Prediction Model for Characteristics of Implementation of Information Systems in Small and Medium Enterprises

Transcription

1 Predcton Model for Characterstcs of Implementaton of Informaton Systems n Small and Medum Enterprses I. Nazor, K. Fertalj, and D. Kalpc Abstract The process of choosng an Enterprse Resource Plannng (ERP) soluton for a Small and Medum Enterprse (SME) s often uncertan and not well defned. In the process of development of a comprehensve methodology for selecton of ERP for SME, a predcton model s desgned, wth the am of assstng buyers of ERP solutons by predctng certan characterstcs of the mplementaton process. All necessary elements for the predcton process have been desgned: parameter set, predcton model, onlne questonnares for experts and for the ERP buyer - clent, and a software tool. The tool collects, verfes and analyzes collected data and predcts relevant propertes of the new ERP mplementaton project, such as cost, duraton, ntensty of Busness Process Reengneerng (BPR) or ntensty of users tranng. The tool nteracts wth the clent n the form of an ntervew. After each answered queston, estmates for all propertes of the new project are recalculated, wth an ncreased precson, based on addtonal data. In order to make the ntervew process flexble, any queston n the ntervew can be skpped, or the ntervew aborted at any pont and the tool wll calculate the best possble predcton for the new project propertes based on the avalable nformaton. Index Terms ERP, mplementaton, knowledge base, project features predcton, SME. I. INTRODUCTION In today's world, the competton s generally ferce, especally n the area of Enterprse Resource Plannng systems (ERP). There are lterally thousands of ERP systems n the world, and even more companes that mplement them. Whle a potental buyer of an ERP wll obvously narrow the search to the locally avalable ERP systems and the ones compatble wth local busness practces and legal requrements, optons for a typcal Small and Medum Enterprse (SME) are usually stll numerous. A classcal approach to ERP system selecton descrbed n lterature, typcally conssts of these steps: ) Formng a team of experts, usually consstng of both nternal and external experts, n charge of defnng the goals and strategy of ERP mplementaton, project feasblty [] ) Assessment of the current stuaton, whch ncludes documentng busness processes and requrements for the new system, 3) Defnng the set of crtera for evaluaton of the new ERP soluton [], [], [4], [6], [8], [], [5], [8], [9], 4) Collectng offers from the vendors, 5) Evaluatng offers accordng to the gven crtera, []-[3], [7] and 6) Makng the fnal selecton. Manuscrpt receved September 6, ; revsed October 3.. I. Nazor s wth Unversty of Splt, Croata (e-mal: nazor@oss.unst.hr). K. Fertalj and D. Kalpc are wth Unversty of Zagreb, Croata ( e-mal: damr.kalpc@fer.hr). There are ample references n lterature on herarches of parameters descrbng propertes of companes buyng ERP []. Characterstcs descrbed comprse the level of nvolvement of the clent's key users [5], IT lteracy and flexblty of employees [6], level of nvolvement of management and ts effcency n dealng wth current problems [], [], clent's capacty of managng complex projects [7], [9], [5], organzatonal effcency and the level of BPR performed durng mplementaton []-[3], clent's negotatng strength, and mportant characterstcs of the vendor s company [5]. SME can be defned as a company havng between and 5 employees, or the annual ncome of less than 4M [3]. SMEs by default have lmted resources, both n terms of staff and fnances [5], there s usually a lack of knowledge needed to select and manage an ERP mplementaton project. When choosng a new ERP system, a typcal decson maker n SME, usually the owner or chef executve offcer s constraned wth tme, money, IT knowledge and nternal organzatonal support. As the result, purchase decson s often taken based on less relevant factors, whch are emphaszed durng ERP vendors' fancy presentatons. As a further consequence, the project of mplementaton of the new ERP system s often late, exceeds budget and fals to meet the customer's expectatons. Accordng to [4], 4% of software mplementatons n SMEs are never completed and % are consdered falures. Results of an onlne survey about satsfacton of small companes wth ther ERP system show a relatvely hgh level of dssatsfacton. Compared to ERP mplementaton projects n large