Basic Quality Control Tools

8/18/2019 Basic Quality Control Tools

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The Tools of Quality

Dr. Rathanraj K J

Professor, BMSCE


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Improving the System

“The objective of the system is tosatisfy the cstomer!.

“" quality system ses thebsiness mo#e$ %ith a focs onthe cstomer an# inc$#es

#ynamics of contina$im&rovement, chan'e, &$annin',an# rene%a$!.


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Quality System Model

Customer

Procedures

Legal

Req’ts

R&D

Technology

People

Plant &

Equipment

R e n

e w a l

C h a n

g e

P

l a n n i n g

C o n t i n u a l

I m p r o v e m

e n t

Adapted from S.

Thomas Foster


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Quality System Model

The variab$es an# re$ationshi&s of the

(a$ity system are an interconnecte#

net%or) that interact to achieve an en#.

Peo&$e are the most im&ortant as&ect

of the system.

*o% &eo&$e are mana'e# is im&ortant

in re$easin' an or'ani+ations &otentia$.


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Basic 7 (B7) Tools of Qualityig

Picture

Data

!nalysis

Data

Collection

Pro"lem

Identi#icationPrioriti$ation

%low

Chart

Chec

'heet

(isto)

grams

'catter

Plots

Control

Charts

Cause

E##ect

Pareto

!nalysis

Adapted from S.

Thomas Foster


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Flowcharts

- ra&hic re&resentation of system

or &roce#re f$o%

-Can be se# for both macro an#micro $eve$ &rocesses

- Promotes system n#erstan#in'

an# %i$$ i#entify 'a&s in &roce#ra$

$o'ic


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Flowcharting Steps

- "'ree on a stan#ar# set ofsymbo$s

- C$ear$y commnicate &r&ose ofthe f$o%chart

- /bserve the %or)0 sha#o% the%or)ers

- Deve$o& f$o%chart of the &rocess- Revie% %ith the em&$oyees an#

a#jst as necessary.


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Flowcharts

- 1$o%charts

2 ra&hica$ #escri&tion of ho% %or) is

#one.

2 3se# to #escribe &rocesses that are to

be im&rove#.


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Flow iagrams

4 Dra% a f$o%chart for %hatever yo

#o. 3nti$ yo #o, yo #o not )no%

%hat yo are #oin', yo jst have a

job.!

55 Dr. 6. E#%ar#s Demin'.


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Flowchart

"ctivity

Decision7es7es

8o8o


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Flowchart


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Flow iagrams


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Flow iagrams


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!hec" Sheets

Chec) Sheets 2 tab$ar or schematic#ata 'atherin' too$s se# to formhisto'rams an# other 'ra&hs

To set & a chec) sheet9:. ;#entify common #efects occrrin' in the

&rocess.


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- The format for recor#in' a$$ the

necessary information to e>ecte

&reventive &$ans an# to enab$e

#eve$o&ment of strate'ies for

im&rovement, common$y )no%n

as Data Sheet, ?o' Boo),

;ns&ection Recor#, Sche#$e ofEn(iry has been terme# chec)

sheet in this conte>t.

!hec" Sheets


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!hec" Sheet #$ample

Pro"lem Type

Set& rotines

not stan#ar#i+e#

Missin' e(i&ment

for set&

1ai$re to se&arate

interna$ an# e>terna$

tas)s

E>tensive machineresettin' an# &a&er

chan'e

/ther

M o n # a

y

T e s # a

y

6 e # n e

s # a y

T h r s #

a y

1 r i # a y

T o t a $

Adapted from S.

Thomas Foster


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!%&T#&TS %F ' !#! S##T

- Identity &artic$ars to enab$e traceability $i)e or'ani+ation, #ate,

&roject, cstomer, s&&$ier, s&ecifications, batch or $ot nmber, asa&&$icab$e.

