7 . CLASSIC Tools of Quality

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Seven Tools of Quality


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INTRODUCTION

Collecting and analyzing data is a foundation on whichthe effective management of quality rests.

"seven tools of quality" will help you effectively collectand analyze data.


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The seven tools of quality are:1) Cause and effect diagram2) Check sheet

3) Control chart4) Flow chart5) Histogram6) Pareto chart7) Scatter diagram


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1.CAUSE AND EFFECT DIAGRAM



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Cause and effect diagram ( or Fishbone diagram)

Fig 1


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Cause and effect diagram

Figure 1 shows a simple cause and effect diagram. Also called a fishbone diagram because it looks like

skeleton of a fish.

Also, this diagram is called Ishiwaka diagram , namedafter a Japanese quality expert who came up with thisconcept.

The idea is first to identify and state the problem, whichis in essence an effect of something that happened in aprocess, and think through various causes that mayhave resulted in an undesired effect.


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Cause and effect diagram

Drawing a cause and effect diagram helps one thinksystematically and logically.

It graphically illustrates the relationship between a givenoutcome and all the factors that influence this outcome.


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2.CHECK SHEET



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Check sheet A check sheet is nothing but a form used to collect data

in such a way that it makes not only the collection of dataeasy, but also the analysis of that data automatic.

Figure 2 is an example of a check sheet. Each mark in the check sheet indicates a defect. The type of defects, number of defects, and their

distribution can be seen at a glance, which makes

analysis of data very quick and easy. Check sheets provide a logical display of data that are

manually derived and yield results from whichconclusions can be easily drawn.


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Check sheet Fig 2


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3.CONTROL CHART



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Control chart

FIG - 3


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Control chart

A control chart is a simple graph or chart with time on the horizontal (X) axis vs. the quality characteristicmeasured on a vertical (Y) axis , with the control limits for the quality characteristic measured.

In other words, a control chart is a continuous graphicindication of the state of a process with respect to aquality characteristic being measured.

You go out on the production floor and just beforeshipping pull a number of samples, inspect them, andnote the number of defects, and calculate percentdefective for several days.


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Control chart

The results may look something like the following:

No of Samples inspected No of sample defective


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Control chart Variations or fluctuations in data are generally caused by

a large number of small differences in materials,equipment, the surrounding atmospheric conditions,physical and mental reactions of people involved, etc.

These small differences cause data to vary in a manner called "normal" or "random" and such variations aretermed normal variations.

In other words, these are variations normal to the

process.


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Control chart Occasionally, however, there will be a large or unusual

difference, much more important than all those smalldifferences put together.

For example, material is taken from a different lot, themachine setter makes a new setting, an inexperiencedoperator takes the place of an experienced operator, etc.

These large differences cause changes in a processresulting in variation in the characteristics measured in amanner called "abnormal" and these variations arecalled abnormal variations.

In other words, these are variations that are not normalto the process.


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Control chart Experience has shown that there are definite detectable differences

between "normal" or "natural" variations and "abnormal" or "unnaturalvariations.

It is possible to detect this difference or to make this distinctionusing a statistical tool known as the control chart .

Abnormal or unnatural variations have identifiable, assignable causes. This makes the diagnosis and correction of many production troubles

and often brings substantial improvements in product quality andreduction in scrap and rework.

Normal or natural variations have no assignable causes. So byidentifying certain quality variations as having no assignable causes or being natural to the process, the control chart tells us when to leave aprocess alone and thus prevents unnecessarily frequent adjustmentsthat tend to increase the variability of the process rather than decreaseit.


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4.FLOW CHART



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Flow chart

A flow chart is a schematic diagram of a process including allthe steps or operations in the sequence as they occur.

The logic here is that the act of constructing a flow chart willhelp you clarify various steps involved in a process and result

in a better overall understanding of that process. One must understand a process clearly to be better able to

identify and solve its problems. Flow chart can help understand the complete process, identify

the critical stages of a process, locate problem areas, andshow relationships between different steps in a process. Figure 4 is an example of a flow chart.


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Manufacturing process flow chart, mensshirts FIG - 4


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Manufacturing process flow chart, dress shirts, men's.(1) Marker lay made according to cutting ticket. Marker lay

checked 100%.(2) Marker and material delivered to spreading operation.

Material spread.(3) Machine knife cut.

(4) Die cutting small parts.(5) Cut parts delivered to plant.(6) Collar department: Fuse stays. Run collar tops, trim

points. Turn and press (shape), top stitch bands. Trim

and baste. Quarter mark band. Buttonhole. Button sew.(7) Cuff department: Hem cuff, run cuff. Shape cuff topstitch. Buttonhole, Button sew.

(8) Under fronts: Baste neck. Crease front. Center pleat.


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(9) Buttonhole. Set pocket. Set pocket. Set flap.(10) Sleeves: Piece binding. Bind sleeve. Tack binding.

(11) Backs: Pleat. Backs.(12) Yokes: Label. Sew.(13) Attach yoke backs.(14) Assemble complated bundles of parts, any size,

section, ply number and/or shade.(15) Join shoulder seam.(16) Join collar to shirt.(17) Set sleeve, join side and underarm seams (sidefell).

(18) Cuff attach, hem shirt,'trim threads.(19) Button shirt, roll collar, press, fold.(20) Pack.


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5.HISTOGRAM



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Histogram

FIG - 5


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Histogram A histogram is a bar chart or a bar graph. It is a graphical chart of a number of occurrences of an

event. For example, if you were to draw a histogram of the data

contained in Table 1, it would look like figure 5. A histogram simply shows the distribution of sample data

and gives some idea about variability of that data. Histogram is a graphic summary of variation in a set of

data, and is a simple but powerful tool for elementaryanalysis. A histogram can help understand the total variation of a

process, and quickly and easily determine the under laying distribution of a process.


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6.PARETO CHART



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F i g .

6 P a r e

t o C h

a r t


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Pareto chart

A Pareto chart is nothing but a histogram where a number of occurrences of an event are arranged in descending order.

For example, a Pareto chart of the data contained in Table 1will look like the Figure 6.

Dr. Joseph M. Juran, the world famous quality managementexpert, observed in mid-1920s, as a young engineer, thatquality defects are unequal in frequency, that is, when a longlist of defects is arranged in order of frequency, generally,relatively few of the defects account for the bulk of defectiveness.

Dr. Juran named this phenomenon the Pareto principle.


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Pareto chart Thus, Pareto chart helps identify those defects that

cause most problems, and by addressing those defects,most of the quality problems can be solved andimprovement be made.

For example, of the 208 defects shown in the Figure 6,45 or 21.6% are fabric defects. So in this instance, it would be most effective to address

fabric quality first because any improvement in fabricquality will significantly improve overall quality of theproduct.

The next defect to address should be open seams, andso forth.


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7.SCATTER DIAGRAM



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FIG - 7Scatter diagram


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Scatter diagram

A scatter diagram is a plot of one variable vs. another variable, which is

dependent on the first variable. For example, yarn strength may depend on twists per inch (twists per

centimeter); moisture absorbancy in a fabric may depend on fabricthickness, and so on.

By plotting one variable against another, it may or may not become obvioushow they are related; in other words, a pattern may or may not emerge.

Various possible patterns of a scatter diagram are shown in Figure above.

7 . CLASSIC Tools of Quality

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