Ranking the Criteria of ABC Implementing and Lunching by FAHP in Iranian Automobile Industry

Transcription

1 Australn Journal of Basi and Applied Sienes, 5(0): 60-68, 0 ISSN Raning the Criter of ABC Implementing and Lunhing by FAHP in Irann Automobile Industry Amin Rezan Reza Fallah and Mohammadreza Pourali Department of Civil, Gorgan Branh, Islami Azad University, Gorgan, Iran Department of Aounting, Chalous Branh, Islami Azad University, Mazandaran, Iran Abstrat: Ativity based osting system is one approah whih its lunhing and implementing in pratie will bring advantages for the users. The present study tried to point out fators affeting launhing and implementing Ativity Based Costing System among in Irann automobile industry ompanies; Fuzzy AHP was employed as one of the important items regarding multi-riter deision maing. Finally, those fators were raned aording to their importane. To this end, first of all, major fators were lassified and raned into four main groups suh as; organizational, environmental, individual, and tehnl fators; then minor fators were examined and then raned. Regarding results of Fuzzy AHP tehnique, organizational fators are the most important ones and then are individual, tehnl, and environmental ones and among all minor fators, enough and on time training, onsidering informational needs of different setions of the organization, logl timing of the designing and running and partiipation of non finanl segments in planning and running system are of vital importane. Enough attention to fators affeting launhing and implementing Ativity Based Costing System is of rul help for useful appltion and more effiieny of suh systems and managements should try to put the approahes suggested in suh an artile from theory to pratie onsiously. Key word: Ativity Based Costing System, Fuzzy AHP, Fators, Launhing, Implementing. INTRODUCTION In global developed and ompetitive marets, to be adapted with ompetitive environment, dynami and omplted environment is essentl for organization to ontinue their ativity. For this purpose, many information systems have been presented for servies and produt osting and budgeting, ost reduing, ontinuous improvement, performane measurement, and eventually value adding (Khozein, 009). Traditional ost system using some ost drivers supported finanl and budget reporting in a wrong way and presented many false information. In suh a ondition, many organizations have been trying to improve their osting systems. Improved osting system is a system for evaluating the soure usage through servies, prodution or onsumers in an effetive way. Tehnology Developing and ompetition inreasing were an effetive fator for designing suh a system. One new method was ABC (Ativity Based Costing) whih is of many advantages ompared to traditional osting system based on volume. So in order to enjoy its advantage, ompanies are willing to implement ABC system. They should implement ABC systems onsiously, intelligently and onservatively. Pratlly, Ativity Based Aounting System reognizes the relationship between neessary ativities and osts in order to render servies whih mae eonomi value for the organization (Horngren, et al., 005). Ativity Based Aounting System is derived from this belief that produts use ativities and ativities use resoures and it leads to deimation of value adding ativities and non-value adding ativities (Wiramsinghe and Alawattage, 007). In fat, by the use of this method, ost of eah produt or servie equals total ost of ativities relevant to the prodution at that produt or servie. In traditional osting, osts are generally alloated on the basis of the volume while aording to the osting thining and ativity based management, produts and the produed servies are not diretly users of the resoures but are users of the ativities (Johnson and Kaplan, 987). Therefore, Ativity Based Aounting System is one of the most modern osting systems; suh as system an alone or with urrent osting systems be applied to provide essentl information to mae deisions (Horngren, et al., 005). As the zone of maret hanges, ompanies as well as organizations try world ompetitions in whih more and more information and tehnology of information is of need to win. ABM and ABC are of pioneer systems in this field (Kuhta and Trosa, 007) and have made