Advaces i Evirometal Biology, 7(9): 2509-2521, 2013 ISSN 1995-0756 2509 This is a refereed joural ad all articles are professioally screeed ad reviewed ORIGINAL ARTICLE A New Algorithm for ERP System Selectio Based o Fuzzy DEMATEL Approach 1 Hasa Jahashahi, 2 Behrouz Farhadzareh, 3 Hatef Fotuhi, 4 Abdorreza Golpour, 5 Mohammad Bagher Mokhtari 1 Departmet of Idustrial egieerig, Imam hosei comprehesive Uiversity, Ira, Tehra 2 Departmet of Idustrial egieerig, Imam hosei comprehesive Uiversity, Ira, Tehra 3 Departmet of Idustrial egieerig, Imam hosei comprehesive Uiversity, Ira,Tehra 4 Departmet of Idustrial egieerig, Imam hosei comprehesive Uiversity, Ira,Tehra 5 Departmet of Idustrial egieerig, Imam hosei comprehesive Uiversity, Ira,Tehra Hasa Jahashahi, Behrouz Farhadzareh, Hatef Fotuhi, Abdorreza Golpour, Mohammad Bagher Mokhtari: A New Algorithm for ERP System Selectio Based o Fuzzy DEMATEL Approach ABSTRACT I this paper, a fuzzy expert system comprehesive method is proposed for selectig proper eterprise resource plaig (ERP) system by aalytical etwork plaig based o heuristic algorithm. After makig the etwork model, the iterdepedecies betwee elemets of each criteria is aalyzed ad the overall relatio matrix (T) is built for each criteria. The relative weights of each criteria with regard to relevat criteria is obtaied by applyig a simple heuristic algorithm to each overall relatio matrix (T). This algorithm has a acceptable accuracy ad makes us idepedet of buildig supermatrix ad usig SUPER DECISION software ad thus icreases the speed ad makes the problem easily doe. The obtaied results are aalyzed by compariso betwee algorithm results ad software outputs. Fially the algorithm will propose a fuzzy expert system with regard to relative weights of elemets. To completely explai the applicatio of this algorithm, the proposed method is implemeted i oe of the idustrial uits of Ira ad the results are aalyzed. Key words: Eterprise Resource Plaig, system selectio, fuzzy logic, heuristic algorithm, fuzzy expert system; Itroductio Eterprise Resource Plaig system (ERP) is implemeted i may orgaizatios ad idustrial uits. However globally ad especially i Ira, may compaies avoid to buy ad implemet ERP. The reaso is the fear of idustrial maagers from failure i implemetig the system. Oe of the most importat reasos of these failures is lack of kowledge amog idustrial maagers which result to select improper alterative for the orgaizatio [7]. ERP systems implemetatio is oe of the most costly ivestmets ad the problem with adaptig orgaizatio processes with ERP system [22] the it is essetial to select a proper ERP system for implemetig the system i orgaizatio successfully. Studies show that aligig busiess ad IT processes is oe of the most cocerig issues of maagers ad adaptig busiess processes with implemeted IT is the most importat part of a successful IT project. Thus a successful ERP system is a advatage poit for orgaizatios [21]. I this paper desigig ad utilizig fuzzy expert system for selectig ERP is proposed. There are differet approaches to evaluate a IT system that make us to use a structured method for decisio makig. Expert systems aalyze various cases of a subject by usig a series of if-the rules ad fially results to a optimal alterative [18]. Fuzzy expert system is a ewer versio of expert system that uses fuzzy logic for processig. I this system, a series of membership fuctio ad fuzzy logic is used for eterig data istead of certai logic [14]. It seems that usig fuzzy logic ca leads to a decrease i the risk of selectig optimal alterative for ERP system. Evaluatig alteratives ad purchasig orgaizatios terms by fuzzy logic eable us to select the optimal alterative more