qcc & 7qc tools

QCC &7 Quality Control Tools

For Problem Solvings

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MODULE 1

UNDERSTANDINGQC CIRCLE

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Overview

• What are Quality Circles?

• How Do Quality Circles Work?

• How Can They be Used in an Organization?

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What is a Quality Circle?

•Voluntary groups of employees Voluntary groups of employees who work on similar tasks or share who work on similar tasks or share an area of responsibilityan area of responsibility

•They agree to meet on a regular They agree to meet on a regular basis to discuss & solve problems basis to discuss & solve problems related to work.related to work.

•They operate on the principle that They operate on the principle that employee participation in decision-employee participation in decision-making and problem-solving making and problem-solving improves the quality of workimproves the quality of workPlan CheckDo Act

How Do Quality Circles Work?

• Characteristics

• Volunteers

• Set Rules and Priorities

• Decisions made by Consensus

• Use of organized approaches to Problem-Solving

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How Do Quality Circles Work?

• All members of a Circle need to receive training

• Members need to be empowered

• Members need to have the support of Senior Management

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How Can They be Used in an Organization?

• Increase Productivity• Improve Quality• Boost Employee Morale • Increase in employee quality consciousness• Problem prevention becomes habitual• Promotion of employee motivation• Improvement in the human relations• More effective company communication

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How Can They be Used in an Organization?

• More active job involvement• Utilization of problem solving capabilities• Contribution to personnel development• Encouragement of teamwork• Improvement of work environment• Development of safety awareness• Control and improvement of quality• Productivity improvement• Increased job security

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Problems with Quality Circles

• Inadequate Training

• Unsure of Purpose

• Not truly Voluntary

• Lack of Management Interest

• Quality Circles are not really empowered to make decisions.

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MODULE 2

DATA COLLECTION

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CATEGORIES OF DATA

• Primary Data

• Secondary Data

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PRIMARY DATA

The data, which are collected from the units or individual respondents directly for the purpose of certain study or information.

Example:•If an experiment is conducted to know the effect of fertilizer doses on the yield OR the effect of a drug on the patients, the observation taken on each plot or patient constitute the primary data.

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SECONDARY DATAThe data, which had been collected by certain people or agency, and statistically treated.

Now the information contained in it is used again from records, processed and statistically analyzed to extract some information for other purpose.

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SECONDARY DATAExample:

Secondary data is obtained from year books, census report, survey reports, official reports or reported experimental findings.

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ANALYSIS OF DATA

One of the most important objectives is to process the observed data and transform it to a form most suitable for decision making.

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DATA PROCESSING

Before tabulation of primary data, it should be edited for:

• Completeness

• Consistency

• Accuracy

• Homogeneity

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ANALYSIS OF DATA

The measures of central tendency and dispersion are parts of data analysis along with the estimation and testing of hypothesis:

• Mean

• Median

• Mode• Standard deviation

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ANALYSIS OF DATA

The data: 4,5,5,4,8,4,3,7.

• Mean

• Median

• Mode• Standard deviation

• Range

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ANALYSIS OF DATA

The data: 4,5,5,4,8,4,3,7.

• Mean = 4+5+5+4+8+4+3+7

8

Mean = 40/8 = 5

MedianMedian = 3,4,4,4,5,5,7,8 = 3,4,4,4,5,5,7,8 ( Select Two centered Data)( Select Two centered Data)

MedianMedian = ( 4+5)/2 = 4.5 = ( 4+5)/2 = 4.5

Mode = 3,4,4,4,5,5,7,8Mode = 3,4,4,4,5,5,7,8 = 4 = 4 (Most Frequently Occurring Number)(Most Frequently Occurring Number)Plan CheckDo Act

ANALYSIS OF DATA

The data: 4,5,5,4,8,4,3,7.

• Standard deviation = 1.69 (X-Bar – X)2 =

( n – 1)

• Range = Max – Min

= 8 – 3 = 5

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Median =4.5Median =4.5

Mean = 5Mean = 5

Range = 5Range = 5

Mode = 4Mode = 4

STD STD Dev=1.69Dev=1.69

P – Value = 0.1377 Data is NormalP – Value = 0.1377 Data is Normal

This tool is given freeThis tool is given free

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MODULE 3

The Basic 7 Quality Tools

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The Basic 7 Quality Tools.

Ishikawa believed that 90% of all quality problems could be solved through the use of the 7 tools listed below:

• Frequency Diagrams ( Histograms )• Cause and Effect (Ishikawa) Diagrams• Check Sheets• Pareto diagrams• Flowcharts• Scatter Diagrams• Control Charts

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Where did the Basic Seven come from?

Kaoru Ishikawa

• Known for “Democratizing Statistics”

• The Basic Seven Tools made statistical analysis less complicated for the average person

• Good Visual Aids make statistical and quality control more comprehensible.

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Fishbone Diagrams

• No statistics involved

• Maps out a process/problem

• Makes improvement easier

• Looks like a “Fish Skeleton”Plan CheckDo Act

Possible CausesPossible CausesPROBLEMPROBLEM(EFFECT)(EFFECT)

Area AArea A Area BArea B

Area DArea DArea CArea C

11

22

3366

55

4411

22

3366

55

44

11

22

3366

55

44

11

22

3366

55

44

Fishbone DiagramsFishbone Diagrams

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• Step 1 - Identify the ProblemStep 1 - Identify the Problem• Step 2 - Draw “spine” and Step 2 - Draw “spine” and “bones”“bones”• Step 3 - Identify different Step 3 - Identify different areas where problems may arise areas where problems may arise fromfrom• Step 4 - Identify what these Step 4 - Identify what these specific causes could bespecific causes could be• Step 5 – Use the finished Step 5 – Use the finished diagram to brainstorm solutions diagram to brainstorm solutions to the main problems.to the main problems.