- "ssociate# hman resorce an# E(i&ments if any

- ;n&t@sA, im&$yin' consm&tion of resorces of a$$ )in#s %ith

&ertinent #etai$s of their (antity an# a$$ie# &arameters $i)e &rity or

stren'th or si+e as the case may be.- Process &arameters

- Environmenta$ &arameters of concern if any

- Metho# of sam&$in', sam&$e si+e, fre(ency of sam&$in'

- ;ns&ection an# test faci$ities se# or avai$e#

- ;ns&ection an# test metho#, $east cont or visa$ stan#ar#s a#a&te#- Corres&on#in' ot&t@sA %ith #etai$s of &arameters of interest

- ;n#ices or a&&ro&riate fnctions of observations ma#e


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'ttri*ute Inspection

"n i$$strative chec) sheet for an attribte #ata


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+aria*le Inspection


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,ac"ing ,rocess " ty&ica$ chec) sheet for $ist of items to be &ac)e# an# or #is&atche#

to a cstomer sch as a stan#ar# $ist of s&ares accom&anyin' amachine too$ as accessories


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-or" Sampling Study


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' specimen chec" sheet for recording

Feed *ac" from !ustomers


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' specimen chec" sheet for setting

approval

k≤2


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istograms

*isto'rams 2 bar chart 'ra&hica$re&resentation of #ata, a$so se# toobserve the sha&e of #ata

R$es for #eve$o&in' histo'rams9 6i#th of bars mst be consistent

C$asses mst be mta$$y e>c$sive an#

a$$5inc$sive R$e of thmb for the nmber of c$asses9

nk ≤2

2log/lognk =


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istograms (cont.)

n nmber of ra% #ata va$es

k nmber of c$asses

3sin' this form$a, %e fin#9

2log/lognk ≥

Number of Observations Number of Classes

9 to 16 4

17 to 32 5

33 to 64 6

65 to 128 7

129 to 256 8


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istogram #$ample

Reference9

?ab :, C;T =


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istogram of weight of ta*lets in grams


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istogram (It is called Bar !hart/ when applied to

attri*ute data) showing fre0uency of non1

conforming *o**ins in samples of si2e 34 each.


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istogram of honing angle of shaving *lades.


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!ause1and1#ffect iagrams

Case5an#5Effect Dia'rams 2 se# tomove to $o%er $eve$s of abstraction inso$vin' &rob$ems

".K.".9 ;shi)a%a Case5an#5Effect or1ishbone or ;shi)a%a Dia'ram

1ish s)e$eton sha&e, the &rob$em is thehea#, major cases are the ribs, sb5cases form sma$$er bones of the ribs


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!ause1and1#ffect (cont.)

1ishbone #ia'rams create# #rin'brainstormin' %ith these ste&s9

:. State the &rob$em c$ear$y as the hea#.


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Fish*one iagram #$ample

Ref9 http://courses.bus.ualberta.ca/orga432reshe!/!ishbone.html

http://www.smartdraw.com/examples/pert/groupsculpture.htmhttp://www.smartdraw.com/examples/pert/groupsculpture.htm


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!ause and #ffect diagram of nonconforming

thic"ness of coating


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!ause and #ffect diagram of slag

inclusion in a cast iron product


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!ause and effect diagram of a thermos flas" failing

to pass the temperature retention test.


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!ause and effect diagram for preparation of tasty

tea


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Improved version of the cause and effect diagram

for preparation of tasty tea


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!ause and effect diagram of performance

of a team in a game of sport


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!ause and effect diagram for national status on

0uality in the world mar"et


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,areto ,rinciple

- i$fre#o Pareto @:5:H


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,areto !harts

Pareto Charts 2 se# to i#entify

an# &rioriti+e &rob$ems to be

so$ve#

*isto'rams ai#e# by the


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,areto !harts (cont.)

R$es for constrctin' Pareto

charts9 ;nformation mst be se$ecte# base#

on ty&es or c$assifications of #efectsthat occr as a res$t of a &rocess.

Data mst be co$$ecte# an#

c$assifie# into cate'ories. " histo'ram or fre(ency chart is

constrcte# sho%in' the nmber of

occrrences.


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,areto !harts (cont.)

Ste&s se# in Pareto "na$ysis9

:. atherin' cate'orica$ #ata re$atin'

to (a$ity &rob$ems.


