7 qc tools rev 5

8/3/2019 7 QC Tools Rev 5

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7 QC Tools • Brainstorming• Data Collection• Pareto Diagram• Cause & Effect Diagram• Scatter Diagram• Graphs & Control Charts• Histogram

• Stratification

7 QC Tools



Brainstorming

Brainstorming is a group technique for generating new,useful ideas. This promotes unconventional, creativethinking. Brainstormed ideas are mixture of Creative,imaginative as well as Logical thinking.

Group has to discourage analytical or critical thinking and aimfor large number of new ideas in the shortest possible time.

The ideas generated can’t be used as substitute for data.



When to use Brainstorming ?

1. For Defining Projects2. Formulating theories of Causes during Diagnostic Journey3. Designing Solutions



Steps for Conducting Brainstorming 1. Phrasing the statement

- Specific for clear focus

- Broad enough to ensure all useful ideas- Should not be biased

2. Preparation for brainstorming- Inform the participants in advance to initiate thinking

3. Do brainstorming- Make contribution in turn- Only 1 idea in a turn- Say „Pass‟ if no idea in a turn - Do not provide explanation for your idea.

- Write all contributions on flip chart / transparency.- Don‟t extend beyond 45 minutes.

4. Processing of Ideas- Clarify each contribution

- Combine & group similar ideas- Agree on evaluation criteria for short listing



Data Collection

The important principle of TQM is „ Decision based on

Facts & Data. Without facts, Problem Solving efforts arereduced to Guessing or trial & error.

• Objective of collecting data is to generate useful informationwhich in turn help in decision making

• For generating useful information

- Formulate the question/s we are trying to answer

- Collect the data & facts relating to the question/s.- Analyze the data to determine factual answer- Present the data in a way that clearly communicatesthe answers to our question/s.



Planning for Data collection

• What question do we need to answer• How we will recognize & communicate the answers to the

questions -Tells about required data• What Data Analysis tools we need to use• What type of data do we need in order to construct the tool

• Where in the process can we get this data• Who in the process can give this data• How we can collect data from those people with minimum

efforts & chance of error.

• What additional information do we need to capture for futureanalysis, reference & traceability



Types of Data collection forms -

1. Check sheets - It is a simple data recording form

designed in such a way that the results can be readilyinterpreted. E.g. Line Graph recording data & at the sametime showing trend of variation, Putting tally marks againstapplicable listed specific defect types while inspecting,Concentration Diagram.

2. Data sheet - The data is gathered in simple tabular form.Unlike check sheet, data sheet does not readily tell us abouttrend. Further processing like constructing Histogram isrequired to get useful information.

3. Check Lists - It contains items relevant to a specific issue orsituation.Though the primary purpose is to guide operations& not for collecting data, check list provide valuable data foranalysis by Quality Improvement Teams. A example of

Check List is a Audit Check List.



How much Data ?

The Sample data has to be truly representative of the

actual situation on continuos or long run of the process.

Data has to be collected in sufficient sample size , collectedover a period of time accounting to all possible variations.

This will ensure drawing right conclusions & inferences.

Data can be Representative by ensuring to cover full spectrumof the operations, covering all times (Shift start,end, Lunch / teabreaks, week ends etc..) , all operating people, all concernedequipment.



Pareto Analysis Pareto analysis is a ranked comparison of factors related to a

quality problem. It helps a quality improvement project teamto identify and focus on the vital few factors.

Pareto analysis gets its name from the Italian born economist

Vilfredo Pareto who observed that a relative few people held themajority of the wealth.

Dr.Juran was the first to point out that what Pareto had observedwas a „Universal‟ principle.

Pareto principle - In any group of factors contributing to acommon effect ,a relative few account for the bulk of the effect.Few contributors - Vital Few

Many other contributors - Useful many



When to use Pareto Analysis ?

• Prioritizing Problems - By far the most common use of Paretoanalysis is in selecting & defining Quality Improvement Projects.

• Analyzing Symptoms - After the project is identified, it usuallyneeds to be refined further to determine the vital few componentsof the symptoms.



How to Construct a Pareto Diagram 80 %

100 %

1. Total the data on effect of each contributor & sum these to

determine grand total.2. Re order the contributors from largest to the smallest

3. Determine the cumulative percent of total

4. Draw & label the left vertical axis.

5. Draw & label the horizontal axis.

6. Draw & label the right vertical axis.

7. Draw bars to represent the magnitude of each contributor’s effect.

8. Draw a line graph to represent the cumulative percent of total

9. Analyze the diagram

10. Title the chart, label the ‘ vital few’ and ‘useful many’ & show the cumulative percent contribution of the vital few.

