A Hybrid Grey-Game-MCDM Method for ERP Selecting Based on BSC. M. H. Kamfiroozi, 2 A. BonyadiNaeini

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1 Int. J. Manag. Bus. Res., 3 (1), 13-20, Winter 2013 IAU A Hybrid Grey-Gae-MCDM Method for ERP Selecting Based on BSC 1 M. H. Kafiroozi, 2 A. BonyadiNaeini 1,2 Departent of Industrial Engineering, Iran University of Science and Technology (IUST), Tehran, Iran ABSTRACT: An enterprise resource planning (ERP) software is needed for industries and copanies that want to develop in future. Many of the anufactures and copanies have a proble with ERP software selection. An inappropriate selection process can affect both the ipleentation and the perforance of the copany significantly. Although several odels are proposed to solve this proble any of the did not consider uncertainty as an effective environental factor. In the current paper a new odel is designed. This odel is based on balanced score card (BSC) and in addition, uncertainty is considered. This paper used three-paraeter interval grey nubers concept that derived fro Grey-theory in order to reduce uncertainty. Beside, hybrid odel for weighting based on Shapley and Entropy ethods are used. This cobination approach is also because of reducing uncertainty. And at the end, a new ethod naed proection attribute function ethod is used for ranking. There is a case study at the end of this paper that shows how this odel works. Keywords: Balanced Score card (BSC), Enterprise resource planning (ERP), Proection attribute function ethod, Shannon entropy, Shapley value INTRODUCTION Nowadays odern organizations operate in an econoic environent where custoer deands are continuously change and increases as there are unpleasant conditions (Yazgan et al., 2009). These organizations strive to reduce total cost through supply chain, production cycle, and inventory. Additionally, they request increasing diversity of products, ore accurate delivery dates and coordinating the supply and production effectively (Xiuwu, et al., 2007). An enterprise resource planning (ERP) syste is an inforation syste that is designed in order to plan and integrate all of an enterprise's subsystes including production, sales, purchase and finance (Gürbüz et al., 2012). An ERP syste typically ipleents a coon enterprise-wide database together with a range of application odules (Davenport, 1998). Though an ERP syste is costly and cople, but it is vital for copanies to face the rapidly changing and copetitive business environent (Chang et al., 2012). The offered ERP software packages cannot provide a once-for-all business odel for each process of all copanies. In other words, no single ERP packaged software can eet all copany functionalities or all special business requireents (Wei et al., 2005). ERP software autoates and integrates business processes and allows inforation sharing in different business functions. In addition ERP software supports the finance, huan resource, operations and logistic, sale and arket. At the sae tie it iproves the perforance of organization s functions by Corresponding Author, Eail: Mohaad.kafirozi@iustn.ac.ir, Mohaad Hassan Kafiroozi

2 M. H. Kafiroozi; A. BonyadiNaeini controlling the (Hallikainen, et al., 2006).An inappropriate selection process can significantly affect not only the ipleentation but also the perforance of the copany (Cebeci, 2009). ERP selection proble was studied in any papers and several odels were introduced. For eaple, Wei and others presented an ERP selection odel based on AHP (Wei051). They proposed two ain attributes, suitable syste and suitable salesan. Cebeci was presented a odel to select an ERP syste for tetile industries with BSC approach (Cebeci, 2009). Another odel that is used to integrate QFD, fuzzy linear regression and 0 1 goal prograing was presented by Bernroider and Sti to solve ERP selection proble (Bernroider and Sti, 2006). Also an ANP odel for ERP software selection proble with BSC approach was developed by Ravi and his college (Raviet al., 2005). A hybrid odel based on Gae-Entropyproection attribute function ethod over three paraeter interval grey nubers with BSC approach is introduced in the present paper. Cobination of Gae and Entropy ethod is used to weight the criteria in uncertain conditions. This cobination ethod can cover weaknesses of each ethod in copared to a tie when they are used separately. Besides, three-paraeter interval grey nubers concept that etracted fro Grey syste theory is used to change linguistic variables into quantitative types. At last, a case study of an Iranian anufacture is brought to show how this odel works. Preliinaries BSC The need for perforance easureent systes at different levels of decision aking, either in the industry or service contets, is undoubtedly not soething new (Bititici, Cavalieri, & Cieinski, 2005)BSC have been proposed by Kaplan and Norton (Kaplan and Norton, 1992) (Kaplan and Norton, Using the balanced scorecard as a strategic anageent syste, 1996) This eans evaluates perforance by four different perspectives: the financial, the internal business, the custoer, and the innovation and learning (Kuar and Bhagwat, 2007). These perspectives are shown in figure 1. Figure 1: Four perspectives of BSC The BSC sees to serve as a control panel, pedals and steering wheel (Mali, 2001). Many copanies are adopting the BSC as the foundation for their strategic anageent syste. Soe anagers have used it as they align their businesses to new strategies, oving away fro cost reduction and towards growth opportunities based on ore custoized, valueadding products and services (Martinsons et al., 1999). Three Paraeter Interval Gray Nubers Grey syste theory was introduced by J. Deng (Deng, 2002) (Deng, The introduction of grey syste, 1989) and was etended by others (Liu et al., 2005). In abbreviation, If black represents the inforation that is copletely unknown and white represents the data that is quite clear, gray is other inforation that soewhat are clear and soewhat are unclear. A syste which contains gray Inforation is called Gray-syste. Figure 2 shows the concept of Gray syste. Figure 2: Concept of grey syste 14