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S%M# #5'M,6#S

Tab$e Sho%in' 8atre, 1re(ency an# Ran) of 8on5conformity.

/r'anisation9 /R Pro#ct9 PR/ Month9 M/7R


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Tab$e Sho%in' Ran), Percent fre(ency an# &ercent cases.

/r'anisation9 /R Pro#ct9 PR/ Month9 M/7R


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,areto iagram of nonconforming *atches of an

adhesive


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eavy #lectrical ,lant

Tab$e Sho%in' case %ise &ercent contribtion to

nonconformities amon' Trbine B$a#es


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,areto analysis of nonconformities among Tur*ine

Blades


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Scatter iagrams

Scatter P$ots @Dia'ramsA 2 se# toe>amine re$ationshi&s bet%eenvariab$es

Ste&s for settin' & a scatter &$ot9:. Determine x @in#e&en#entA an# y

@#e&en#entA variab$es


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Scatter iagram #$ample

..H:.:.:*"served+alue5


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Figure S owing relations ip *etween initial

and final setting times of cement


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!%MM%&6 %BS#8+# S!'TT#8

I'98'MS

%igure 'howing scatter diagrams indicating no

worthwhile relationship/


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Figure S owing scatter diagrams indicating

wea" linear relations ips


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Figure S owing scatter diagrams indicating

strong linear and curvilinear relations ips


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!ontrol !harts


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!ontrol !harts

Contro$ Charts 2 se# to #etermine if a

&rocess %i$$ &ro#ce a &ro#ct or

service %ith consistent measrab$e

&ro&erties

N an# R charts are se# to'ether, an#

are ca$c$ate# from the same ra% #

Contro$ Charts 2 se# to #etermine if a

&rocess %i$$ &ro#ce a &ro#ct or

service %ith consistent measrab$e

&ro&erties

N an# R charts are se# to'ether, an#

are ca$c$ate# from the same ra% #ata

5 d 8 !h t # l


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5 and 8 !hart #$amples


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Control Charts

!ontrol chart functions

- Contro$ charts are #ecision5ma)in'too$s 5 they &rovi#e an economic basisfor #eci#in' %hether to a$ter a &rocessor $eave it a$one

- Contro$ charts are &rob$em5so$vin'too$s 5 they &rovi#e a basis on %hich toform$ate im&rovement actions

- SPC e>&oses &rob$ems0 it #oes notso$ve themO


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Control Charts

!ontrol charts

- Contro$ charts are &o%erf$ ai#s to

n#erstan#in' the &erformance of

a &rocess over time.

PROCESSPROCESS

Input Output

What’s causing variability?

! t l h t id tif


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Control Charts

!ontrol charts identify

variation- Chance cases 5 “common case!

2 inherent to the &rocess or ran#om an# not

contro$$ab$e

2 if on$y common case &resent, the &rocess

is consi#ere# stab$e or “in contro$!

- "ssi'nab$e cases 5 “s&ecia$ case!

2 variation #e to otsi#e inf$ences

2 if &resent, the &rocess is “ot of contro$!


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Control Charts

!ontrol charts help us learn more

a*out processes

- Se&arate common an# s&ecia$

cases of variation

- Determine %hether a &rocess is in a

state of statistica$ contro$ or ot5of5contro$

- Estimate the &rocess &arameters

@mean, variationA an# assess the&erformance of a &rocess or its

ca&abi$ity


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Control Charts

!ontrol charts to monitor

processes

- To monitor ot&t, %e se a contro$ chart

2 %e chec) thin's $i)e the mean, ran'e,

stan#ar# #eviation

- To monitor a &rocess, %e ty&ica$$y se t%o

contro$ charts

2 mean @or some other centra$ ten#ency

measreA

2 variation @ty&ica$$y sin' ran'e or stan#ar#

#eviationA

!ontrol chart


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Control Charts

!ontrol chart

components

- Center$ine

2 sho%s %here the &rocess avera'e is

centere# or the centra$ ten#ency of

the #ata- 3&&er contro$ $imit @3C?A an#

?o%er contro$ $imit @?C?A

2 #escribes the &rocess s&rea#


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Control Charts

!ontrol chart for varia*les

- ariab$es are the measrab$emeasrab$e

characteristics of a &ro#ct or

service.