organizations and ompanies use them. If we are aware of fators affeting implementing and running, it will help the system to reah its goals suessfully and prevents waste of finanl as well as intelletual apital and then leads to more organizational partiipation and trust (Coins, 996) Companies that implement ativity based osting run the ris of spending too muh time, effort, and even money on gathering and going over the data that is olleted. Too many details an prove frustrating for managers involved in ABC. On the other hand, a la of detail an lead to insuffiient data. Corresponding Author: Amin Rezan Department of Civil, Gorgan Branh, Islami Azad University, Gorgan, Iran. a.rezan@srbu.a.ir 60

2 Aust. J. Basi & Appl. Si., 5(0): 60-68, 0 Another obvious fator that tends to ontrute to the downfall of ativity based osting is the simple failure to at on the results that the data provide (Khozein, 009). This generally happens in businesses that were relutant to try ABC in the first plae. Now, it is time to refer to the researh question: What are raning major and minor fators implementing and launhing Ativity Based Costing System among Irann automobile industry ompanies? Review of the literature: ABC system an be taen into aount as a development and extension of two-stage assignment for osting and is underlying for modern ost system, measuring produts and program ost, more effiient operational budgeting and approprte produt priing (Khozein, 009). ABC system will put an emphasis on ativity as a ost objet sine ativity is the main reason for osting. In suh a system first, osting will be assigned to the ativities and then through the ativities, it will be given to other objets suh as programs, plans, produts, servies (Coins, 996). Having, determined the ost, the managers will give muh more redene to the new ost objet i.e. ativities. Measuring the produts and program ost approprtely, produt prie, produt proessing improvement, eliminating the additional ativities, identifying the ost drivers, value added ativities identiftion, eliminating non value added ativities, operation programming, determining trade strategy of entity and finally measuring its operation approprtely need some information provided by ABC system muh better ompared to the management aounting traditional systems. Horngren, et al., (005) believes that Ativity Based Aounting System is one the most modern osting systems whih an provide proper information for deision maing. Prior to this, Cooper and Kaplan (Cooper and Kaplan, 99) laimed that ompanies an derease osts, run modern priing politis, reognize improvement opportunities and speify produt ombinations if they apply relble finanl statements approved by Ativity Based Costing System system. Most ABC pratitioners find that spel-purpose ABC software is required to mae the tas manageable (Henris, 999). Gary Coins (999) wrote an artile aimed at ertified publi aountants that have diffiulty embraing ativity based osting. In "Learning to Love ABC'', Coins explains that ativity based osting usually wors best with a minimum amount of detail and estimated ost figures. He bas this up by stating that "typlly, when aountants try to apply ABC, they strive for a level of exatness that is both diffiult to attain and timeonsuming-and that eventually beomes the projet's iss of death." Coins (000) wrote another artile entitled "Overoming the Obstales to Implementing Ativity-Based Costing." In this wor Coins noted that "ativitybased osting projets often fail beause projet managers ignore the ardinal rule: It is better to be approximately orret than to be preisely inaurate. When it omes to ABC, lose enough is not only good enough; lose enough is often the seret to suess." Coins also notes that the use of average ost rates, the use of overly detailed information, and the failure to onnet information to ation an also hinder ABC projets. By understanding these onepts, Coins feels that CPAs an enhane their roles as business partners and onsultants. Coins (996) lassified problems (failures) ahead of running osting projets and ativity based management into four ategories whih are as follows: the biggies or showstoppers, the users rejetion, the organization obstales and the nuisanes. Some of the items inlude: unemploying all finanl and nonfinanl setions