enterprses, projects n SME are much smpler, depend on a smaller number of parameters, and are managed by less people. Furthermore, t s assumed that the manager of such project has a good overvew of all detals of the project, and hs knowledge s relatvely easy to collect n a formalzed and comparable way. The am of ths research s to assemble a pool of knowledge about past ERP projects and to formulate an algorthm that gudes the potental buyer through a seres of questons, provdng an nsght nto certan aspects of ther ERP project, whch becomes more precse wth each queston answered. Tools for selecton of ERP solutons are avalable as Internet servces, but they are not adapted to a typcal decson maker n a SME, who usually lacks nsght nto the problem and the knowledge to mpartally answer the seres of complex questons that are asked. Furthermore, solutons that have been analyzed, mostly focus on comparng features of dfferent ERP systems based on specfed customer requrements, wthout provdng a deeper nsght nto what the entre ERP mplementaton process would look lke. DOI:.7763/IJIMT..V

2 II. PREDICTION MODEL The frst research hypothess s that a typcal decson maker n a SME does not have enough knowledge and tme to make a precse specfcaton of hs or her needs, specfy the relevant crtera for an ERP soluton to meet, and make objectve evaluatons of the proposed alternatves. The second hypothess s that vast knowledge s exstng about mplementaton of ERP systems n SMEs, whch can be easly collected because a large number of such mplementatons have been already completed. The proposed method s based on the descrbed hypotheses, and conssts of three man steps: ) Defnton of herarches of parameters to store the collected knowledge, wth correspondng metrcs and domans of values. Determnng groups of parameters that hold specfc vews, contexts, relatons between contexts, as well as the approprate set of rules for nterpretaton of measurements across dfferent domans. ) Collecton of expert knowledge, va onlne questonnares. The man source of nformaton are consultants who have managed ERP projects n SMEs n the past. Ths knowledge s stored n the parameters of the context "Past ERP Projects". Addtonally, generc knowledge about mutual nfluence of dfferent propertes of ERP mplementaton projects (software, clent, mplementer s staff), vewed n the context of "Generc ERP knowledge" can be collected and used to determne weght factors n the smulaton phase. After the knowledge s gathered, statstcal processng (Fg.), s run to calculate the elements for regresson analyss. Predcton s run by calculatng ndvdual values of parameters n the "New Project Propertes" context, whch are lnearly combned partcular weghts. Four methods of determnng parameter weghts were nvestgated: assgnng equal weghts to all parameters, usng values obtaned from regresson analyss, nterpretng the results of the generc knowledge questonnare from the prevous step, and usng the number of measurements avalable for each parameter as ts weght coeffcent. NPP_ID NPP_Sesson_ID NPP_Value Global Param. ID Global Param. ID Stat. Param. ID Stat.Param.Value New ERP Project Predctons M Predctons Statstcal Parameters Global Param. ID PPP_Value N. M Name Input Set PPP_ID Param. X ID Param Y ID Global Parameter Lst Past ERP Projects Knowledge Base N Calculated from Param Y ID Param. X ID Weghts from r^ EBP_ID EBP_Sesson_ID EBP_Value Global Param. ID Global Param. ID Expert Evaluaton. Global Param.ID Global Param.ID Global Param. ID Global Param. ID Fg.. E-R dagram of the conceptual data model used for predcton N Wj ERP Buyer Parameters Generc ERP Knowledge Knowledge Base Weghts from Expert Knowledge Parameter Weghts 3) Intervew sesson wth the potental ERP buyer, where the ntervewee s asked to grade certan characterstc of hs company. The questons are amed at determnng the level of potental buyer s motvaton, fnancal strength, and organzatonal maturty. The results are compared aganst the knowledge stored n the database usng elements of regresson analyss. The process of predctng elements of a future ERP project s run after every answer receved from the clent, wth ncreased precson as the ntervew progresses. III. PARAMETER SET Parameter descrbes a property of an ERP functonalty, a characterstc of clent's company or property of an ERP mplementaton project. Attrbutes of a parameter are: name, global dentfer, one or more context-related dentfers, nput set and the mappng