- Measrement #ata is ta)en an#

arraye# on charts.


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Control Charts

51*ar and 8 charts

- The N5bar chart 5 se# to #etect

chan'es in the mean bet"een

sb'ro&s

2 tests centra$ ten#ency or $ocationeffects

- The R chart 5 se# to #etect

chan'es in variation %ithinsb'ro&s

2 tests #is&ersion effects


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Control Charts

Step : efine the pro*lem

- 3se other (a$ity too$s to he$&

#etermine the 'enera$ &rob$em

thats occrrin' an# the &rocess

thats ss&ecte# of casin' it. 2 brainstorm sin' case an# effect

#ia'ram, %hy5%hy, Pareto charts,

etc.


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Control Charts

Step 3 Select a 0uality

characteristic to *e measured

- ;#entify a characteristic to st#y 5 for

e>am&$e, &art $en'th or any other

variab$e affectin' &erformance

2 ty&ica$$y choose characteristics %hichare creatin' (a$ity &rob$ems

2 &ossib$e characteristics inc$#e9 $en'th,

hei'ht, viscosity, co$or, tem&eratre,ve$ocity, %ei'ht, vo$me, #ensity, etc.

St ; !h * i


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Control Charts

Step ; !hoose a su*group si2e

to *e sampled

- Choose homo'eneos sb'ro&s

2 *omo'eneos sb'ro&s are &ro#ce#

n#er the same con#itions, by the same

machine, the same o&erator, the same mo$#,

at a&&ro>imate$y the same time.

- Try to ma>imi+e chance to #etect

#ifferences bet%een sb'ro&s, %hi$e

minimi+in' chance for #ifference %ith a'ro&.


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Control Charts

%ther guidelines

- The $ar'er the sb'ro& si+e, the moresensitive the chart becomes to sma$$

variations.

- This increases #ata co$$ection costs.

- Destrctive testin' may ma)e $ar'e

sb'ro& si+es infeasib$e.

- Sb'ro& si+es sma$$er than arent

re&resentative of the #istribtion avera'es.- Sb'ro&s over : sho$# se S chart.


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Control Charts

Step < !ollect the data

- Rn the &rocess ntoche# to 'ather

initia$ #ata for contro$ $imits.

- enera$$y, co$$ect


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Control Charts

Step = etermine trial

centerline

- The center$ine sho$# be the &o&$ation mean, µ

- Since it is n)no%n, %e se N #ob$e bar, or the

'ran# avera'e of the sb'ro& avera'es.

m

m

i

i∑=

= 1

X

X

St > t i t i l t l li it


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Control Charts

Step > etermine trial control limits

1 5*ar chart

- The norma$ crve #is&$ays the

#istribtion of the sam&$e avera'es.

- " contro$ chart is a time5#e&en#ent

&ictoria$ re&resentation of a norma$crve.

- Processes that are consi#ere# n#er

contro$ %i$$ have HH.G=L of their

'ra&he# avera'es fa$$ %ithin si>stan#ar# #eviations.


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Control Charts

?!6 6!6 calculation

deviationstandard

3XLCL

3XUCL

=

−=

+=

σ

σ

σ

t i i lt ti l f


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Control Charts

etermining an alternative value for

the standard deviation

m

m

i

i∑== 1R

R

R AXUCL2

+=

R AXLCL2

−=

Step 7 etermine trial control


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Control Charts

Step 7 etermine trial control

limits 1 8 chart

- The ran'e chart sho%s the s&rea# or#is&ersion of the in#ivi#a$ sam&$es %ithin

the sb'ro&.

2 ;f the &ro#ct sho%s a %i#e s&rea#,then the in#ivi#a$s %ithin the

sb'ro& are not simi$ar to each other.

