seven tools of tqm

7/31/2019 Seven Tools of TQM

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SEVEN TOOLSOF

TQMIPE 381: Measurement and Quality Control


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TOTAL QUALITY MANAGEMENT

It refers to the organization wide effort toachieve quality.

It extends to suppliers as well as customers.

In TQM, customer is the focal point.

Customer satisfaction is the main drivingforce.

Quality is affected by the activities of all thedepartments in the organization.


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TQMAPPROACH

Find out what customers want

Design the product according to customerneeds.

Design a production process that facilitatesdoing the job right the first time.

Keep track of results.

Extend these concepts to suppliers anddistribution.


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CHARACTERISTICSOF TQM

Continual improvement

Customer focus

Organization wide activity

Employee empowerment

Team approach

Competitive benchmarking

Team approachKnowledge of tools

Internal and external customers


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SEVENBASICTOOLSOF TQM

Kaoru Ishikawa contends that 95% of a company'sproblems can be solved using these seven tools. Thetools are designed for simplicity.

Flow Charts

Ishikawa Diagrams or Cause-Effectdiagram

Checklists

Pareto Charts Histograms

Scatter diagrams

Control Charts


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FLOW CHARTS

A flow chart shows the steps in a process i.e.,actions which transform an input to an output forthe next step.

This is a significant help in analyzing a process

but it must reflect the actual process used ratherthan what the process owner thinks it is or wants itto be.

The differences between the actual and theintended process are often surprising and providemany ideas for improvements.

Often, non-value-added steps become obviousand eliminating these provides an easy way toimprove the process.


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ISHIKAWA DIAGRAMS/ CAUSE EFFECTDIAGRAMS/FISHBONE DIAGRAM

Dr. Kaoru Ishikawa, a Japanese quality controlstatistician, invented the fishbone diagram. Therefore, itmay be referred to as the Ishikawa diagram.

Their function is to identify the factors that are causing anundesired effect (e.g., defects) for improvement action.

Because of the function of the fishbone diagram, it maybe referred to as a cause-and-effect diagram.

The design of the diagram looks much like the skeletonof a fish. Therefore, it is often referred to as the fishbonediagram.


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HOWISAFISHBONEDIAGRAM

CONSTRUCTED?

This is a three step process.

Step 1

Write down the effect to be investigated and draw the 'backbone' arrow to it. Inthe example shown below the effect is 'Incorrect deliveries'.


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HOWISAFISHBONEDIAGRAMCONSTRUCTED?

Step 2

Identify all the broad areas of enquiry in which the causes of theeffect being investigated may lie. For incorrect deliveries thediagram may then become:

For manufacturing processes, the broad areas of enquiry whichare most often used are Materials (raw materials), Equipment(machines and tools), Workers (methods of work), andInspection (measuring method).


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HOWISAFISHBONEDIAGRAM

CONSTRUCTED?

Step 3 This step requires the greatest amount of work and

imagination because it requires you (or you and yourteam) to write in all the detailed possible causes in each

of the broad areas of enquiry. Each cause identifiedshould be fully explored for further more specific causeswhich, in turn, contribute to them.

You continue this process of branching off into more and

more directions until every possible cause has beenidentified. The final result will represent a sort of a 'minddump' of all the factors relating to the effect beingexplored and the relationships between them.


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CE diagram


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CATEGORIESOF MAJOR CAUSES

The 6 Ms (used in manufacturing) Machine (technology)

Method (process)

Material (Includes Raw Material, Consumables and

Information.) Man Power (physical work)

Measurement (Inspection)

Mother Nature (Environment)

The original 6Ms used by the Toyota Production Systemhave been expanded by some to included the followingand are referred to as the 8Ms.

Management/Money Power

Maintenance


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CATEGORIESOF MAJOR CAUSES

The 5 Ss (for service

industry)

Surroundings

Suppliers Systems

Skills

Safety

The 8 Ps (for service

industry)

Product=Service

Price Place

Promotion/Entertainment

People(key person)

Process Physical Evidence

Productivity & Quality


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TYPESOF CE DIAGRAM

Cause Enumeration

This identifies one by one all possible causes frombrainstorming sessions and then classifies into groups.

Process Analysis

It follows the process step by step and causes are listedas per process step.

Prior to develop a process type CE diagram, processflow chart is a must.


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CHECKLIST

Checklists are a simple way of gathering data so thatdecisions can be based on facts, rather than anecdotalevidence.

The following figure shows a checklist used to

determine the causes of defects in a hypotheticalassembly process. It indicates that "not-to-print" isthe biggest cause of defects, and hence, a goodsubject for improvement.

Checklist items should be selected to be mutuallyexclusive and to cover all reasonable categories.


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PARETO CHARTS

Alfredo Pareto was an economist who noted that a fewpeople controlled most of a nation's wealth. According tohim 80% of the countrys wealth is occupied by 20% of thepopulation.

"Pareto's Law" has been applied to many other areas,including quality control, where a few causes are

responsible for most of the problems.

These few(say 20%) are known as vital few and the restmany (say 80%) are known as trivial many.

These "vital few causes can be separated from the"trivial many using a diagram known as a Pareto chart.

They are actually histograms aided by the 80/20 rule adaptedby Joseph Juran.

Remember the 80/20 rule states that approximately 80% ofthe problems are created by approximately 20% of thecauses.