of the organization, la of enough and on time training, ourage of implementation team, full time wor, grossly underestimating of the magnitude of resistane to hange, underestimating the degree of disbelief of the newly alulated numbers by projet team, overengineering the design of the new system, autoratlly mandating the new system by higher-level organization unit, and (Coins, 996). Coins in his following findings asserted that 90 perent of suess in implementation and running Ativity Based Costing System is due to organizational fators and the rest due to math fators. Aountants as well as other organization setions are responsle for implementing suh system. To this aim, fators suh as eduation and plan to implement and ommunte, true seletion of ativity are of importane (Coins, 00). Sine too muh attention to essentl tehnl onsiderations to implement is neessary, little attention is paid to withstanding versus implementation (Roth, 995). To sum up, Ativity Based Aounting System is better and more aurate in omparison with traditional Aounting system, though there are a lot of obstales ahead of them. One of the biggest problems that this system faes is that ompanies now how to implement a new system but they do not now enough why they should do suh ativities and ignore them (Kuhta and Trosa, 007). Khozein et al., (0) tried to point out major fators affeting launhing and implementing Ativity Based Costing System among Irann ompanies. To this end, major fators were lassified and raned into five main groups suh as; organizational, managerl, environmental, individual, and tehnl fators. Regarding results organizational fators are the most important ones and then are Managerl, environmental, individual, and tehnl ones. Thus regarding former researhes, fators affeting implementing and running Ativity Based Costing Systems in Irann automobile industry ompanies are lassified into four groups suh as: organizational, individual, environmental and tehnl fators; minor fators are shown in table 3. Organizational fators 6

3 Aust. J. Basi & Appl. Si., 5(0): 60-68, 0 onsider formal and informal relationships between employees and managers. These fators an be ontrolled and managed by managers. On the other hand, environmental fators are related to onditions whih are outside the enterprise and managers an not ontrol them. Individual fators inlude harateristis and attrute of the planning and implementing (projet) team. Finally, tehnl fators ontain items whih are derived from the nature of Ativity Based Costing and Management System (Khozein et al., 0). The present study tries to prioritize and ran suh fators. Researh Method: In this researh, first of all, fators (riter) affeting implementing and launhing ABC System were reognized then using Fuzzy Analytl Hierarhy Proess (FAHP) they were raned. Analyti Hierarhy Proess (AHP) is one of the well-nown Multi-riter deision maing tehniques that was first proposed by Saaty (980). Although the lassl AHP inludes the opinions of experts and maes a multiple riter evaluation, it is not apable of refleting human s vague thoughts. The lassl AHP taes into onsideration the definite judgments of deision maers (Wang and Chen, 007). Different methods for the fuzzftion of AHP have been proposed in the literature. Experts may prefer intermedte judgments rather than ertain judgments. Thus the fuzzy set theory maes the omparison proess more flexle and apable to explain experts preferenes (Kahraman, Cebei and Uluan, 003). Fuzzy set theory-zadeh in 965 introdued fuzzy set theory to solve problems involving the absene of sharply defined riter (Zadeh, 965). If unertainty (fuzziness) of human deision-maing is not taen into aount, the results an be misleading. A ommonality among terms of expression, suh as ''very liely'', ''probably so'', ''not very lear'',''rather dangerous'' that are often heard in daily life, is that they all ontain some degree of unertainty (Tsaur, Tzeng, and Wang, 997: Tsaur, Chang, and Yen, 00). Fuzzy theory thus is used to solve suh ind of problems, and it has been applied in a variety of fields in the last four deades. Theory of fuzzy sets has evolved in various diretions, and two distint diretions are: treating fuzzy sets as preisely defined mathematl objets subjet to the rules of lassl logi, and the linguisti