functon. Context s a specfc vew on a parameter, dependng on the purpose for whch t s used. There are four contexts: Past Project Propertes, contanng formalzed expert knowledge, Dependences, used for collectng generc expert knowledge on ERP mplementatons, and Buyer s Company Propertes, contanng characterstcs of the clent s company and ther plans regardng the new ERP system, collected durng the predcton sesson va an ntervew, and the context New Project Propertes contans the fnal result of the smulaton process,.e. the predctons of sgnfcant propertes of the new ERP mplementaton project. Dfferent contexts are shown as enttes n the dagram n Fg.. In the proposed model there are 9 parameters, observed n four dfferent contexts. Pror to processng, all measurements are mapped to a real subset [,], by means of the mappng functon B : C,, () where A s a parameter s nput set, and C s the real nterval [, ], scaled for calculatons. In case where the nput set A s a lngustc varable wth a fnte set of n lngustc values, the nterval [,] s dvded nto n- equal sectons, and the nput values are mapped to the approprate value n the [,] set, wth the frst element mapped to, and the n th to. As an example, the lngustc varable wth elements: "very low", "low", "medum", "hgh", and "very hgh" would be mapped to values,.5,.5,.75, and, respectvely. When mappng of lngustc varables, one must dfferentate between two knds, where A denotes an ascendng seres, a a,a A and when ts elements are not strctly arranged n ascendng order. The frst case s Lkert scale varables ("very low", "low", "medum", "hgh", and "very hgh"), where "very low" obvously means less then "low". In ths case, the mappng rules for A are the same as for a contnuous set of numercal values. The other possblty s that a varable wth a number of lngustc terms s used for classfcaton, where one cannot compare two values n terms of magntude. An example would be classfcaton accordng to busness operatons, wth possble values: "trade", "manufacturng", "other". In ths case, a dfferent approach should be consdered, 74

3 possbly usng bnary varables. Classfcaton varables are planned for use n the extended verson of the descrbed methodology, and at that tme, the correct approach would be devsed. In the case of standard lnear mappng, f A s an ascendng seres or a contnuous set of numercal values, and B s a lnear mappng functon B wthn the two bounds, ther functonal dependency s: A Lbound A; Lbound A Hbound Hbound Lbound B( A) ; A Lbound () ; A Hbound Hbound and Lbound are upper and lower values for whch the functon s lnear. It was often observed that the sgnfcance of values could not be well explaned only by a lnear functon, so an addtonal mappng opton was ntroduced. An ascendng seres of steps S s ntroduced between Lbound and Hbound wth B(S ) dstrbutng mappngs evenly throughout the range C. S R, S S ;,,.., k; k N (3) B( S ) ; Lbound S Hbound (4) n Therefore, the frst opton of mappng functon () s merely a specal case of (3), wth =. In an example shown on Fg., the parameter P39, "Average age of employees", has numerc set A, Lbound 3, and Hbound 6. Therefore, any age between 3 and 6 s mapped lnearly. On the other hand, the parameter P, "Increase / Shortenng of planned mplementaton tme", n addton to Lbound = and Hbound = 8, has steps 3, 6, 5. Fg.. Lnear mappng functon wthout and wth steps In practce, mappng wth steps would mean that an ncrease n varance of mplementaton tme between and 3% has a stronger mpact than the same ncrease, but after an already exstng delay of 6%. For the purpose of nterpretaton of the results, the results of calculatons are mapped back from the set [, ] nto the orgnal nput set. Mappng from real set [,] back nto the lngustc set s done by fndng the closest step value, and mappng t nto the correspondng lngustc value. For example, the value of.4 s mapped to "low" because ts closest step value n the [,] set s.5. As an example, the parameter "Average age of employees" has global dentfer 39. It s tagged "E" n the "New Project Propertes" context, and "K6" n the Buyer s Company Propertes context. Its set of nput values s numerc, and the value denotes a person s age. Its mappng functon s lnear. Parameter P5, "Level of BPR durng mplementaton" has lngustc nput set wth possble values: none, low, medum, hgh, very hgh. If the result obtaned after calculaton s,.95, value would be used as the nput value for mappng to the lngustc set, because.95 - < The lngustc value equvalent of s very hgh. If one vews value mappng n terms of Fuzzy set theory, n ths case trangular Fuzzy membershp functon s used. In a dfferent approach, any other type of Fuzzy membershp functon could be used. At ths tme, however, the sutablty of other membershp functons has not been nvestgated, whch s left for further research. IV. KNOWLEDGE ACQUISITION The dea s to formalze the expert knowledge about the mplementaton of ERP systems. There are two sources of expert knowledge, ntervews wth leaders of specfc ERP projects n SME and ntervews collectng generc knowledge about dependences between ERP mplementaton crtcal success factors. In both cases, the knowledge s gathered va onlne questonnares, whch store responses nto an onlne spreadsheet document. The raw data s later pasted nto an Excel sheet wth added functonalty for data parsng, data nterpretaton, and runnng smulatons. The frst questonnare collects data n the context Past ERP Projects, that contans 59 parameters. Durng each sesson, the expert answers questons about a sngle ERP mplementaton project. The questonnare s dvded nto 5 sectons: General nformaton, Quotaton phase, Implementaton process, End of mplementaton, and Clent's staff and organzaton. Sectons descrbe dfferent phases of an mplementaton project, (before, durng, and after the mplementaton) and certan organzatonal and personnel characterstcs of the clent's company. Some questons ask for descrptve answers, whch are not numercally processed later, whle most of them requre ether a number or a lngustc varable as an answer, and are nterpreted n a formalzed way. Questons n the Quotaton phase assess how well the elements of the project were estmated n the quotaton, ncludng the project's duraton, hours of staff tranng, overall costs, and project's revenue. 74

4 Assumpton based on the researcher s experence from managng ERP mplementaton projects s that, n the ntal phase of mplementaton, an ntense drect contact s essental between mplementer s staff and key users from all nterested unts n the clent s organzaton. Reducng ths contact or replacng t wth alternatves such as teleconferencng, remote access, e-mal, phone calls, chat etc., may result n problems. The key players may not get acquanted personally wth the mplementer's staff, the need to communcate well between each other and between departments on all levels may not be clarfed enough, makng a poor foundaton for resolvng ssues that may arse later n the mplementaton process. Questons n the Implementaton phase deal wth such characterstcs as physcal dstance between people and departments n the organzaton and the dstance the mplementer s staff needs to travel to reach the clent's company. The questons n the End of mplementaton secton determne the accuracy of estmates of project's scope and sze (duraton, number of lcenses, prce), outsde problems that may have had an mpact on the project flow (fnancal ssues, trackng of work performed, ssues when completng the project), as well as the effcency of usng remote access for dealng wth clent's staff. Surprsngly, the parameters that ndrectly descrbe nternal effcency of the mplementer have receved relatvely hgh negatve grades. The last secton, Characterstcs of clents organzaton and staff, classfes clent's busness operatons, evaluates certan organzatonal characterstcs (process maturty wth questons usng a very smplfed Capablty Maturty Model - CMM questonnare, effcency of organzatonal structure, level of nterdepartmental communcaton), effcency of management's resolvng ssues durng mplementaton, and some characterstcs of employees (age, IT lteracy, experence, flexblty, speed to adopt new sklls). The second questonnare, "General ERP mplementaton knowledge", deals wth the parameters n the Dependences context. It s amed at collectng generc expert knowledge, not related to a sngle mplementaton. Dependences between 9 parameters are evaluated, by means of an ascendng seres of values, rangng from ( nsgnfcant ) to 9 ( extremely sgnfcant ). Questons are amed at fndng out how much a certan characterstc of a potental ERP buyer nfluences the outcome of the entre mplementaton. One can argue that that ths approach s mprecse, and that a more detaled questonnare should be used, gradng nfluence of the set of characterstcs of a potental customer to the full set of requred characterstcs of an mplementaton, n all possble combnatons, and not just one "outcome of