J A M S. Enterprise Resource Planning (ERP) System Selection Using a Novel Integrated Weight Decision Making Method. R. V. Rao 1* and B. K.

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1 Internatinal Jurnal f Advanced Manufacturing Systems Vlume 2 G Number 1 G January-June 2011 G pp Internatinal Science Press I J A M S Enterprise Resurce Planning (ERP) System Selectin Using a Nvel Integrated Weight Decisin Making Methd R. V. Ra 1* and B. K. Patel 1 Enterprise resurce planning (ERP) systems have becme imprtant part f industries as they enable industries t make their peratins and resurces mre efficient and integrate business data prcess. In rder t put an ERP prject successfully in service, it is necessary t select an ERP system which can meet the requirements f the industries. Thus, a decisin making apprach fr ERP system selectin requires bth industry requirements and characteristics f the ERP system and their interactins t be taken int accunt. This paper prpses a nvel integrated weight multiple attribute decisin making (MADM) methd fr ERP system selectin. Keywrds: Decisin making, Enterprise resurce planning system, Statistical variance, Objective weights, Subjective weights, Integrated weights, Fuzzy lgic. 1. INTRODUCTION THE rapid grwth f infrmatin and cmmunicatin technlgies has influenced all aspects f cmputing applicatins acrss rganizatins. At the same time, the business envirnment is becming increasingly cmplex with functinal units requiring mre and mre inter-functinal data flw fr decisin making, timely and efficient prcurement f prduct parts, management f inventry, human resurces and distributin f gds and services. T deal with these challenges, new sftware systems knwn as enterprise resurce planning (ERP) systems have emerged in the market targeting mainly large cmplex business rganizatins [1]. ERP is a system r sftware that is intended t manage all the infrmatin and functins f a cmpany frm shared data strages. Irrespective f the size f the cmpany, whether it is a multi-millin dllar cmpany r small business cmpany, the bjective f the system selectin is t get a system that can fully supprt the functinality f the business prcess and prvide significantly better prfit at the end f the day. ERP attempts t integrate all departments and functins acrss a cmpany nt a single cmputer system that can serve all thse different departments particular needs. ERP vanquishes the ld standalne cmputer systems in finance, human resurces, manufacturing and warehuse, then replacing them with a single unified sftware prgram divided int sftware mdules that rughly apprximate the ld 1 S. V. Natinal Institute f Technlgy, Surat, Gujarat , India (*Crrespnding authr:

2 22 Internatinal Jurnal f Advanced Manufacturing Systems (IJAMS) standalne systems. Finance, manufacturing, human resurces and the warehuse still get their wn sftware, except nw the sftware is linked tgether using the same database s that smene in finance can lk int the warehuse sftware t see if an rder has been shipped. Mst vendrs ERP system is flexible enugh that yu can install sme mdules withut buying the whle package. Many cmpanies, fr example, will just install an ERP finance r HR mdule and leave the rest f the functins fr anther day. Careful planning, right timing and methdlgy selectin is vital in ERP implementatin. With the right decisin, the cmpany will reap the benefit nt nly during the implementatin but als during the life f ERP system. Althugh implementing an ERP system may be cstly and time-cnsuming, its benefits are wrthwhile. Hwever, there are a number f examples where rganizatins have nt been successful in reaping the ptential benefits that mtivated them t make large investments in ERP implementatins. ERP adptin invlves initiating apprpriate business prcess changes as well as infrmatin technlgy changes t significantly enhance perfrmance, quality, csts, flexibility and respnsiveness. There is a grwing cnsensus amng ERP system implementers that selecting an inapprpriate system is a majr reasn fr ERP implementatin failure. Due t the cmplexity f the business envirnment and the diversity f ERP alternatives, ERP system selectin is a tedius and lengthy task. The selectin prcess fr determining the mst apprpriate ERP system amng a set f pssible alternatives available in the market depends n many attributes which affect its selectin. Hence, ERP system selectin is a multiple attribute decisin making (MADM) prblem. Many researchers in the past have attempted ERP system selectin using different techniques. Owing t its simplicity, the scring methd is ne f the mst ppular methds fr ERP system selectin [2]. Analytic hierarchy prcess (AHP) based appraches cnstitute a general class f anther cmmnly used IS selectin techniques. Teltumbde [3] prpsed a methdlgy based n the nminal grup technique and the AHP fr evaluating ERP systems. Wei and Wang [4] suggested a hierarchical attribute structure mdel t evaluate the ERP alternatives systematically by emplying an integratin mdel that uses the fuzzy average methd and fuzzy integral value ranking fr ERP system selectin cmbining data btained frm prfessinal studies with that surveyed frm interviews with vendrs. Fisher et al. [5] used DEA t analyze and cmpare the perfrmance f ERP packages. Hwever, the evaluatins are based n infrmatin prvided by ERP vendrs. Wei et al. [6] used the AHP t systematically cnstruct the bjectives f ERP selectin t supprt the business gals and strategies f an enterprise, identify the apprpriate attributes and set up a cnsistent evaluatin standard fr facilitating a grup decisin prcess. Lall and Teyarachakul [7] prvided a case study n hw DEA can be applied fr ERP perfrmance evaluatin based n the real crprate data reflecting the rganizatin s needs and requirements. Lin et al. [8] prvide a cmprehensive review f sftware selectin applicatins. Karsak and Ozgul [1] develped a decisin framewrk fr ERP system selectin based n quality functin deplyment (QFD), fuzzy linear regressin and zer ne gal prgramming.

