Inzinerine Ekonomika-Engineering Economics, 2012, 23(5),

Transcription

1 Inznerne Ekonomka-Engneerng Economcs, 2012, 23(5), Decson Makng on Advertsement Strategy Selecton Based on Lfe Cycle of Products by Applyng FAHP and TOPSIS GREY: Growth Stage Perspectve; a Case about Food Industry n IRAN Sarfaraz Hashemkhan Zolfan 1, 2, Nahd Rezaenya 3, Morteza Pourhossen 4, Edmundas Kazmeras Zavadskas 2 1 Amrkabr Unversty of Technology P.O. Box , Tehran, Iran e-mal: sa.hashemkhan@gmal.com 1, 2 Vlnus Gedmnas Techncal Unversty Sauleteko al. 11, LT-10223, Vlnus, Lthuana e-mal: edmundas.zavadskas@vgtu.lt 3 Alghadr Insttute of hgher Educaton P. O. Box , Tabrz, Azarbaan Shargh, Iran e-mal: nah.rezaenya@gmal.com 4 Shomal Unversty P. O. Box 731, Amol, Mazandaran, Iran e-mals: poorhossenm@yahoo.com In recent decades, Marketng management and Advertsng have been maor concepts n busness feld. The man reason that attracts attenton to them s growth of competton among companes. There are dfferent ways to ncrease the competton n organzatons such as mprovng productvty by acqurng nformaton technology. In ths paper hybrd MCDM methods appled for selectng Advertsng strategy by consderng product lfe cycle of products. Growth stage s one of the crtcal stuatons for companes and advertsng has an mportant role n ths step. Hence Growth stage selected as a case study for ths research and the proposed model s mplemented for most famous Iranan s factory n food ndustry, Kalleh. Fve crtera are selected as mportant crtera by studyng n lterature and experts decson nclude: Audence ft, Content, Impresson rate, Monthly cost and Look and feel. In ths artcle Fuzzy Analytc Herarchy Process (FAHP) appled for calculatng weght of each crteron and fnally TOPSIS grey appled for selectng the best Advertsng strategy among Televson, Rado, Newspaper, Internet & SMS and Envronmental advertsng. The results of ths research llustrated that monthly cost, content and educatonal level, are three mportant crtera n advertsng strategy and also newspaper s the best way for advertsng and after that televson s next and rado, envronmental advertsng and nternet & SMS are after those n ths case n Growth step of Product Lfe Cycle. Ths research can be useful as a valuable framework for companes and dfferent ndustres. Each company n dfferent ndustry can apply ths methodology and develop that due to ther needs and prorty. Keywords: Advertsng; Fuzzy Analytc Herarchy Process (FAHP); Multple Crtera Decson Makng (MCDM), TOPSIS Grey, Product Lfe Cycle (PLC). Introducton Whether we turn on TV or pass by bllboards along freeways, casual observaton suggests the omnpresence of advertsng n modern cultures. There s a wdespread percepton of advertsng as powerful economc and socal force. Advertsng descrpton or presentaton of a product, dea or organzaton, n order to nduce ndvduals to buy, support or approve of t. The advertsng essentally represents nformaton whch helps consumers to decde and choose ther needs. Due to global ntensve competton, many companes prortze quck and specfc responses to customers' varous demands and mprove ther advertsng. Nowadays many people now mprove ther busness, one of the steps that they need to do s to select the rght meda where they wll advertse and promote ther busness. Industry proponents argue that advertsng does not nfluence consumer choce (Hoek & Gendall, 2006)

2 Sarfaraz Hashemkhan Zolfan, Nahd Rezaenya, Morteza Pourhossen, Edmundas Kazmeras Zavadskas. Decson However, f they really beleve such a proposton, how could they ustfy the large sums they nvest n advertsng (Story & French, 2004)? Advertsng actually has a maor role n renforcng and normalzng behavor (Hoek & Gendall, 2006). One of the ndustres that advertsng have a bg and mpressve nfluence on t s food ndustres. Pror research shows that advertsng nfluences food preferences, food purchases, consumpton behavor, and brand and category sales (Caraher & Landon, 2007). Three man categores of attrbutes affect consumer demand for (food) products, Prce, narrow