revised qfd

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Fuzzy Quality Function Deployment (FQFD) to Assess CustomerRequirement in Passenger car segment: An Indian Prospective

Astract

The concept of QFD evolved from the belief that Total Quality Control must include not only

checking of the control points during production, but an understanding of the exact customersrequirements prior to the design phase. t is useful basis that differentiates bet!een a average

!ith an excellent product. This concept is not devoid of a passenger car segment as !ell. The

present study hybridi"es QFD !ith fu""y dominance order to asses the customer requirementsin #assenger car segment. t uses content analysis and nominal group technique $%&T' to

identify customer and technical requirements. Fu""y dominance order !here used to establish

democratic !isdom to pinpoint exact customer requirements. (erein the relation matrix has been used in con)unction !ith conventional QFD replaced !ith aggregated fu""y relation

matrix to )ustify sub)ective nature of responses. The result !ith this approach and conventional

QFD approach is then compared using nterpretive *tructural +odel $*+' for deployableelements of technical requirements as a reference.

Key words: Quality Function Deployment , Fu""y dominance, Fu""y Quality Function

Deployment $FQFD', nterpretive *tructural +odel $*+', Quality assurance !" IntroductionQFD was developed in Japan during the 60s by Akao and Mizuno as a method of produt development! whih aims at ful"lling ustomer demands# $he primaryob%etive of this method is to assure &uality sine the earlier stages of the pro%etdevelopment $he QFD onept is broken down into the two main ob%etives'(rodut &uality deployment and deployment of the &uality funtion# (rodut&uality deployment translates the )voie of the ustomer* in to the produt ontrolharateristis# +hereby! deployment on the &uality funtion ativities needed to

assure that ustomer re&uired &uality is ahieved# Deployment of the &ualityfuntion e,amines the ompany response to the ustomer voie through anorganize team approah -./#Aording to ohen and 1an -2! 3/ there are si, stages of hierarhial frameworkof QFD as in table4.#

#ale$! %ierarc&ical Frame'or o QFD5tage. oie of the ustomer Developing ategorizing and prioritizing

ustomer re&uirements2 ompetitive Analysis omparing the performanes with

ompetitors and set target levels for

ustomer re&uirements#

3 oie of 7rganization $ranslating the voie of the ustomer tothe voie of organization##

8 Design $argets 5peifying targets value for designre&uirement and determining the pro%etost#


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9 :elationship matri, ;valuating impat of design re&uirementson ustomer re&uirements#

6 orrelation matri, speifying tradeoien ?/# 1owever! in real world situation where human prefereneand logi is involved the opinions often follow sub%etivity thereby bringingelement of fuzziness in the system# QFD methodology does address suh fuzzinessin its relationship between ustomer and tehnial re&uirements# @t therefore!seems logial if fuzzy set approah is utilized in QFD tehni&ue in order to e,pet amore realisti solution# $he present paper hybridizes QFD with fuzzy dominaneorder to asses the ustomer re&uirements in engineering eduation system# @t usesontent analysis and nominal group tehni&ue B>$C to identify ustomer andtehnial re&uirements# Fuzzy dominane order has been used to establishdemorati wisdom to identify ustomer re&uirements# :elation matri, used in

traditional QFD has been replaed with aggregated fuzzy position matri, to %ustifysub%etive responses# $he result obtained by this approah and traditional QFDapproah is ompared using @nterpretive 5trutural Model @5MC for deployableelements of tehnial re&uirements as yardstik#

*"+ ,ngineering ,ducation -ystem:;ngineering eduation world over is urrently under tremendous strain as it istrying to ope up with the eduational aspets of globalization of eonomy! rapidtehnologial advanes! emergene of totally new tehnologies! and ontinuingshortening of half life of engineers eroup $ehni&ue B>$C hasbeen utilized to obtain overall integrated desriptive ustomer re&uirementsthrough a onsensus G driven interative4iterative proess#

$hree workshop sessions in two stages were organized to prepare the integratedlist of ustomer re&uirements# @t onsisted of .9 domain e,perts derived fromaademia! student population! e,perts in administration of engineering eduationplanning and "nane inluding the authors# During the "rst stage several lists of ustomer re&uirements were prepared from primary and seondary soures# $heseomplied lists were further lari"ed! merged! edited! oded and keyworded# @n all.0 elements have been onsidered# -$able42/

$able42 ustomer :e&uirement


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5#Bo ustomer re&uirement ode. (laement .