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Bad SolderJoints

Machines Manpower

MaterialsMethods

Solder Gun

Size

Heat sink

Power Source

Skill

Training

Physical limits

Terminals

Stripping

TechniqueManual Flux

Solder

Wire Gauge


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In the above example, I’ve added some causes and some common categories – know as the 4 “M’s” – Machines, Manpower, Methods and Materials.How do we arrive at the possible causes? The best (and most common) method is brainstorming. This generates a large number of ideas in a short period of time.Once the diagram is complete, then we can continue with the evaluation.We obviously can not tackle all the problems at once because there are too many and besides some will have such small effects that they will not be worth bothering about.

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Bad SolderJoints

Machines Manpower

MaterialsMethods

Solder Gun

Size

Heat sink

Power Source

Skill

Training

Physical limits

Terminals

Stripping

TechniqueManual Flux

Solder

Wire Gauge


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On this diagram, three of the causes have been highlighted.

These are thought to be the most important, and the ones to tackle. WHY?

There are a number of ways of choosing the front runners.

We could design a series of experiments to determine the biggest influence, OR

We could use existing data or experience, OR

We could make a judgement purely on opinion

Fishbone Fishbone DiagramsDiagrams

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CAUSE & EFFECT DIAGRAM - Example

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WORKSHOP & PRESENTATION

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WHAT IT IS? Histograms are effective Q.C. tools which are used in the analysis of data. They are used as a check on specific process parameters to determine where the greatest amount of variation occurs in the process, or to determine if process specifications are exceeded.

This statistical method does not prove that a process is in a state of control. Nonetheless, histograms alone have been used to solve many problems in quality control.

HISTOGRAM

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HISTOGRAM ANALYSIS

• How well is the histogram centered?

The centering of the data provides information on the process aim about some mean or nominal value.

• How wide is the histogram?

Looking at histogram width defines the variability of the process about the aim.

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HISTOGRAM ANALYSIS

•What is the shape of the histogram?

Remember that the data is expected to form a normal or bell-shaped curve. Any significant change or anomaly usually indicates that there is something going on in the process which is causing the quality problem.

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NORMAL

Depicted by a bell-shaped curve

• Most frequent measurement appears as center of distribution • Less frequent measurements taper gradually at both ends of distribution Indicates that a process is running normally (only common causes are present).

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BI-MODAL

• Distribution appears to have two peaks • May indicate that data from more than one process are mixed together

Materials may come from two separate vendors Samples may have come from two separate machines. Plan CheckDo Act

CLIFF-TYPE

• Appears to end sharply or abruptly at one end • Indicates possible sorting or inspection of non-conforming parts. Plan CheckDo Act

SAW-TOOTHED

• Also commonly referred to as a comb distribution, appears as an alternating jagged pattern• Often indicates a measuring problem

Improper gage readings Gage not sensitive enough for readings.

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SKEWED

Positively Skewed Negatively Skewed

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Excercise

• Construct an Histogram for the four distribution from the set data “Plot Data”.

• Can you determine the type of distribution from the Histogram?

• Can you determine the bimodal from the Histogram?

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Dist 1Dist 1Dist 2Dist 2

Dist 3Dist 3 Dist 4Dist 4


Measurements of 50 items from process XYZ

147 179 185 125 210

131 137 141 142 166

198 142 205 150 141

190 161 157 165 155

165 155 169 158 150

170 125 177 108 193

178 181 155 186 145

157 135 148 171 124

168 141 151 162 150

145 177 154 137 160

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TALLY CHART

RANGE TALLY NUMBER

100-109 | 1

110-119 0

120-129 | | | 3

130-139 | | | | 4

140-149 | | | | | | | | | 9

150-159 | | | | | | | | | | | 11

160-169 | | | | | | | | 8

170-179 | | | | | | 6

180-189 | | | 3

190-199 | | | 3

200-209 | 1

210-219 | 1

TOTAL 50Plan CheckDo Act

A tally is a simple form of categorising the data so as to let it speak for itself. In the first column, we have the basic categories themselves: 100 – 109, 110 – 119, and so on. In the second column, there is the tally – the actual count of the number of items found in that category. In the third column is the actual number in the category, or the frequency. We now have the data in a form which “speaks” to us. The next obvious step is to display the data on a graph.

HISTOGRAM

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Data

Frequency

200180160140120100

12

10

8

6

4

2

0

Mean 158.4StDev 21.80N 50

Histogram of DataNormal

Data

Frequency

200180160140120100

12

10

8

6

4

2

0

125 185Mean 158.4StDev 21.80N 50

Histogram of DataNormal

If we add the tolerance limits to the graph, we can see that we are going to have a large proportion of rejects from the process. From this it is easy to see how vital the concept of the frequency diagram is to analysing process capabilities

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Compute the

-Mean

-Median

-Standard Deviation

-Range

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Check Sheets Counting and accumulating data

WHAT IS A CHECK SHEETS?