approah. The underlying logi of linguisti approah is that the truth-values are fuzzy sets and the rules of inferene are approximate rather than exat (Gupta, Saridis and Gaines, 977). A trngular fuzzy number, a spel ase of a trapezoidal fuzzy number, is very popular in fuzzy appltions. As shown in Fig., the trngular fuzzy number is represented by (a,b,), and the membership funtion is defined as: M x a, a x b b a x ( x ), b x b 0, otherwise () Fig. : Membership funtion of a trngular fuzzy number. a, b, The strongest grade of membership is parameter b, that is, f M (b) =, while a and are the lower and upper bounds.an important onept of fuzzy sets is the ut. For a fuzzy number and any number 0,, the ut, C, is the risp set (Klir and Yan, 995). 6

4 Aust. J. Basi & Appl. Si., 5(0): 60-68, 0 () C { x C ( x) } The a-ut of a fuzzy number is the risp set that ontains all the elements of the universal set U whose membership grades in are greater than or equal to the speified value of, as shown in Fig.. Fig. : ut of a trngular fuzzy number. By defining the interval of onfidene at level a, the trngular fuzzy number an be haraterized as (Cheng 999: Cheng 996: Cheng and Mon 994). (3) a, C b a a, b, 0, The distane between two trngular fuzzy numbers an be defined by the vertex method (Chen, 000). Let a, b, and a, b, be two trngular fuzzy numbers, the distane between them is: (4) d ( M, M ) [( a a ) ( b b ) ( ) ] 3 Many different methods have been devised to ran fuzzy numbers, and eah method has its own advantages and disadvantages (Klir & Yan, 995). A popular method is the intuition raning method, whih rans trngular fuzzy numbers by drawing their membership funtion urves. A higher mean value and lower spread fuzzy number is preferred by human intuition (Lee and Li 988). Another popular fuzzy number raning method is the a-ut method (Adamo 980). Centroid raning method is also often used to ran fuzzy numbers (Yagar 978). A fuzzy mean and spread method was proposed by Lee and Li (988) by using a generalized mean and standard devtion based on the probability measures of fuzzy events. A good deision-maing model needs to tolerate vagueness or ambiguity beause fuzziness and vagueness are ommon harateristis in many deision-maing problems (Yu, 00). Sine deision maers often provide unertain answers rather than preise values, the transformation of qualitative preferenes to point estimates may not be sensle. Conventional AHP that requires the seletion of arbitrary values in pairwise omparison may not be suffiient and unertainty should be onsidered in some or all pairwise omparison values (Yu, 00). Sine the fuzzy linguisti approah an tae the optimism/pessimism rating attitude of deision maers into aount, linguisti values, whose membership funtions are usually haraterized by trngular fuzzy umbers, are reommended to assess preferene ratings instead of onventional numerl equivalene method (Lng and Wang 994). As a result, the fuzzy AHP should be more approprte and effetive than onventional AHP in real pratie where an unertain pairwise omparison environment exists. Fuzzy analyti hierarhy proess (FAHP)- FAHP is used to generate the weighting of the four fators of the launhing and implementing Ativity Based Costing System. There are six essentl steps:. Construt the hierarhl struture with deision elements (e.g., riter and detailed riter). Eah deision maer is ased to express relative importane of two deision elements in the same level (e.g. two riter) by a nine-point sale. Collet the sores of pairwise omparison, and form pairwise omparison matries for eah of the K deision maers.. Analyze onsisteny. The priority of the elements an be ompared by the omputation of eigenvalues and eigenvetors: 63