3 ENTERPRISE RESOURCE PLANNING (ERO) SYSTEM SELECTION USING A NOVEL 23 The next sectin describes steps f a nvel integrated weight MADM methd fr ERP system selectin. 2. PROPOSED DECISION MAKING METHODOLOGY The methdlgy presented in this paper fr ERP system selectin in the manufacturing envirnment is described belw: Step 1: Preparatin f the decisin table. Table 1 Decisin Table in MADM Methds [9] Attributes Alternatives B 1 B 2 B 3 B m (w 1 ) (w 2 ) (w 3 ) ( ) ( ) (w m ) A 1 y 11 y 12 y 13 y 1m A 2 y 21 y 22 y 23 y 2m A 3 y 31 y 32 y 33 y 3m A n y n1 y n2 y n3 y nm Identify the selectin attributes fr the cnsidered decisin making prblem and shrt-list the alternatives n the basis f the identified attributes satisfying the requirements. The attributes are f tw types, beneficial (i.e. higher values are desired) and nn-beneficial (i.e. lwer values are desired). A quantitative r qualitative value r its range may be assigned t each identified attribute as a limiting value r threshld value fr its acceptance fr the cnsidered prblem. An alternative with each f its attribute, meeting the acceptance value, may be shrt-listed. After shrt-listing the alternatives and determining the values assciated with the attributes, a decisin table, similar t that given in Table 1 including the measures r values f all attributes (y ij ) fr the shrt-listed alternatives, can be prepared and the values f attributes can be nrmalized fr different alternatives using equatin (1). n y * ij = y ij / Σ y ij. Where, y * ij is the nrmalized value f y ij. (1) i=1 It may be added here that the abve equatin can deal with quantitative attributes. Hwever, there exists sme difficulty in the case f qualitative attributes (i.e. quantitative value is nt available). A ranked value judgment n a fuzzy cnversin scale is prpsed in this paper by using fuzzy set thery. This apprach is based n the wrk f Chen and Hwang [11]. The presented numerical apprximatin system systematically cnverts linguistic terms t their crrespnding fuzzy numbers. An eleven-pint scale is prpsed in this paper fr better understanding and representatin f the qualitative attribute (Table 2). Once a qualitative attribute is represented n a

4 24 Internatinal Jurnal f Advanced Manufacturing Systems (IJAMS) scale then the alternatives can be cmpared with each ther n this attribute in the same manner as that fr quantitative attributes. Step 2: Determinatin f weights f imprtance f the attributes The weights f relative imprtance f the attributes may be decided by the decisin maker fr the cnsidered applicatin either, (a) based n the data f the attributes fr varius alternatives given in the decisin table and nrmalized subsequently r (b) based n his/her subjective preferences n the attributes r (c) based n a cmbinatin f bjective weights and the subjective preferences. These three cases are explained belw. Table 2 Values f Selectin Attribute Qualitative measures f selectin attribute Fuzzy number Assigned crisp scre Exceptinally lw M Extremely lw M Very lw M Lw M Belw average M Average M Abve average M High M Very high M Extremely high M Exceptinally high M Step 2.1: Objective weights f imprtance f the attributes The statistical variance is prpsed in this paper fr determining the bjective weights f imprtance f the attributes using equatin (2). n V j = (1/n) Σ (y ij * (y ij * ) mean ) 2 (2) i=1 Where, V j is the variance f the data crrespnding t the j th attribute. (y ij* ) mean is the mean r average value f y ij*. The bjective weight f the j th attribute ( w j ) can be cmputed using equatin (3). m w j = V j / Σ V j (3) j=1 The statistical variance cncept f determining the bjective weights f the attributes is cmparatively simpler than the entrpy methd. Step 2.2: Subjective weights f imprtance f the attributes The weights f relative imprtance f the attributes may be assigned based n the decisin maker s preferences ver the attributes fr the cnsidered applicatin.