accurately. Expert system compare alteratives directly ad i the basis of huma thikig istead of doig paired compariso amog them. The approach ad a case study of idustrial uit i Ira are described i this paper. Various models for selectig ERP system for a orgaizatio have bee proposed. Wei [20] has proposed a aalytical hierarchy process (AHP) model for this purpose. There are two mai criteria as proper system ad proper marketer that may sub criteria have bee cosidered for them. Aother model that uses AHP method is proposed too. We ca refer to a model i textile idustries that select Correspodig Author Hatef Fotuhi, Departmet of Idustrial egieerig, Imam hosei comprehesive Uiversity, Ira,Tehra
Adv. Eviro. Biol., 7(9): 2509-2521, 2013 the best ERP alterative with regard to BSC model i which has 3 mai criteria as cost, system features ad supplier status with 13 sub criteria [4]. I aother model that is based o 9126 stadard, mai criteria as system operatio, system istallatio, maiteace capability, system efficiecy, system reliability ad capability with 21 sub criteria are cosidered [11]. I aother model which is about project cotrol we have 4 mai criteria as system quality, supplier advatages, cost ad time ad 18 sub criteria [12]. We ca refer to other approaches used i this cotext such as 0-1 plaig model, goal programig [9,8]. Ziaee et al (Ziaee, Fathia, & Sadjadi) proposed a 0-1 plaig model that cosiders 3 mai criteria as system, project ad supplier i ERP decisio makig. Yag et all [21] proposed a multistage approach that icludes self-evaluatio of the orgaizatio that wats to purchase ERP, RFB provisio, evaluatig ERP alteratives ad egotiatig cotracts [3]. ANP approach is beig used i oe of the studies which mai criteria are system ad supplier features [17]. Studies show that there is ot ay study that uses expert system for selectig ERP system. But we must otice that there are may studies that discuss about expert system such as usig fuzzy expert system ecoomic ivestmet aalysis i Radio Frequecy Idetificatio [19], decisio makig i global marketig [10], ad developig BSC [2]. I Ira we have some studies too, such as rakig stock market exchage usig fuzzy expert system [9], aalyzig stock market [5], improvig the operatio of wheat combie [16], steel productio process [23], evaluatig efficiecy of health ad safety system of gas [1] refiery [23]. Method: A case study is used I order to fully explai the approach. This fuzzy expert system approach has 2510 bee implemeted i oe of the idustrial uits i Ira ad the results are preseted i this paper. This approach is described as follow: First we must defie alteratives ad criteria of the orgaizatio ad build the etwork model. We could defie the orgaizatio alteratives for selectig proper ERP system ad specify orgaizatio criteria for implemetig the system by developig a expert group cosist of two groups as iteral ad exteral orgaizatio cosultat. The by obtaiig relative weights of each elemet with regard to relevat criteria, we move to the ext stage. I this stage, relative weights of each elemet of criteria with regard to relevat criteria is obtaied for desigig fuzzy sets for each decisio makig criteria. It is essetial to aalyze iterdepedecies betwee elemets of each criteria by fuzzy DEMATEL ad buildig overall relatio matrix for each criteria. Despite the fact that fuzzy DEMATEL approach reflects the output of iterdepedecies betwee elemets, better tha fuzzy-anp, i this paper we will use fuzzy DEMATEL for aalyzig cause ad effect relatioships betwee each criteria. I order to use DEMATEL approach we eed to use experts opiios ad these opiio cosist of vague terms. The it is essetial to trasform these terms to fuzzy umbers. Defiig decisio makig