mplementaton". An mprovement of ths model, probably usng a full Analytcal Herarchy Process (AHP) evaluaton s consdered n future work. context New Project Propertes. Wth each new answer, the smulaton should gve a more precse estmate of all of the expected propertes of the new ERP project. Smulaton Expert past project panager Expert - generalst Generated predctons Past projects Generc knowledge ERP Buyer Parameters Predctns New ERP project Past ERP projects knowledge base Parameter weghts Statstcal parameters Buyer's answers Predcton Onlne questonnare past projects parameters Onlne questonnare generc expert knowledge Queston / Response Generator Graphcal vew Calculated weghts r, r, alpha, beta, sgma Predcton Answers to questons Graphcal vew Statstcal processng Generc ERP mpl. knowledge Generc ERP mplemetaton knowledge base Fg. 3. Data flow dagram of the predcton model ERP buyer Analyst Choce of evaluaton method Predctons are calculated usng parameters obtaned through regresson analyss, whch attempts to fnd a functon that best descrbes the dependence between two statstcal varables, based on ther measured values. Input values for the regresson analyss are taken from the Past Project Parameters knowledge base. The frst step s to extract the measurements for the parameters analyzed. A graphcal dsplay of ths s shown by dots on X-Y graph on Fg. 4. The next step s to determne the nature of the functon that wll be used to approxmate the measured values. Usual optons are a lnear functon or a hgher-level polynomal. The requrement on the functon s that t should pass at a smallest possble squared dstance from all the ponts on the graph (Fg. 4). Once obtaned ts parameters, the regresson functon can be used to predct the value of varable Y when the value of X s known. Fg. 4 shows an example of the regresson functon for the parameters P4 Level of resstance of employees to change from the context Buyer s Company Propertes, on X axs, and P Extenson / shortenng of planned mplementaton tme, from context New Project Propertes, as a dependent varable, on Y axs. V. SIMULATION Smulaton s performed by means of an ntervew, where the clent answers questons n a questonnare, formed from the parameters n the context Buyer s Company Propertes. Each answer provdes an nput value for the predcton model, whch then values for the varables n the Fg. 4. Lnear regresson functon example 743

5 Lnear regresson functon, F( x) x was used n ths case, wth the formula, wth β as the lne slope and α as ntercept. In ths example β =.577 and α =.5. The nput set of P4 s a lngustc varable wth fve possble values: very low, low, medum, hgh, and very hgh. P has a numerc nput set, and a stepped lnear mappng functon wth Lbound 3, Hbound 8, S 6 and S 5. If the clent estmates the Level of Resstance of Employees n ther company to medum, t s mapped to value.5. By usng x.5, F( x).577 x. 5, the projected value s.895, whch, when mapped nto the nput set of the parameter Extenson / shortenng of mplementaton tme equals 33.7%. Hence, for a medum level of employees resstance to change, the project s expected to be prolonged by 33.7%. Coeffcent of correlaton, r, determnes how precsely the functon of regresson descrbes the relatonshp between two varables. Value of r vares between - and, and closer ts absolute value s to, the better t approxmates measured values. In an deal case of r, all the measured values (dots on X-Y graph) would be lad exactly on the regresson functon, the two varables would have a functonal dependence, and one could make predctons wth hgh certanty. In the example shown on Fg. 4, the coeffcent of correlaton s relatvely low,.74, snce, as can be seen on the graph, the measurements are relatvely far from the lne of regresson. Usng the descrbed method, and based on known value of one parameter, (.e. one answer from the clent) one can make predctons for all the parameters, whch are statstcally related to the known parameter. In ths example, the nformaton from the buyer that the value of parameter P4 s medum s enough to make predctons on most of parameters n the New Project Propertes context. Naturally, one must consder the fact that the accuracy of predcton of each parameter depends on the correlaton coeffcent between two varables n queston. The smulaton process s performed on two parameter sets (contexts): New Project Propertes (P), and Buyer s Company Propertes (B). After k th teraton of queston and