One of the most common form of data collection, the check sheet is a structured form containing a list of things you want to measure, inspect or record. Plan CheckDo Act

WHAT DOES CHECKLIST PREVENT

Forget to inspect

Late inspection

Ineffective inspection

Partial inspection

Not knowing who did the inspection

No record for inspection done

No action for inspection done

Check Sheets

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Process Name: MouldingProduct Name: Widgits

TOTAL3/8{F}

2/8{T}

1/8{W}

31/7{T}

30/7 {M}

Defective part

61811151413TOTAL

7| | || | ||Other

2||Cracks

9|| || | | ||Pinholes

14| | | || | || || | | |Grit

8|| | | || |Fibres

21| | | || | || | | | || | || | | |Mould Cracked

Line Name: Auto1Product Number: 123456

Check Sheets

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Check sheets are used to systematically record data from historical sources or from observations as they happen so that patterns and trends can be clearly detected and shown.Check sheets minimise clerical effort since the operator merely adds a mark to the tally on the prepared sheet rather than writing out a figure.

Check Sheets

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Product: Copper Pipe Date:14th April 2003

Manufacturing Stage: Final insp Inspector’s Name: Sam

Type of Defect: scratch, incomplete, misshapen Lot No: 24

Total No. inspected: 2530 Remarks: All inspected

Type Check Sub-Total

Scratches

Cracks

Incomplete

Misshapen

Others

//// //// //// //// //// //

//// //// //// //// ///

//// //// //// //// //// //// /

////

//// ///

22

19

25

4

7

Grand Total 77

Total Rejects //// //// //// //// //// //// ////

//// //// //// //// //// //// // 54

PARETO DIAGRAM

The 80/20 Principle :Achieving More With Less

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Pareto Discovery• In the late 19th century, an Italian economist by the

name of Vilfredo Pareto undertook a study on the distribution of wealth in Italy.

• Pareto discovered that about 80% of the wealth in Italy was distributed to only 20% of the Italian families.

• In society, 80% of the value of all crimes committed was caused by 20% of the criminals

• In life, most happiness enjoyed by a person occurred during 20% of his lifetime.

• At work, 80% of one’s valuable output occurred in 20% of his time

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The 80/20 Thinking & Analysis

• To engage in 80/20 thinking, we must constantly ask ourselves:

• “What is the 20% that is leading the 80%?”

• “ What are the vital few causes or inputs as opposed to the trivial many ? “

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The 80/20 Principle, as applied to Quality Management

• The pioneer of the 80/20 principle was Joseph Juran :

- the great Quality Management Practitioner - the man behind the global quality revolution

of the late 20th century

• This Romanian-born US Engineer Juran alternately called the “Pareto Principle” or the “80/20 Principle” the so-called “The Rule of the Vital Few and the Trivial Many” – virtually synonymous with the search for high quality products and services.

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Why The 80/20 Principle Is So Important ?

Whether you realize it or not, the 80/20 Principle applies to:

• Your life• Your social world• The place where you work• Your business For each individual and each business, it is

always possible to obtain much more that is of value and avoid what has negative value, with much less input of effort, expense or investment.

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80/20 As Applied To Daily Life

• Only do things we are best at doing and enjoy most• In every important aspect in life, work out where 20% of

effort will lead to 80% of return• Choose your career and employers with care, and if

possible, employ others rather than being employed yourself

• Make the most of the lucky “few streaks” in your life• Strive for excellence in few things, rather than good

performance in many• Calm down, work less and target a limited number of

goals

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80/20 Thinking Is Reflective

The objective of the 80/20 Thinking is to generate actions which will make sharp improvements in your life and that of others

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80/20 Thinking Is Reflective

To be strategic is to concentrate on what is important in the long run, on those few things that can give us a comparative advantage, on what is important to us than others, and to plan and execute the resulting plan with determination and steadfastness.

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80/20 Insights For Individuals• 80 % of achievements and happiness in life,

takes place in 20% of our time• Our lives are profoundly affected for good

and ill, by a few events and a few decisions in our life

• Everyone can achieve something significant. The key is not effort, but finding the right thing to achieve

• There are winners and losers – and always more of the latter. You can be a winner by choosing the right competition, the right team and the right methods to win

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80/20 As It Applies To Business

o That losses (manufactured goods that have to be rejected because of poor quality) do not arise from a large number of causes. Rather the causes are always mal-distributed in such a way that a small percentage of the quality characteristics always contributes a high percentage of the quality loss

o And if you remedy the critical 20% of your quality gaps, you will realize 80% of the benefits.

o The first 80% typically includes the first breakthrough in continuous improvementPlan CheckDo Act

Pareto Diagrams Focus on key problems

• Kadang-kadang sebagian besar masalah disebabkan oleh segelintir sebab. Sebagai contoh, 80% dari downtime disebabkan oleh 20% dari mesin; 80% Revenue berasal dari 20% pelanggan.

• Aturan 80/20 adalah satu cara untuk memprioritaskan usaha supaya tertumpu (fokus) pada apa yang lebih penting.

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Part Cum % Annual Cost Annual Cum Cum % Inv.# Of Total Usage Per Cost Ann. Cost Of Total Class

1 10% 5000 98.00$ 490,000$ 490,000$ 49% A2 20% 395000 0.79$ 312,050$ 802,050$ 80% A3 30% 10000 8.25$ 82,500$ 884,550$ 88% B4 40% 75000 0.87$ 65,250$ 949,800$ 95% B5 50% 5000 2.00$ 10,000$ 959,800$ 96% B6 60% 1000 9.75$ 9,750$ 969,550$ 97% C7 70% 125000 0.07$ 8,750$ 978,300$ 98% C8 80% 30000 0.26$ 7,800$ 986,100$ 99% C9 90% 250000 0.03$ 7,500$ 993,600$ 99% C

10 100% 600 10.70$ 6,420$ 1,000,020$ 100% C

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Fault No.

A. DRY JOINT

B. MISSED COMPONENT

C. REVERSED COMPONENT

D. ARCING

E. OPEN CIRCUIT

F. OTHER

2

5

8

4

1

3

TOTAL 23

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Fault No.