5 ENTERPRISE RESOURCE PLANNING (ERO) SYSTEM SELECTION USING A NOVEL 25 He/She may assign the weights f imprtance arbitrarily as per his/her preferences r may use any f the systematic methds f assigning relative imprtance such as analytic hierarchy prcess (AHP) methd, pints methd, digital lgic methd, etc. Step 2.3: Integrated weights f imprtance f the attributes If the decisin maker wishes t utilize bth the bjective and subjective weights f the attributes as described in steps 2.1 and 2.2, then he/she may use the integrated weights described by equatin (4). w j i = W O w j + W S w j s (4) Where, w ji is the integrated weight f j th attribute and W S and W O are the weightages given t the bjective and subjective weights respectively and the values f W S and W O are between 0 and 1. Step 3: Cmputatin f preference index The preference index fr each alternative indicates the scre r merit f the alternative with respect t the ther alternatives andcan be calculated using the fllwing equatins. m P i = Σ w j y ij ** (5) j=1 m P i s = Σ w j s y ij ** (6) j=1 m P i i = Σ w j i y ij ** (7) j=1 Where, y ij ** = [y ij *b /(y ij *b ) max ] fr beneficial attributes and [(y ij *nb ) min /y ij *nb ] fr nn-beneficial attributes. y ij *b and y ij *nb indicate the nrmalized values f the beneficial and nnbeneficial attributes, respectively. (y ij *b ) max indicates the maximum value f the j th beneficial attribute and (y ij *nb ) min indicates the minimum value f the j th nn-beneficial attribute. Equatin (5) can be used when the decisin maker wishes t use the bjective weights f the attributes; Equatin (6) can be used if he/she wishes t use the subjective weights f the attributes; and Equatin (7) can be used if he/she wishes t use the integrated weights f the attributes. The alternatives may be arranged in the descending rder f the preference index values. The alternative fr which the value f preference index is highest is the best chice fr the cnsidered decisin making prblem. Step 4: Final selectin A final decisin may be taken keeping in view the practical cnsideratins. All pssible cnstraints likely t be experienced by the user are lked in during this stage. These include cnstraints such as management cnstraints, scial cnstraints, plitical cnstraints, etc. Hwever, cmprmise may be made in favr f an alternative with a higher value f preference index.

6 26 Internatinal Jurnal f Advanced Manufacturing Systems (IJAMS) Table 3 Data f the ERP System Selectin Example [7] ERP system Cmplexity f Estimated cst f Functinal Vendr implementatin implementatin match (FM) prfile (CI) (ECI)($, millins) (VP) A B C D E F EXAMPLE: ERP SYSTEM SELECTION Lall and Teyarachakul [7] prvided a case study n hw DEA can be applied fr ERP perfrmance evaluatin based n the real crprate data reflecting the rganizatin s needs and requirements. The ERP system selectin prblem cnsiders fur selectin attributes and six ptential alternatives. The data f the attributes f ERP system selectin are given in Table 3. The cmplexity f implementatin (CI) and estimated cst f implementatin (ECI) are cnsidered as nn-beneficial attributes as lwer values are desire. Whereas, the ther tw attributes functinal match (FM) and vendr prfile (VP) are cnsidered as beneficial attributes. Nw, the prblem f ERP system selectin is slved using the nvel MADM methd described in sectin 2. Table 4 Nrmalized Data f the ERP System Selectin Attributes f the Example ERP system Cmplexity f Estimated cst f Functinal Vendr implementatin implementatin (ECI) match (FM) prfile (CI) ($, millins) (VP) A B C D E F Step 1: In the present wrk, the alternatives and the attributes cnsidered are same as in [7] and are shwn in Table 3. Here, all the attributes are expressed quantitatively. If any attribute is expressed qualitatively, equivalent ranked value judgements n fuzzy cnversin scale are made as shwn in Table 2. The values f the attributes fr the shrt-listed alternatives, given in Table 3, are nrmalized fr different alternatives using Equatin (1) and are given in Table 4. Step 2.1: The statistical variance prpsed in this paper (i.e., Equatin (2)) is used fr determining the bjective weights f imprtance f the attributes. The variance