answer, the value of j th parameter n the set P s: p j n k j p P, b B, n N, m N j w ( b ) j k where m and n are counts of elements n sets P and B. Answer to each queston should bear a certan weght n the total value of the estmated varable. w j s the weght of th projecton n the resultng value of j th element of P. In order to normalze the total value to the range [,], t s dvded wth the sum of weghts for p j over. j w j j (5) Snce all values are normalzed before calculaton, the resultng y j s also n the nterval [, ]. After calculaton, the result s mapped nto the nput set usng each parameter's mappng functon. Varous methods of determnng weght factor w j are consdered: Assgnng equal weghts to all answers, ; m, j n w j Usng coeffcent of determnaton r between x and y as weght factor, adjusted for the sum of weghts over all varables x, xy r l ( xk x) ( yk y) k ; ( xk x) ( yk y) l N Determnng weghts between parameters by gatherng expert knowledge, as descrbed n the secton "Knowledge Acquston". Takng nto consderaton the number of measurements used for calculatng statstcal values. Snce the questonnare s flexble, allowng respondents not to provde an answer, t often occurs that a relaton between two varables s calculated on the bass of a small number of measurements n, whch ntutvely leads to a less relable measure. Ths could be taken nto account when calculatng w j, by multplyng t wth rato n/n max. Durng testng of the model s precson, ths led to a small ncrease n precson. VI. CONCLUSIONS As t s very often the case whle collectng large amount of nformaton from humans, avalablty of respondents s the man bottleneck. When creatng the data collecton methodology and the parameter set, conflctng demands need to be addressed: The clent needs precse answers, smply and quckly, t s dffcult to predct ther areas of expertse, and whch of the nformaton provded s the most relevant for the knowledge database. The knowledge database needs to address many detals on many topcs, n order to provde relevant connectons between the buyer s answers and the knowledge stored. Fllng an extremely detaled knowledge base would be a bg challenge, due to unavalablty of experts, and the tme they would be wllng to spend answerng questons. Furthermore, many questons are not answered for varous reasons, be t senstvty of nformaton asked, or lack of tme or knowledge. The proposed parameter set s an attempt to mnmze all of the problems mentoned. Durng research, many parameter sets from the avalable lterature were consdered, and those that would be most understandable to an unsklled end user, whle beng easy and straghtforward to estmate for experts, were chosen. As the collecton of data from experts s stll n progress, the model s precson s ncreasng wth more data collected. At ths stage, the am s to valdate the model, select the rght (6) 744

6 smulaton methodology, and eventually make correctons to the parameter set, whle retanng the mentoned propertes of understandablty and ease of answerng. VII. FUTURE WORK The precson of estmates of the proposed methodology remans to be tested wth more expert knowledge. At ths stage, the am s to valdate the model, select the rght smulaton methodology, and eventually make correctons to the parameter. It s estmated that at least questonnares about specfc ERP projects, and at least generc-knowledge questonnares need to be collected n order to stablze the regresson parameters. Then t wll be possble to make a better judgment as to whch method of determnng weght factors, and whch regresson functon gve the best results. When the knowledge base ncreases, t wll make sense to classfy the projects nto groups accordng to dfferent crtera, such as ther ndustral actvty, number of employees or geographcal locaton. The buyer s company wll then be compared aganst the closest-matchng group n the knowledge base, and addtonal parameters, such as "ERP system mplemented n the closest-matchng company". Havng a detaled classfcaton on a small number of responses would decrease the number of avalable measurements below a statstcally relevant number. Another way of ncreasng the relevance of the knowledge stored n the knowledge base could be to classfy expert s answers accordng to the overall success of the project that they were managng. Metrcs for the successfulness of a project would be devsed, and the grade obtaned could act as weght factor for all parameter s grades n a partcular evaluaton. Parameters lke project schedule delay, budget cost overrun, level of BPR durng mplementaton or mprovement of process maturty durng mplementaton can be consdered as a measure of ERP project s success. REFERENCES [] K. 