criteria ad buildig etwork diagram: Defiig ERP system decisio makig criteria is based o the followig cotexts: Reviewig used criteria by former researches. Reviewig success idicator of ERP system from the maagers ad users view The mai criteria are takig ito the accout by former approach for selectig the best ERP system Table 1 preset a series of most importat criteria used i former researches. These criteria is cosidered by frequecy ad emphasis used i the researches. Table 1: Most importat criteria used i former researches Project criteria: cost, time, set up time 1 System features: adaptatio with orgaizatio process, quality ad reliability, beig user friedly ad havig a good user iterface, updatig ad developig capability Supplier state: used techology, support, traiig, fiacial stability, reputatio 2 System features: software fuctioality, reliability, user friedly, efficiecy (o-fuctioal features), teability Maagerial criteria: supplier state, implemetatio cost ad time 3 System compatibility, support ad cosult quality, system acceptace by users, set up time, maiteace ad adaptatio ad cost 4 System features: cost, implemetatio time, system fuctioality, user-friedly ad user iterface, flexibility, reliability Supplier state: reputatio, techical capability, support service 5 Cost: purchase cost, implemetatio cost System features: software fuctioality, flexibility, reliability, user friedly ad user iterface, developig ad updatig capability, adaptatio with orgaizatio processes Supplier state: support, reputatio 6 System quality: correctess, reliability, user-friedly ad user iterface, itegratio, teability, testability Supplier state: market share, huma resources, reputatio, traiig, willigess to corporate Cost: software cost, hardware cost Set up time 7 System features: software fuctioality, flexibility, user-friedly ad user iterface, set up cost, cost, reliability Supplier state: market share, ecoomic stability, implemetatio capability, support, update capability From aother aspect proper operatio of the software, flexibility, beig user friedly ad high quality traiig are criteria that must be take ito accout for implemetig a successful ERP system
Adv. Eviro. Biol., 7(9): 2509-2521, 2013 [13]. Also based o oe of the studies coducted i Ira we must cosider cost, supplier experiece, busiess process reegieerig, software support, software quality, adaptatio with orgaizatio processes i order to success i implemetig ERP system [15]. Fially accordig to the above topics, 3 criteria as system set up, system features ad supplier status is cosidered as mai decisio makig criteria. System set up criteria: System set up criteria icludes two sub criteria as cost ad set up time accordig to their importace ad emphasize. 2511 System features criteria: There are six sub criteria for these criteria accordig to the literature: adaptatio capability with orgaizatio curret processes, flexibility, reliability, software fuctioality, user quality ad user iterface, data aalysis ad report capability. Supplier status criteria: There are 4 sub criteria for these criteria as follow: supplier credit, traiig quality, support quality, software updatig quality. Fig. 1: Network model for criteria ad subcriteria Usig fuzzy DEMATEL: The purpose of usig fuzzy DEMATEL i this method is to obtai relative weights of each criteria with regard to other criteria ad makig the overall relatio matrix. I order to use Fuzzy DEMATEL i this method, we must go through followig steps. Fig. 2: Overall relatio matrix Buildig direct relatio matrix betwee system factors as figure 1.
