A fuzzy model for achieving lean attributes for competitive advantages development using AHP-QFD-PROMETHEE

Roghanian, E.; Alipou , Mohammad A icle A uzzy model o achie ing lean a ibu es o compe i i e ad an ages de elopmen using AHP-QFD-PROMETHEE Jou nal o Indus ial Enginee ing In e na ional P o ided in Coope a ion wi h: Islamic Azad Uni e si y (IAU), Teh an Sugges ed Ci a ion: Roghanian, E.; Alipou , Mohammad (2014) : A uzzy model o achie ing lean a ibu es o compe i i e ad an ages de elopmen using AHP-QFD-PROMETHEE, Jou nal o Indus ial Enginee ing In e na ional, ISSN 2251-712X, Sp inge , Heidelbe g, Vol. 10, pp. 1-11, h ps://doi.o g/10.1007/s40092-014-0068-4 This Ve sion is a ailable a : h ps://hdl.handle.ne /10419/157407 S anda d-Nu zungsbedingungen: Die Dokumen e au EconS o dü en zu eigenen wissenscha lichen Zwecken und zum P i a geb auch gespeiche und kopie we den. Sie dü en die Dokumen e nich ü ö en liche ode komme zielle Zwecke e iel äl igen, ö en lich auss ellen, ö en lich zugänglich machen, e eiben ode ande wei ig nu zen. So e n die Ve asse die Dokumen e un e Open-Con en -Lizenzen (insbesonde e CC-Lizenzen) zu Ve ügung ges ell haben soll en, gel en abweichend on diesen Nu zungsbedingungen die in de do genann en Lizenz gewäh en Nu zungs ech e. Te ms o use: Documen s in EconS o may be sa ed and copied o you pe sonal and schola ly pu poses. You a e no o copy documen s o public o comme cial pu poses, o exhibi he documen s publicly, o make hem publicly a ailable on he in e ne , o o dis ibu e o o he wise use he documen s in public. I he documen s ha e been made a ailable unde an Open Con en Licence (especially C ea i e Commons Licences), you may exe cise u he usage igh s as speci ied in he indica ed licence. h p://c ea i ecommons.o g/licenses/by/2.0/ ORIGINAL RESEARCH A uzzy model o achie ing lean a ibu es o compe i i e ad an ages de elopmen using AHP-QFD-PROMETHEE E. Roghanian •Mohammad Alipou Recei ed: 5 Feb ua y 2014 / Accep ed: 31 May 2014 / Published online: 14 June 2014 The Au ho (s) 2014. This a icle is published wi h open access a Sp inge link.com Abs ac Lean p oduc ion has become an in eg al pa o he manu ac u ing landscape as i s link wi h supe io pe - o mance and i s abili y o p o ide compe i i e ad an age is well accep ed among academics and p ac i ione s. Lean p oduc ion helps p oduce s in o e coming he challenges o ganiza ions ace h ough using powe ul ools and ena- ble s. Howe e , mos companies a e aced wi h es ic ed esou ces such as inancial and human esou ces, ime, e c., in using hese enable s, and a e no capable o imple- men ing all hese echniques. The e o e, iden i ying and selec ing he mos app op ia e and e icien ool can be a signi ican challenge o many companies. Hence, his li - e a u e seeks o combine compe i i e ad an ages, lean a ibu es, and lean enable s o de e mine he mos app o- p ia e enable s o imp o emen o lean a ibu es. Quali y unc ion deploymen in uzzy en i onmen and house o quali y ma ix a e implemen ed. Th oughou he me hod- ology, uzzy logic is he basis o ansla ing linguis ic judgmen s equi ed o he ela ionships and co ela ion ma ix o nume ical alues. Mo eo e , o inal anking o lean enable s, a mul i-c i e ia decision-making me hod (PROMETHEE) is adop ed. Finally, a case s udy in au o- mo i e indus y is p esen ed o illus a e he implemen a- ion o he p oposed me hodology. Keywo ds Lean p oduc ion Fuzzy quali y unc ion deploymen (Fuzzy-QFD) House o quali y (HOQ)  Fuzzy analysis hie a chy p ocess (Fuzzy-AHP)  PROMETHEE In oduc ion Today, he wo ld is wi nessing he globalized ad ancemen o science and echnology en iched by new heo ies, models and app oaches, which has made manage s e hink abou p oduc ion echniques. Sho e p oduc li ecycles, inc ease in p oduc a ie y, and also in ensi ica ion o challenges ahead o compe i o s a global le el, ha e mo i a ed many manu ac u ing i ms o s ep owa ds new manu ac u ing echniques (Ho e e al. 2012; P iyan o e al. 2012). Because o he g owing dynamics and a ia ion in demand, mode n p oduc ion sys ems ha e o be lean and lexible in o de o s eng hen he company’s compe i i e posi ion (Abele and Reinha 2011). Up o now, some ha e succeeded in o e coming he challenges h ough u ilizing mode n p oduc ion concep s, echniques and models. One o he la es p oduc ion app oaches is lean p oduc ion, which has powe ul ools and enable s, and holds big p omises o p oduce s (Chen e al. 2013). Lean p oduc ion sys em is one o he leading app oaches adop ed by many leading businesses in he wo ld o keep hei compe i i e ad an ages in he g owing global ma ke (Schonbe ge m 2007). Lean p oduc ion is o en ega ded as he gold s anda d o mode n ope a ions and supply chain managemen (Guinipe o e al. 2005; Goldsby e al. 2006). I holds signi ican p omises o subs an ial ad an age o imp o emen o communica ion and in eg a ion wi hin he o ganiza ion and supply chain (Sche e -Ra hje e al. 2009). E. Roghanian Depa men o Indus ial Enginee ing, K. N. Toosi Uni e si y o Technology, Teh an, I an e-mail: [email p o ec ed] M. Alipou (&) Sus ainable Ene gies G oup, AUT O ice o Sus ainabili y, Ami kabi Uni e si y o Technology (Teh an Poly echnic), P.O. Box: 15875-4413, Teh an, I an e-mail: [email p o ec ed] 123 J Ind Eng In (2014) 10:68 DOI 10.1007/s40092-014-0068-4 Lean enable s a e conside ed as a se o ools, ech- niques and sugges ions o implemen a ion and execu ion o lean p inciples and help business uni s o s ep owa d leanness (De T e ille and An onakis 2006; Hopp and Spea man 2004; Na asimhan e al. 2006). Nume ous business uni s ei he in manu ac u ing o se ice sec o s ha e been helped by lean enable s o enhance hei e i- ciency, p o i abili y and compe i i eness. De eloped o ig- inally o Toyo a P oduc ion Sys em, he applica ion o lean enable s has empowe ed a ious o ganiza ions o imp o e hei quali y, p oduc i i y and cus ome se ices, signi ican ly (Riezebos 2009). Lean manu ac u ing pa adigm encompasses se e al ools/ echniques o achie e leanness in manu ac u ing o ganiza- ions (Vinodh e al. 2010) and i ms ha e a p oblem o selec ing he bes lean concep o he immedia e imple- men a ion as mos en e p ises a e no capable o imple- men ing all hese echniques (Vinodh e al. 2012). Besides, mos o ganiza ions ha e limi ed esou ces such as budge , human esou ces, ime, e c. Hence, iden i ying and selec ing he mos app op ia e and e icien ool can be a signi ican challenge o many companies. In his way, his s udy ies o p opose an in eg a ed app oach, based on quali y unc ion deploymen (QFD) and in pa icula HOQ echnique, owa ds imp o ing leanness o business uni s. The i s objec i e is o de e mine he ela ionship be ween he o ga- niza ion’s compe i i e ad an ages and lean a ibu es in he HOQ, and hen, anking he mos necessa y and e icien lean a ibu es o achie e compe i i e ad an ages. The second goal is o de e mine he mos signi ican and e icien lean enable s and hen, anking hem h ough PROMETHEE. In addi ion, he p esen app oach uses uzzy logic o minimize he gap be ween judgmen and sco ing and he eali y. The p oposed model o his pape uses compe i i e ad an ages, lean a ibu es and lean enable s based on uzzy analy ical hie a chy p ocess (F-AHP), while geome ic mean me hod is applied o calcula ing uzzy weigh s. Finally, o show me i s o he me hodology, a case s udy is ep esen ed as well. The emainde o his s udy is o ganized as ollows: ‘‘Li e a u e e iew’’ p esen s a li e a u e e iew o com- pe i i e ad an ages, F-QFD and HOQ, PROMETHEE, and F-QFD calcula ions. The Fuzzy-AHP-QFD-PROMETHEE model o inc easing leanness o he o ganiza ion is p o- posed in ‘‘Fuzzy-AHP-QFD-PROMETHEE model’’. A case s udy in an indus y is discussed in ‘‘Case s udy’’ as an illus a i e example along wi h ou comes o he model. And inally, concluding ema ks a e gi en in ‘‘Conclusion’’. Li e a u e e iew This pape d aws on and con ibu es o he ollowing esea ch s eams: compe i i e ad an ages, F-QFD and HOQ, and PROMETHEE. An o e iew o he ela ed wo ks in each a ea and hei implica ions o his esea ch a e p o ided as ollows. Fuzzy-QFD calcula ions a e p o- ided a he end o his sec ion. Cu en wo ld o business is a dynamic space in which he a e o change and de elopmen is ex ensi ely high. The e o e, i ms seek o unde s and cus ome