B. MISSED COMPONENT

D. ARCING

F. OTHER

A. DRY JOINT

E. OPEN CIRCUIT

8

5

4

3

2

1

TOTAL 23


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This tool is given freeThis tool is given freePlan CheckDo Act

88

REVERSEDREVERSEDCOMPONENTCOMPONENT

55

MISSED MISSED COMPONENTCOMPONENT

44

ARCINGARCING

33

OTHEROTHER22

DRY JOINTDRY JOINT 11OPEN CIRCUITOPEN CIRCUIT

BBCC DD FF AA EE


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Fault No. COST TOTAL COST


B. MISSED COMPONENT

D. ARCING

F. OTHER

A. DRY JOINT

E. OPEN CIRCUIT

8

5

4

3

2

1

2

2

5

1

4

6

16

10

20

3

8

6

TOTAL 23 - 63


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Fault No. COST TOTAL COST

D. ARCING


B. MISSED COMPONENT

A. DRY JOINT

E. OPEN CIRCUIT

F. OTHER

4

8

5

2

1

3

5

2

2

4

6

1

20

16

10

8

6

3

TOTAL 23 - 63


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£20

ARCING

£16

REVERSEDCOMPONENT

£10

MISSED COMPONENT

£8

DRY JOINT

£6

OPEN CIRCUIT£3

OTHER

CD B A E F


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Flowcharts Picturing the process

• Overview

• Detailed look at flowcharting

• Real world examples

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Overview of Flowcharts

• What is a flowchart?

• How are they useful?

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What is a flowchart?

• Process Flow Diagram

• A diagram illustrating the activities of a process

• One of Ishikawa’s seven basic tools of quality

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Brief History

• No originator, or “father” of flowcharts

• Forms of flowcharts have always been used

• Give us insight into historical processesPlan CheckDo Act

Flowcharts - Picturing the processFlowchart Symbols

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Start/EndThe terminator symbol marks the starting or ending point of the system. It usually contains the word "Start" or "End."

Action or ProcessA box can represent a single step ("add two cups of of flour"), or and entire sub-process ("make bread") within a larger process.


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DocumentA printed document or report.

DecisionA decision or branching point. Lines representing different decisions emerge from different points of the diamond.


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Input/OutputRepresents material or information entering or leaving the system, such as customer order (input) or a product (output).

ConnectorIndicates that the flow continues where a matching symbol (containing the same letter) has been placed.Plan CheckDo Act


Flow LineLines indicate the sequence of steps and the direction of flow.

DelayIndicates a delay in the process.

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MergeIndicates a step where two or more sub-lists or sub-processes become one.

SubroutineIndicates a sequence of actions that perform a specific task embedded within a larger process. This sequence of actions could be described in more detail on a separate flowchart.

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Manual LoopIndicates a sequence of commands that will continue to repeat until stopped manually.

Data storageIndicates a step where data gets stored.

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How are they useful?

• Create visual maps of a process• Help with planning a project• Quality improvement tool

– Identify processes that need improvement

– Identify unnecessary/ problem steps in a process

– Good communication toolPlan CheckDo Act

Common Rules of Flowcharts

• Indicate and label all elements of the project

• Sequence of events is clear

• No gaps or dead ends

• Must be logical to the user

• Use correct symbols


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Real World Use of Flowcharts

• Production – Manufacturing– Used to identify critical path

• Accounting– Help visualize money flow

• Services– Restaurants– Real estate


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Real World (cont’d)

• Education– Curriculum flowcharts– Student flow through process

• Hospitals– Patient flow– Medical processes

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Summary :• Valuable and unique quality improvement tool• Simple and effective way of visualizing and

understanding a process• Entire organization has an effect on the

flowchart • Everyone involved can take part in improving

the process


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Scatter Diagrams Measuring relationships between variables

WHAT IS IT?• Shows relationship between 2 characteristic values

HOW DOES IT RELATE?• Number of working years and the salary!

• The plating time and the plating thickness!

• Dimensions before and after assembly!

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Scatter Diagrams Measuring relationships between

variables

When Scatter Diagram are preparedcheck the following:•Is there any correlation?

• Are there any abnormally plotted points?

• Is there a need for stratification?

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Example Exercise :The rise in temperature in the motor coil of an electric shaver needs to be controlled so it does not exceed 500C.

The data shown in the next slide is the results of a survey carried out to find out the relationship between the coil temperature (X) and the surface temperature of the motor case (Y).

Collect more then 30 pairs of data to show relationship

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Scatter Diagrams No X Y No X Y No X Y No X Y

1 30.6 15.9 17 37.0 23.2 33 39.5 24.5 49 43.8 26.3

2 33.0 20.0 18 37.1 21.5 34 40.0 22.0 50 44.5 26.8

3 33.2 17.7 19 37.2 22.4 35 40.3 23.0

4 33.5 19.0 20 37.5 20.1 36 40.4 20.9

5 34.2 22.5 21 37.5 23.3 37 40.5 21.3

6 34.3 19.9 22 37.8 21.8 38 40.5 29.9

7 34.7 20.9 23 37.8 23.0 39 40.6 25.7

8 35.6 20.3 24 38.3 23.3 40 41.0 23.7

9 35.6 22.9 25 38.6 22.9 41 41.2 24.4

10 35.7 19.7 26 38.7 24.5 42 41.3 22.2

11 35.7 21.9 27 38.8 20.7 43 41.3 25.7

12 35.9 23.7 28 38.8 21.5 44 41.8 26.8

13 36.0 18.9 29 38.9 23.1 45 42.0 25.0

14 36.2 21.2 30 39.2 23.1 46 42.1 23.1

15 36.3 20.5 31 39.3 23.3 47 42.8 26.6

16 36.4 22.3 32 39.5 22.0 48 42.9 25.5


X-Bar = 38.308 and Y-Bar = 22.608 Gradient, m = 0.645

Y = mX + C , C = Y – mX

C = 22.608 – (0.645*38.308)