5 Aust. J. Basi & Appl. Si., 5(0): 60-68, 0 w max. w (5) Where w is the eigenvetor, the weight vetor, of matrix R, and max is the largest eigenvalue of R.The onsisteny property of the matrix is then heed to ensure the onsisteny of judgments in the pairwise omparison. The onsisteny index (CI) and onsisteny ratio (CR) are defined as (Saaty 980). (6) max n CI n CI CR RI Where n is the number of items being ompared in the matrix, and RI is random index, the average onsisteny index of randomly generated pairwise omparison matrix of similar size, as shown in Table. As suggested by Saaty (994), the upper threshold CR values are 0.05 for a 3*3 matrix, 0.08 for a 4*4 matrix, and 0.0 for larger matries. If the onsisteny test is not passed, the original values in the pairwise omparison matrix must be revised by the deision maer. (7) Table : Random index (RI) (Saaty, 980) N RI 3. Construt fuzzy positive matries. The sores of pairwise omparison are transformed into linguisti varbles,whih are represented by positive trngular fuzzy numbers listed in Table. Table : Trngular fuzzy numbers. Linguisti varbles Extremely Strong Intermedte Very Strong Intermedte Strong Intermedte Moderately Strong Intermedte Equally strong Positive trngular fuzzy numbers (9,9,9) (7,8,9) (6,7,8) (5,6,7) (4,5,6) (3,4,5) (,3,4) (,,3) (,,) Positive reiproal trngular fuzzy numbers (/9,/9,/9) (/9,/8,/7) (/8,/7,/6) (/7,/6,/5) (/6,/5,/4) (/5,/4,/3) (/4,/3,/) (/3,/,) (,,) Aording to Buley (985),the fuzzy positive reiproal matrix an be defined as: R r ij (8) Where R :a positive reiproal matrix of deision maer ; r : relative importane between deision elements i and j; ij r ij, i j, and, i, j.,..., n r ij rji 4. Calulate fuzzy weights. Based on the Lambda Max method proposed by Csutora and Buley (00), alulate the fuzzy weights of deision elements. The proedures are: Apply ut. Let to obtain the positive matrix of deision maer, R b r,and let ij 0 to b obtain the lower bound and upper bound positive matries of deision maer, R a r and ij a R r.based on the weight alulation proedure proposed in AHP, alulate weight matrix, ij Wb wi W b a wi and W a wi i,,3,..., n 64

6 Aust. J. Basi & Appl. Si., 5(0): 60-68, 0 In order to minimize the fuzziness of the weight, two onstants, M M W W a min W W max i i n i n M, M and,are hosen as follows: a (9) (0) w w * * i The upper bound and lower bound of the weight are defined as: M w a M w i The upper bound and lower bound weight matries are: * * i a i,,..., n W a w * * i,,..., n W wi () () (3) (4) By ombining defined as i * W * a, * i W b and W * W w, w, w, i,,..., n,the fuzzy weight matrix for deision maer an be btained and is 5. Integrate the opinions of deision maers. Geometri average is applied to ombine the fuzzy weights of deision maers (5) W i Wi,,..., K where W i :ombined fuzzy weight of deision element i of K deision maers. W i :fuzzy weight of deision element i of deision maer. K: number of deision maers. 6. Obtain final raning. Based on the equation proposed by Chen (000), a loseness oeffiient is defined to obtain the raning order of the deision elements. The loseness oeffiient is defined as follows: CC i d ( W i, 0 ) i,,..., n * d ( W i, ) d ( W i, 0 ) (6) 0 CC i d where CC i is the weight for deision element i, and ( W i,0 ) [( W 3 0 ) ( W 0 ) ( W i 0 ) ] (7) 65

7 Aust. J. Basi & Appl. Si., 5(0): 60-68, 0 d * ( W i, 0 ) 3 [( W i ) ( W ) ( W i ) d * ( Wi,0) and d ( Wi,) are the distane measurement between two fuzzy numbers. ] (8) The respondent of this researh were managers, finanl managers, researhers, university professors and experts of ABC system. For gathering data needed for FAHP tables, the researhers used interviews, questionnaire and maing expert wor groups. After reording the answers, ombining pair wise omparison matrix for eah partiipant would be started. Data input and analysis: Computer software paages, suh as the Expert Choie Expert Choie, 006, have been applied abundantly in solving AHP problems. The responses olleted from questionnaires are input to the FAHP system, and the results are analyzed by the FAHP. The pairwise omparison results of deision maers filled on the questionnaires are then input by seleting the number on the nine-point sale as is shown in table. Maximum eigenvalue of the matrix is alulated by Eq. (5), and the onsisteny property of the matrix is heed by Eqs.