7 ENTERPRISE RESOURCE PLANNING (ERO) SYSTEM SELECTION USING A NOVEL 27 values f the 6 attributes are cmputed using Equatin (2) as V CI = , V ECI = , V FM = and V VP = Objective weights f the attributes are cmputed using Equatin (3) as w CI = , w ECI = , w FM = and w VP = Step 2.2: All fur attributes are cnsidered equally imprtant in this case. Table 5 Rankings f Alternative ERP Systems f the Example Lall and Prpsed decisin making methd ERP Teyarachakul W S =1 W S =0.8 W S =0.6 W S =0.5 W S =0.4 W S =0.2 W S =0 system [7] using DEA W O =0 W O =0.2 W O =0.4 W O =0.5 W O =0.6 W O =0.8 W O =1 A B C D E F Step 2.3: The integrated weights f imprtance f the attributes are cmputed using Equatin (4) fr different weightages given t the bjective and subjective weights f the attributes. It may be mentined here that fr W S =0 and W O =1, the integrated weights are same as that f bjective weights f step 2.1 and fr W S =1 and W O =0, the integrated weights are same as that f subjective weights f step 2.2. Step 3: The preference index values are cmputed fr different alternatives using Equatins (5) t (7). The 6 alternative ERP systems are ranked based n the cmputed values f the preference index and the ranks are given in Table 5. Frm the results given in Table 5, it can be understd that usage f nly subjective weights f the attributes (i.e., W O =0 and W S =1 shwn in the last clumn f Table 5) leads t ERP systems ranking f E-B-A-C-D-F which suggests ERP system E as the first chice and F as the last chice. These results match well with the results presented by Lall and Teyarachakul [7] using DEA methd. Hwever, the DEA methd used by Lall and Teyarachakul [7] des nt cnsider the decisin makers perceptin. Hence the ranking suggested by Lall and Teyarachakul [7] using DEA methd is nt reliable. The prpsed methd gives an ptin t the decisin maker either t use nly subjective weights, r bjective weights, r integrated weights f imprtance f the attributes. Furthermre, the prpsed methd has a prvisin t quantify the qualitative attributes using fuzzy lgic, if such attributes are present in the prblem. Thus, the prpsed methd is mre lgical and has ranked the alternative ERP systems in few steps with simple calculatins. 4. CONCLUSIONS A suitable enterprise resurce planning (ERP) system can radically imprve the future cmpetitiveness and the market perfrmance f the industry. The ERP system selectin prblem is invlves multiple attributes and alternatives because a large number f

8 28 Internatinal Jurnal f Advanced Manufacturing Systems (IJAMS) ERP systems are available having their wn strengths and limitatins. The cmplexity f ERP systems and the diversity f alternatives make this critical selectin prcess cmplicated. A nvel integrated weight multiple attribute decisin making methd is prpsed in this paper t deal with the decisin making situatins f the ERP system selectin cnsidering bth qualitative and quantitative attributes. Statistical variance is suggested fr determining the bjective weights f imprtance f the attributes and AHP is suggested fr cnsidering the decisin maker s preferences n attributes. The prpsed methd helps the decisin maker in the selectin, based n either the bjective r subjective r integrated weights f imprtance f the attributes. The methdlgy develped in this paper can simultaneusly cnsider any number f quantitative and qualitative ERP system selectin attributes and a preference index can be btained which evaluates and ranks alternatives fr a given selectin prblem. Furthermre, a ranked value judgment n a fuzzy cnversin scale t represent the qualitative selectin attribute (i.e. quantitative value is nt available) is suggested in the paper. The methdlgy is lgical, simple and cnvenient t implement when cmpared t the ther MADM methds. Acknwledgement Authrs acknwledge the financial supprt prvided by Cuncil f Scientific and Industrial Research (CSIR), India in carrying ut the present research wrk. References [1] Karsak, E. E. and Ozgul, C. O. (2009), An Integrated Decisin Making Apprach fr ERP System Selectin. Expert Systems with Applicatins, 36, [2] Ptak, C. A. (2000), ERP Tls, Techniques, and Applicatins fr Integrating the Supply Chain. New Yrk: St Lucie Press. [3] Teltumbde, A. (2000), A Framewrk fr Evaluating ERP Prjects. Internatinal Jurnal f Prductin Research, 38, [4] Wei, C.-C. and Wang, M.-J. J. (2004), A Cmprehensive Framewrk fr Selecting an ERP System. Internatinal Jurnal f Prject Management, 22, [5] Fisher, D. M., Kiang, M. Y., Fisher, S. A. and Chi, R. T. (2004), Evaluating Mid-level ERP Sftware. Jurnal f Cmputer Infrmatin Systems, [6] Wei, C.-C., Chien, C.-F. and Wang, M.-J. J. (2005), An AHP-based Apprach t ERP System Selectin. Internatinal Jurnal f Prductin Ecnmics, 96, [7] Lall, V. and Teyarachakul, S. (2006), Enterprise Resurce Planning (ERP) System Selectin: A Data Envelpment Analysis (DEA) Apprach. Jurnal f Cmputer Infrmatin Systems, 47(1), [8] Lin, H.-Y., Hsu, P.-Y. and Sheen, G.-J. (2007), A Fuzzy-based Decisin Making Prcedure fr Data Warehuse System Selectin. Expert Systems with Applicatins, 32, [9] Ra, R.V. (2007), Decisin Making in the Manufacturing Envirnment Using Graph Thery and Fuzzy Multiple Attribute Decisin Making Methds. Springer-Verlag, Lndn.

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