2 E(a$ avera'es can be #eceivin'.

- Ca$c$ate# simi$ar to >5bar charts0

2 3se D= an# DD


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Control Charts

81*ar chart e$ceptions

- Becase ran'e va$es cannot be

ne'ative, a va$e of is 'iven for

the $o%er contro$ $imit of sam&$e

si+es of si> or $ess.

Step @ #$amine the process


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Control Charts

Step @ #$amine the process 1

Interpret the charts

- " &rocess is consi#ere# to be stab$e an# in a

state of contro$, or n#er contro$, %hen the

&erformance of the &rocess fa$$s %ithin the

statistica$$y ca$c$ate# contro$ $imits an#e>hibits on$y chance, or common cases.

!onse0uences of


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Control Charts

!onse0uences of

misinterpreting the process

- B$amin' &eo&$e for &rob$ems that they cannotcontro$

- S&en#in' time an# money $oo)in' for

&rob$ems that #o not e>ist

- S&en#in' time an# money on nnecessary

&rocess a#jstments

- Ta)in' action %here no action is %arrante#

- "s)in' for %or)er5re$ate# im&rovements%hen &rocess im&rovements are nee#e# first


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Control Charts

,rocess variation

- 6hen a system is sbject to on$ychance cases of variation, HH.G=L of

the measrements %i$$ fa$$ %ithin =

stan#ar# #eviations

2 ;f : sb'ro&s are measre#, HHG %i$$

fa$$ %ithin the si> si'ma $imits.

!h t


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Control Charts

!hart 2ones

- Base# on or )no%$e#'e of the norma$ crve, a

contro$ chart e>hibits a state of contro$ %hen9

2 T%o thir#s of a$$ &oints are near the center

va$e.

2 The &oints a&&ear to f$oat bac) an# forthacross the center$ine.

2 The &oints are ba$ance# on both si#es of the

center$ine.

2 8o &oints beyon# the contro$ $imits. 2 8o &atterns or tren#s.

Id tif i tt


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Control Charts

Identifying patterns

-Tren#s 2 stea#y, &ro'ressive chan'es in $eve$

- Chan'e, jm&, or shift in $eve$

- Rns 5 G &oints above or be$o%0 si>

increasin' or #ecreasin', c$sters

- Recrrin' cyc$es

- T%o &o&$ations

- Mista)es


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Control Charts

Step A 8evise the charts

- ;n certain cases, contro$ $imits arerevise# becase9

2 ot5of5contro$ &oints %ere inc$#e# in

the ca$c$ation of the contro$ $imits. 2 The &rocess is in5contro$ bt the

%ithin sb'ro& variation si'nificant$y

im&roves.

8 i i th h t


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Control Charts

8evising the charts

- ;nter&ret the ori'ina$ charts

- ;so$ate the cases

- Ta)e corrective action

- Revise the chart 2 /n$y remove &oints for %hich yo

can #etermine an assi'nab$e case

Step :4 'chie e the p rpose


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Control Charts

Step :4 'chieve the purpose

- /r 'oa$ is to #ecrease thevariation inherent in a &rocess over

time.

- "s %e im&rove the &rocess, thes&rea# of the #ata %i$$ contine to

#ecrease.

- a$ity im&rovesOO


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Process Capability

,rocess !apa*ility

efining process


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Process Capability

efining process

capa*ility

- Process ca&abi$ity refers to theabi$ity of the &rocess to meet the

s&ecifications set by the cstomer

or #esi'ner.


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Process Capability

,rocess capa*ility analysis

- /bjective is to #etermine ho% %e$$ theot&t from a &rocess meetss&ecification $imits

-Com&are tota$ &rocess variation an#to$erance.

LSL USL

-3σ +3σ arg!t

Individual values compared


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Process Capability

Individual values compared

with averages

- 6hen #istribtions of avera'es arecom&are# to #istribtions of in#ivi#a$

va$es, the avera'es are 'ro&e#

c$oser to the center va$e than are the

in#ivi#a$ va$es, as #escribe# by the

centra$ $imit theorem.

