093

7 8th Jan 07 910 145 145/910 =0.159

8 9th Jan 07 1080 90 90/1080 =0.083

9 10th Jan 07 985 81 81/985 =0.82

Total 9155 1022 1.006

p ChartStep 2: Calculate UCL & LCL

P = 1.006 / 9 =0.112

UCL = 0.112 + 3 0.112(1-0.112)/9 = 0.422UCL = 0.112 + 3 0.112(1-0.112)/9 = 0.422

LCL = 0.112 – 3 0.112(1-0.112)/9 = - 0.203

= 0

p ChartStep 3: Draw Chart

0.250

0.300

0.350

0.400

0.450

Frac

tion

Def

ecti

ve

UCL = 0.422

0.000

0.050

0.100

0.150

0.200

0.250

Frac

tion

Def

ecti

ve

LCL = 0

P = 0.112

c Chart

C Chart is used where each item inspected may have

several nonconformities and each nonconformity is counted,

and sample size is constant.

C Chart is used where each item inspected may have

several nonconformities and each nonconformity is counted,

and sample size is constant.

c ChartFormula’s Used

Center line c = c /n

UCL = c + 3 * c

LCL = c - 3 * c

Where ‘c’ is the number of nonconformities in each sample

‘n’ is the number of lot

LCL = c - 3 * c

c ChartExample

Lot Number Number of pinholes

1 8

2 9

3 5

4 8

Lot Number Number of pinholes

11 6

12 4

13 7

14 64 8

5 5

6 9

7 9

8 11

9 8

10 7

14 6

15 14

16 6

17 4

18 11

19 7

20 8

Total 152

c ChartStep 1: Calculate UCL & LCL

Center line c = c /n

UCL = c + 3 * c

c = 152/20 = 7.6

UCL = 7.6 + 3 * 7.6 = 15.85UCL = c + 3 * c

LCL = c - 3 * c

UCL = 7.6 + 3 * 7.6 = 15.85

LCL = 7.6 - 3 * 7.6 = -0.65

= 0

c ChartStep 2: Draw Chart

1012141618

No.

of P

inho

les

UCL = 15.85

02468

10

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

No.

of P

inho

les

c = 7.6

LCL = 0

u Chart

A u-chart is an attributes control chart used with data

collected in subgroups of varying sizes

u ChartFormula’s Used

Center line u = u /n

UCL = u + 3 * u /N

LCL = u - 3 * u /N

Where ‘u’ is the number of nonconformities in each sample

‘n’ is the number of items in the sample

‘N’ is the average sample size

LCL = u - 3 * u /N

u ChartExample

S. No. No. of parts Inspected Number of Nonconformities

1 200 5

2 80 7

3 100 3

4 300 154 300 15

5 120 4

6 90 6

7 250 10

8 50 1

9 100 6

10 70 2

Total 1360 59

u Chart

S. No. No. of parts Inspected( A )

Number of Nonconformities( B )

U = B/A

1 200 5 0.025

2 80 7 0.088

3 100 3 0.030

4 300 15 0.050

5 120 4 0.033

Step 1: Calculate u

5 120 4 0.033

6 90 6 0.067

7 250 10 0.040

8 50 1 0.020

9 100 6 0.060

10 70 2 0.029

Total 1360 59

u ChartStep 2: Calculate UCL & LCL

Center line u = u /n

UCL = u + 3 * u /N

N = 1360 / 10 = 136

u = 59/1360 = 0.043

UCL = 0.043 + 3 0.043/136 = 0.097UCL = u + 3 * u /N

LCL = u - 3 * u /N

UCL = 0.043 + 3 0.043/136 = 0.097

LCL = 0.043 - 3 0.043/136 = -0.011

= 0

u ChartStep 3: Draw Chart

0.06

0.08

0.1

0.12

u

UCL = 0.097

0

0.02

0.04

0.06

0 1 2 3 4 5 6 7 8 9 10

u

u = 0.043

LCL = 0

Thank You!Thank You!

7 qc tools

Documents

plan problem

conclusion problem

technical analysis

step problem

use of analysis

technological analysis

benefits of systematic

qualityimprovement process