Contributors

%Grand total



Cause - Effect Diagram

Basic to Quality Improvement is the need to identify theRoot Causes of the Problem. The Cause - Effect Diagramis an effective way to organize and display the varioustheories about what those Root Causes might be.

This tool was first introduced by Dr.Kaoru Ishikawa in 1943.The diagram is also called Ishikawa Diagram.

C- E diagram shows only theories of causes or probable causesemerged while Brain storming & not facts.Some of the theories become facts or Root Causes only after

testing for validity with the help of data.



Lost Control

of Car

Flat Tire Slippery Road

Driver Error Mechanical Failure

Rock

GlassBlow out

Nail Ice

SnowRain

Oil

Poor reflexes

Sleepy

Chemicallyimpaired

Poor Training

Reckless

Stuck

Accelerator

Broken Tie rod

Worn Pads

Fluid Loss

BrakeFailure

Cause - Effect Diagram for Lost Control of Car



Referring to the Diagram, it is evident that the C - E diagram is a visual presentation of the „theories of causes‟ arranged to show the inter relationship between the effect and the main

cause - sub cause - sub - sub - cause and so on. This inter -relationship is called as “causal - chain”. E.g. „ Worn pads cause brakes failure, which in turn causes mechanical failure resulting in lost control of the car.‟ or

„The car lost control - Why ? - Due to Mechanical Failure - Why?- Brakes‟ failure - Why ? - Worn pads.‟

• This causal chain is the heart of any C - E diagram . Withoutestablishing it, the C - E diagram is meaning less.

• All possible sources of causation should be considered.- Manufacturing Problems - 5 M viz. Man,Material, Method,Machines, Measurements

- Service Problems - 5 P viz. People, Provisions, Procedures,

Place and Patrons (Customers)



How to construct C - E Diagram ?

Step 1 - Define the Effect or the Symptom for which the causesare to be identified. It should be specific & not vague.

Step 2 - Use Brain storming to identify the possible causes.Identify major main causes & then probe for sub causes,sub - sub causes ( Why - Why analysis ) & so on.

Step 3 - Categorize major causes, normally not more than 6categories. Put each category in box.

Effect

Category

Cause

Sub Cause

Sub sub cause

How much to Probe?

To the point where it becomes controllable & that if same is eliminated, the

problem effect will be either eliminated or reduced.



Step 4 - Check the logical validity of each causal chain

Step 5 - Check for completeness.We may doubt completeness in following cases -- Main branches with fewer than 3 causes- Main branches with substantially fewer causes than

most others.- Fewer levels of subsidiary causes than do the other

main branches have.- Main branches that have substantially more causes

than most of the others.



Scatter Diagram

A Scatter diagram is a graphic presentation of the relationshipbetween two variables. In quality improvement, scatter diagramsare usually used to explore cause - effect relationships in thediagnostic journey.

It establishes correlation

A example - A Hydraulic system was tripping forindication of overload. The tripping mechanism is providedas safety. The electronic control circuitry was executing safetyshut down of the hydraulic system , with Voltage as a signal.

The complaint was more in Summer months from areas thatwere close to large bodies of Water.



One of the Probable cause was “ The higher Relative

Humidity causes sticky ness in the system , leading to increased

load and subsequent tripping.”

•

• •

•

• •

• •

•

• •

•

• • •

Cause

Effect

Voltage

Relative

Humidity

The diagram clearly shows that as Relative Humidity increasesthe sensor reads higher voltage. This causes circuitry to believethat the hydraulic system had exceeded it limits & trips.



The graphic nature of the scatter diagram helps a team to seethe relationship between the variables.

The patterns of Correlation -

Positive Correlation Negative correlation

Or

No Correlation

A) Linear

B) Non Linear

C)

It can be statistically described as Pearson‟s Correlation Coefficient

( It can be strong or Weak correlation)



What is Graph ?

Graph is a diagram which explains connections orinter relations amongst 2 or more things. These connectionsare represented by a number of distinctive dots, lines, bars,columns.

Common type of graphs -

• Line Graph• Column / Bar Graph

• Pie Graph



Definition of Control Chart

A Control Chart is a special type of trend chart used todetect the special causes of variation in the process.