C = 22.61– 24.71

C = - 2.1

The Regression Equation is Y = 0.645X – 2.101

m = m = ∑xy – n(x-Bar)(y-Bar)∑xy – n(x-Bar)(y-Bar) ∑ ∑x² -n(x-Bar)²x² -n(x-Bar)²

r = r = ______n∑xy - ∑x∑y______ = .771 ______n∑xy - ∑x∑y______ = .771 √ √[n∑x² - (∑x)²][n∑y² - (∑y)²][n∑x² - (∑x)²][n∑y² - (∑y)²]

See Scatter See Scatter plot Data file.plot Data file.

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Regression

Parameter Est value St dev t studentProb(>|t|)

b0 -2.11 2.96 -0.720.48

b1 0.65 0.08 8.390.00

Residual St dev 1.64y = b0 + b1.x1

R2 0.59

R2(adj) 0.59

F 70.42

Prob(>F) 0.00

Y = -2.11 + 0.65X

R2 = 0.59

P-Value = 0

There is a CorrelationPlan CheckDo Act


X

Y

464442403836343230

30.0

27.5

25.0

22.5

20.0

17.5

15.0

S 1.64175R-Sq 59.5%R-Sq(adj) 58.6%

Fitted Line PlotY = - 2.114 + 0.6453 X

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There is a positive correlation between X and Y.

But how good is the correlation???

The value of r = 0.7711

The Regression Model is 59.59 % accurate.Plan CheckDo Act


3.5

4

4.5

5

150 400 650

An increasAn increase in y may dependupon an increase in x.

PositivPositive Correlation

3.5

4

4.5

5

150 400 650

Negative Correlation

An decrease in y may dependupon an increase in x.

3.5

4

4.5

5

150 400 650

No Correlation

There is no demonstrated connection between x and y.

3.5

4

4.5

5

150 400 650

Positive Correlation?

If X is increased, y may also increase.

3.5

4

4.5

5

150 400 650

Negative Correlation?

If X is increased, y maydecrease.

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Control Charts Identifying Sources Of Variations

•Control charts are used to monitor, control and improve process performance by focussing on its variation and its cause.•The control chart can be thought of as a target. The average line is the bull's-eye and the control limits are the extremes of the target. •Control charts are used by taking periodic measurements or observations of products or processes. These results are compared with calculated control limits, and if the limits are exceeded action is taken (and recorded) to bring the process “back into control”.

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About This Module…

Six Sigma, A Quest for Process PerfectionMeet Goals and Attack Variation

Control charts portray process performance andseparate causes of variation:

• Random• Assignable

Control Chart Systems are:• A proven technique for improving productivity• Effective in defect prevention• Prevent unnecessary process adjustments• Provide diagnostic information• Provide information about process capability

\DataFile\Attribut mtw\DataFile\Variable.mtw

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1. Control charts are a powerful tool to hold the gains.

2. How control charts discriminate between common cause and assignable cause variation.

3. Why control charts must be designed to fit the data type and the control purpose.

What We Will Learn.

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Uses of Control Charts1) Attain a state of statistical control:

• All subgroup averages and ranges within control limits - no assignable causes of variation present

2) Monitor a process

3) Determine process capability

What happens after an out-of-control situation occurs at the core of a successful SPC program?

Juran’s Quality Control Handbook, 4th edition, page 24.7

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General Conceptsw = some characteristic of interest

= mean of each sample

Sw = standard deviation of w

Upper Control Limit

Centerline =

Lower Control Limit

Therefore 99.73% of points will be within the control limits unless there is an assignable cause

WX

3 wUCL X S

3 wLCL X S X

Plan CheckDo Act

Components of a Control Chart

20100

4

3

2

1

0

Sample Number

Sam

ple

Co

unt

U Chart for Defects

U=1.930

UCL=3.794

LCL=0.06613

Upper Control Limit

Lower Control Limit

Center Line

How many points do we need to set the initial

control limits?

Plan CheckDo Act

Control Chart Selection TreeType of data

Count or Classification

Discrete

Fixed or variable

opportunity?

Count

C Chart

Fixed

U Chart

Variable Fixed or variable

opportunity?

Attribute

NP Chart

Fixed

P Chart

Variable

Subgroup >1?

Variable

IMR Chart

No

X Bar and Ror

X Bar and S

Yes

Supplement with EWMA if

CTQ is sensitive to

small process shifts

Plan CheckDo Act

DefinitionsNon-conformance (defect)

A single instance of a failure to meet some requirement

Non-conforming Unit (defective)A single item containing one or more non-conformance

Plan CheckDo Act

Types of Attribute Control Charts

*Juran’s Quality Control Handbook, Fourth Edition

c chart: Number of non-conformances in a sample

Use & effectiveness: • All subgroups are the same size • Effective when the number of non-conformances possible

on a unit is large, but the percentage of any single non-conformance is small

Example:• Surface irregularities, flaws, pinholes on continuous or

extensive products such as yarn, wire, paper, textiles or other sheeted materials. The chance of a non-conformance occurring at any one spot is small, but the overall opportunity for non-conformance may be great.*

Plan CheckDo Act

Types of Attribute Control Chartsu chart: Defects Per Unit (DPU)

Use & effectiveness: Use when several independent (required) non-conformities may occur in one unit, document, etc.