Adv. Eviro. Biol., 7(9): 2509-2521, 2013 2512 Fig. 3: Direct relatio matrix betwee system factors Defuzzificatio of iitial direct matrix: Liguistic terms must be covertig to fuzzy umber by oe of the fuzzy spectrum. Triagular fuzzy umber is used i this paper. We ca use table 2 to covert vague judgmets to triagular fuzzy umbers [24]. Table 2: Verbal judgmets to triagular fuzzy umbers Liguistic variable Relevat triagular fuzzy umber Effectless (0, 0, 0.25) Very low effect (0, 0.25, 0.5) Low effect (0.25, 0.5, 0.75) High effect (0.5, 0.75, 1) Very high effect (0.75, 1, 1) The curret paper uses CFCS approach (covertig fuzzy data to certai poits) proposed by Opriacovic ad tzeg for defuzzy [24]. After defuzzificatio, expert opiios are aggregated ad iitial direct relatio matrix is built by absolute umbers a ij which is the effect of factor I o factor j. Normalizig direct relatios matrix: First we must ormalize iitial direct relatios matrix. Normalized direct relatio matrix X x ij is * obtaied by Eq 1. All the matrix compoets are beig applied i 0 x ij < 1 costrait ad all the mai diagoal compoets of the matrix are equal to zero. X s A (1) 1 1 s mi, (2) maxi aij max j aij j 1 i 1 Buildig overall relatio matrix: We compute sum of a ifiitive sequece of direct ad idirect effects of elemets amog each other (I additio to all possible feedbacks) as a geometric progressio ad o the basis of curret graph rules. The sum of progressio is overall relatio matrix (T). TX (1-X) -1 (3) Applyig the heuristic algorithm: By applyig the heuristic algorithm to each overall relatio matrix of criteria:, we ca build the supermatrix ad obtai the weights of elemets of each criteria by usig super decisio software to calculate the limit supermatrix accordig to etwork model ad output of fuzzy DEMATEL approach (overall relatio matrix T) ad relative weights of alterative with regard to elemets of each criteria. I the preseted etwork model, alteratives are idepedet from criteria ad there are o depedecies betwee criteria. I this case the relative weights of elemets of each criteria are oly affected by the overall relatio matrix of relevat criteria. Therefor we ca apply the proposed method to each overall relatio matrix (T) ad obtai relative weight of each criteria with regard to relevat criteria. This algorithm has a acceptable accuracy ad made us idepedet of buildig the supermatrix ad usig SUPER DECISION software. Therefor augmet us with more speed ad comfort. The accuracy of the method is compared with output of SUPER DECISION software. The proposed method stages are as follow: first we must obtai U from ormalizigu ad S from ormalizig S by Eq (4) ad (5) the we ca compute relative weights of each criteria with regard to relevat criteria by arithmetic average method. u i wcc 1,2,, umber of elemets i J J 1 of relevat criteria (4) w CC : Relative weight of elemet with regard to i J elemet u1 U u u1 u 1 u1+ u2 + u u 2 u 2 U u1+ u2 + u (5) u u u1+ u2 + u
Adv. Eviro. Biol., 7(9): 2509-2521, 2013 Z Oracle system ( ) 0.32 0.68 0.82 1 2 2 µ z. zdz 2z dz + 0.64zdz + 2z + 2 zdz +.36zdz A 0 0.32.068 0.68 0.32 0.68 0.82 1 µ (z) dz 2zdz + 0.64dz + 2z + 2 dz +.36dz A 0 0.32.068 0.68 0.248056 0.5305 0.4676 s1 s 2 S s s1 s 1 s1 s2 s + + + s2 S s 2 s1+ s2 + + s s s s1+ s2 + + s S i + U i W C i 1,, i 2 2513 (6) (7) (8) For better uderstadig the proposed method we apply this method o "system feature criteria". First we obtai relative weights of elemets of "system feature" criteria with regard to "system feature". Table 3: Overall relatio matrix of system features With regard to system feature C1 C2 C3 C4 C5 C6 C1 0.2172 0.1489 0.0879 0.1478 0.2179 0.1371 C2 0.212 0.1151 0.1572 0.1169 0.1103 0.1981 C3 0.1241 0.2104 0.1741 0.2519 0.0761 0.231 C4 0.2412 0.1217 0.2362 0.1781 0.2471 0.1476 C5 0.1895 0.1981 0.2514 0.24517 0.1463 0.1841 C6 0.016 0.2058 0.0932 0.0636 0.2023 0.1021 U w +w + +w 2172+0.1489+0.0879+0.1478+0.2179+0.13710.9568 1 CC 1 1 CC 1 2 CC 1 6 0. 9568 0. 9096 1.0676 U 1.1719 1.21457 0.683 S 6 J 1 i CC i J CC J i J 1 0. 159374 0.151512 0.17783 w CC 1, 2,, 6 U i J 0.195204 0.202311 0.113768 w w i 1,2,,,
