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By:

Albertus Ch. W. / 1106067886

Ficky Augusta I. / 1106070483

Vincentius Himawan / 1106070520

Walter Andrew Shewhart

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Background Walter Andrew Shewhart(pronounced like

"shoe-heart", March 18, 1891March 11, 1967)

was an American

physicist, engineer and statistician, sometimes

known as the father of statistical qualitycontroland also related to the Shewhart cycle.

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Contribution

On May 16, 1924 while working for BellLabs. schematic control chart. That diagram, andthe short text which preceded and followed it, setforth all of the essential principles and considerations

which are involved in what we know today asprocess quality control.

He understood data from physical processes neverproduce a normal distribution curve (Gaussiandistribution).

Dr. Shewhart concluded that while every processdisplays variation, some processes displaycontrolled variation that is natural to the process,while others display uncontrolled variation that is not

present in the process causal system at all times.

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Schematic Control Chart

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Each point represents a summary statisticcomputed

from a sample of measurements of a quality

characteristic. The vertical axisof a Shewhart chart is scaled in the

same units as the summary statistic.

The samples from which the summary statistics are

computed are referred to as rational subgroupsorsubgroup samples. The organization of the data into

subgroups is critical to the interpretation of a Shewhart

chart. This makes the chart more sensitive to shifts in

the process level. The horizontal axisof a Shewhart chart identifies the

subgroup samples. Frequently, the samples are

indexed according to the order in which they were

taken or the time at which they were taken.

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The central lineon a Shewhart chart indicates the

average (expected value) of the summary statistic

when the process is in statistical control.

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The upper and lower control limits (UCL and LCL),

respectively, indicate the range of variation to be

expected in the summary statistic when the process isin statistical control. The control limits are commonly

computed as 3 limitsrepresenting three standard

errors of variation in the summary statistic above and

below the central line. Limits determined by the latter

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The control limits are also determined by the

subgroup sample size because the standard error of

the summary statistic is a function of sample size. If

the sample size is constant across subgroups, the

control limits are typically horizontal lines, as in the

picture. However, if the sample size varies from

subgroup to subgroup, the limits are usually adjustedto compensate for the effect of sample size, resulting

in step-like boundaries.

Control limits can be estimated from the data being

analyzed, or they can be standard, previouslydetermined values. Estimated limits are often used

when statistical control is being established, and

standard limits are often used when statistical control

is being maintained.

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A point outside the control limitssignals the presence

of a special cause of variation. Additionally, tests for

special causes(also referred to as Western Electricrulesand runs tests) can signal an out-of-control

condition if a statistically unusual pattern of points is

observed in the control chart. For example, one

pattern used to diagnose the existence of a trend isseven consecutive steadily increasing points.

When the process is in statistical control, a point may

fall outside the control limits purely by chance,

resulting in a false out-of-control signal. However,

when the Shewhart chart correctly signals the

presence of a special cause, additional action is

needed to determine the nature of the problem and

eliminate it.

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Example

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Shewhart Control Charts Run chartsuse the middle value (median) and so the

rules rely on addressing whether points are above orbelow that middle value. No account is taken of therelative distances from the median, only whether a value isabove or below.

Shewhart control chartsuse the arithmetic mean as thecentre line. Because the relative distances from the meanare taken into consideration, Shewhart charts are a moresensitive way of detecting whether observed variation isdue to common or special causes.

If a process is stable (i.e. data points are randomlyarranged within the control limits), Shewhart charts allowus to predict future performance. This allows us tocalculate if the current process is capable of producing adesired result (i.e. achieve a numeric aim or target) or

whether the process still needs to be improved orreplaced.

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Types of Shewhart control charts should be

used:

In order to plot accurate control limits you need 20-

30 data points but for X-bar, P, C and U charts trial

limits can be used with as few as 12 points. Youshould alwa s lot a run chart first.

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Standard control chart rules for detecting

special causesSpecial cause (non-random) variation is detected

using a variation of 2 of the 4 rules used on runcharts (the shifts and trends rules) with three extra

ones that rely on the position of data points relative

to the mean (centreline) and control limits.

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The formula to calculate the control limits differ for

each type of control chart so producing controlcharts requires specialist software and/or a skilled

data analyst. The control limits are sometimes

marked 3 sigma.

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PDSA

Simply :

Plan a step or process needed to achieve a goal

or result.

Do what you planned.

Study, that is, reflect on the result.

Act, based on what you learn, to improve the next

similar step or process.

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Shewhart Charts

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Illustrate

Plan

- If a company isn't experiencing the success it

would like in a given area, the company is wise to

brainstrom ideas for improvement. This is the "plan"

phase of the cycle.

Do

- Next, the company chooses a course of action to

pursue, then pursues it, which logically, constitutesthe "do" phase. It is important that Do step carefully

follow the plan.

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Illustrate Study The "study" phase that follows consists of the

company observing the results of their actions, andsubsequently, making judgments as to their efficacy.This step is crucial. It serves as the foundation for thenext and final "act" phase. In study phase, we ask ourself, what we learn? What went wrong?

The act phase instructs the company to analyze theobserved results. If the results are pleasing, change

course to pursue this direction further. If they are not,this phase instructs the company to circle back to theoriginal brainstorming pool in order to start theprocess over again and repeat the cycle until thecompany is pleased with the results.

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PDCA

Shewhart and Deming ( his student ) revised themodel PDSA to PDCA ( plan, do, check, act)

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PDCA

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Shewhart Quote

The object of industry is to set up economicways of satisfying human wants and in so doing

to reduce everything possible to routines

requiring a minimum amount of human effort.

Through the use of the scientific method,extended to take account of modern statistical

concepts, it has been found possible to set up

limits within which the results of routine efforts

must lie if they are to be economical. Deviationsin the results of a routine process outside such

limits indicate that the routine has broken down

and will no longer be economical until the cause

of trouble is removed.

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Reference wikipedia.org/wiki/Walter_A._Shewhart "Western Electric - A Brief History". The Porticus

Centre. Retrieved 2009-04-10.

Neave, Henry R.; British Deming Association(1992). Why SPC?. Knoxville, Tennessee: SPC

Press. ISBN 978-0-945320-17-3. skymark.com/resources/leaders/shewart.asp

en.wikipedia.org/wiki/Statistical_process_control

en.wikipedia.org/wiki/PDCA

http://www.qihub.scot.nhs.uk/knowledge-centre/quality-improvement-tools/shewhart-control-charts.aspx

http://support.sas.com/documentation/cdl/en/qcug/63922/HTML/default/viewer.htm#qcug_shewhart_a0000003557.htm

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