use value for the consumer, and performance for ssues lke ts mpact on the commons or on nature, on future generatons, and on dstant people whch the consumer does not expect to ever meet (Mchalopoulos et al., 2008). Determnng the communcaton and promoton budget and allocatng that budget across dfferent promotonal tools are mportant marketng decsons, partcularly for manufacturers, who spend consderable money on promotng ther brands. From a retaler s vewpont, manufacturer spendng decsons on consumer and trade promotons are crtcal as they affect ther prcng and promotonal polces (Shankar, 2008). The brand manager further allocates the brand s promotonal budget wthn each promotonal tool. For example, wthn advertsng, the manufacturer allocates spendng between tradtonal meda (e.g., TV, prnt, rado) and new meda (e.g., the Web, emal, blog, socal meda, moble meda). Manufacturers allocate marketng budgets to dfferent promotonal tools on the bass of relatve compettve elastcty (Shankar, 2008). We know that Food ndustry almost s more mportant for the consumer packaged goods (CPG) ndustry. For most CPG frms, the bulk of the marketng budget goes to advertsng and sales promoton (consumer and trade promoton). Over the past two decades, the allocaton for CPG frms has shfted from advertsng toward sales promoton due to three key reasons: ncreasng consumer decson-makng at the pont of purchase, the rse of retaler power, and the fragmentaton of mass meda communcaton vehcles (Shankar, 2008). MCDM can gve managers many dmensons to consder related elements, and evaluate all possble optons under varable degrees. Group decson-makng s a process where experts make decsons and consoldate an optmal strategy (Chu et al., 2007). Decson-makng or problem solvng, as a broader term, s the process of selectng one or a few alternatves that should be the most favorable (Ulubeyl & Kazaz, 2009; Zavadskas et al., 2009; Saparauskas et al., 2011; Medneckene et al., 2011; Yazdan et al., 2011). An mportant step to developng your sales and marketng plan s to select the rght meda to send out your message. There are no hard-and-fast rules as to whch meda s better. The rght meda for one busness may be wrong for another. Therefore selectng the rght meda for advertsng s a decson under a varety of factors and can be evaluated accordng to dfferent aspects, Therefore t can be vewed as a multple crtera decson makng (MCDM) problem (Zavadskas & Tursks, 2011). The food ndustry ncludes the restaurant, beverage, confectonery, and processed food ndustres (Story et al., 2002). Pror research shows that advertsng nfluences food preferences, food purchases, consumpton behavor, and brand and category sales (Caraher & Landon, 2007). After revewng the evdence, Royne & Levy (2008) conclude that the food ndustry's marketng s undermnng publc health. Food ndustry marketng appears to have a substantal nfluence on obesty. Research usng chldren found that chldren choose the brands they have seen n advertsements (Story & French, 2004). The food ndustry spends bllons each year to reach chldren and youth. Advertsng ncreases food purchase requests by chldren to parents, and nfluences ther product and brand preferences (Insttute of Medcne, 2004). Advertsng may also nfluence adults although not to the same extent as chldren. There s a lot of research n lterature that focused on selecton the best advertsng, they ntroduce several crtera and consder them for advertsng selecton, these crtera are: Educatonal Level (Hyun et al., 2011;Roas- Méndez et al., 2009), age (Capella et al., 2011; Farrs & Buzzell, 1979; McKay-Nesbtt et al., 2011; Luyt, 2011; Roas-Mendez et al., 2009), Content (Wu et al. 2008; Song, et al., 2011), Impresson Rate (Alpert & Maltz, 2005; Vann et al., 1987), Monthly cost (Josh & Hanssens, 2009; Walters et al., 2008), User- Frendlness (Brown et al., 1998; Lee et al., 2006; Krshnamurthy, 2001), Desgn (Feasely et al., 1987; Lecknby & Km, 2002; Aaker et al., 1990), and n ths study we use lterature and expert's