4.1 Quality Control

8/18/2019 4.1 Quality Control

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Quality Control

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Overview

• Inspection and quality control

-Types of Inspection, Control Charts -Introduction, Defnition, Classifcation, Types,Attributes, Variables

• Total Quality Management- Concept, Features, Kaizen model and 7 QualityControl Tools - Procedures, Variables, Quality level

• ecent concepts in Quality Control

- Introduction to !i" sigma, #pproaches,$ene%ts and Types& - DAIC, DADV, !reen belt,"lac# belt, aster blac# belts$

• Tutorial

- 'ro(lems in control charts&%


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Introduction

• Quality control &QC' includes t(e activities )ro*t(e suppliers, t(rou+( production, and to t(ecusto*ers$

• Inco*in+ *aterials are ea*ined to *a#e sure

t(ey *eet t(e appropriate specifcations$• T(e uality o) partially co*pleted products are

analy.ed to deter*ine i) production processes are)unctionin+ properly$

•

/inis(ed +oods and services are studied todeter*ine i) t(ey *eet custo*er epectations$

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QC T(rou+(out Production yste*s

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3aw aterials,Parts, andupplies

ProductionProcesses

Products andervices

Inputs Con)ersion *utputs

Control C(artsand

Acceptance Tests

Control C(artsand

Acceptance TestsControl C(arts

Quality o) Inputs

Quality o) Outputs

Quality o) Partially Co*pleted

Products


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ervices and T(eir Custo*er4pectations

• 5ospital

• Patient receive t(e correct treat*ents6

• Patient treated courteously by all personnel6

• 5ospital environ*ent support patient recovery6

• "an#• Custo*er7s transactions co*pleted wit( precision6

• "an# co*ply wit( +overn*ent re+ulations6

• Custo*er7s state*ents accurate6

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Products and T(eir Custo*er4pectations

• Auto*a#er

• Auto (ave t(e intended durability6

• Parts wit(in t(e *anu)acturin+ tolerances6

• Auto7s appearance pleasin+6

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a*plin+

• T(e :ow o) products is bro#en into discretebatc(es called lots$

• 3ando* sa*ples are re*oved )ro* t(ese lots and*easured a+ainst certain standards$

• A rando* sa*ple is one in w(ic( eac( unit in t(elot (as an eual c(ance o) bein+ included in t(esa*ple$

• I) a sa*ple is rando*, it is li#ely to be

representative o) t(e lot$

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a*plin+

• 4it(er attributes or variables can be *easuredand co*pared to standards$

• Attributes are c(aracteristics t(at are classifedinto one o) two cate+ories, usually de)ective &not

*eetin+ specifcations' or nonde)ective &*eetin+specifcations'$

• Variables are c(aracteristics t(at can be*easured on a continuous scale &wei+(t, len+t(,etc$'$

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i.e and /reuency o)a*ples• As t(e percenta+e o) lots in sa*ples is increased=

• t(e sa*plin+ and sa*plin+ costs increase, and

• t(e uality o) products +oin+ to custo*ers increases$

• Typically, very lar+e sa*ples are too costly$

• 4tre*ely s*all sa*ples *i+(t su>er )ro*statistical i*precision$

• ?ar+er sa*ples are ordinarily used w(en sa*plin+)or attributes t(an )or variables$

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en o nspecDurin+ t(e Production

Process• Inspect be)ore costly operations$• Inspect be)ore operations t(at are li#ely to

produce )aulty ite*s$

• Inspect be)ore operations t(at cover up de)ects$

• Inspect be)ore asse*bly operations t(at cannotbe undone$

• On auto*atic *ac(ines, inspect frst and lastpieces o) production runs, but )ew in-between

pieces$• Inspect fnis(ed products$

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Central ?i*it T(eore*

• T(e central li*it t(eore* is= Samplingdistributions can be assumed to be normallydistributed even though the population (lot)distributions are not normal$