(6) and (7). If the onsisteny test is not passed, the questionnaire an either be revised by the deision maer or be disregarded. In this researh the onsisteny rate is that is aeptable. Fuzzy positive matries based on the input questionnaire results are generated next by Eq. (8) and Eqs. (9) (4) are adopted next to alulate the omparison weights of deision elements. The fuzzy weights from different deision maers are finally ombined by Eq. (5) to generate the overall fuzzy matrix, as shown in table 3.The final priority weights and raning are obtained by Eq. (6). RESULTS AND DISCUSSION In this study, the effet of 6 minor fators (riter) in 4 major groups on implementing and launhing Ativity Based Costing System among some aepted ompanies in Tehran Sto Exhange were examined. As table 3 illustrates, regarding findings of the researh, the importane of organizational fators (0.308) is more than other fators; then are other fators suh as: individual fators (0.93), tehnl fators (0.0), and environmental fators (0.97). Among organizational fators, enough and on time training (0.068), onsidering informational needs of different setions of the organization (0.0650), and Partiipation of non finanl segments in planning and running system (0.065), is more than other fators. Among individual fators, logl timing of the designing and running (0.067), eligility of the designing team (0.06), and full time wor of projet team (0.0577) is more than other fators. Among tehnl fators, the strong orrelation of reinfores and osts (0.0440), finding the lear ost objets (0.046) and onsidering all osts of hain value (0.044) is more than other fators. Among environmental fators, standardization and diretions relevant to the new system (0.045) and shortage of papers, researh and boos (0.0408) is more than other fators. Among all minor fators, enough and on time training, onsidering informational needs of different setions of the organization, logl timing of the designing and running and partiipation of non finanl segments in planning, running system, eligility of the designing team, monitoring and supervising the operation and full time wor of projet team are of vital importane; and logl timing of the designing and running, little ontradition between the results taen from the new system, applying simulating tehniques are the least important fators. Conlusion: To implement new informational management and budgeting systems lie ABC in organization is inevitable. So, for many different reasons the suess is suh systems when they are implementing is not so muh. Suh reasons an be lassified in the four following groups inluding organizational fators, environmental fators, individual fators and tehnl fators. The best solution to the problems existing for implementing ABC systems is to train employees and managers in a good way and to mae them muh more aware of the new system and also to introdue their advantages in the organization onerned. Ativity based osting programs require proper planning and a ommitment from upper management. If possle, it is best to do a trl study or test run on a department whose profit maing performane is not living up to expetations. These types of situations have a greater hane of sueeding and showing those in harge that ABC is a vble way for the ompany to save money. If no ost-saving measures are determined in this pilot study, either the ativity-based osting system has been improperly implemented, or it may not be right for the ompany. 66

8 Aust. J. Basi & Appl. Si., 5(0): 60-68, 0 Table 3: Raning major and minor riter implementing and launhing ABC System by fuzzy AHP Criterion Organizational riterion (I) Environmental riterion (I) Weight of Criterion Minor Criter Partiipation of nonfinanl segments in planning and running system Weight of Minor Criterion Total Weight Ran Readiness of the employees' of different setions Enough and on time training Monitoring and supervising the operation Considering informational needs of different setions of the organization Identiftion of all advantages of the system by soiety Standardization and diretions relevant to the new system Teahing the system in universities and eduational enters No withstand from employees' part versus new information system Shortage of papers, researh and Boos Logl timing of the designing and running Individual riterion (I3) Full time wor of projet team Enough ourage from projet team Eligility of the designing team No strit attention to details while planning Considering all osts of hain value Tehnl riterion (I4) Strong orrelation of reinfores and osts Finding the lear ost objets Aurate definition of ativities Little ontradition between the results taen from the new system Another thing a business must do when using ABC is set up a team that will be responsle for determining whih ativities are neessary for the produt