dea for evaluaton crtera. The am of ths research s developng a framework for advertsng strategy selecton based on new hybrd MCDM methods. Accuracy of ths ssue s so mportant. Advertsng has a key role n busness and companes all around the world have dfferent plans for ther advertsng strateges. Authors do beleve n mportance of Product Lfe Cycle (PLC) n process of advertsng. Due to ndustres mportance of PLC s dfferent. Authors propose that each stage of PLC about products needs dfferent strategy. Prevously there wasn t enough attenton to PLC n advertsng strateges and ths research tres to show mportance of that n advertsng and busness. Case study of ths research s n food ndustry and also Growth stage of PLC selected as a case of stages. At the end authors beleve that Growth step s crtcal stage of PLC. Process of ths research at frst s dentfyng mportant crtera that should be consdered n the process of selectng alternatves of advertsng. In second step Fuzzy AHP wll be appled for calculatng weghts and mportance of each crteron and n thrd step TOPSIS Grey wll be appled to llustrate mportance of each alternatve and fnally rank them. Fuzzy AHP s evaluatng crtera n more detals and t seems sutable for applyng n research because accuracy s very mportant for ths research. Grey analyss n evaluatng alternatves s very useful hence TOPSIS Grey appled n ths research for evaluatng alternatves and prortzng them. In the rest of ths paper there are descrbed product lfe cycle, methodology, case study and the concluson summarzed n the last secton

3 Inznerne Ekonomka-Engneerng Economcs, 2012, 23(5), Product lfe cycle Levtt defned a PLC as the lfe of a product to that of a lvng organsm (Levtt, 1965; Dckson, 1997). The concept of PLC explans the general tendency of products development process or a depcton of a product s sales hstory from ts brth or marketng begnnng, to ts death, or wthdrawal from the market (Zkmund & d'amco, 1999). Schnaars (1991) defned the concept of PLC to be a pattern-based approach whch tracks trends n product sales hstores from ther ncepton to ther demse. Saka et al., (2003) and Chen et al., (2006) mentoned that PLC could descrbe the possble product polces n dfferent stages of PLC as well as helps the enterprse to compare ts product wth the former smlar product to estmate the performance of the products that would be ntroduced to the market. Assael (1993) thought PLC to be a useful tool for suggestng strateges over the lfe of a brand and n ndcatng when changes n strateges should take place. It s even more useful for helpng marketers to develop successful, proftable product strateges as the market for a product changes over tme (Solomon & Stuart, 1997). Based on the defntons beng provded by Kotler and Keller (2006), a PLC has four dstnct stages: ntroducton, growth, maturty and declne: 1. Introducton: Introducton s a perod of slow sales growth as the product s ntroduced n the market. Profts are nonexstent n ths stage because of the heavy expenses of product ntroducton. (Product development begns when the frm fnds and develops a new product dea. Durng the product development stage, sales are zero and the frm s nvestment costs mount. 2. Growth: Growth s a perod of rapd market acceptance and ncreasng profts. 3. Maturty: Maturty s a perod of the slowdown n sales growth because the product has acheved acceptance by most potental buyers. Profts level off or declne because of ncreased marketng outlays to defend the product aganst competton. 