• T(e t(eore* allows use o) t(e nor*al distributionto easily set li*its )or control c(arts andacceptance plans )or bot( attributes andvariables$

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a*plin+ Distributions•

T(e sa*plin+ distribution can be assu*ed to benor*ally distributed unless sa*ple si.e &n' isetre*ely s*all$

• T(e *ean o) t(e sa*plin+ distribution & ' is eual tot(e population *ean &µ'$

• T(e standard error o) t(e sa*plin+ distribution &σ ' is

s*aller t(an t(e population standard deviation &σ ' by

a )actor o) 1

1%

-

n


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Population and a*plin+Distributions

10

f(x)f(x) Population Distribution

Sampling Distribution

of Sample Means

Mean = µ

Std. Dev. = σx

Mean = x = µStd. Error =

=

x

x

xx

σσ =

n

f(x)


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Control C(arts

• Pri*ary purpose o) control c(arts is to indicate ata +lance w(en production processes *i+(t (avec(an+ed suEciently to a>ect product uality$

• I) t(e indication is t(at product uality (as

deteriorated, or is li#ely to, t(en corrective ista#en$

• I) t(e indication is t(at product uality is bettert(an epected, t(en it is i*portant to fnd out w(yso t(at it can be *aintained$

• Fse o) control c(arts is o)ten re)erred to asstatistical process control &PC'$

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Constructin+ ControlC(arts• Vertical ais provides t(e scale )or t(e sa*ple

in)or*ation t(at is plotted on t(e c(art$

• 5ori.ontal ais is t(e ti*e scale$

• 5ori.ontal center line is ideally deter*ined )ro*

observin+ t(e capability o) t(e process$• Two additional (ori.ontal lines, t(e lower and

upper control li*its, typically are 0 standarddeviations below and above, respectively, t(ecenter line$

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Constructin+ ControlC(arts• I) t(e sa*ple in)or*ation )alls wit(in t(e lower

and upper control li*its, t(e uality o) t(epopulation is considered to be in controlGot(erwise uality is Hud+ed to be out o) control

and corrective action s(ould be considered$• Two versions o) control c(arts will be ea*ined

• Control c(arts )or attributes

• Control c(arts )or variables

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Control C(arts )orAttributes• Inspection o) t(e units in t(e sa*ple is per)or*ed

on an attribute &de)ectivenon-de)ective' basis$

• In)or*ation provided )ro* inspectin+ a sa*ple o)si.e n is t(e percent de)ective in a sa*ple, p, or

t(e nu*ber o) units )ound to be de)ective in t(atsa*ple divided by n$

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Control C(arts )orAttributes•

Alt(ou+( t(e distribution o) sa*ple in)or*ation )ollowsa bino*ial distribution, t(at distribution can beapproi*ated by a nor*al distribution wit( a• *ean o) p

• standard deviation o)

•

T(e 0σ control li*its are

1<

-

)/n p(100 p −

)/n p(100 p3 -/ p −+


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4a*ple= AttributeControl C(art

4very c(ec# cas(ed or deposited at ?incoln"an# *ust be encoded wit( t(e a*ount o) t(ec(ec# be)ore it can be+in t(e /ederal 3eserveclearin+ process$ T(e accuracy o) t(e c(ec#

encodin+ process is o) up*ost i*portance$ I)t(ere is any discrepancy between t(e a*ount ac(ec# is *ade out )or and t(e encoded a*ount,t(e c(ec# is de)ective$

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Twenty sa*ples, eac( consistin+ o) %8Bc(ec#s, were selected and ea*ined$ T(enu*ber o) de)ective c(ec#s )ound in eac( sa*pleis s(own below$