needs in o de o achie e a compe i i e ad an age and imp o e he o ganiza ional pe o mance. To speci y he impac o compe i i e ad an ages, se e al MCDM me hods ha e been p o ided. Nayebi e al. (2012) applied VIKOR o anking he indices o o ganiza ional en ep eneu ship de elopmen . Sun and Lin (2009) used uzzy TOPSIS me hod o e alua ing he compe i i e ad an ages o shopping websi es. In measu ing he compe i i e ad an age and isk o a p oduc supply chain, Hung (2011) used decision-making ial and e alua ion labo a o y (DEMA- TEL) o analyze and de e mine he in e dependence ela- ionships be ween he c i e ia. Hsu e al. (2008) adop ed an op imal esou ce-based alloca ion s a egy o senio ci i- zen housing o gain a compe i i e ad an age. They used analy ic hie a chy p ocess (AHP) o de e mine each c i e ia weigh and he a angemen o impo ance. Like hei wo k, his esea ch uses AHP o de e mine he weigh o compe i i e ad an ages; which uzzy logic is suppo ed o deal wi h he de iciency in he classical AHP, e e ed o as FAHP. The adi ional AHP equi es c isp judgmen s and also due o he complexi y and unce ain y in ol ed in eal wo ld decision p oblems, a decision make (DM) may some imes eel mo e con iden o p o ide uzzy judgmen s a he han c isp compa isons. The weigh s de e mined by F-AHP unlike app oaches like ex en analysis me hod, ep esen he ela i e impo ance o decision c i e ia o al e na i es and can co ec ly be adop ed as hei p io i ies (Wang e al. 2008). QFD is a me hodology o ansla ing cus ome equi emen s (CRs) in o p oduc design equi emen s (DRs). I helps o add ess cus ome equi emen s in o enginee ing speci ica ions o a p oduc by p io i izing each p oduc a ibu e while simul aneously assigning de elopmen goals o he same p oduc . The house o quali y (HOQ), o ins ance is an app o ed ool used by QFD whe ein isually appealing g aphical illus a ions a e used o de ine he ela ionships be ween cus ome desi es and he p oduc ea u es (Smi h 2011). Va ious op imiza- ion me hods ha e been applied in he ield o QFD o helping decision make s in p oduc planning and imp o emen (Chen and Ko 2009,2010; Lai e al. 2007; Delice and Gu ¨ngo ¨ 2009; Bha acha ya e al. 2010; Chien e al. 2010; Hajji e al. 2011; Ka ipidis 2011). Mo eo e , se e al esea che s ha e gene a ed subs an ial a en ion on uzzy QFD du ing he las decades. Zaim e al. (2014) syn hesized enowned capabili ies o ANP and Fuzzy 68 Page 2 o 11 J Ind Eng In (2014) 10:68 123 Logic o be e ank echnical cha ac e is ics o a p oduc (o a se ice) while implemen ing QFD. Raissi e al. (2012) p io i ized enginee ing cha ac e is ic in QFD using uzzy common se o weigh . Bo ani (2009) p esen ed an app oach aims a iden i ying he mos app op ia e enable s o be implemen ed by companies s a ing om compe i i e cha ac e is ics o he ela ed ma ke . The app oach was based on QFD and in pa icula HOQ, in which he whole sca old exploi s uzzy logic, o ansla e linguis ics judg- men s equi ed o ela ionships and co ela ions ma ixes in o nume ical alues. As one o he key issues in F-QFD is o de i e he echnical impo ance a ings o design equi emen s (DRs) in uzzy en i onmen s and p io i ize hem, Wang and Chin (2011) in es iga ed how he ech- nical impo ance o DRs can be co ec ly a ed in uzzy en i onmen s. In his pape , F-QFD is adop ed ins ead o c isp QFD model o de e mine he e ec o lean a ibu es on compe i i e ad an ages. The dis inc ions be ween he F-QFD sys em and he adi ional QFD me hodology is ha he QFD ele an da a a e symbolized as linguis ic e ms a he han c isp numbe s, and he linguis ic da a is p o- cessed by algo i hms embedded in he sys em’s in e nal en i onmen (Meh je di 2010). Mul iple c i e ia decision making (MCDM), o en called mul i-c i e ia decision aid (MCDA) and mul i-c i e ia analysis (MCA), is a se o me hods which allows he agg ega ion and conside a ion o nume ous (o en con- lic ing) c i e ia in o de o choose, ank, so o desc ibe a se o al e na i es o aid a decision p ocess (Zopounidis 1999). The e is no single me hod conside ed as he mos sui able o all ypes o decision-making si ua ions, and i has also been acknowledged ha se e al me hods can be