- 6hat #oes this im&$y for avera'es in

contro$ $imits verss in#ivi#a$ va$es ins&ecification $imitsI

#stimation of population sigma


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Process Capability

#stimation of population sigma

from sample data

- The re$ationshi& bet%een the stan#ar##eviation for in#ivi#a$ va$es an# the

stan#ar# #eviation for their avera'es is 'iven

by the form$a9

n x

σ

σ = Wh!r! n " subgr#up sa$pl! si%!


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Process Capability

To simplify calculation

- ;f the &rocess can be assme# to be norma$,the &o&$ation stan#ar# #eviation can beestimate# from either the stan#ar# #eviationassociate# %ith the sam&$e stan#ar#

#eviation or the ran'e9

d 2

R =∧

σ #r

c4

S=∧

σ

!ontrol limits and specification


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Process Capability

!ontrol limits and specification

limits

- Nbar charts #o not ref$ect ho% %i#e$ythe in#ivi#a$ va$es com&osin' the

&$ottin' avera'es s&rea#.

- The s&rea# can on$y be seen byobservin' %hat is ha&&enin' on the s

or r chart.

The Si$ sigma spread versus


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Process Capability

The Si$ sigma spread versus

specification limits

- Case ;9 Fσ Q 3S? 5 ?S?- Most #esirab$e0 in#ivi#a$ va$es fa$$ %ithin

s&ecification $imits

- Case ;;9 Fσ 3S? 5 ?S?- /)ay, as $on' as the &rocess remains in contro$

- Case ;;;9 Fσ 3S? 5 ?S?- 3n#esirab$e0 &rocess inca&ab$e of meetin'

s&ecifications

!alculating process capa*ility


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Process Capability

!alculating process capa*ility

indices

- Process ca&abi$ity in#ices9 ratiosthat (antify the abi$ity of a

&rocess to &ro#ce %ithin

s&ecifications


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& 7 (&7) T l f I t


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&ew 7 (&7) Tools for Improvement

1ocses on 'ro& &rocesses an##ecision ma)in'

Recommen#e# that the 8G too$s

be se# in a “cyc$e of activity,!

%herein on too$ &rovi#es in&ts to

another too$ @&.


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y y p

0nnown 1nown

LogicalCreative

!##inity Diagram

!ctivity -etwor

Diagram

Process Decision

Program Chart

2atri,

Diagram

Prioriti$ation

2atrices

Tree Diagram3

'ystem %low

Interrelationship

Diagraph

'ffinit iagrams


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'ffinity iagrams

"ffinity Dia'rams 2 creates a hierarchyof i#eas on a $ar'e srface

3sef$ to srface a$$ isses re$ate# to a&rob$em

*e$&s 'ro&s conver'e on a set

nmber of themes or i#eas

'ffinity iagram Steps


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'ffinity iagram Steps

:. ;#entify &rob$em. Create a c$ear,concise statement of the isse.


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'ffinity iagram Steps (cont.)

F. "$$ &artici&ants si$ent$y an# (ic)$ymove car#s into &i$es %ith simi$arthemes.

G. Do not #iscss #isa'reement on car#

&$acement, sim&$y move it.. /nce consenss @a$$ car#s in 'ro&s

an# car# movement has sto&&e#Acreate hea#er car#s.

H. Dra% finishe# affinity #ia'ram an#&rovi#e a %or)in' co&y for a$$&artici&ants.

'ffinity iagram #$ample


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Ref9

http://mot.vuse.van

derbilt.edu/mt322/#

!!inity.htm

Ref9

http://""".in!odesign.com.au/usab

ilityresources/general/a!!initydi agramming.asp

Interrelationship iagrams

http://www.flowbiz.com.au/flowbiz/products/charter/http://www.flowbiz.com.au/flowbiz/products/charter/http://www.flowbiz.com.au/flowbiz/products/charter/http://www.vanderbilt.edu/Engineering/CIS/Sloan/web/es130/quality/newtool.htmhttp://www.vanderbilt.edu/Engineering/CIS/Sloan/web/es130/quality/newtool.htmhttp://www.vanderbilt.edu/Engineering/CIS/Sloan/web/es130/quality/newtool.htmhttp://www.vanderbilt.edu/Engineering/CIS/Sloan/web/es130/quality/newtool.htmhttp://www.vanderbilt.edu/Engineering/CIS/Sloan/web/es130/quality/newtool.htmhttp://www.vanderbilt.edu/Engineering/CIS/Sloan/web/es130/quality/newtool.htmhttp://www.flowbiz.com.au/flowbiz/products/charter/http://www.flowbiz.com.au/flowbiz/products/charter/http://www.flowbiz.com.au/flowbiz/products/charter/