%B

2 1 8 0 % ; 2 8 % 0

% < 8 0 9 2 % 8 0 9


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T(e *ana+er o) t(e c(ec# encodin+depart*ent #nows )ro* past eperience t(atw(en t(e encodin+ process is in control, anavera+e o) 1$9 o) t(e encoded c(ec#s are

de)ective$(e wants to construct a p c(art wit( 0-

standard deviation control li*its$

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%%

σ

− −= = = =

&1 ' $B19&1 $B19' $B18;22$BB;@09

%8B %8B p

p p

n

UCL = 3 =.016+3(.00!36)= .03!"0" or 3.!"# p

p σ +

LCL = 3 =.016-3(.00!36)=-.00"0"= 0# p

p σ −


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%0

p Chart for Lincoln Bank

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

0.045

0 5 10 15 20

Sample Number

S a m p l e P r o p o r t i o n p


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Control C(arts )orVariables• Inspection o) t(e units in t(e sa*ple is per)or*ed

on a variable basis$

• T(e in)or*ation provided )ro* inspectin+ asa*ple o) si.e n is=

• a*ple *ean, , or t(e su* o) *easure*ent o)eac( unit in t(e sa*ple divided by n

• 3an+e, 3, o) *easure*ents wit(in t(e sa*ple, ort(e (i+(est *easure*ent in t(e sa*ple *inus t(elowest *easure*ent in t(e sa*ple

%2


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Control C(arts )orVariables•

In t(is case two separate control c(arts are used to*onitor two di>erent aspects o) t(e process7s output=• Central tendency

• Variability

• Central tendency o) t(e output is *onitored usin+ t(e

-c(art$• Variability o) t(e output is *onitored usin+ t(e 3-

c(art$

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-C(art•

T(e central line is , t(e su* o) a nu*ber o) sa*ple*eans collected w(ile t(e process was considered tobe Jin controlK divided by t(e nu*ber o) sa*ples$

• T(e 0σ lower control li*it is - A3

• T(e 0σ upper control li*it is L A3

• /actor A is based on sa*ple si.e$

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3-C(art•

T(e central line is 3, t(e su* o) a nu*ber o) sa*pleran+es collected w(ile t(e process was considered tobe Jin controlK divided by t(e nu*ber o) sa*ples$

• T(e 0σ lower control li*it is D13$

• T(e 0σ upper control li*it is D%3$

• /actors D1and D% are based on sa*ple si.e$

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0σ Control C(art /actors)or Variables

Control ?i*it /actor Control ?i*it /actora*ple )or a*ple ean )or a*ple3an+e

i.e n A D1 D%

% 1$


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4a*ple= VariableControl C(art

%@

x Chart for eo! Cho!

4".#

4".$

4"."

50.0

50.1

50.2

50.3

0 5 10 15 20Sample Number

S a m p l e

e a n

%CL

LCL


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4a*ple= VariableControl C(art

0B

& B C ' ( )* Chart for eo! Cho!

0.00

0.10

0.20

0.30

0.400.50

0.+0

0.#0

0.$0

0 5 10 15 20Sample Number

S a m p l e * a n , e *

LCL

%CL


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Acceptance Plans

• Trend today is toward developin+ testin+ *et(odst(at are so uic#, e>ective, and inepensive t(atproducts are sub*itted to 1BBinspectiontestin+

•

4very product s(ipped to custo*ers is inspectedand tested to deter*ine i) it *eets custo*erepectations

• "ut t(ere are situations w(ere t(is is eit(eri*practical, i*possible or unecono*ical

• Destructive tests, w(ere no products survive test

• In t(ese situations, acceptance plans are sensible

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Acceptance Plans

• An acceptance plan is t(e overall sc(e*e )oreit(er acceptin+ or reHectin+ a lot based onin)or*ation +ained )ro* sa*ples$

• T(e acceptance plan identifes t(e=•

i.e o) sa*ples, n• Type o) sa*ples• Decision criterion, c, used to eit(er accept or reHect

t(e lot

• a*ples *ay be eit(er sin+le, double, or

seuential$

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in+le-a*plin+ Plan

• Acceptance or reHection decision is *ade a)terdrawin+ only one sa*ple )ro* t(e lot$