Adv. Eviro. Biol., 7(9): 2509-2521, 2013 2514 ( w w ) ( w w ) ( w w ) S + + + 1 CC 1 1 CC 1 1 CC 1 2 C2C1 CC 1 6 CC 6 1 ( ) ( ) ( ) S 0.2172 0.2172 + 0.1489 0.212 + 0.1371 0.016 0.16876 1 0. 168786 0.163538 0.154736 0.144925 0.174454 0.16903 S, S 0.211063 0.204501 0.201503 0.195238 0.121547 0.117768 S i + U i W C i 1,, i 2 S1+ U 1 0.159374 + 0.163538 WC 0.16145 1 2 2 Table 4: Result compariso betwee super decisio software ad heuristic method with regard to system features criteria Relative weights of system features criteria elemets SuperDecisio output Heuristic output WC1 0.161456 0.161443 WC2 0.14713 0.150717 WC3 0.173827 0.173374 WC4 0.200807 0.199798 WC5 0.201742 0.198734 WC6 0.11456 0.11575 Ad we ca obtai the other relative weights of elemets with regard to relevat criteria by the same method Table 5: Overall relatio matrix of supplier state With regard to supplier state C7 C8 C9 C10 C7 0.263 0.4231 0.5463 0.1412 C8 0.4556 0.227 0.1384 0.2634 C9 0.1413 0.1234 0.2324 0.1412 C10 0.1413 0.2276 0.084 0.4556 Table 6: Result compariso betwee super decisio software ad heuristic method with regard to supplier state criteria Relative weights of supplier state criteria elemets Super Decisio output Heuristic method output WC7 0.324271 0.328704 WC8 0.295914 0.276278 WC9 0.149416 0.149639 WC10 0.230399 0.244074 Table 7: Overall relatio matrix of system set up criteria With regard to system set up C11 C12 C11 0.7229 0.6875 C12 0.2771 0.3125 Table 8: Result compariso betwee super decisio software ad heuristic method with regard to system set up criteria With regard to system set up criteria Super Decisio output Heuristic method output WC11 0.693 0.709134355 WC12 0 0.291462356 Desigig fuzzy sets of decisio makig criteria: I this stage three fuzzy sets as low, medium, high is desiged for each decisio makig criteria. Fuzzy sets are desiged accordig to obtaied weights of elemets ad ERP purchaser orgaizatio status. We must otice that summatio of relative weights of elemets with regard to relevat criteria is equal to 1. For coveiece, a bigger scale is cosidered as score scale. Fuzzy sets ad membership fuctios are defied accordig to cosidered domai (scale) for each criteria ad curret score ad orgaizatio relative strategy. It is otable that defied scale for each criteria, ca be differet.
Adv. Eviro. Biol., 7(9): 2509-2521, 2013 2515 Table 9: Sub criteria score Mai criteria Sub criteria Sub criteria score Criteria score summatio System features c1 0.16*10016 100 c2 0.15*10015 c3 0.17*10017 c4 20 c5 20 c6 12 Supplier state c7 33 100 c8 28 c9 15 System set up c10 24 100 c11 71 c12 29 Now as we metioed above, we must defie membership fuctios for each criteria i curret problem. Membership fuctios for system set up criteria are as follow: 0, x < 0 1, 0 < x < 6 System features low (0, 6, 50) 50 x, 6 < x < 50 44 1, x > 50 0, x < 6 x 6, 6 < x < 50 44 System features medium (6, 50, 94) 94 x, 50 < x < 94 50 0, x > 94 0, x < 50 x 50, 50 < x < 94 System features high (50, 94,100) 44 1, 94 < x < 1000 0, x > 1000 Membership fuctios for supplier state criteria: 0, x < 0 1, 0 < x < 8 Supplier state low (0, 8, 50) 50 x, 8 < x < 50 42 0, x > 50 0, x < 8 x 8, 8 < x < 50 42 Supplier state mediumي ث (8, 50, 92) 92 x, 50 < x < 92 42 0, x > 92 (9) (10) (11) (12) (13)