3 Int. J. Manag. Bus. Res., 3 (1), 13-20, Winter 2013 A three paraeter interval gray nuber like a( ) can be shown within a( ) [ a, a%, a] that a is lower bound, a% center of gravity (the nuber has the highest possibility) and a upper bound. When center of gravity is not deterined, we face with the typical gray nubers. Operators of Three Paraeter Interval Grey Nubers Let a( ) [ a, a%, a] and b( ) [ b, b %, b] be two three paraeter interval grey nubers, then a( + ) b( ) [ a+ b, a% + b%, a+ b] a() / b() [in {a/b,a/,/b,/}, /, a {a/b,a/,/b,/} Decision Making Matri Noralization Assue our decision aking atri is like below: S { () () (,, ), 0, 1,2,, 1,2,, We use the following ethod for atri noralization that is naed poor transfor ethod. Desired value for efficiency i i And desired value for costing i At the above equations, When u u u u i i u u i u % % i i u% u i u % u i u i u u 1 i n i 1 i n i u 0 u a { u }, u in { u },then we can eliinate this. i u atribute fro decision aking atri,because it is an effectless paraetre. i ( i, % i, i ) is a three-paraeter interval grey nuber in [0,1]. Now we have a standardized decision aking atri as follows: R K M 1 L Cooperative Gae and Shapley Value Gae theory is rapidly becoing established as one of the cornerstones of the social sciences (Shaun and Varoufakis, 1995). This branch is the study of ulti person decision probles (Gibbons, 1992) which they can have coalition (cooperative gae) or not (non-cooperative gae). Cooperative gae in the characteristic function for (also called a TU-gae) is a function : 2 with a finite set N as the grand coalition of the players. For each coalition S (a subset of N), v(s) represents the worth of S (the gain possible to be achieved ointly by all the players fro S when they collaborate) (Radzik, 2012). The Shapley value (Auann and Shapley, 1974) (Shapley, 1953) is a well-known solution concept in cooperative gae theory. Iagine the situation where if soe players (for eaple soe econoic agents) ake up a cooperative relationship, (i.e., a coalition) then they can get ore gains than those if they do not do so. In such situations, one of their interests is how uch share can be got by each of the when the coalitions are foring. The Shapley value shows a vector whose eleents are agents' share derived fro several reasonable bases (Tsurui et al., 2001). Denoting Φ ( 1 (v), 2 (v),..., n (v)) as an allocation schee, the Shapley value is denoted by the condition: ( n 21 O M n!!! 15

4 M. H. Kafiroozi; A. BonyadiNaeini where S is any available coalition of N, S is the nuber of the players in the coalition. v (S \ {i}) is the characteristic function of the coalition S ecept i (Barron, 2008). Shannon Entropy Method This easure of uncertainty is given by Shannon (Shannon and Weaver, 1947) as E S{ P 1, P 2,..., Pn } k [ PLnP] i 1 i i K is a positive constant here. When decision atri is clearly eplained, entropy can be used as a tool in criteria evaluation. Here we present this ethod in a step-bystep approach (Hwang and Yoon, 1970): Let the decision atri D of alternatives and n attributes (criteria) be D K 1 n 21 M O M 1 L n The proect outcoes of attribute can be defined as i p i i i 1 The entropy of the set of proect outcoes of attribute is E k [ p Lnp ] i 1 i i k is a constant as: 1 k Ln( ) this guarantees that 0 E 1 The degree of diversification of inforation provided by the outcoes of attribute can be defined as d 1 E Then the weights of attributes can be obtained by w If the DM has a prior, subective weight, then this can be adapted in a new for ' λ w w n λ w 1 In this paper weights of attributes are gained fro lower band, gravity and upper band atri separately. Then the ean value of weights that outcoes fro each atri is considered as weights of every attributes. Method of Proection Attribute Function Denote d n d 1 a {, a, in, in, in Definition: Suppose evaluation vector of alternatives is denoted by (1) ( ) ( ( ), ( ),..., ( )), i 1,2,..., n i i1 i2 i Then the diension three-paraeter nonnegative interval grey nuber vectors are called ideal optial alternative evaluation vector and critical alternative evaluation vector, respectively, in which,,,,, for 1,2,, λ ( ) ( ( ), ( ),..., ( )), i 1,2,..., n ( ) ( ( ), ( ),..., ( )), i 1,2,..., n For the sake of siplicity, the noralized evaluation vector of A by (1) can be rewritten as the following decision aking atri 16