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Interrelationship iagrams

;nterre$ationshi& Dia'rams 2 sho%re$ationshi&s bet%een #ifferent

isses

*e$&f$ in i#entifyin' the most

im&ortant isse

Interrelationship iagram Steps


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:. P$ace car#s %ith re$ate# isses from

affinity #ia'ram in co$mns %ith 'a&sbet%een them.

amine car#s in#ivi#a$$y $oo)in' forisses case# or inf$ence# by the

isse. Dra% a one5%ay arro% fromthe “case! to the “one inf$ence# bythe case.! Re&eat nti$ a$$ isses#iscsse#.

=. Revie%, ma)e revisions, then contthe of arro%s &ointin' to each notean# %rite the s on the notes.



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. ;#entify car#s %ith the most arro%s as“)ey factors! @no more than to :A.Bo>es %ith severa$ arro%s a$tho'hnot )ey factors can be #ro&&e# at this

&oint. Bo>es %ith ot'oin' arro%sten# to be root cases, those %ithincomin' arro%s ten# to be&erformance in#icators.

. Dra% a #ob$e bo> aron# )eyfactors an# brainstorm %ays toa##ress these isses.

Interrelationship iagram #$ample


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Ref9

http://""".sm

artdra".com/t

utorials/bpm/

bpm$%.htm

Tree iagrams

http://www.smartdraw.com/tutorials/bpm/bpm13.htmhttp://www.smartdraw.com/tutorials/bpm/bpm13.htmhttp://www.smartdraw.com/tutorials/bpm/bpm13.htmhttp://www.smartdraw.com/tutorials/bpm/bpm13.htmhttp://www.smartdraw.com/tutorials/bpm/bpm13.htmhttp://www.smartdraw.com/tutorials/bpm/bpm13.htmhttp://www.smartdraw.com/tutorials/bpm/bpm13.htmhttp://www.smartdraw.com/tutorials/bpm/bpm13.htm


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Tree iagrams

Tree Dia'rams 2 sef$ to i#entify theste&s nee#e# to a##ress the &rob$em

Ste&s to com&$ete tree #ia'ram9

:. 1rom the affinity #ia'ram hea#er car#s,choose the most im&ortant isse.

haste# i#eas for ste&s.

Tree iagram #$ample


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Ref9

""".smartdra".com/resources/e&am les/business/or chart'.htm

,rioriti2ation 9rids

http://www.smartdraw.com/resources/examples/business/orgchart9.htmhttp://www.smartdraw.com/resources/examples/business/orgchart9.htmhttp://www.smartdraw.com/resources/examples/business/orgchart9.htmhttp://www.smartdraw.com/resources/examples/business/orgchart9.htm


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,rioriti2ation 9rids

Prioriti+ation ri# 2 se# to ma)e#ecisions base# on m$ti&$e criteria

Ste&s to com&$ete a &rioriti+ation 'ri#9:. Determine 'oa$, a$ternatives, an# criteria

to ma)e the #ecision %ith.


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,rioriti2ation 9rid Steps (cont.)

=. "&&$y &ercenta'e %ei'ht @tota$in' to:A to each of the criteria for each

o&tion.

. "## the in#ivi#a$ ratin' &er criterionto fi're overa$$ ran)in', #ivi#e by

of o&tions to fin# avera'e ran)in'.

. Ran) each o&tion %ith res&ect to

criteria, avera'e ran)in's, an# a&&$ya com&$ete# ran)in'.

,rioriti2ation 9rid Steps (cont )


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,rioriti2ation 9rid Steps (cont.)