4. Declne: Declne s the perod when sales fall off and profts drop. The slow ntroducton phase reflects the dffculty of overcomng buyer nerta and stmulatng tral of a new product. Rapd growth then occurs as many new buyers are attracted once the product s perceved as successful. Maturty of the product s potental buyers s eventually reached, causng the rapd growth to level out to the underlyng rate of growth of the relevant target market. Fnally, the declne wll set n as new substtute products appear (Doyle & Stern, 2006). Durng a product s lfe tme, a frm wll normally reformulate ts marketng strateges several tmes. Not only do economc condtons and compettve stuatons change. However, n addton, the product also passes through new stages of buyer nterest and requrements. Product Lfe Cycle (PLC) has dfferent roles n many areas of scence and can be consdered n dfferent scences. In prevous secton we descrbed brefly the am of ths research and after descrbed each secton of PLC. We want to focus on growth stage n developng products wth materals of meda advertsng. In fact PLC s appled n ths research as a powerful tool for advertsng n more detals. Products of food ndustry have exprng dates and coordnatng between processes s so crtcal and hard. Authors beleve that selectng growth stage as a case study n ths ndustry s more crtcal than other secton of PLC and for ths mean growth stage s selected for ths research. Methodology Over the past decades the complexty of economc decsons has ncreased rapdly, thus hghlghtng the mportance of developng and mplementng sophstcated and effcent quanttatve analyss technques for supportng and adng economc decson-makng (Zavadskas & Tursks, 2011). Multple crtera decson makng (MCDM) s an advanced feld of operatons research, provdes decson makers and analysts a wde range of methodologes, whch are overvewed and well suted to the complexty of economcal decson problems (Hwang & Yoon, 1981; Fguera et al., 2005). Multple crtera analyss (MCA) provdes a framework for breakng a problem nto ts consttuent parts. MCA provdes the means to nvestgate a number of alternatves n lght of conflctng prortes. Over the last decade scentsts and researchers have developed a set of new MCDM methods (Kaplnsk & Tupenate, 2011; Antuchevcene et al., 2011; Kaplnsk & Tamosatene, 2010;Tamosatene et al., 2010). They modfed methods and appled them to solve practcal and scentfc problems (Kalbatas et al., 2011; Gnevcus et al., 2008a; Zavadskas et al., 2011). Solvng of modern decson makng problems n most cases s based on ntegrated model of dfferent approaches. There s a wde range of methods based on mult-crtera utlty theory: SAW (MacCrmon, 1968); TOPSIS (Hwang & Yoon, 1981); VIKOR compromse rankng method (Oprcovc and Tzeng, 2004); COPRAS-G (Zavadskas et al., 2008); and other methods (Tursks et al., 2010 a,b). Decson-makers n ther actvtes deal wth uncertan future. The mult crtera decson-makng could be appled to assess dfferent alternatves of future actvtes. Hu et al. (2009) ncorporated the fuzzy concept n lnear programmng to obtan the best possble outcome n portfolos, when drect real estate nvestment s ncluded. The best strategy could be selected from avalable scenaros, and nformaton. In strategc decsons, dealng wth uncertanty, the values of crtera could be determned at ntervals from pessmstc value to optmstc value. The lmts of crteron value could also be determned by expert and determnaton of lmts depends on the qualfcaton and experence of expert. Therefore t s better to collect the obectve data (Zavadskas et al., 2010a). Deng (1982) developed the Grey system theory and descrbed operatons wth grey numbers. Grey relatonal analyss possesses advantages (Deng, 1989): nvolves smple calculatons, requres smaller samples, a typcal dstrbuton of samples s not needed, the quantfed outcomes from the Grey relatonal grade do not result n contradctory conclusons to qualtatve analyss, the Grey relatonal grade model s a transfer functonal model that s effectve n dealng wth dscrete data