po en ially alid o a pa icula decision-making si ua ion (Mulline e al. 2013). The e is a well-de eloped body o esea ch on MCDM me hods wi h a iew o ank and selec he bes al e na i es. Fo example, Faghihinia and Molla e di (2012) p esen ed a model o es ablishing a p ope main enance policy o make he bes comp omise be ween h ee c i e ia and es ablish eplacemen in e - als using PROMETHEE II. As a p ocess o selec ing a sui able manu ac u ing sys em is highly complex and s a egic in na u e, Anand and Kodali (2008) applied PROMETHEE o analyze how companies make a s a- egic decision o selec ing lean manu ac u ing sys ems (LMS) as pa o hei manu ac u ing s a egy. The e a e also o he me hods like TOPSIS, AHP, ELECTRE, COPRAS,e c. ha esea che s apply o p io i izing hei op ions conside ing he ela ed p oblem. Each one e lec s a di e en app oach o sol e a gi en disc e e MCDM p oblem o choosing he bes among se e al p eselec ed al e na i es. These me hods can be adop ed app op ia ely based on he c i e ia, al e na i es, and gene ally he p oblem ha a esea che is aced wi h. Fo example, ELECTRE is especially con enien when he e a e decision p oblems ha in ol e a ew c i e ia wi h a la ge numbe o al e na i es. This s udy deals wi h he lean enable s selec ion in business uni s and se e al c i e ia ha e o be conside ed in his selec ion p ocess, he e o e i is a ypical MCDM p oblem and ou anking me hod would be sui able o his concep selec ion. Hence, in he p esen s udy, PROMETHEE II, an ou anking me hod is used as a MCDM me hod o anking he bes sui able concep . The ma hema ical model in PROMETHEE is ela i ely easy o he deci- sion make s o unde s and (Gilliams e al. 2005). I is closely coinciding wi h he human pe spec i es and can easily ind ou he p e e ences among mul iple decisions (Ballis and Ma o as 2007) and he e o e, PROM- ETHEE me hods occupies an signi ican place among he ou anking me hods (Vinodh and Gi ubha 2012). Fuzzy-QFD calcula ions The main objec i e o F-QFD is o ind inal weigh o impo ance o ‘‘hows’’. The gene al s uc u e o house o quali y ma ix wi h uzzy echnique is equal o he men- ioned house o quali y. F-QFD calcula ions and HOQ ma ix a e desc ibed in he ollowing h ee s eps (Bo ani 2009). S ep 1: Iden i ica ion o impo ance o cus ome equi e- men s (CRs)/‘‘wha s’’ Fi s , W i as weigh ed impo ance o i- h CR is calcula ed di ec ly by cus ome s o expe s using linguis ic e ms o ob ained using mo e accu a e echniques such as pai ed compa ison and F-AHP and o he uzzy weigh ing me hods. S ep 2: Calcula ion o ela i e weigh /impo ance o enginee ing cha ac e is ics (ECs)/‘‘hows’’ RI j as ela i e impo ance o j- h EC is ob ained based on i s e ec on CRs as ollows: RIj¼X n i¼1 WiRij;j¼1;...;mð1Þ whe e, R ij is uzzy numbe , which ep esen s he ela ion be ween i- h CR and j- h EC. S ep 3: Calcula ion o inal weigh o ECs Ha ing co ela ion be ween ECs in he oo o he HOQ ma ix ob ained, ollowing ela ion gi es us i s e ec on RI j and consequen ly RI j *as inal weigh o j h EC. RIj¼RIjX k¼j Tkj RIk;j¼1;...;mð2Þ J Ind Eng In (2014) 10:68 Page 3 o 11 68 123 whe e T kj shows he deg ee o co ela ion be ween k- h and j- h EC, and RI k is he ela i e impo ance o k- h EC. Fuzzy-AHP-QFD-PROMETHEE model The amewo k applied by Fuzzy-AHP-QFD-PROM- ETHEE o achie e leanness has h ee majo pa s which a e depic ed in Fig. 1. QFD and HOQ, in he app oach, a e ansla ed om he ield o new p oduc de elopmen in o lean con ex . Exploi a ion o HOQ o i s compe i i e ad an ages o lean a ibu e is ecommended, as i is shown in Fig. 2. This sec ion del es deepe in o how o build a HOQ. Phase I: In oduce compe i i e ad an ages and de e mine he weigh o compe i i e ad an ages using uzzy-AHP Compe i i e ad an ages Facing igh compe i ion om all o e he wo ld, an o ganiza ion is equi ed o ha e a s ong s a egy o be able o s ay a loa (P iyan o e al. 2012). In o he wo ds, i he wan s o emain compe i i e, he mus ha e a sus ainable compe i i e ad an age (Hi e al. 2001). Majo i y o o ganiza ions, along wi h in ensi ica ion o compe i ion in he ma ke , s i e o ealiza ion o such ad an ages. Lean p oduc ion, among many, is one o he app oaches o make his possible; mainly due o he high e iciency o enabling. To educe cos s, emo e was e o esou ces, inc ease p o i abili y, and imp o e pe o mance as much as possible, a i m mus use equi emen s o leanness, and employs he mos e icien lean enable s. This s age is cons i u ed o 2 s eps. S ep 1: De e mining and selec ing he mos signi ican compe i i e ad an ages The p esen wo k seeks o each lean equi emen s o empowe ing compe i i e ad an ages o he o ganiza ion. Mo eo e , ega ding he necessi y o eaching compe i i e ad an ages o each o ganiza ion, he i s s ep is o selec , based on expe s’ opinion and da a collec ed h ough ques ionnai e and in e iews, he mos necessa y and e i- cien compe i i e ad an ages. S ep 2: De e mining uzzy weigh s o compe i i e ad an ages h ough pai compa ison and using Fuzzy- AHP Pai ed compa ison ma ix and AHP a e implemen ed o de e mine ela i e weigh s o compe i i e ad an ages. P ope linguis ic a iables a e employed by he expe s o ca y ou he compa ison and de e mine ela i e impo - ance and weigh o ad an ages. Because o quan i a i e na u e o linguis ic a iables, ambigui y is in insic o hem and uzzy logic helps emo al o he ambigui ies. Hence, iangula uzzy numbe s a e adop ed o de e mine alue o he a iables. Linguis ic a iables in his wo k imple- men ed o pai wise compa ison, de e mina ion weigh s and co esponding uzzy numbe s a e lis ed in Table 1. By asce aining consis ency o he judgmen s, an AHP me hod mus be implemen ed o calcula ing uzzy weigh s o compe i i e ad an ages. The e a e nume ous me hods START In oduce compe i i e ad an ages and de e mine he weigh o compe i i e ad an ages using uzzy-AHP Selec he mos impo an lean a ibu es and de e mine he e ec o lean a ibu es on compe i i e ad an ages h ough Fuzzy-QFD, in HOQ Selec he mos impo an lean enable s and anking lean h ough PROMETHEE END enable s Fig. 1 Schema ic ep esen a ion o he algo i hm Final de uzzied weigh s o lean a ibu es Rela ionships ma ix Fuzzy weigh s o compe i i e ad an ages by AHP Compe i i e Ad an ages Lean A ibu es Rela i e Impo ance o lean a ibu es (Rij) Co ela ion ma ix Final impo ance/weigh o lean a ibu es (Rij*) Fig. 2 S uc u e o he house o quali y 68 Page 4 o 11 J Ind Eng In (2014) 10:68 123 in oduced by esea ches o de i ing p io i y weigh s in he AHP. In ope a ion p ocess o applying AHP me hod, i is mo e easy o e alua o s o assess ‘‘c i e ion A is much mo e impo an han c i e ion B’’ han o conside ‘‘ he signi icance o p inciple A and p inciple B is se en o one’’. The e o e, Buckley ex ended Saa y’s AHP o he case whe e he e alua o s a e allowed o employ uzzy a ios in place o exac a ios o handle he di icul y o people o assign exac a ios when compa ing wo c i e ia and de i e he uzzy weigh s o c i e ia by geome ic mean me hod (Chen e al. 2011). Hence, uzzy weigh s o he compe i i e ad an ages ob ained by geome ic mean me hod a e lis ed in he second column o HOQ (Fig. 3). Phase II: Selec ing he mos signi ican lean a ibu es and de e mine he e ec o lean a ibu es on compe i i e ad an ages h ough Fuzzy-QFD, in HOQ When compe i i e ad an ages ( he inal objec i e o he o ganiza ion) and uzzy weigh s o he ad an ages a e acqui ed h ough F-AHP, su eying lean a ibu es is unde conside a ion a e wa d. The e a e ou s eps in ol ed in his pa . S ep 1: Su eying a ibu es unde conside a ion by he o ganiza ion and selec ing he bes a ibu es (lean a ibu es) o minimizing was es The ul ima e goal o lean p oduc ion is o educe cos s h ough emo ing was e o esou ces in he sys em. To s ep owa d being lean, o ganiza ions mus ocus on emo al o was es and consequen ly educ ion o cos s and mo e p o i abili y and p oduc i i y. Hence, i is essen ial o lead he i m owa ds emo al o was e and imp o emen o he a ibu es, i i is supposed o be lean. Among se e al a ibu es abou o ganiza ions’ objec i es and p ocesses, hose e ec i e on emo al o educ ion o was e and consequen ly on leanness o o ganiza ion, he e o e, a e adop ed as mos impo an lean a ibu es. Table 1 Linguis ic a iables and co esponding uzzy numbe s o exp essing he ela ionships Linguis ic a iables Fuzzy numbe s Equal impo ance (1; 1; 1) Weak impo ance (2/3; 1; 3/2) Mode a e impo ance (3/2; 2; 5/2) S ong impo ance (5/2; 3; 7/2) Absolu e impo ance (7/2; 4; 9/2) Based on Dagde i en e al. (2008)—dis o ed da a Fig. 3 HOQ J Ind Eng In (2014) 10:68 Page 5 o 11 68 123 S ep 2: De e mining e ec o lean a ibu es on compe i i e ad an ages, in HOQ An o ganiza ion no mally ies o each p e-se com- pe i i e ad an ages h ough concen a ing he a ibu es de e mined as lean a ibu es and equi emen s. Thus, supe ision o expe ienced expe s o de e mina ion o he impac s o lean a ibu es o o ganiza ion on compe i i e ad an ages is i al. F-QFD as one o s onges and mos common quali y managemen ools is implemen ed in his s age. The HOQ, used in his wo k as pic u ed in Fig. 3,is comp ised o compe i i e ad an ages in ma ix ows (wha s) and lean a ibu es in columns (hows). Cen al cells o HOQ, known as ela ionships ma ix, a e de o ed o impac o he lean a ibu es on compe i i e ad an ages. These cells ough o be illed ou based on linguis ic a i- ables unde expe s’ opinions. Linguis ic a iables in Table 1a e used o pai wise compa ison o lean a ibu es. In he same way, expe s o he o he compe i i e ad an ages pe o m pai compa isons o lean a ibu es in sepa a e ma ixes. By asce aining consis ency o he pai wise compa ison ma ixes, loga i hmic leas squa e (LLS) nonlinea p o- g amming model o each ma ix is acqui ed. A e wa ds, he models a e sol ed using a simple ma hema ical op i- miza ion so wa e and uzzy weigh s o e e y a ibu e, ela i e o o he a ibu es om he same compe i i e ad an age, a e calcula ed. The ob ained igu es a e hen inse ed in he ela ed ow in HOQ. The same p ocess is epea ed o all pai wise compa ison ma ixes and all ows o ela ionships ma ix a e illed ou in he HOQ ma ix. S ep 3: De e mining e ec o lean a ibu es on each o he , in he oo o HOQ (co ela ion ma ix) Among lean a ibu es, some ha e posi i e o nega i e co ela ion wi h o he s. Tha is, imp o emen o one a i- bu e may a ec o he s, ei he posi i ely o nega i ely. Thus, s udying co ela ion among all he a ibu es is essen ial. In doing so, expe s’ opinions and linguis ic a iables de ined in Table 2a e used o de e mine co e- la ions, i any, in he oo o HOQ. S ep 4: Fuzzy-QFD calcula ions o HOQ and ob aining inal uzzy weigh s and anking o he lean a ibu es Fi s , uzzy weigh o each compe i i e ad an age is inse ed acco ding o ma ix (Eq. 1) and ela i e uzzy impo ance/weigh o each lean a ibu e (RI j ) is calcula ed. Subsequen ly, co ela ions among lean a ibu es a e employed in ela i e impo ance o each a ibu e (RI j ) (Eq. 2) o ob ain inal impo ance/weigh s o he a ibu es (RI j *). Finally, uzzy weigh s can be de uzzied ia he ollowing equa ion. DFi¼liþmiþui 3ð3Þ Phase III: Selec he mos impo an lean enable s and anking lean enable s h ough PROMETHEEII S ep 1: Su eying enable s used in he o ganiza ion and elec ing he mos e ec i e lean enable s in achie emen o lean a ibu es Lean p oduc ion comp ises a ie y o ools and echniques known as lean enable s. Se e al esea che s ha e in o- duced and employed a ious se s o echniques as lean enable s o implemen a ion o he p inciples o leanness (Shah and Wa d 2003; B owning and Hea h 2009) Thus, he e is a lack o a gene ally accep ed se o ools as lean enable s. Conside ing impac in ealiza ion o lean a i- bu es, some o he mos impo an enable s used o po en ially usable by he o ganiza ion a e selec ed in his s ep. S ep 2: Ranking lean enable s h ough PROMETHEEII The PROMETHEE me hod is applied o he p oblems like he ones below: Max ðMinÞ 1ðaÞ; 2ðaÞ;...; ðaÞjaAgð4Þ whe e A ep esen s he se o decision c i e ia and (a) and i=1, 2,…, consis o a se o indices based on which he c i e ia a e assessed. In PROMETHEE II, he ne low /(a) (di e ence in lea ing lows minus en e ing lows) is used, which pe mi s a comple e anking o all al e na i es (Gu umu hy and Kodali 2008). The al e na i e wi h he highes ne low is supe io . A decision make always demands a comple e anking, as making decision will be mo e con enien . Calcula ion o a ne lows o anking p o ides such condi ions: /aðÞ¼/þaðÞ/aðÞ ð5Þ The comple e anking h ough PROMETHEE II a