Types of Control Charts -

• For Variables , i.e. For the characteristics that can bemeasured - X - R Chart

• For Attributes - For characteristics that can be judges as

pass or fail, go or no-go, defective or non-defective etc.- np chart, p chart, c chart, u chart



Advantages of using control charts -

• It is a effective tool to control the process statistically (SPC)

•It helps to detect changes in the process over a period of timeand take corrective action.

• It differentiates chance cause variation & assign able causevariation in the process.

• It is an effective tool which helps to reduce variations in the

process

• It provides information about process capability.



Histograms

A Histogram is a Graphic summary of variation in a setof data. The pictorial nature of the Histogram enables usto see patterns that are difficult to see in a simple tableof numbers.

This is a good tool to analyze data that contain variation.

It tells us about pattern of variation of the process.

The tool is most useful to work out Process Capability Index.

Hence this tool is used to compare before project &after project process variation spread, pattern, improvementin Cp.



Steps for Constructing a Histogram

1. Collect the measurements / data.2. Arrange / Group the data column wise in Table form3. Find & mark the largest and smallest number / value in each group.4. Find the largest(Max) and smallest value(Min.) in whole set.5. Calculate the range of measurements, i.e. Range = Max. - Min.6. Determine the number of class intervals for the Frequency diagram

Guidelines :-No of Readings No of Class intervals

< 50 5 to 750 to 100 6 to 10> 100 7 to 12

7. Determine Intervals and BoundariesInterval = Range / Class Interval

8. Determine the frequencies of each class interval with tallies.9. Prepare the frequency Histogram

- mark & label the vertical scale (Frequency)- Mark & label the horizontal scale ( Measurement value)- Draw the columns according to the frequency tallies.

- Label the Histogram.



Data of 40 Diameter measurements

( Nominal value 25 mm)

23 22 24 25

27 25 26 2426 26 25 23

24 27 25 25

22 24 24 26

25 25 25 26

24 23 25 27

28 27 27 28

23 28 28 26

24 23 26 26

Range= 28 - 22=6

No of Class interval = 6

Interval = Range/Class interval =1

The Boundries - Frequency

21.5 - 22.5 2

22.5 - 23.5 5

23.5 - 24.5 7

24.5 - 25.5 9

25.5 - 26.5 8

26.5 - 27.5 5

27.5 - 28.5 4

Total 40

Histogram

2

5

7

9

8

5

4

01

2

3

4

5

6

7

8

9

10

21.5 - 22.5 22.5 - 23.5 23.5 - 24.5 24.5 - 25.5 25.5 - 26.5 26.5 - 27.5 27.5 - 28.5

Diameter

F r e q u e n c y

Note - The bars have to be attached to each other- No gap between bars

Because of software limitation the Compromise is done



Normal Distribution

What is variation ?

Variation is a natural phenomena. No 2 components/ Products areexactly identical in manufacturing process. This may occur due todifferences in machine, tool, material, human beings etc.

Causes of Variation

Variation are caused due to - a) Chance causes b) Assignable causes

a) Chance cause - These causes can‟t be economically eliminated. E.g. Slight variation in raw material, slight machine vibration,

Backing of threading , variations of ambient temperature etc.b) Assignable cause - These are predictable and result in large process

variation. E.g. Defective batch of raw material, Tool worn out , SOP

not followed etc.



What is normal distribution ?

If a process is operating under chance causes only ( No assignablecause) then the measurement data ( minimum 40 readings) collectedfrom such process when put into histogram, it could be observed thatthe distribution is having two characteristics viz. Central tendency &Dispersion and the curve of distribution( If all mid points of top of

histogram bars are connected), reveals smooth curve like bellshape, which is a Normal distribution curve.

Important - For normal distribution, process has to be stable,having completeabsence of assignable causes. Such Process is said to be statistically under

control, which is a pre requisite for establishing process capability.



Stratification What is stratification ?

Stratification is used to classify details according to some specificparameters / grade / category.

This classification enables to focus greater attention on different layers

of the problem.

Stratified data Mix data



Examples for application of Stratification -

Example 1 -

Machine shop rejection can be stratified as follows -

- Shift wise- Machine wise- Operator wise- Component wise- Defect wise etc.

Example 2 -

There is need to improve the living facilities at Mumbai. To know what

are all the facilities to be added , the population is to be studied.The population may be classified as follows -

- Age group- Income- Education

- Occupation etc

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