F. M$ti&$y criteria %ei'ht by its assoc.criterion ran) for each criterion in thematri> @ is the best an# : the %orstA.Res$t in each ce$$ is ca$$e# an

“importance score.!G. "## im&ortance scores for each

a$ternative.

. Ran) a$ternatives accor#in' to

im&ortance

,rioriti2ation 9rid #$amples


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Ref9

http://""".smartdra".

com/tutorials/bpm/bpm

$3.htm

Matri$ iagrams

http://www.smartdraw.com/tutorials/bpm/bpm10.htmhttp://www.smartdraw.com/tutorials/bpm/bpm10.htmhttp://www.smartdraw.com/tutorials/bpm/bpm10.htmhttp://www.smartdraw.com/tutorials/bpm/bpm10.htmhttp://www.smartdraw.com/tutorials/bpm/bpm10.htmhttp://www.smartdraw.com/tutorials/bpm/bpm10.htm


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Matri$ iagrams

Matri> Dia'rams 2 brainstormin'too$s se# in a 'ro& to sho%

re$ationshi&s bet%een i#eas or

isses

Can be se# in t%o, three, or for

#imensions

Matri$ iagram Steps


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Matri$ iagram Steps

:. Determine the nmber of issesor #imensions to be se# in the

matri>.

.

=. P$ace the a&&ro&riate symbo$s in

the matri>, base# on the $e'en#.

Matri$ iagram #$ample


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Ref9http://""".vanderbil

t.edu/(ngineering/)I

*/*loan/"eb/es$3%/qu

ality/ne"tool.htm+ma

tri&

,rocess ecision ,rogram !harts

http://www.infodesign.com.au/usabilityresources/general/affinitydiagramming.asphttp://www.infodesign.com.au/usabilityresources/general/affinitydiagramming.asphttp://www.infodesign.com.au/usabilityresources/general/affinitydiagramming.asphttp://www.infodesign.com.au/usabilityresources/general/affinitydiagramming.asphttp://www.infodesign.com.au/usabilityresources/general/affinitydiagramming.asphttp://www.infodesign.com.au/usabilityresources/general/affinitydiagramming.asphttp://www.infodesign.com.au/usabilityresources/general/affinitydiagramming.asphttp://www.infodesign.com.au/usabilityresources/general/affinitydiagramming.asphttp://www.infodesign.com.au/usabilityresources/general/affinitydiagramming.asphttp://www.infodesign.com.au/usabilityresources/general/affinitydiagramming.asp


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,rocess ecision ,rogram !harts

Process Decision Pro'ram Charts 2 too$ to he$& brainstorm &ossib$econtin'encies or &rob$ems

associate# %ith theim&$ementation of some &ro'ramor im&rovement

Can be in either tree form orot$ine form

,, !hart Steps


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,, !hart Steps

:. P$ace the first $eve$ bo>es inse(entia$ or#er.


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Ref9

http://""".!lo

"bi,.com.au/!l

o"bi,/products

/charter

'ctivity &etwor" iagrams

http://mot.vuse.vanderbilt.edu/mt322/Affinity.htmhttp://mot.vuse.vanderbilt.edu/mt322/Affinity.htmhttp://mot.vuse.vanderbilt.edu/mt322/Affinity.htmhttp://mot.vuse.vanderbilt.edu/mt322/Affinity.htmhttp://mot.vuse.vanderbilt.edu/mt322/Affinity.htmhttp://mot.vuse.vanderbilt.edu/mt322/Affinity.htmhttp://mot.vuse.vanderbilt.edu/mt322/Affinity.htmhttp://mot.vuse.vanderbilt.edu/mt322/Affinity.htm


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'ctivity &etwor" iagrams

"ctivity 8et%or) Dia'rams 2 too$se# in contro$$in' &rojects

".K.".9 PERT @Pro'ram

Eva$ation an# Revie% Techni(eA

Dia'ram or Critica$5Path @$on'est

&ath in time from be'innin' toen#A Dia'ram

,#8T iagram #$ample


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Basic Quality Control Tools

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Transcript of Basic Quality Control Tools