• I) t(e nu*ber o) de)ectives, c7, does not eceedt(e acceptance criteria, c, t(e lot is accepted$

00


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in+le-a*plin+ Plan

02

Lot of % &te'

ando'

Sa'p*e of

n &te' % - n &te'

&npet n &te'

, , ep*ae

Defet/ve

n %ondefet/ve

, Defet/ve

ond /n Sa'p*e

e2et Lot ept Lot


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Double-a*plin+ Plan

• One s*all sa*ple is drawn initially$

• I) t(e nu*ber o) de)ectives is less t(an or eual toso*e lower li*it, t(e lot is accepted$

• I) t(e nu*ber o) de)ectives is +reater t(an so*e

upper li*it, t(e lot is reHected$• I) t(e nu*ber o) de)ectives is neit(er, a second

lar+er sa*ple is drawn$

• ?ot is eit(er accepted or reHected on t(e basis o)

t(e in)or*ation )ro* bot( o) t(e sa*ples$

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Double-a*plin+ Plan

09

Lot of % &te'

ando'

Sa'p*e of

n1 &te' % 4 n1 &te'

&npet n1 &te'

1, 5 1, 1

ep*ae

Defet/ve

n1 %ondefet/ve

1, Defet/ve

ond /n Sa'p*e

e2et Lot

ept Lot

Cont/ne

1 1, 5

(to next */de)


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(1, + 5,) 5

Double-a*plin+ Plan

0;

% 4 n1 &te'

ando'

Sa'p*e of n5 &te'

% 4 (n1 + n5)&te'

&npet n5 &te'

ep*ae

Defet/ve

n5 %ondefet/ve

5, Defet/ve

ond /n Sa'p*e

e2et Lot

ept Lot

Cont/ne

(1, + 5,) 5

(fro' prev/o */de)


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euential-a*plin+ Plan

• Fnits are rando*ly selected )ro* t(e lot andtested one by one$

• A)ter eac( one (as been tested, a reHect, accept,or continue-sa*plin+ decision is *ade$

• a*plin+ process continues until t(e lot isaccepted or reHected$

0<


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euential-a*plin+ Plan

0@0 10 50 30 0 $0 60 0 "0 !0 100 110 150 130

3

1

5

Un/t Sa'p*ed (n)

6

%'7er of Defet/ve

$

0

e2et Lot

ept Lot

Cont/ne Sa'p*/n8


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Defnitions

• Acceptance plan - a*ple si.e &n' and *ai*u*nu*ber o) de)ectives &c' t(at can be )ound in asa*ple to accept a lot

• Acceptable uality level &AQ?' - I) a lot (as no

*ore t(an AQ? percent de)ectives, it is considereda +ood lot

• ?ot tolerance percent de)ective &?TPD' - I) a lot(as +reater t(an ?TPD, it is considered a bad lot

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Defnitions

• Avera+e out+oin+ uality &AOQ' M !iven t(e actual o) de)ectives in lots and a particular sa*plin+plan, t(e AOQ is t(e avera+e de)ectives in lotsleavin+ an inspection station

•

Avera+e out+oin+ uality li*it &AOQ?' M !iven aparticular sa*plin+ plan, t(e AOQ? is t(e*ai*u* AOQ t(at can occur as t(e actual de)ectives in lots varies

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Defnitions

• Type I error - "ased on sa*ple in)or*ation, a +ood&uality' population is reHected

• Type II error - "ased on sa*ple in)or*ation, a bad&uality' population is accepted

• Producer7s ris# &α' - /or a particular sa*plin+ plan,t(e probability t(at a Type I error will beco**itted

• Consu*er7s ris# &β' - /or a particular sa*plin+plan, t(e probability t(at a Type II error will beco**itted

2%

C id ti i


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Considerations inelectin+ a a*plin+ Plan• Operatin+ c(aracteristics &OC' curve