4 Sarfaraz Hashemkhan Zolfan, Nahd Rezaenya, Morteza Pourhossen, Edmundas Kazmeras Zavadskas. Decson Ths paper presents the applcaton of Fuzzy AHP and TOPSIS grey methods for the case study of selecton the best advertsng strategy n growth stage of product lfe cycle. The framework of research s shown n fgure 1. Constructon of selecton crtera herarchy Fnal evaluatng results Lterature survey Identfyng crtera Fgure 1. Framework of research Analytc Herarchy Process Crtera weghts by Fuzzy AHP Evaluatng and Rankng wth TOPSIS GREY Analytc herarchy process (AHP), proposed by Thomas L. Saaty n 1971 (Saaty, 1980), s able to solve the multple crtera decson makng problems. AHP utlze three prncples to solve problems (Aydogan, 2011; Podvezko, 2009; Svlevcus, 2011): 1) Structure of the herarchy, 2) The matrx of par wse comparson ratos, and 3) The method for calculatng weghts. AHP can decompose any complex problem nto several sub-problems n terms of herarchcal levels where each level represents a set of crtera or attrbutes relatve to each sub-problem. Durng the past, there were 13 maor condtons that have dscovered to well ft the utlzaton of AHP such as settng prortes, generatng a set of alternatves, choosng a best polcy alternatves, determnng requrements, allocatng resources, predctng outcomes, measurng performance, desgnng system, ensurng system stablty, optmzaton, plannng, resolvng conflct, and rsk assessment (Saaty, 1980). Besdes, recent condtons encompass to reduce the nfluence of global clmate change (Berrttella et al., 2007), to choose unversty faculty (Grandzol, 2005), to decde the locaton of offshore manufacturng plants (Walalak and McCarthy, 2002), to evaluate rsk n conductng cross-country petroleum ppelnes (Dey, 2003) and so on. Wth ths method, a complcated system s converted to a herarchcal system of elements. In each herarchcal level, par-wse comparsons of n elements are made by usng a nomnal scale and the value s assgned to represent the udgment concernng the relatve mportance of decson element e over e. These comparsons compose a par-wse comparson matrx M = {m }. In order to fnd the weght of each element, or the score of each alternatve, the prorty vector (or egenvector) W= (w 1,w 2,,w n ) T of ths comparson matrx s calculated based on solvng the equaton (1): = W, (1) It ndcates that the egenvector correspondng to the largest egenvalue of the par-wse comparsons matrx reflects the relatve mportance of the decson elements. Ths conventonal AHP approach gves reasonably good approxmaton only when the decsonmaker s preferences are consstent. However, the descrptons of lngustc varable (such as udgment or preference ) are usually vague and the verbal atttudes of decson-maker s requrements on evaluaton process always contan ambguty and multplcty of meanng. AHP s neffectve when appled to ambguous problem. Thus, fuzzy sets could be ncorporated wth the par-wse comparson, as an extenson of AHP, to solve ths knd of uncertanty (Lee, 2010). Fuzzy AHP method Zadeh (1965) frst ntroduced the fuzzy set theory to deal wth the uncertanty due to mprecson or vagueness. Let the unverse of dscourse X be the subset of real numbers R. A fuzzy set Ã=,μ à s a set of ordered pars, where t s called the membershp functon whch assgns to each obect a grade of membershp rangng between zero and one. Trangular fuzzy number s the most wdely used membershp functon n many applcaton felds because of ts ntutve appeal and computatonal effcency. A trangular fuzzy number, defned to be a normal and convex fuzzy subset of and denoted as à =(,, ), has the followng membershp functon (Kaufmann & Gupta, 1991) The parameter " " gves the maxmal grade of, the parameters " " and " " are the upper and lower bounds whch lmt the feld of possble evaluaton (Lee, 2010). In ths research, trangular fuzzy numbers are used to represent subectve par-wse comparsons of experts udgments among the optons such as ust equal, equal mportance, weak mportance, strong mportance, very strong mportance, and extremely preferred. The trangular fuzzy converson scale used to convert such lngustc values nto fuzzy scales n the evaluaton model of ths paper s shown n Table 1. The recent applcatons of AHP method n shortly are lsted below: Hashemkhan Zolfan et al., (2012a) appled fuzzy AHP for performance evaluatng rural ICT centers (Telecenters) Aghdae et al., (2012) appled fuzzy AHP n market segment evaluaton and selecton. Basrat Baygg et al., (2011) appled fuzzy AHP to Develop ntellectual captal evaluaton model n Hotel Industry; Aghdae el al., (2011) used fuzzy AHP and fuzzy TOPSIS for market segment evaluaton and selecton; Kersulene, Tursks (2011) fuzzy AHP and ARAS for archtect selecton; Gungor et al., (2009) used fuzzy AHP approach to personnel selecton problem (qualty1); Onut et al., (2008) appled fuzzy AHP and fuzzy TOPSIS for machne tool selecton