• Samples not required to be the same sizeExample:

Complex assembly or document; electronic assembly, purchase order, bill of material, etc.

np chart: Number Non-conforming Use & effectiveness:

Use when direct count of the number of non-conforming in a subgroup is desired.

• All subgroup sizes must be the same (Juran 24.22).

p chart: Fraction or Proportion Non-conformingUse & effectiveness:

Use to describe a single quality characteristic or two or more characteristics considered collectively.

• Samples not required to be the same size

Plan CheckDo Act

c (Count of Defects) Chart Formulae

Center Line c

c3cLCL

c3cUCL

Plan CheckDo Act

LCL uu

n 3

Center Line u

UCL uu

n 3

u (Defects per Unit) Chart Formulae

Plan CheckDo Act

np Number Nonconforming Chart Formulae

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

CL = n p

Plot the number of nonconforming not the percentage of nonconforming. Variable sample sizes are OK.

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

Plan CheckDo Act

LCL pp p

n

3

1( )

UCL pp p

n

3

1( )

Center line = p

To estimate p ( ) measure 20 - 25 samples calculate the average proportion defective. Use this as a trial p until more data is available. Variable sample sizes are OK.

p

P (Proportion Defective) Chart Formulae

Plan CheckDo Act

C Chart ExampleFile= /Datafiles/Attribut.mtwStat>Control Charts>Attribute Charts>C Chart

Plan CheckDo Act

464136312621161161

50

40

30

20

10

Sample

Sam

ple

Cou

nt

_C=20.7

UCL=34.35

LCL=7.05

1

1

C Chart of C Chart Data

Worksheet: Attribut.MTW

The C Chart

Data points 6 and 15 were the result of errors. Replace them with asterisks to indicate missing data then replot.Plan CheckDo Act

The New Chart

464136312621161161

35

30

25

20

15

10

5

Sample

Sam

ple

Cou

nt

_C=19.58

UCL=32.86

LCL=6.31

C Chart of C Chart Data

Worksheet: Attribut.MTW

Plan CheckDo Act

The U Chart

Plan CheckDo Act

The U Chart

Note: the control limits changed as the sample size changed.

191715131197531

2.0

1.5

1.0

0.5

0.0

Sample

Sam

ple

Count Per

Unit

_U=0.763

UCL=1.439

LCL=0.086

1

U Chart of Defects found

Worksheet: Attribut.MTWTests performed with unequal sample sizes

Plan CheckDo Act

The NP Chart

NP charts should be used only when the subgroup size is uniform.

Plan CheckDo Act

The NP Chart

464136312621161161

40

30

20

10

0

Sample

Sam

ple

Count

__NP=19.17

UCL=30.98

LCL=7.36

1

1

1

NP Chart of Batch 1

Worksheet: Attribut.MTWNote: The session window describes the special causes identified on the chart.

Plan CheckDo Act

The P Chart

Plan CheckDo Act

The P Chart

252321191715131197531

0.20

0.15

0.10

0.05

0.00

Sample

Pro

port

ion

_P=0.0955

UCL=0.1885

LCL=0.0026

1

P Chart of Defectives

Worksheet: Attribut.MTWTests performed with unequal sample sizes

Plan CheckDo Act

Control Charts for Variable Data

Much more sensitive than charts for attribute data charts

• X bar and R• X bar and s

Critical decisionsSample size - the width of the control limits is inversely

proportional to the sample size for any multiple of s

Subgroups - chances of differences due to assignable causes within subgroups should be minimized (same operator, shift, head, material etc.)

Plan CheckDo Act

X and R Control Chart Formulae & Constants

2

4

3

X Control Limits =X ± A R

R Upper Control Limit = D R

R Lower Control Limit = D R

SampleSize

A2 D3 D4 d2

2 1.880 - 3.267 1.1283 1.023 - 2.574 1.6934 .729 - 2.282 2.0595 .577 - 2.114 2.3266 .483 - 2.004 2.5347 .419 .076 1.924 2.7048 .373 .136 1.864 2.8479 .337 .184 1.816 2.970

10 .308 .223 1.777 3.078

Plan CheckDo Act

X and s Chart Formulae & Constants 1

4

3

X Control Limits = X ± A s

s Upper Control Limit = B s

s Lower Control Limit = B s

N A1 B3 B4

2 2.121 0 3.267

3 1.732 0 2.568

4 1.500 0 2.089

5 1.342 0 2.089

6 1.225 .030 1.970

Plan CheckDo Act

Creating an X-bar and R Chart

Plan CheckDo Act

An X-Bar and R Chart

Sample

Sam

ple

Mean

45403530252015105

41

40

39

38

__X=40.000

UCL=41.294

LCL=38.706

Sample

Sam

ple

Range

45403530252015105

4.5

3.0

1.5

0.0

_R=2.243

UCL=4.743

LCL=0

5

1

66

1

11

1

1

1

Xbar-R Chart of measure1, ..., measure5

Worksheet: Variable.MTW

The numbers show violations of the assumption of control. The nature of the violation is given in the session window.

Plan CheckDo Act

StatGuide Interprets the Tests

Plan CheckDo Act

Rules of Standard Deviation“where the data is?”