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PARETO CHART

Usually Pareto chart contains both bars and a line graph,

where individual values are represented in descendingorder by bars, and the cumulative total is represented bythe line.

The left-side vertical axis of the pareto chart is labeled

Frequency (the number of counts for each category), theright-side vertical axis of the pareto chart is thecumulative percentage, and the horizontal axis of thepareto chart is labeled with the group names of your

response variables.

To take the example in the next slide, in order to lowerthe number of defects by 80%, it is sufficient toconcentrate and analyze the causes of defects in the first

three workstations.


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STRATIFICATION ANALYSIS

Stratification is a technique used in combination withother data analysis tools.

When data from a variety of sources or categories havebeen lumped together, the meaning of the data can be

impossible to see. This technique separates the data so that patterns can

be seen.

When to Use Stratification Before collecting data.

When data come from several sources or conditions, such asshifts, days of the week, suppliers or population groups.

When data analysis may require separating different sourcesor conditions.


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STRATIFICATION ANALYSIS(CONTD)

For example, the following figure plots defects against threepossible sets of potential causes.

The figure shows that there is no significant difference indefects between production lines or shifts, but product typethree has significantly more defects than do the others.

Finding the reason for this difference in number of defectscould be worthwhile.


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STRATIFICATION ANALYSIS(CONTD)


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The ZZ400 manufacturing team drew a scatter diagram totest whether product purity and iron contamination wererelated, but the plot did not demonstrate a relationship. Thena team member realized that the data came from three

different reactors. The team member redrew the diagram,using a different symbol for each reactors data:

Now patterns can be seen. The data from reactor 2 andreactor 3 are circled. Even without doing any calculations, it isclear that for those two reactors, purity decreases as ironincreases. However, the data from reactor 1, the solid dotsthat are not circled, do not show that relationship. Somethingis different about reactor 1.


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HISTOGRAM

Histograms are another form of bar chart in whichmeasurements are grouped into bins; in this caseeach bin representing a range of values of someparameter.

The histogram graphically shows the following:

center (i.e., the location) of the data;

spread (i.e., the scale) of the data;

skewness of the data;

presence of outliers; and

presence of multiple modes in the data.


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VARIOUSSHAPESOF HISTOGRAM


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SCATTER DIAGRAMS

Scatter diagrams are a graphical, rather than statistical,means of examining whether or not two parameters arerelated to each other.

It is simply the plotting of each point of data on a chart

with one parameter as the x-axis and the other as the y-axis.

If the points form a narrow "cloud the parameters areclosely related and one may be used as a

predictor of the other.

A wide "cloud" indicates poor correlation.


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Vertical axis: variable Y--usually the response variableHorizontal axis: variable X--usually some variable we suspect

may be related to the response


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No Relationship

This plot shows that there is lack

of predictability in determining Y

from a given value ofXand thus

the associated amorphous, non-

structured appearance of the

scatter plot leads to the

conclusion: no relationship.Strong Positive Linear

Correlation

The slope of the line is positive

(small values ofX correspond to

small values ofY; large values of

Xcorrespond to large values ofY),

so there is a positive co-relation

betweenXand Y.


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Strong Negative Correlation

The scatter about the line is quite

small, so there is a strong linear

relationship. The slope of the line isnegative i.e. small values of X

correspond to large values ofY; large

values of X correspond to small

values ofY, so there is a negative co-

relation between Xand Y.

Exact Linear Relationship

The scatter about the line is zero--

there is perfect predictability

betweenXand Y


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Quadratic Relationship

In order to describe the relationship

between X and Y--a curved (or

curvilinear, or non-linear) function isneeded. The simplest such curvilinear

function is a quadratic model

Exponential RelationshipIn this example, the large values of X

correspond to nearly constant values ofY,

and so a non-linear function beyond the

quadratic is needed. Among the many

other non-linear functions available, one

of the simpler ones is the exponential

model


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Sinusoidal Relationship

This scatter plot reveals that the

amount of swing does not appear to

be constant but rather is decreasing

(damping) asXgets large.


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PROCEDUREOF CONSTRUCTION

Divide points in the graph into four quadrants. Count x/2 points from top to bottom and draw a

horizontal line.

Count x/2 points from left to right and draw a verticalline.

If number of points is odd, draw the line through themiddle point.

Count the point in each quadrants. Do not count thepoints on a line.

Add diagonally opposite points.

A= points in upper left+ points in lower right

B= points in upper right+ points in lower left

Q= smaller of A and B

N=A+B

Look N on the correlation test table

If Q < limit, then two variables are related.


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CONTROL CHARTS

The charts are made by plotting in sequence themeasured values of samples taken from a process.

For example, the mean length of a sample of rods froma production line, the number of defects in a sample of aproduct, the miles per gallon of automobiles tested

sequentially in a model year, etc.

These measurements are expected to vary randomlyabout some mean with a known variance. From themean and variance, control limits can be established.

Control limits are values that sample measurementsare not expected to exceed unless some specialcause changes the process.


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A sample measurement outside the control limitstherefore indicates that the process is no longer stable,and is usually reason for corrective action.

Other causes for corrective action are non-randombehavior of the measurements within the control limits.

Control limits are established by statistical methodsdepending on whether the measurements are of aparameter, attribute or rate.


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