Adv. Eviro. Biol., 7(9): 2509-2521, 2013 0, x < 50 x 50, 50 < x < 92 Supplier state high (50, 92, 100) 42 1, 92 < x < 100 0, x > 100 Membership fuctios for system set up criteria 0, x < 0 1, 0 < x < 15 System set up low (0, 15, 50) 50 x, 15 < x < 50 35 0, x > 50 0, x < 15 x 15, 15 < x < 50 35 System set up lmedium (8, 50, 92) 85 x, 50 < x < 85 35 0, x > 85 0, x < 50 x 50, 50 < x < 92 System set up high (50, 92, 100) 42 1, 92 < x < 100 0, x > 100 iputs fuzzificatio: Expert system aalyze preseted data from supplier compaies ad obtaied iformatio from oral iterview ad determie orgaizatio optimal system score ad alteratives score with regard to each elemet. Alteratives ad orgaizatio optimal system score with regard to each criteria is obtaied 2516 (14) (15) (16) (17) by summatio of alteratives ad orgaizatio optimal scores with regard to each elemet of criteria. Now for makig the iputs fuzzy, membership degree of alteratives ad orgaizatio optimal system i each criteria are determied by defied membership fuctios for relevat criteria. Table 10: Optimal orgaizatio system score ad the alteratives score for system features criteria: Sub criteria Score upper limit Optimal Alterative2 Alterative1 oracle system Sage c1 16 11 13 3 c2 15 12 11 5 c3 17 10 10 7 c4 20 10 18 10 c5 20 10 14 6 c6 12 5 12 7 sum 100 58 78 38 Alterative3 MFG 5 6 7 14 16 12 60 Table 11: Optimal orgaizatio system score ad the alteratives score for purchaser state criteria: sub criteria upper limit optimal alterative1 oracle alterative2 sage alterative3 MFG system c7 33 10 17 10 13 c8 28 20 23 10 13 c9 15 23 23 13 13 c10 24 17 26 20 20 Sum 100 70 89 53 59
Adv. Eviro. Biol., 7(9): 2509-2521, 2013 2517 Table 12: Optimal orgaizatio system score ad the alteratives score for system set up criteria: sub criteria upper limit score optimal alterative1 oracle alterative2 sage alterative3 MFG system c7 71 36 5 40 31 c8 29 12 6 20 9 sum 100 48 11 60 40 Now Membership degree of the optimal system ad the alterative is obtaied accordig to defied membership fuctio for each criteria. Table 13: Optimal orgaizatio system ad the alteratives membership degree i membership fuctio of system feature criteria Optimal system Alterative1 Alterative2 Alterative3 obtaied score 58 78 38 60 membership degree i fuzzy membership fuctio low 0 0 0.27 0 medium 0.82 0.36 0.73 0.77 high 0.18 0.64 0 0.23 Table 14: Optimal orgaizatio system ad the alteratives membership degree i membership fuctio of purchaser state criteria. Optimal system Alterative1 Alterative2 Alterative3 obtaied score 70 89 53 59 membership degree i fuzzy membership fuctio low 0 0 0 0 medium 0.52 0.07 0.93 0.79 high 0.48 0.93 0.07 0.21 Table 15: Optimal orgaizatio system ad the alteratives membership degree i membership fuctio of system set up criteria. Optimal system Alterative1 Alterative2 Alterative3 obtaied score 48 11 60 40 membership degree i fuzzy membership fuctio low 0.06 1 0 0.29 medium 0.94 0 0.71 0.71 high 0 0 0.29 0 Defiig fuzzy expert system rules: Reasoig ceter of expert system icludes a set of if-the rules. I fuzzy expert system rules are expressed by a set of liguistic terms. Expert system rules compare optimal orgaizatio status with the alterative with regard to each mai criteria ad express coicidece degree by liguistic terms. I this fuzzy expert system, ie rule is defied for each criteria as follow: The coformity is high whe optimal orgaizatio system with regard to cosidered criteria is low ad the alterative is low. The coformity is medium whe optimal orgaizatio system with regard to cosidered criteria is low ad the alterative is medium. The coformity is low whe optimal orgaizatio system with regard to cosidered criteria is low ad the alterative is high. The coformity is medium whe optimal orgaizatio system with regard to cosidered criteria is medium ad the alterative is low. The coformity is high whe