UCL

LCL

99-99.9%

90-98%

60-75%1 Sigma

1 Sigma

2 Sigma

2 Sigma

3 Sigma

3 Sigma

A

A

B

B

C

C

Time

Me

as

ure

d V

aria

ble

% of Data

Plan CheckDo Act

Plan CheckDo Act

Test 1

Test 2 One or more points beyond the 3 limit

2 out of 3 pts > 2 std Dev from the center line (same side)

Plan CheckDo Act

Test 4

Test 3

4 out of 5 pts > 1 Std Dev from the center line (same side)

8 pts in a row > 1 Std Dev from the center line (either side)

Plan CheckDo Act

One or more points beyond the 3 limit

8 pts in a row > 1 Std Dev from the center line (either side)

Test for special causes (pattern)( For Range Chart )

Plan CheckDo Act

Cycle Pattern

* The cycle pattern repeats continuously.* This is an indication of special causes

Plan CheckDo Act

* Trend is identified with - Points moving in one direction ( up or down )- Points does not change direction continuously.

Trends are easily noticeable

Trends occurs when more than six points continuously moves upwards or downwards.

Plan CheckDo Act

Mixture

Points falling near the UCL or LCL crossing the center line.

Mixture pattern contains 2 different types of patterns on the same chart….one falling on the UCL and the other on the LCL.

Mixture pattern occurs when 8 points continuously fall on both side of the centerline without any point on Zone C.

Plan CheckDo Act

Systematic

Continuously points are alternating …up, down, up, down without changes.

Points not necessarily alternating only, as long it is moving up anddown, it is termed systematic.

Systematic pattern is occuring as long as 14 points continuously is alternating up and down.

Plan CheckDo Act

Hugging at centerline

All the points distribution comparing with the width between UCLand LCL : Points are distributed at the centerline. No points at UCL and LCL

Hugging shows:- There is special causes existing or the process has changed.- Sampling method is not good- Two population existing (Bimodal)

Hugging happens if more than 15 points are distributed in Zone C.

Shewhart’s concept of variation “Every process has variation; some process exhibits

controlled variation , while others exhibits uncontrolled variation ” - (Walter Shewhart)

• Controlled variation exhibits patterned variation characteristic which is stable and consistent against time.

• Uncontrolled variation exhibits inconsistent variationwhich changes against time. This type of variation is not consistent and not stable.

Plan CheckDo Act

Causes of Variation

Controlled Variation = Common CausesControlled Variation = Common Causes

We will call any unknown random cause of variation a chance cause or a common cause, the terms are synonymous and will be used as such. If the influence of any particular chance cause is very small, and if the number of chance causes of variation are very large and relatively constant, we have a situation where the variation is predictable within limits. You can see from the definition above, that a system such as this qualifies as a controlled system. Where Dr. Shewhart used the term chance cause, Dr. W. Edwards Deming coined the term common cause to describe the same phenomenon.

Uncontrolled Variation = Special CausesUncontrolled Variation = Special Causes

At times, the variation is caused by a source of variation that is not part of the constant system. These sources of variation were called assignable causes by Shewhart, special causes of variation by Dr. Deming. Experience indicates that special causes of variation can usually be found without undue difficulty, leading to a process that is less variable.

Plan CheckDo Act

Plan CheckDo Act

Common causes Vs Special CausesSpecial Causes

Common causes

BreakthroughImprovement

To achieve our goal, which will we concentrate, common causes or special causes?

0.01

0.02

0.03

J F M A

Party TimeParty Time

Com

plai

ns p

er m

illio

n un

its

Year 1 Plan CheckDo Act

Intuitive SPC -Case Study 1~The factory scrap level is at a year low of 2%

~Manager presents an award to the plant

~Ceremony in the cafeteria:Pizza and refreshment for all!

~Everyone should be proud of what you have accomplished

0.01

0.02

0.03

J F M A M J J

Manager wantsto take backthe award

Manager wantsto take backthe award

Com

plai

ns p

er m

illio

n un

its


Intuitive SPC -Case Study 1~Three consecutive months of scrap increases

~Manager wishes he could take back the award.

~Instead of holding the gains, scrap went right back up

~Manager decides:"This group just needs tough management

0.01

0.02

0.03

J F M A M J J A S O

Scrap is highest this year. Action needed!!!

Scrap is highest this year. Action needed!!!

Com

plai

ns p

er m

illio

n un

its


Intuitive SPC -Case Study 1~ Scrap rises to a value of 2.6%

~ Manager decides to take action.

~ A "special meeting" is called to solve this problem once and for all.

~ After a sound lecture on the importance of scrap, the manager leaves. Employees aren't sure what to do.

~ Besides, they have other metrics which have more importance. So they do nothing.

0.01

0.02

0.03

J F M A M J J A S O N D J F M A

Com

plai

ns p

er m

illio

n un

its

Year 1 Year 2

Manager Conclude: Tough management style gets results

Plan CheckDo Act

Intuitive SPC -Case Study 1~Manager has seen reduced scrap levels since the end

of last year. "Things are looking - up!"

(Although nothing had been done to change the system)

~His takeaway: "A tough management style gets results"

In this excercise, what’s your opinion withregards to the Manager’s action?

Is the Manager’s action valid?

Is the Manager’s action fruitful?

Kalau dilihat mengunakan control chart,bagaimana rupanya?

Plan CheckDo Act

Intuitive SPC -Case Study 1

0.01

0.02

0.03

J F M A M J J A S O N D J F M A

Com

plai

ns p

er m

illio

n un

its

Year 1 Year 2

Control Chart shows the VOP

LCL

UCL

Plan CheckDo Act

Manager: "Hey, I made my decision based on data - How can I go wrong ?"

Black Belt: "Your decisions were made from observing high and low points as signals. When in reality, it was all noise.

Look at the data, there was no significant change in the

process."