• Avera+e out+oin+ uality &AOQ' curve

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O ti C( t i ti


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Operatin+ C(aracteristic&OC' Curve• An OC curve s(ows (ow well a particular sa*plin+

plan &n,c' discri*inates between +ood and badlots$

• T(e vertical ais is t(e probability o) acceptin+ a

lot )or a plan$• T(e (ori.ontal ais is t(e actual percent de)ective

in an inco*in+ lot$

• /or a +iven sa*plin+ plan, points )or t(e OC curvecan be developed usin+ t(e Poisson probability

distribution

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O ti C( t i ti


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Operatin+ C(aracteristic&OC' Curve

28

$1B$1B

$%B$%B

$0B$0B

$2B$2B

$8B$8B$9B$9B

$;B$;B

$


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OC Curve &continued'• ana+e*ent *ay want to=

• peci)y t(e per)or*ance o) t(e sa*plin+ procedure byidenti)yin+ two points on t(e +rap(=• AQ? and α

• ?TPD and β

• T(en fnd t(e co*bination o) n and c t(at provides a

curve t(at passes t(rou+( bot( points

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A O t i Q lit


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Avera+e Out+oin+ Quality&AOQ' Curve• AOQ curve s(ows in)or*ation depicted on t(e OC

curve in a di>erent )or*$

• 5ori.ontal ais is t(e sa*e as t(e (ori.ontal ais)or t(e OC curve &percent de)ective in a lot'$

•Vertical ais is t(e avera+e uality t(at will leavet(e uality control procedure )or a particularsa*plin+ plan$

• Avera+e uality is calculated based on t(eassu*ption t(at lots t(at are reHected are 1BB

inspected be)ore enterin+ t(e production syste*$

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AOQ Curve

• Fnder t(is assu*ption,

AOQ πNP&A'1w(ere= π percent de)ective in an inco*in+ lot

P&A' probability o) acceptin+ a lot is

obtained )ro* t(e plan7s OC curve

• As t(e percent de)ective in a lot increases, AOQwill increase to a point and t(en decrease$

2<


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AOQ Curve

• AOQ value w(ere t(e *ai*u* is attained isre)erred to as t(e avera+e out+oin+ uality level&AOQ?'$

• AOQ? is t(e worst avera+e uality t(at will eit

t(e uality control procedure usin+ t(e sa*plin+plan n and c$

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Co*puters in Qualit


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Co*puters in QualityControl• 3ecords about uality testin+ and results li*it a

fr*7s eposure in t(e event o) a product liabilitysuit$

• 3ecall pro+ra*s reuire t(at *anu)acturers

•

now t(e lot nu*ber o) t(e parts t(at areresponsible )or t(e potential de)ects

• 5ave an in)or*ation stora+e syste* t(at can tie t(elot nu*bers o) t(e suspected parts to t(e fnalproduct *odel nu*bers

•5ave an in)or*ation syste* t(at can trac# t(e*odel nu*bers o) fnal products to custo*ers

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Co*puters in Quality


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Co*puters in QualityControl• it( auto*ation, inspection and testin+ can be so

inepensive and uic# t(at co*panies *ay beable to increase sa*ple si.es and t(e )reuencyo) sa*ples, t(us attainin+ *ore precision in bot(

control c(arts and acceptance plans

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Quality Control in ervices

• In all services t(ere is a continuin+ need to*onitor uality

• Control c(arts are used etensively in services to*onitor and control t(eir uality levels

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rap Fp= orld Class


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rap-Fp= orld-ClassPractice• Quality cannot be inspected into products$

Processes *ust be operated to ac(ieve ualitycon)or*anceG uality control is used to ac(ievet(is$

•

tatistical control c(arts are used etensively toprovide )eedbac# to everyone about ualityper)or*ance

• $ $ $ *ore

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4.1 Quality Control

Documents

Transcript of 4.1 Quality Control