5 Int. J. Manag. Bus. Res., 3 (1), 13-20, Winter ( ) ( k ) (2) zı Then 1ε zi zi zi / 1ε zi zi zi / ( ) ( k ) Which,,,,,, 1,2,, (3) Assue w ( 1,2,..., ) are weights of attributes, for any i1,2,,n, denote (4) zi w, zi w, zi w zi w, zi w, zi w zi 1 ε zi zi zi z i 1,2,, is called proection function value of evaluation vector i ( ), in which ε [0,1] is preference coefficient. The proection function value Z() i characterizes the relative closeness of evaluation vectors between i ( ) for alternative Ai () and + ( ) (ideal optial alternative) and for alternative Ai () and ( ) (critical alternative) (Dang, 2009). RESEARCH METHODO In this section the ethod that used in this paper is eplained. At the beginning, the goals are allocated to perspectives of BSC. Then linguistic variables are changed to three paraeter interval grey nubers. Net each perspectives weight is obtained by Shapley ethod and these values are used as obective weights in Entropy ethod. Then the final weights of each perspective coe fro Entropy ethod. At the end, proection attribute function ethod is used to rank and select the best ERP syste. This ethodology is depicted in figure 3. i Definition: Suppose standard evaluation vectors for any alternative, ideal optial alternative and critical alternative are given by (2) and (3), + Zi ( ) k, Zi ( ) k( i 1,2,.., nk ; 1,2,3) are given by (4), denote Figure 3: Research ethod 17

6 M. H. Kafiroozi; A. BonyadiNaeini Case Study and Results One of the anufactures in I.R. Iran would purchase an ERP syste. This decision was ade in order to satisfy soe goals. Goals and their own perspectives are shown in table 1. The alternatives and their values are gathered by consultation with 31 inforation systes eperts. These are showed in table 2. Linguistic variables were changed to three paraeter interval grey nubers by table 3. Table 1: Goals and their own perspectives Aspects Goals Efficiency Increasing Financial Custoer Internal business process Learning and growth Costs Optiizing Achive to Copetetive Price Costoer Satisfaction Custoer Holding Cofortable Access New Market Recognition Adoptability Fleibility Standard of Production Quality Increasing Safety and Security Supporting Traning Knowledge Based Process Table 2: Alternatives and their values Financial Custoer Internal business process Learning and growth Oracle Mediu Weak Very Strong Weak Sage Strong Mediu Strong Mediu MFG Mediu Strong Weak Mediu Table 3: Linguistic variables and their equal three paraeter interval grey nubers Very weak (0.0,0.1,0.2) Weak (0.2,0.3,0.4) Mediu (0.4,0.5,0.6) Strong (0.6,0.7,0.8) Very strong (0.8,0.9,1.0) 18

7 Int. J. Manag. Bus. Res., 3 (1), 13-20, Winter 2013 The weight of every aspect fro coalition values (table 4) was obtained by Shapley ethod. These weights were used as obective weights in Entropy ethod. Every aspect's weights were calculated by Entropy ethod (table 5). Then the alternatives ranks were gained by proection attribute function ethod. This is shown in table 6. CONCLUSION In this paper a new odel was presented for ERP software selection. Enterprise resource planning (ERP) software selection is known as a Table 4: Coalition values ulti-criteria decision aking (MCDM) proble. The proposed odel considers uncertainty with the use of three-paraeter interval grey nubers and a weighting hybrid odel. This hybrid odel that is the result of Shapley (cooperative gae) and Entropy cobination can reduce uncertainty that coes fro decision aking odel. Proection attribute function ethod is used in order to rank alternatives. This ethod is a new one that used over three-paraeter interval grey nubers. An industrial case study was presented at the end to show how this odel can work. V({})V({Financial})V({Custoer})V({Internal business process })V({Learning and growth })0 V({Financial,Custoer})0.15,V({Financial, Internal business process })0.18,V({Financial, Learning and growth })0.24,V({Custoer, Internal business process})0.22,v({custoer, Learning and growth})0.27,v({internal business process, Learning and growth })0.29 V({Financial, Custoer, Internal business process })0.35,V({Financial, Custoer, Learning and growth})0.43,v({financial, Internal business process, Learning and growth})0.47,v({custoer, Internal business process, Learning and growth})0.42 V({Financial, Custoer, Internal business process, Learning and growth})1 Table 5: Shapley weights and entropy weights Financial Custoer Internal business process Learning and growth Shapley Entropy Weights Table 6: Proection function values and rank of each alternative Alternative proection function value Rank Oracle Sage MFG

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