Pokerchip Excercise

(1) Take sample from process Pokerchip once an hour, total sample is 5 each time. Obtain data from Normal distribution with mean = 100 and stdev = 10.

(2) Continue collecting sample until 15 samples are obtained.

(3) Enter data into XBar-R Chart dan estimate: - Mean Sample Xbar-Bar - Range Sample Rbar - Stdev Sample X bar

(4) Save data into file Pokerchip.excel.

Plan CheckDo Act

This Tool Is Given FreeThis Tool Is Given Free



n 5 x-bar-bar 98.98 R-bar 22.23 s-bar 8.976

UCL 111.8 UCL 47.02 UCL 18.75

LCL 86.16 LCL 0 LCL 0

Pokerchip Excercise

Plan CheckDo Act

Excercise:

• Open file ‘Tensile_Strength’.

• Plot Xbar / R chart for Strength.

• An Engineer who has 10 years experience inthe process says he face no problem with the process.

Do you belief what he is saying?Analyze the control chart that has been plotted.

Plan CheckDo Act

252321191715131197531

58

56

54

52

50

Sample

Sam

ple

Mean

__X=55.093UCL=55.803

LCL=54.382

252321191715131197531

8

6

4

2

0

Sample

Sam

ple

Range

_R=1.232

UCL=2.605

LCL=0

11

115

8

1

1

1

1

5

1

1

1

1

1

1

1

1

5

1

2

1

1

1

Xbar-R Chart of Strength

Tensile Strength X-Bar R Chart

Does the process has problems?Is the process in-control?Plan CheckDo Act

Control Chart for Batch process• Range chart shows variation within batch.

• However, X bar R chart is not suitable. X bar chart only uses variation within batch for determining control limit. This assumption when variation between batch can be ignored (negligible).

•Generally, variation between batach is greater than variation within batch.

• For batch process, the type of control chart is IMR-R chart; where every mean sample is expected an individual value.

Batch 1 Batch 2 Batch 3

X-Bar, R X-Bar, R X-Bar, RPlan CheckDo Act

I-MR-R ChartsI-Chart

CL = XUCL = X + 2.66 MRLCL = X – 2.66 MR

MR ChartCL = MRUCL = D4 MRLCL = D3 MR

Range ChartCL = RUCL = D4 RLCL = D3 R

(D4 dan D3 when n=2,because MR is range between2 continuous measurement)

Plan CheckDo Act

I-MR-R Chart Output

252321191715131197531

60

55

50

Subg

roup

Mea

n

_X=55.09

UCL=61.61

LCL=48.57

252321191715131197531

8

4

0

MR

of

Subg

roup

Mea

n

__MR=2.452

UCL=8.010

LCL=0

252321191715131197531

8

4

0

Sample

Sam

ple

Ran

ge

_R=1.232

UCL=2.605

LCL=02

1

1

1

I-MR-R/ S (Between/ Within) Chart of Strength

Batch mean

Batch-to-Batchvariation

Variation withinBatch

Plan CheckDo Act

Comparison between Xbar chart dan I-MRchart for mean sample

252321191715131197531

58

56

54

52

50

Sample

Sam

ple

Mean

__X=55.093

UCL=56.361

LCL=53.825

252321191715131197531

8

6

4

2

0

Sample

Sam

ple

Range

_R=2.198

UCL=4.647

LCL=0

11

66

1

1

1

1

1

1

1

1

1

1

1

1

22

2

2

2

1

1

Xbar-R Chart of Strength

252321191715131197531

60

55

50

Subg

roup

Mea

n

_X=55.09

UCL=61.61

LCL=48.57

252321191715131197531

8

4

0

MR o

f Su

bgro

up M

ean

__MR=2.452

UCL=8.010

LCL=0

252321191715131197531

8

4

0

Sample

Sam

ple

Ran

ge

_R=1.232

UCL=2.605

LCL=02

1

1

1

I-MR-R/ S (Between/ Within) Chart of Strength

Control limit only depictsvariation within batch.Subgroup Mean

Control limitdepictsvariation between and within batch

Plan CheckDo Act

CONTROL CHART FOR INDIVIDUAL DATA.

• Sometimes, we are forced to use one measurement only (compared with taking one sample containing more than one measurement). (Jumlah sample n = 1)

This happens when,

- measurement is expensiveExample : Test destroying the product (destructive test)

- Measurement obtained from standard sources.Example : pH measurement for chemical mixture

Based on total Sample n = 1Based on total Sample n = 1

Plan CheckDo Act

Sample X MR 1 4 ... 2 4 ... 0 3 3.3 ... 0.7 4 4.7 ... 1.4 5 5.3 ... 0.6

I – MR Control Charts

I CHART

Centre Line = X

Upper Control Limit = X + 2.66 MR

Lower Control Limit = X - 2.66 MR

MR CHART

Centre Line = MR

Upper Control Limit = 3.27 MR

Lower Control Limit = NonePlan CheckDo Act

X Chart

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73

Batch Number

X V

alu

e

Pokerchip

UCL X

LCL X

Data Mean

Print Chart Return To Data Entry Screen

This Tool is Given FreeThis Tool is Given Free

Moving Range Control Chart

0

5

10

15

20

25

30

35

40

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73

Batch Number

MR

Val

ue Moving Range

UCL MR

LCL MR

MR Mean

Print Chart Return To Data Entry Screen

This Tool is Given FreeThis Tool is Given Free

END7 QC Tools

qcc & 7qc tools

Documents

act plan module

act module

quality tools plan

act overview

act secondary datathe

act primary datathe

act analysis of dataone

quality problems