2 :egular ourse update 23 Modernization of Hab 38 ompetent faulty and

supporting sta


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approah industries! institutions and publi through building rapport andrepute of building knowledge! reativity and innovativeness inorder to ahieve e,ellene# $his approah ensure healthyompetitive! ooperative ollegial and aademi environment#

@mplementability An K4ability of an engineering institution of adapting thehanges &uikly and ating upon e,eution of planned work for

total &uality of eduation#

ision and Mission A desriptive statement of long term perspetive to have abetter plan and implementation for ahieving them

ompetent anddediated faulty and5upporting 5ta


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I 8 8 8 36 36 20 20 20 20 .60

? 9 9 0 9 29 9 89 89 9 .39

.0 8 20 20 20 36 36 36 36 36 280

Column-um

*01 !7+

*36

036

*1*

0*4

001

*+0 *!4+

$o identify tradeo< of tehnial re&uirements orrelation oe=ient has also beenobtained! whih is shown in table4#

#ale$5 Correlation Coe8cient or #ec&nical Requirement $. $2 $3 $8 $9 $6 $ $I

$. , #?3

#.?8 #I26 #32 #290 #36 #92

$2 , 0?.?

#09 #9?2 #.I. #2? #88

$3 , #6003

#603 #.I0 #.89 88

$8 K #6.2 #02? #330 #990 $9 , #

8..8

#.2 #393

$6 K #3.9 #06. $ , #326 $I K

Finally very strong to moderate relationship between tehnial re&uirements wereobtained as depited in table4I#ale$6 Co relations&ip et'een tec&nical requirements

Relations&ip #ec&nical Requirementsery strong LLC $34$I!$.4$2! $.4$8! $24$8!$24$I5trong LC $34$8!$34$9!$84$9Moderate 44C $24$9!$84$I!$.4$I

>overs -8/ e,plained three ategories that may develop onditions leading tofailure of QFD# $hey may be methodologial problem like risk of too muh detail!organizational problem like lak of ommuniation among ross funtional

proesses and produt poliy like market information#5ome of these problems may well be addressed provided sub%etive information isdiretly inorporated in QFD proess# $his will help respondents give a betterommuniation strategy with out losing fous on detail# 1ybrid QFD approah hastherefore been inorporated to overome suh di=ulty#

0"+ %yrid Quality Function Deployment:@n this method the weighted importane of ustomer re&uirement was obtained onthe basis of fuzzy dominane# Five features namely &uality! utility! aesthetis!performane and servieability were seleted with the help of domain e,perts#5take holders were asked to ompare all ustomer re&uirements on eah feature in


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the form of linguisti variables like A4very good! 4 good! 4 average! D4 belowaverage and D4 poor# .00 ustomers were asked to "ll suh &uestioner in the formof 9,.0 position matries# @n all .00 suh position matries were obtained# Asample position matri, is given in table4?#

$able4? 5ample (osition Matri,

Features . 2 3 8 9 6 I ? .0Quality A A A A A A ANtility A A A AAesthetis A A A A(erformane A A A A A A A A5ervieability

A A A A A A A

;ah matri, was then &uanti"ed# $he membership values are shown in table4.0# $able4.0 Fuzzy membership funtion

ery good 0#?>ood 0#Average 0#9elowaverage

0#3

(oor 0#. $he &uanti"ed position matries were aggregated as shown in table4..#

$able4.. Aggregated (osition Matri,

F

e a t u r e s. 2 3 8 9 6 I ? .

0

Q u a l i t y#

I8

#93

#90

#I6

#I9

#32

#98

#2?

#II

#8

9 t i l i t y

#9

#3?