optimal orgaizatio system with regard to cosidered criteria is medium ad the alterative is medium. The coformity is medium whe optimal orgaizatio system with regard to cosidered criteria is medium ad the alterative is high. The coformity is low whe optimal orgaizatio system with regard to cosidered criteria is high ad the alterative is low. The coformity is medium whe optimal orgaizatio system with regard to cosidered criteria is high ad the alterative is medium. The coformity is high whe optimal orgaizatio system with regard to cosidered criteria is high ad the alterative is high. 9 rule is defied for each criteria the we must defie 27 rule i this paper. Each fuzzy expert system gives us a fuzzy outputs accordig to the computed membership degree for each optimal system ad alterative. For example for first alterative ad i system feature criteria fuzzy outputs are as follow The coformity is high if optimal system is medium ad the system is medium with regard to system features criteria The coformity is medium if optimal system is medium ad the system is high with regard to system features criteria The coformity is medium if optimal system is high ad the system is medium with regard to system features criteria The coformity is high if optimal system is high ad the system is high with regard to system features criteria
Adv. Eviro. Biol., 7(9): 2509-2521, 2013 2518 Fig. 4: Fuzzy output accordig to the computed membership degree for alterative ad i system feature criteria by rule 5 Fig. 5: Fuzzy output accordig to the computed membership degree for alterative ad i system feature criteria by rule 6 Fig. 6: Fuzzy output accordig to the computed membership degree for alterative ad i system feature criteria by rule 8
Adv. Eviro. Biol., 7(9): 2509-2521, 2013 2519 Fig. 7: Fuzzy output accordig to the computed membership degree for alterative ad i system feature criteria by rule 3 The output is zero from the other 5 rules because the membership degree of optimal system or alterative is equal to 1 or both of them are zero. Fuzzy iferece: Fuzzy iferece is the most importat stage of fuzzy expert system process ad is coducted by defied rules. A fuzzy set of coformity of optimal orgaizatio system with the alterative with regard to the relevat criteria defied by itegratig fuzzy outputs obtaied from coformity degree of optimal orgaizatio system with the alterative with regard to oe criteria with 9 defied rule for that criteria. I this paper we have 3 mai criteria that create 3 fuzzy set for each alterative. Fuzzy set for first alterative with regard to system features are equal to: Fig. 8: Fuzzy set for first alterative with regard to system features Defuzzificatio: It is covertig fuzzy set to a umeral value. I this method If is a fuzzy set, umeral value is as follow: Z µ * A µ A ( z ). zdz (z) dz (18) I this stage, we have 3 fuzzy set for each alterative. Numeral value of created fuzzy sets for first alterative with regard to system features is as follow: 2 z, z [0,0.32) µ A (Z) 0.64, z [0.32, 0.68) (19) 2z + 2, z [0.68, 0.82) 0.36, z [0.82,1)
Adv. Eviro. Biol., 7(9): 2509-2521, 2013 2520 Z Oracle system 2 2 µ ( ) A + + + + 0.32 0.68 0.82 1 µ + + + + A 0.32 0.68 0.82 1 z. zdz 2z dz 0.64zdz 2z 2 zdz.36zdz 0.248056 (z) dz 2zdz 0.64dz 2z 2 dz.36dz 0.4676 0 0.32.068 0.68 0 0.32.068 0.68 0.5305 Alterative rakig: After defuzzificatio a umeral value is obtaied for each alterative with regard to each criteria. If criteria have equal weights from maager's view, Arithmetic mea of the values obtaied with each alterative with regard to criteria is the aalyzig method for the alterative. Thus by cosiderig equal weights for criteria we have: Table 16: Fial score of alteratives alterative