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Rppa, A Methodologcal Proposal to Access the Feasblty of ERP Systems Implementaton Strateges, n Proc. the 4 st Hawa Internatonal Conference on System Scences, 8. [3] J. Razm and M. S. Sangar, A Hybrd Mult-Crtera Decson Makng Model for ERP System Selecton, IEEE ICIAFS8, pp , 8. [4] S. C. L. Koh, A. Gunasekaran, and J. R. Cooper, The Demand for Tranng and Consultancy Investment n SME-Specfc ERP Systems Implementaton and Operaton, Internatonal Journal of Producton Economcs, vol., pp. 4-54, 9. [5] D. Alon, R. Dulmn, and V. Mnnno, Rsk Management n ERP Project Introducton: Revew of the Lterature, Journal of Informaton and Management, vol. 44, pp , 7. [6] J. E. Scott and S. Walczak, Cogntve Engagement wth a Multmeda ERP Tranng Tool: Assessng Computer Self-effcacy and Technology Acceptance, Journal of Informaton & Management, vol. 46, pp. -3, 9. [7] C. Lang and Q. L, Enterprse nformaton system project selecton wth regard to BOCR, Internatonal Journal of Project Management, vol. 6, pp. 8-8, 8. [8] A. Jakupovc, M. Pavlc, and K. Fertalj, Analyss and classfcaton of ERP producers by Busness Operatons, Journal of Computng and Informaton Technology, vol. 7, pp , 9. [9] J. Hendler, H. Ktano, and B. Nebel, Handbook of knowledge representaton, Unted Kngdom, 8. [] A. Deep, P.Guttrdge, S. Dana, and N. Burns, Investgatng Factors Affectng ERP Selecton n Made-to-Order SME Sector, Journal of Manufacturng Technology Management, vol. 9, no. 4, pp , 8 [] P. Hawkng and A. Sten, Revstng ERP Systems: Beneft Realzaton, n Proc. the 37th Hawa Internatonal Conference on System Scences, 4. [] A. S. Jadhav and R. M. Sonar, Evaluatng and Selectng Software packages: a revew, Informaton and Software Technology Journal, vol. 5, pp , 9. Igor Nazor was born on March 8, 97 n Splt, Croata. He s currently attendng doctoral study n appled computng, researchng the mplementaton of nformaton systems n small and medum enterprses. The Bachelor of Scences degree was obtaned at Unversty of Zagreb, Faculty of Electrcal Engneerng and Computng, Zagreb, Croata, n 997. He currently holds the poston of Lecturer at the Unversty of Splt, Croata, at the Department for Professonal Studes. Prevously held postons nclude: Sales manager, Marketng manager, Dstrbutor Channel manager, and IT manager wth local and nternatonal companes based n Croata. As a part of doctoral research, he publshed the artcle on dealng wth restructurng SMEs durng mplementaton of ERP systems. Kresmr Fertalj s a full professor at the Department of Appled Computng at the Faculty of Electrcal Engneerng and Computng, Unversty of Zagreb. Currently he lectures a couple of computng courses on undergraduate, graduate and doctoral studes. Hs professonal and scentfc nterest s n computeraded software engneerng, complex nformaton systems and n project management. He partcpated n a number of nformaton system desgns, mplementatons and evaluatons. Fertalj s member of ACM, IEEE, PMI, and Croatan Academy of Engneerng. 745

7 Damr Kalpc graduated at the Faculty of Electrcal Engneerng, Unversty of Zagreb n 97, acheved hs master degree n 974 and PhD degree n 98. He started hs professonal career as assstant at the Faculty of Electrcal Engneerng. Nowadays he s full professor wth tenure at the Department of Appled Computng of the Faculty of Electrcal Engneerng and Computng, Unversty of Zagreb, Croata. At the Faculty, he performed the dutes of chef executve offcer, dean s ade, vce-dean for scentfc actvtes and head of department. Professonally, he desgns and supports the development of nformaton systems, and apples operatonal research n busness, state admnstraton and dfferent servces. He lectures algorthms and data structures and operatonal research. He was leadng tens of practcal projects, advsed hundreds of graduates, tens masters of scence and mentored ten defended doctoral theses. He authored or co-authored journal artcles, delvered nvted lectures, and publshed papers n proceedngs of nternatonal conferences. He can communcate, n descendng fluency, n Englsh, German, Italan, Spansh, French and Portuguese. He was awarded wth Gold medal "Josp Loncar", what s regarded as the hghest recognton at hs nsttuton. 746