5 Inznerne Ekonomka-Engneerng Economcs, 2012, 23(5), Ths paper apples Chang s extent analyss method (Chang, 1996). Accordng to Chang s extent analyss method, the value of fuzzy synthetc extent s defned, usng the standard fuzzy arthmetc, as below: 1 m n m S = M = 1 M g = 1 = 1 g (2) Where s a trangular fuzzy number representng the extent analyss value for decson element wth respect to goal. s the generc element of a fuzzy par-wse comparson matrx lke the one used n the AHP method. The degree of possblty of s defned as: ( ) = sup [mn (,)] (3) x y 1, And can be equvalently expressed as follo: ( )h ( ) 0, (4), h The degree of possblty for a convex fuzzy number to be greater than k convex fuzzy number: ( = 1,2,..., ) can be defned by,,, = [( ) ( 2) ( )] mn ( ), 1, 2, 3,,. (5) Assume that: For = 1,2,..., ;. then the weght vector s gven by: W = ( ( ),, ) (6) (7) Where ( = 1,2,..., ) are n decson elements. Va normalzaton, the normalzed weght vectors are: =(,, ) (8) Where s a non-fuzzy number, compared to conventonal AHP, The fuzzy AHP approach allows a more accurate descrpton of the decson makng process. Table 1 Lngustc varables descrbng weghts of the crtera and values of ratngs (Onüt et al., 2008) Lngustc scale for mportance Trangular fuzzy scale (a,b,c) Just equal (1.0,1.0,1.0) Equal mportance (1.0,1.0,3.0) Weak mportance of one over another (1.0,3.0,5.0) Essental or strong mportance (3.0,5.0,7.0) Very strong mportance (5.0,7.0,9.0) Extremely preferred (7.0,9.0,9.0) If factor has one of the above numbers assgned to t when compared to factor, then has the recprocal value when compared wht Recprocals of above,,. Technque for Order Preference by Smlarty to Ideal Soluton (TOPSIS) The TOPSIS method was developed by Hwang and Yoon (1981). TOPSIS method belongs to MCDM (Multcrtera decson-makng method) group and dentfes solutons from a fnte set of alternatves based upon smultaneous mnmzaton of dstance from an deal pont and maxmzaton of dstance from a negatve deal pont. TOPSIS can ncorporate relatve weghts of crtera. The only subectve nput needed s weghts. Ln et al., (2008) developed TOPSIS method wth grey number operatons to the problem soluton wth uncertan nformaton. TOPSIS method was appled n many felds: For evaluatng rural ICT telecenters (Hashemkhan Zolfan et al., 2012a); About Mult-role artst of rock bands n Iran (Hashemkhan Zolfan et al., 2012b); For team member selectng (Hashemkhan Zolfan & Antuchevcene, 2012); Rsk assessment of constructon proects (Zavadskas et al., 2010b); For selecton of the strategc allance partner (Buyukozkan et al., 2008); For suppler selecton wth TOPSIS method (Boran et al., 2009); To rsk evaluaton n workplaces (Grass et al. 2009); To customer evaluaton usng fuzzy methods based on TOPSIS n safety management (Yang et al., 2009). TOPSIS method wth crtera values determned at ntervals The TOPSIS method s one of the best descrbed mathematcally and not smple for practcal usng. Ln et

6 Sarfaraz Hashemkhan Zolfan, Nahd Rezaenya, Morteza Pourhossen, Edmundas Kazmeras Zavadskas. Decson al., (2008) proposed the model of TOPSIS method wth attrbutes values determned at ntervals that ncludes the followng steps: Step 1: Selectng the set of the most mportant attrbutes, descrbng the alternatves; Step 2: Constructng the decson-makng matrx X. Grey number matrx X can be defned as: (9) =, = 1,, =1, Where denotes the grey evaluatons of the -th alternatve wth respect to the -th attrbute;,,, s the grey number evaluaton seres of the -th alternatve. Step 3: Construct the normalzed grey decson matrces. The normalzed values of maxmzng attrbutes are calculated as: = {( max ) ( b J, mn w J ){ n } = [ x, x 2 xm ] {( w J), ( maxb J ) n} = [ x, x x ] A,... A,... = mn 1 2 m x w b x, b = =,. (10) max( b ) max( b ) max( b ) The normalzed values of mnmzng attrbutes are calculated by Ln et al. (2008): x b w x, w = 1 = 1,1. (11) max( b ) max( b ) max( b ) Step 4: Determnng weghts of the crteraq. Step 5: Construct the grey weghted normalzed decson-makng matrx. Step 6: Determne the postve and negatve deal alternatves for each decson-maker. The postve deal alternatve A, and the negatve deal alternatve A can be defned as: 1 (12) (13) Step 7: Calculate the separaton measure from the postve and negatve deal alternatves, and, for the group. There are two sub-steps to be consdered: the frst one concerns the separaton measure for ndvduals; the second one aggregates ther measures for the group. Calculate the measures from the postve and negatve deal alternatves ndvdually. For decson-maker k, the separaton measures from the postve deal alternatve and negatve deal alternatve are computed through weghted grey number as: d d 1 = 2 1 = 2 m = 1 m = 1 q q x p p [ x w x b ] p w (14) (15) In equatons (14) and (15), for p 1 and nteger, s the weght for the attrbute, whch can be determned by attrbute weght determnaton methods. If p = 2, then the metrc s a weghted grey number Eucldean dstance functon. Equatons (14) and (15) wll be as follows: 1 m 2 k k k k d = [ 2 q x w x b, 2 d = x p w x b p m 1 2 k k k k 2 = 1 = 1 q x 1 b 1 p 2 (16) (17) ( Step 8: Calculate the relatve closeness, to the postve deal alternatve for the group. The aggregaton of relatve closeness for the -th alternatve wth respect to the postve deal alternatve for the group can be expressed as: C = d d d (18) Where 0 1. The larger the ndex value s, the better the evaluaton of alternatve wll be. Step 9: Rank the preference order. A set of alternatves now can be ranked by the descendng order of the value of C. Case study: selecton Advertsng strategy n growth of product lfe cycle based on Fuzzy AHP and TOPSIS grey method Crtera selecton and data survey The am of ths study s to utlze a new hybrd model of MCDM methods n selecton Advertsng strategy n growth of product lfe cycle. A case company s Kalleh Company, the oldest and the most famous company n food ndustrals, n Iran. Kalleh Company tends to select best advertsng strategy n growth of product lfe cycle, because sale of the products n growth stage of the cycle have the rsng growth and the company want to focus on the product's advertsng so the structure of ths decson are shown n fgure 1.Ths fgure shows that there are fve alternatves, Televson Ad ( ), Rado Ad ( ), Newspaper Ad ( ), Internet & SMS Ad ( ), Envronmental Ad ( ) and fve group crtera that are shown n table

7 Inznerne Ekonomka-Engneerng Economcs, 2012, 23(5), Crtera for selecton advertsng strategy n growth of product lfe cycle Table 2 Crtera Audence ft Content Impresson rate Monthly cost Look & feel Educatonal level Age User-frendlness Desgn Sub crtera Select the best advertsng strategy Audence ft Content Impresson rate Monthly cost Look & feel Educatonal level Age Userfrendlness Desgn Televson Rado Newspaper Internet & SMS Envronmental Fgure 2.The herarchy structure of selecton advertsng strategy n product lfe cycle Based on the nature of seven evaluaton crtera, optmzaton drectons for each evaluaton crteron s determned as follows:,,,,, Max Mn Background Informaton of Experts Varable Items NO Varable Items NO 1)Educaton Bachelor 3 3)Sex Male 5 background Master 3 Female 3 PhD 2 2)Groups Laban 4 4)Age Gosht Usng FAHP method for Prortzaton crtera For par wse comparson decson makng n FAHP, a questonnare was sent to a group of 8 experts that are the senor managers of company and are responsble for producton sales. Informaton about experts s shown n Table 3: Table