#69

#6I

#II

#2I

#6I

#32

#6I

#.I

A e s t & e t i #

6

#2

0

#8

I

#2

I

#6

I

#3

2

#8

I

#.

I

#.

I

#2

8

P e r ) o r m a#

I6

#96

#II

#II

#I6

#8I

#2I

#22

#32

#8I


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- e r v i c e a #

8

#9?

#8

#I9

#I8

#2I

#2

#2I

#2I

#92

Dominane order on all features for ustomer re&uirements is given in table4.3

$he weights were assigned as per the dereasing dominane order# $here&uirement of dominane order of . bore a weight of .0 while a dominane orderof .0 bore a weight of .# $hese weights were further normalized with a sale 04.#

$hese normalized weights are given in table.3# $able 4.3 Dominane 7rders of ustomer :e&uirements

Dominaneorder

ustomer re&uirement ustomer re&uirement

@ 7ptimal @nfrastruture #..3

@@ (laement #.6?@@@ ompetent faulty #.32@ Modernization of lab #.90 :egular ourse update #.90

@ 1ealth onsious anteen #0?8@@ Modernization of Hibrary #09@@ 7ptimal fees struture #03@K $ransparent ;valuation 5ystem #0.IK 5u=ient sports and ultural

ativities#096

Fuzzy relation matri, -$able4.8/ between ustomer re&uirement and tehnialre&uirement was obtained in the form of linguisti variables! whose membershipfuntion is given in "gure4.

Figure4. Hinguisti variables and their membership values


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$able4.8 Fuzzy relation matri,

@mportane

$. $2 $3 $8 $9 $6 $ $I

. 0#.6? ( : ( ( : ( ( $2 0#.90 ( ( $ ( : ( ( :

3 0#.90 ( : ( ( $ ( ( (

8 0#.32 ( ( : ( ( : ( (

9 0#..3 : $ ( ( ( : : $

6 0#0?8 $ 0 : ( : ( : $

0#09 : ( ( ( ( ( ( :

I 0#096 $ $ ( ( : : : :

? 0#03 $ 0 $ : ( ( ( (

.0 0#0.I : : : ( ( ( ( (

$hese matries were &uanti"ed and aggregated# $he aggregated relation matri, isgiven in table4.9# ;ah ell entry was multiplied with importane fator by salarmultipliation#

$able4.9 Fuzzy aggregated relation matri,

@mportane

$. $2 $3 $8 $9 $6 $ $I

. 0#.6? #.339 #.08 #.3? #.32 #0II #.8I #.2 #088 #.82 0#.90 #.23 #.. #033 #.32 #02 #.2? #.32 #0I. #.3

3 0#.90 #.. #0I #.26 #.. #02 #.26 #.2? #.2? #.2

8 0#.32 #.0I #..0I #0636 #..0I #.0I #063 #..0I #... #..

9 0#..3 #09I #0203 #0?8 #0? #0?8 #09I #09I #0203 #0?

6 0#0?8 #0.6? 0 #08I #0I2 #08I #0I2 #089 #0229 #0I

0#09 #036 #096 #09I #06.9 #063 #0689 #06.9 #03? #06

I 0#096 #0.89 #0.00 #089? #02?. #0302 #02?. #026I #0302 #08

? 0#03 #006 0 #006 #03.0 #03.0 #0303 #032 #03.I #03

.0 0#0.I #0.. #000I #00?3 #0.8 #0.9I #0.98 #.98 #0.9. #.9

A: #.339 #.. #.3? #.32 #.0I #.8I #.32 #.2?