feature coformity status coformity set up coformity fial score Oracle 0.5305 0.5547 0.5009 0.6566 Sage 0.6232 0.5647 0.6277 0.6052 MFG 0.6566 0.5646 0.6292 0.6168 If criteria do ot have equal weight from maager's view we eed to use paired compariso method of saaty to obtai their weights ad after applyig it to the relevat value, the fial score will be computed. Coclusio: I this paper, a comprehesive method with a heuristic algorithm based o aalytical etwork process is preseted to select a optimal eterprise resource plaig system. I decisio makig by fuzzy expert system with regard to fuzzy logic, havig a error i computig optimal value of a elemet, do ot udermie decisio makig processes because all the cosidered elemets determie optimal value of orgaizatio i each decisio criteria. Fuzzy expert system rules review alterative coditios accordig to orgaizatio demads ad the rakig is applied without direct comparig of alteratives with optimal coditios of orgaizatio ad therefore alterative adaptatio with optimal orgaizatio coditio is the base for selectig the optimal alterative ad optimal alterative will be selected based o orgaizatio desirability ot suppresses to the other alteratives. I this approach selected alterative is based o orgaizatio demad. I the preseted etwork model criteria are idepedet from alteratives ad there is o depedecy betwee criteria. I this case relative weights of each criteria with regard to relevat criteria is oly affected by overall relatio matrix (T). Therefore we ca apply this algorithm for each overall relatio matrix to obtai relative weights of each criteria with regard to relevat criteria. This algorithm has a acceptable accuracy ad makes us idepedet of buildig super matrix ad the decisio makig is doe more quickly ad easily. The algorithm results are compared with software outputs. The comparisos proves the accuracy of the algorithm. Fially a fuzzy expert system is preseted accordig to relative weights of elemets. This expert system has bee implemeted i oe of the idustrial uits i Ira ad the results is preseted. Itegratig this method with other ERP selectig methods (like Fuzzy MADM) is suggested to reduce vulerability ad achieve higher effectiveess. Evaluatig the outputs of ERP selectio by this comprehesive method ad improvig criteria ad their elemets is a sigificat issue Refereces 1. Azadeh, A., I. Fam, M. Khoshoud, & M. Nikafrouz, 2008. Desig ad implemetatio of a fuzzy expert system for performace assessmet of a itegrated health, safety, eviromet (HSE) ad ergoomics system: The case of gas refiery. Iformatio Scieces, pp: 178. 2. Bobillo, F., M. Delgado, J. Gomez-Romero, & E. Lopez, 2009. A sematic fuzzy expert system for a fuzzy balaced scorecard. Expert Systems withapplicatios, pp: 36. 3. Bueo, S., & J. Salmero, 2007. Fuzzy modelig Eterprise Resource Plaig tool selectio. Computer Stadards ad Iterfaces, pp: 30. 4. Cebeci, F., 2009. Fuzzy AHP-based decisio support system for selectig ERP systems i textile idustry by usig balaced scorecard. Expert Systems with Applicatios, pp: 36. 5. Esfahaipour, A., & W. Aghamiri, 2010. Adapted Neuro-Fuzzy Iferece System o idirect approach TSK fuzzy rule base for stock market aalysis. Expert Systems with Applicatios, pp: 37. 6. Fasaghari, M., & A. Motazer, 2010. Desig ad implemetatio of a fuzzy expert system for Tehra Stock Exchage portfolio recommedatio. Expert Systems with Applicatios, pp: 37. 7. Hakim, A., & H. Hakim, 2010. A practical model o cotrollig the ERP implemetatio risks. Iformatio Systems, pp: 35. 8. Karsak, E., & C. Ozogul, (.d.). A itegrated decisio makig approach for ERP system selectio. Expert Systems with Applicatios, pp: 36.
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