e as ollows: aPIIb  i /aðÞ[/bðÞ aIIIb  i /aðÞ¼/bðÞ ð6Þ Table 2 Linguis ic a iables and co esponding uzzy numbe s o exp essing he co ela ions Linguis ic a iables Fuzzy numbe s S ong posi i e (SP) (0.7; 1; 1) Posi i e (P) (0.5; 0.7; 1) Nega i e (N) (0; 0.3; 0.5) S ong nega i e (SN) (0; 0; 0.3) Sou ce: adap ed om Bo ani and Rizzi (2006) 68 Page 6 o 11 J Ind Eng In (2014) 10:68 123 The bes al e na i e is he one wi h he highes ne dominance (Vinodh and Gi ubha 2012). In his model, all he op ions will be compa able. Ha ing inal anking o lean enable s, managemen can make a decision on he ela i e impo ance and p io i y o lean enable s. Consequen ly, he o ganiza- ion may concen a e, wi h less ene gy and ime and highe e iciency, on he mos impo an enable s wi h highes p io i y and s op wande ing be ween a ie ies o echniques and lean enable s. In his way, he o ganiza ion will ha e he uppe hand and e e inc easing de elopmen . Case s udy Au omo i e indus y is he key d i e o any g owing economy. I plays a pi o al ole in any coun y’s apid economic and indus ial de elopmen (Business Knowledge Resou ce 2013). Hence, he case s udy has been elec ed om his indus y.‘‘A in Au o’’, manu- ac u e o dynamo and au omobile s a s, has been selec ed o implemen ing he model. The company aims on wide ange o la ge quan i ies o p oduc s o he company and plans o pene a e in o a wo ld class p oduce o pa s in global supply chain. The company is mo e conce ned on implemen a ion o lean p inciples and u iliza ion o lean enable s. In wha ollows, all s eps in he model a e explained nume ically and based on da a om he company. Phase I: Iden i y compe i i e ad an ages and hei weigh s h ough Fuzzy-AHP S ep 1: Iden i ying all compe i i e ad an ages and selec ing he mos impo an compe i i e ad an ages o he o ganiza ion Se en ad an ages can be iden i ied based on compe i i e ad an ages in oduced by Yusu e al. (2000) and Ren e al. (2003) and opinions o he eam o expe s. These com- pe i i e ad an ages a e aken as mac o goals o he o ga- niza ion, which a e lis ed in Table 3as he main compe i i e ad an ages. S ep 2: De e mining he uzzy weigh s o compe i i e ad an ages h ough pai compa ison by Fuzzy-AHP Se en ad an ages a e compa ed using pai compa ison ma ix. Table 4 ep esen s ela i e impo ance o he compe i i e ad an ages. As he able lis s, he expe s used linguis ic a iables and co esponding iangula uzzy numbe s o he compa ison. Then, uzzy weigh s o compe i i e ad an ages we e ob ained by geome ic mean me hod. Phase II: Selec he mos impo an lean a ibu es and de e mine he e ec o lean a ibu es on compe i i e ad an ages using Fuzzy-QFD, in HOQ S ep 1: Iden i ying a ibu es unde conside a ion in he o ganiza ion and selec ing he bes a ibu es, as lean a ibu es, based on was es educ ion When he eam o he expe s app aises all he a ibu es unde conside a ion by he o ganiza ion, he main a en ion, o de e mina ion o lean a ibu es, is shi ed o he a i- bu es e ec i e on emo al o was es. Consequen ly, eigh a ibu es we e selec ed as he mos impo an a ibu es (Table 3). S ep 2: De e mining e ec s o lean a ibu es on compe i i e ad an ages, in HOQ The impac s o he eigh lean a ibu es on achie e- men o he se en compe i i e ad an ages (HOQ) a e discussed in his s ep. HOQ (Fig. 3) is comp ised o compe i i e ad an ages in he ows (wha s) and lean a ibu es o he o ganiza ion in he columns (hows). Table 3 Compe i i e ad an ages, lean a ibu es and was es ela ed o each a ibu es, and lean enable s o A in Au o Compe i i e ad an ages Lean a ibu es Was e ela ed o each a ibu es Lean enable s Financial esou ces P oduc ion planning success a e Wai s Con inuous imp o emen (Kaizen) Ma ke sha e Educa ion e ec i eness Wai s P e en i e main enance (PM) Quali y Machine ies ailu e Unnecessa y p ocess Supply chain managemen (SCM) Success o planning T anspo a ion cos o sale cos a e Excess p oduc ion Pull sys em (KANBAN p oduc ion con ol) Human esou ce Ex a wo ks In en o y Failu e mode and e ec analysis (FMEA) P oduc s a ie y Ac ual in en o y o s anda d in en o y a e Faul y p oduc Job o a ion A e -sales se ices Pa pe million (PPM) T anspo a ion