:: @@@ @ @@ @ @@ @ @

@n order to "nd out the relative importane between tehnial and ustomerre&uirements ma,ima of eah row entry and olumn entry was taken instead of row sum and olumn as in traditional QFD#5ine eah olumn of matri, represents a fuzzy vetor therefore! fuzzy ;ulideandistane between olumns vetor were obtained to "nd out loseness between twotehnial re&uirements given preferenes of ustomer re&uirements#


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#ale$!4 ,uclidean matric or #ec&nical Requirement

$. $2 $3 $8 $9 $6 $ $I $. , #0??8 0#2. 0#I?? 0#?03 0#.28 0#0909 0#?0. $2 , 0#.90 0#0I2. 0#86 0#2.3 0#982 0#0I?I $3 K 0#.396 0#I6 0#I62 0#2.6 0#0I36

$8 K 0#.22 0#I36 0#.36 0#2.6 $9 , 0#.89 0#I96 0#.26 $6 K 0#0683 0#28I $ , 0#.0?2 $I K

1"+ #&e I- Process:

@5M provides a method for making the elements of a omple, issue into an agreeddiagrammati struture# $he struture may be obvious as in managementorganizations or may be less obvious as in the value struture of a deision maker#onept elements are no e,eption to suh vagueness#

@5M has its roots in the systemati appliation of some elementary notions of graph theory! set theory! mathematial logi and matri, theory# $heoretial!oneptual and omputational leverage is e,ploited to e=iently onstrut adireted graph of omple, system under a spei"ed onte,tual relationship amonga set of elements of the system#

1"! -tructural -el Interaction atri2 (--I)55@M on the onte,tual relation a


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$he 55@M has been onverted into reahability matri, by replaing symbols into .and 0 in its @! %C th entry#

$able4. :eahability Matri, for $ehnial :e&uirements

$. $2 $3 $8 $9 $6 $ $I $? $. K 0 . . 0 0 . . .

$2 . K 0 0 0 0 0 0 . $3 0 . K . 0 0 . . . $8 0 0 0 K . 0 . . . $9 . . . . K . . . . $6 . . . . . K . . . $ 0 . 0 0 . . K . . $I 0 . 0 . 0 0 0 K . $? 0 0 0 0 0 0 0 0 K

(artitions on the reahability matri, were done! yles identi"ed and it wasonverted into anonial form! whih was appended to give @5M# $he @5M of

tehnial re&uirement so obtained is given in "gure 3#

Figure43 @5M for tehnial re&uirements

3"+Conclusion:@t is evident from the @5M of tehnial re&uirements that re&uirements like $6@mplementabilityC! $9 Bon beauroati approahC! $3 Alloation a&uisition of fundsC $ ision and missionC and $I ompetent and dediated faulty and sta


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Further! regarding the importane of weights obtained in ase of traditional QFDfor ustomers re&uirements! it is observed that it has more ties as ompared to theproposed approah where it is none# @t is beause of the fat that in hybrid QFDustomer re&uirements has been %udged on the basis of seleted features andgives overall dominant opinion of the stakeholders in terms of their linguistipereptions# $his gives better larity in deiding the level of importane of

ustomer re&uirements and hene assigning distint weights#

Reerence.# Phoo H#( and 1o B# )Framework of a fuzzy &uality funtion deployment

system* @nternational Journal of (rodution :esearh! ol#38! Bo# 2! pp 2??43..!.??6#

2# ohen #H )Quality funtion deployment' how to make QFD work for you!Nnited 5tates of A Ameria! Addison4 +esley (ublishing ompany! .??9#

3# 1an# et# al# )A oneptual QFD planning model*! @nternational Journal of Quality and reliability management! ol#.I! Bo I! 200.#

8# >overs! #(#M# )QFD not %ust a tool but a way of &uality management*@nternational Journal of (rodution ;onomis! ol#6?! Bo 2! 200.#

9# (atriia rakin )Assessing ;ngineering ;duation' an @ndustrial Analogy*@nternational Journal of ;ngineering ;duation! ol .I! Bo 2! pp .9.4.96!2002#

6# :ihard #P# Fung! Dingweihlang and Jiafu $ang )An @ntelligent Fuzzy@nferene Model for ustomer re&uirement Management* @@; 1PC Annual

Journal ?4?I## :ambabu Podali ) Multi4Attribute Deision model using Analytial 1ierarhy

(roess for the Justi"ation in ;,ellene of $ehnial @nstitutions in @ndia* $he @ndian Journal of $ehnial ;duation! ol#2.!Bo3!July45ept .??I


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revised qfd

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