8 Pared comparson matrx crtera s one of the matrces shown n Table 4. FAHP method s then used for whch were completed wth nformaton of experts s prortzng. Table 4 Crtera pared comparson matrx X 1 X 2 X 3 X 4 X 5 X X X X X Chang s extenson method s then used to synthesze the data. The values of fuzzy synthetc extents are calculated. As an example, the values of fuzzy synthetc extents for audence ft s sub crtera are calculated as below: = 0.153,0.212, , 3.265, = 0.313, 0.694, The mnmum of the degrees of possblty are calculated as below: ( )=1 Prorty weghts of crtera and sub crtera Same calculatons are performed for the other crtera and sub-crtera and these yeld the followng weghts vector: = (1.000, 0.416) Then every weght s dvded nto ths total to become normalzed: = 0.706, After all comparsons and weghng processes are done, we obtan the overall prorty weght of each crtera and sub-crtera that s shown n table 5. Table 5 weghts Accordng the weghts n table 5,,, were three of the most mportant consderng crtera. Selecton of the best advertsng strategy: The ntal decson-makng matrx wth values determned at ntervals s presented n Table 6. In Table 6 gven notatons are the crtera weghts and,, are alternatves. In ths table the group of experts evaluated each canddate accordng to each crteron. The evaluaton was done on a scale from 1 to 9, where 9 meant very mportant and 1 not mportant at all. Table 6 Intal decson-makng matrx wth values (TOPSIS grey method) Rankng of alternatves by applyng TOPSIS grey technque and the weghts that are calculated n last stage, s performed. Alternatves Crtera Optmum max max max max mn max max A A A A A Optmal value In Table 7 the normalzed decson-makng matrx wth value of each crteron expressed at ntervals s presented

9 Inznerne Ekonomka-Engneerng Economcs, 2012, 23(5), Normalzed decson-makng matrx (TOPSIS grey method) Table 7 Alternatves Normalzed values of crtera Weghts q A A A A A The results of the calculaton for each alternatve are presented n Table 8. Alternatves Weghted-normalzed decson-makng matrx (TOPSIS grey method) Weghted-normalzed values of crtera d d - A A A A A A A c Rank Table 8 Accordng to the TOPSIS grey and the weght that calculated wth FAHP methods the order of alternatves ranks s: f f f f. The frst alternatve newspaper Ad must be selected as best advertsng meda. Conclusons In ths age of dynamc and ncreased compettve markets, advertsng s an mportant tool for condensng cross-doman ntegraton energy and a source of core competency. The ndustres spends bllons each year to reach chldren and youth, therefore selectng the rght meda for advertsng s a very mportant decson and t and can be evaluated accordng to dfferent aspects. The man am of ths study s to consder Product Lfe Cycle (PLC) n process of advertsng. In our case of Iran, Kalleh Company s one of the oldest and the most famous company n food ndustry that s workng nternatonally and have a sutable strategy of advertsng s very mportant for t. Ths problem s crtcal for Kalleh Company to acqure compettve advantages. In food ndustry strateges, growth stage n product lfe cycle of products has a crtcal poston. Ths research tred to show the mportance of ths stage as a case study. In ths research a hybrd model of MCDM method utlzed. At frst FAHP appled to weght the seven evaluaton crtera nclude: and TOPSIS Grey method for evaluatng the performance of advertsngs Medas. The results of the study showed that monthly cost, content and educatonal level, are three mportant crtera n advertsng strategy. FAHP appled n ths secton because authors beleve that fuzzy nformaton can help to dentfy mportance of crtera more carefully. Results of TOPSIS Grey llustrated that newspaper s the best way for advertsng and after that televson s next and rado, envronmental advertsng and nternet & SMS are after those. The newly developed methods TOPSIS grey could be successfully appled for the assessment of alternatves descrbed by multple crtera wth values expressed at ntervals. TOPSIS Grey help experts to descrbe ther deas more carefully and n ths research appled for ths mean. Product Lfe Cycle s a crtcal ssue n many ndustres and ths research can be helpful for other ndustres as a framework and can be developed n other stuatons

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