Human esou ce managemen (HRM) Ex a cos s o anspo a ion a e Mo emen s J Ind Eng In (2014) 10:68 Page 7 o 11 68 123 The impac o lean a ibu es on compe i i e ad an ages is lis ed in in e nal cell o HOQ ( ela ionships ma ix). Fo illing ou he ela ionships ma ix, eigh lean a ibu es a e compa ed pai wise om he i s compe - i i e ad an age iewpoin and ela i e impo ance o he a ibu es, ega ding hei e ec on ealiza ion o he i s compe i i e ad an age, is ob ained based on lin- guis ic a iables. Fuzzy pai compa ison ma ix o lean a ibu es ega ding he i s compe i i e ad an age ( inancial esou ces) is ep esen ed in Table 5.In he same way, pai compa ison o lean a ibu es is ca ied ou based on he expe s’ opinion o o he iewpoin s o compe i i e ad an ages. The ob ained uzzy weigh s (by LLSM) o he i s ma ix o pai compa ison o a ibu es ( inancial esou ces) is lis ed on i s ow o HOQ ma ix, while he o he six ows o he ela ion- ships ma ix a e dedica ed o he emaining six pai compa ison ma ixes sol ed by nonlinea p og amming model. S ep 3: De e mining mu ual e ec o lean a ibu es in he oo o HOQ (co ela ion ma ix) The e a e posi i e/nega i e co ela ions among some o lean a ibu es, be ween machine ies ailu e and PPM, o ins ance, he e is a posi i e co ela ion; so ha , he mo e ailu e o he machine ies, he mo e a e o b oken p oduc s and ice e sa. Addi ionally, a nega i e co ela ion is ound be ween educa ion e ec i eness index and a e o ex a wo ks. Tha is o say, a e o pa allel wo ks declines wi h mo e e icien educa ion. Linguis ic a iables and equi alen iangula uzzy numbe s (Table 2) a e used o inding co ela ion, i any, be ween he lean a ibu es. S ep 4: Fuzzy QFD calcula ions o HOQ and ob aining inal uzzy weigh s and anking o he lean a ibu es Fuzzy weigh o each compe i i e ad an age is i s implemen ed in he ela ionships ma ix (Eq. 1) o ob aining ela i e uzzy impo ance/weigh o each lean Table 4 Fuzzy pai compa ison ma ix o compe i i e ad an ages Financial esou ces Ma ke sha e Quali y Success o planning Human esou ce P oduc s a ie y A e -sales se ices Financial esou ces (1,1,1) (2/3,1,3/2) (3/2,2,5/2) (2/3,1,3/2) (7/2,4,9/2) (3/2,2,5/2) (5/2,3,7/2) Ma ke sha e (2/3,1,3/2) (1,1,1) (2/3,1,3/2) (3/2,2,5/2) (5/2,3,7/2) (5/2,3,7/2) (3/2,2,5/2) Quali y (2/5,1/2,2/3) (2/3,1,3/2) (1,1,1) (3/2,2,5/2) (5/2,3,7/2) (3/2,2,5/2) (5/2,3,7/2) Success o planning (2/3,1,3/2) (2/5,1/2,2/3) (2/5,1/2,2/3) (1,1,1) (5/2,3,7/2) (3/2,2,5/2) (5/2,3,7/2) Human esou ce (2/9,1/4,2/7) (2/7,1/3,2/5) (2/7,1/3,2/5) (2/7,1/3,2/5) (1,1,1) (2/7,1/3,2/5) (2/5,1/2,2/3) P oduc s a ie y (2/5,1/2,2/3) (2/7,1/3,2/5) (2/5,1/2,2/3) (2/5,1/2,2/3) (5/2,3,7/2) (1,1,1) (2/3,1,3/2) A e -sales se ices (2/7,1/3,2/5) (2/5,1/2,2/3) (2/7,1/3,2/5) (2/7,1/3,2/5) (3/2,2,5/2) (2/3,1,3/2) (1,1,1) Table 5 Fuzzy pai compa ison ma ix o lean a ibu es ega ding he i s compe i i e ad an age ( inancial esou ces) Financial esou ces Educa ion e ec i eness Machine ies ailu e Ex a wo ks P oduc ion planning success a e Ac ual in en o y o s anda d in en o y a e Pa pe million (PPM) T anspo a ion cos o sale cos a e Ex a cos s o anspo a ion a e Educa ion e ec i eness (1,1,1) (2/7,1/3,2/5) (2/7,1/3,2/5) (2/9,1/4,2/7) (2/9,1/4,2/7) (2/7,1/3,2/5) (2/3,1,3/2) (2/9,1/4,2/7) Machine ies ailu e (5/2,3,7/2) (1,1,1) (2/5,1/2,2/3) (2/7,1/3,2/5) (2/5,1/2,2/3) (2/5,2,2/3) (2/3,1,3/2) (2/7,1/3,2/5) Ex a wo ks (5/2,3,7/2) (3/2,2,5/2) (1,1,1) (2/7,1/3,2/5) (2/7,1/3,2/5) (2/5,1/2,2/3) (2/3,1,3/2) (2/5,1/2,2/3) P oduc ion planning success a e (7/2,4,9/2) (5/2,3,7/2) (5/2,3,7/2) (1,1,1) (2/3,1,3/2) (2/5,1/2,2/3) (2/3,1,3/2) (3/2,2,5/2) Ac ual in en o y o s anda d in en o y a e (7/2,4,9/2) (3/2,2,5/2) (5/2,3,7/2) (2/3,1,3/2) (1,1,1) (3/2,2,5/2) (7/2,4,9/2) (3/2,2,5/2) Pa pe million (PPM) (5/2,3,7/2) (3/2,2,5/2) (3/2,2,5/2) (3/2,2,5/2) (2/5,1/2,2/3) (1,1,1) (2/3,1,3/2) (3/2,2,5/2) T anspo a ion cos o sale cos a e (2/3,1,3/2) (2/3,1,3/2) (2/3,1,3/2) (2/3,1,3/2) (2/9,1/4,2/7) (2/3,1,3/2) (1,1,1) (2/3,1,3/2) Ex a cos s o anspo a ion a e (7/2,4,9/2) (5/2,3,7/2) (3/2,2,5/2) (2/5,1/2,2/3) (2/5,1/2,2/3) (2/5,1/2,2/3) (2/3,1,3/2) (1,1,1) 68 Page 8 o 11 J Ind Eng In (2014) 10:68 123