or servie in question. This team should inlude experts from different areas of the ompany (inluding finane, tehnology, and human resoures) and perhaps also an outside onsultant. The head of the designing team of Ativity Based Costing System should be brave enough; he/she should be the most interested person among all qualified individuals; he/she should be interested in onsulting the matters with experiened ounselors. Universities should also train students regarding Ativity Based Costing System. All taen together, implementing and launhing Ativity Based Costing System leads to less failures in ompanies; otherwise, while implementing and launhing the system, different wea points of the system would be nown; items whih already have been preditable and ontrollable but not enough attention has been paid to them. When we are aware of the reasons of the failures, we an easily solve the problems and predit the probable problems and find solution for them. As results, we will experiene more suess and an enjoy benefits of ABC system more than ever and finally value of the organization would be added. REFERENCES Buley, J.J., 985. Fuzzy hierarhl analysis. Fuzzy Sets and Systems, 7: Chang, D.Y., 99. Extent analysis and syntheti deision. Optimization Tehniques and Appltions, : 35. Chen, C.T., 000. Extensions of TOPSIS for group deision-maing under fuzzy environment. Fuzzy Sets and Systems, 4: -9. Cheng, C.H. and D.L. Mon, 994. Evaluating weapon system by analytl hierarhy proess based on fuzzy sales. Fuzzy Sets and Systems, 63: -0. Cheng, C.H., 996. Evaluating naval tatl missile systems by fuzzy AHP based on the grade value of membership funtion. European Journal of Operational Researh, 96: Coins, Gary, 996. Ativity Based Cost Management: M Graw-Hill Companies, In., USA. 67

9 Aust. J. Basi & Appl. Si., 5(0): 60-68, 0 Coins, Gary, 999. Learning to Love ABC, Journal of Aountany. Coins, Gary, 000. Overoming the Obstales to Implementing Ativity-Based Costing, Ban Aounting and Finane. Coins, Gary, 00. Ativity Based Cost Management: an Exeutive's Guide, John Wiley & Sons, In., USA. Cooper, R. and R.S. Kaplan, 99. Profit Priorities From Ativity-Based Costing. Harvard Business Review, 69(3): Csutora, R. and J.J. Buley, 00. Fuzzy hierarhl analysis: the Lambda-Max method. Fuzzy Sets and Systems, 0: Gupta, M.M., G.N. Saridis and B.R. Gaines, 977. Fuzzy automata and deision proesses, New Yor: Elsevier North-Holland. Henris, Mar, 999. Beneath the Surfae, Entrepreneur. Horngren, C.T., G. Foster, S. Datar, M. Rajan and C. Ittner, 005. Cost Aounting A Managerl Emphasis, th edition: Pearson Eduation, Upper Saddle River, New Jersey. Johnson, H., Thomas and Robert S. Kaplan, 987. Relevane Lost: The rise and Fall of Management Aounting, Boston, MA: Harvard Business Shool Press. Kahraman, C., U. Cebei and Z. Uluan, 003. Multi-riter supplier seletion using fuzzy AHP. Logistis Information Management, 6(6): Khozein, A., 009. The Fators Effetive in a Sueed Implementing Ativity Based Costing and Management. ICIME, pp: , International Conferene on Information Management and Engineering, Kuala Lumpour, Malays, 3-5April. Khozein, A., G. Barani and R. Ranjbar, 0. Major Criter of Target Costing Implementing and Launhing. World Applied Sienes Journal, 3(6): Klir, G.I. and B. Yan, 995. Fuzzy sets and fuzzy logi theory and appltions. London: Prentie-Hall International. Kuhta, D. and M. Trosa, 007. Ativity-based Costing and Customer Profitability, Cost Management. Roth, W., 995. Ativity Based Costing in servie industries, in Reev,j. (ed) Readings and Issues in Cost Management, Warren, Gorham & Lamont. Saaty, T.L., 980. The analyti hierarhy proess. New Yor: MGraw- Hill. Saaty, T.L., 980. The Analytl Hirarhy Proess, Planning, Priority, Resoure Alloation, RWS Publtion, USA. Saaty, T.L., 994. How to mae a deision: the analyti hierarhy proess. Interfaes, 4(6): Wang, T.C. and Y.H. Chen, 007. Applying onsistent fuzzy preferene relations to partnership seletion, International Journal of Management Siene, 35: Wiramsinghe, D. and C. Alawattage, 007. Management Aounting Change: approahes and Perspetives, Rowtledge. Yu, C.S., 00. A GP-AHP method for solving group deision-maing fuzzy AHP problems. Computers and Operations Researh, 9. Zadeh, L.A., 965. Fuzzy sets. Information and Control, 8: