THE ROLE OF BUSINESS INTELLIGENCE IN DECISION PROCESS MODELING

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1 EJAE 2015, 12(2): ISSN UDK: DOI: /ejae Origial paper/origiali auči rad THE ROLE OF BUSINESS INTELLIGENCE IN DECISION PROCESS MODELING Višja Istrat 1 *, Saja Staisavljev 2, Brako Markoski 2 1 Faculty of Orgaizatioal Scieces, Uiversity of Belgrade, Serbia 2 Faculty of Techical Scieces Mihajlo Pupi, Uiversity of Novi Sad, Zrejai, Serbia Abstract: Decisio makig is a very sigificat ad complex fuctio of maagemet that requires methods ad techiques that simplify the process of choosig the best alterative. I moder busiess, the challege for maagers is to fid the alteratives for improvig the decisio-makig process. Decisios directly affect profit geeratio ad positioig of the c ompay i the market. It is well-kow that people dealt with the pheomeo of decisio makig i each phase of the developmet of society, which has triggered the eed to lear more about this process. The mai cotributio of this paper is to show the sigificace of busiess itelligece tools ad techiques as support to the decisio makig process of maagers. Research results have show that busiess itelligece plays a eormous role i moder decisio process modelig. Key words: busiess itelligece, decisio-makig, decisio. INTRODUCTION Decisio makig is the selectio of oe of the offered alteratives. It is a extremely complex process that should result i makig the right maagemet decisios ad thus, it is essetial to cotiuously explore ad improve moder decisio makig methods ad techiques. There is o ideal solutio for the specified problem. Therefore, it should be orieted towards fidig the most optimal solutio. The maager should be coversat with the basic theory of the decisio-makig process ad should possess ecessary experiece to make busiess decisios that will result i maximum profit. There are three dimesios that determie full developmet of this disciplie: qualitative, quatitative ad iformatio ad commuicatio aspects. These three aspects of the decisio-makig process fully satisfy all cocepts of developmet of moder decisio makig o both theoretical ad practical level. Quatitative approach to moder decisio makig is defied as the basic formalism of the decisio makig problem (Suković & Delibašić, 2010). Accordig to (Suković, 2001), the decisio makig problem is cetered aroud the five items (A, X, F, Θ, ) where: A: fial group of available alteratives (actios) that the sessio participat raks with the goal of selectig the most suitable; X: group of possible results that follow as a cosequece of the alterative selectio; Θ: group of the state of the facts, depeds o the ukow state θ Θ, because the cosequeces of the selectio of the alterative a A ca be differet; F: A x Θ X determie for each state of the fact v ad for each alterative a, resultig i cosequece x = F (a,ϖ) : relatio of weak ifluece to X, i.e. biary relatio that satisfies the followig coditios: (i) x y or y x, x, y X (ii) Is trasitive, x y, y z x z. Relatio determies the decisio maker ad is called the relatio of the preferece. Strog preferece x y, meas that x y, but ot y z. Relatio of i- 44 * visja.istrat@gmail.com

2 differece x ~ y meas that it is valid x y ad y z. The most frequet solutio to the decisio makig problems is i trasformatio of weak item to X i usual item over real umbers with the fuctio of utility (Cetre for busiess decisio makig, 2015). I the coditios of geeral problem of decisio makig, it is assumed that the state of the facts v is kow, X is multidimesioal ad kow for each alterative as a group of relevat values of the attribute. Based o the level ad degree of complexity of the problem, ad the umber of participats (Suković & Delibašić, 2010), moder decisio makig as a huma pheomeo is split ito the followig categories: Idividual decisio makig. This decisio makig form is the most simplified ad most ofte explaied ad used, which is cofirmed by umerous refereces. Activities of the decisio makig process are dedicated to oe decisio maker. Group decisio makig team work. It is described by the higher level of the phases ad activities of the decisio makig process. Greater umber of participats (of the sessio) is ivolved i the selectio of the most optimum alterative. It cotais sigificat methods, models ad up-to-date tools to support group decisio makig. Orgaisatioal decisio makig. This form of decisio makig is described by the high level of ustructured problems the orgaizatios face. This type of decisio makig is followed by umerous research experimets, but it is ot yet come to systematical kowledge that would sigificatly improve it. Metaorgaizatioal decisio makig. It is the highest level of the applicatio of systematical kowledge i the field of decisio makig. More iformatio o kowledge discovery ca be see i (Guruler et al., 2010). It is practically oted at the level of oe coutry (state), ow atioal iterest through social well-beig, culture, icome, etc. (Suković & Delibašić, 2010). The result of successful decisio makig process is gettig the most optimal decisio for the defied topic, because there is o perfect solutio. Decisio is the fial outcome of the decisio makig process, most ofte choosig the most acceptable alterative from the group. The process should be followed by a ituitio, strog aalytical tools ad logical thikig. The selectio of the alterative is possible by decisio makig techiques, decisio makig rules or skills. Accordig to (Suković & Delibašić, 2010), the decisio makig momet is the most creative ad most critical momet i the etire decisio makig process. DEVELOPMENT OF THE BUSINESS DECISION MAKING MODEL Data miig is the research ad aalysis of large amouts of data with the aim to reveal sigificat patters ad rules. I order to icrease productivity of moder compaies, the goal is to improve the orgaizatio's fuctios through better uderstadig of customers. More iformatio o customer value maagemet ca be see i (Verhoef et al., 2007). Data miig techiques ad tools have various fields of applicatio law, astroomy, medicie, idustry, etc. Not eve oe data miig algorithm has bee firstly itroduced for commercial purposes. More iformatio o data miig strategy ca be foud i (Horick et al., 2007). The selectio of certai combiatio of techiques that will be used i certai situatio depeds o the ature of research, available data ad skills ad customer prefereces. More iformatio o data ca be see i (Verhoef et al., 2010). Data miig ca be direct ad idirect. Direct data miig explais or categorizes certai fields, like fiacial icome, whereas idirect data miig tries to fid the patters or similarities betwee target groups of data with o use of certai field or collectio or predefied classes. Data miig is mostly orieted towards defiig the model. The model is simply a algorithm or a set of rules that coect the collectios of iput (iput elemets) with certai goal or output elemets. Regressio, eural etworks, decisio makig trees ad most other techiques are orieted towards model creatio. Uder the right circumstaces, model ca result i the explaatio o how output elemets of certai iterest, like the precise order or usuccessful bill payig, are coected ad ca be predicted usig the available facts. Models are also used to get the results. The result is the way of expressig the fidigs of the model by umbers. Results ca be used to sort the list of customers from the most to least loyal, willig or ot to cooperate or to pay back the loa. Data miig process is also called kowledge discovery or kowledge discovery i Databases. Oe of the syoyms is also kowledge creatio. 45

3 May assigmets related to itellectual, ecoomic ad busiess fields that will be solved by data miig ca be divided ito six categories: Classificatio Estimatio Predictio Associatio rules Clusterig Descriptio ad profilig The first three types are the descriptio of direct data miig, where the goal is to fid certai target variables. Associatio rules ad clusterig are idirect assigmets where the goal is to fid the data structure, regardless of the variables value. Profilig is the descriptive assigmet that ca be direct or idirect. The assigmet of the associatio rules is to determie which items go together. A typical example is groupig the items that ca be bought together i shoppig at a supermarket market basket aalysis. Supermarket chais use associatio rules to pla the timetable of items o the shelves or i the catalogue so that the items that ca be bought together are most ofte together o the shelves. They are used to idetify chaces for cross-sellig ad desigig of attractive packagig or groupig of products or services. It is a simple approach o how to create the rules from large databases. If two items, i.e. computer ad web camera are ofte bought together, we ca produce two associatio rules: Buyers that buy computer ca also buy web camera with probability of P1 Buyers that buy web camera ca also buy computer with probability of P2 The assigmets ad the process of data miig ca be differet. Depedig o data miig results, the assigmets ca be: Exploratory data aalysis: i databases large amouts of data are at disposal. This data miig assigmet has two purposes: search of kowledge that customer demads ad data aalysis. These techiques are iteractive ad visual for the customers. Descriptive modelig: describes all data, icludig models for the whole probability of data distributio, split of multidimesioal space ito groups ad models that describe the relatios betwee variables. Predictive modelig: this model allows predictio of values of some variables more tha kow values of other variables. Data miig: this assigmet has bee used to fid the hidde patters ad the rules i cluster. There are differet sizes at disposal i clusters. The goal of this is to reveal "how it is best to fid the rules". This ca be achieved by usig the rules for iductio ad may other techiques i data miig algorithms. This is called clusterig of algorithms. Research of the cotet: the primary goal of this assigmet is fidig the data set that is ofte used for audio/video tasks, as well as photos. Such data miig is similar to those that are of iterest i dataset. The most frequetly used data miig techiques are: Decisio makig trees ad their rules, No-liear regressios ad classificatio methods, Methods based o examples, Models of graphical depedece of the probability, Relatioal models of learig, etc. I geeral, we classify these data miig methods as: Olie aalytical processig (OLAP), classificatio, clusterig, associatio rules, temporary data miig, time-sheet aalysis, space research, web research, etc. More iformatio o classificatio ca be foud i (Grabmeier & Lambe, 2007). These methods use differet types of algorithms ad data. The source of data ca be data warehouse, database, regular or textual file. Algorithms ca be statistical, based o the decisio makig tree, eural etworks, geeric algorithms, etc. I geeral, data miig algorithms are completely depedet o two factors: type of datasets used demads of customers Kowledge Discovery Process icludes raw data of choosig data miig algorithm, as well as processig of data results. More iformatio o data miig models ca be see i (Johasso et al., 2004). There are programs that are called assistats for kowledge discovery (Itelliget Discovery Assistats - IDA), that help i the applicatio of valid kowledge i the research process. These assistats provide three beefits to the users: Systematic validatio of process for kowledge discovery, 46

4 Effective rakig of valid processes o differet criteria, that help i choosig the suitable optio ad Ifrastructure for kowledge sharig, that leads to exteralizatio of etworks. There have bee attempts to make this process automatic ad desig the geeral tool for data miig that uses busiess itelligece while selectig data ad data miig algorithms, ad to a certai extet, kowledge discovery. Kowledge maagemet ad data miig are still i the developmet phase ad they represet iterestig areas for researchers. Although there is a itegrative framework for kowledge maagemet i the cotext of marketig, there are critical research challeges that should be devoted cosiderable attetio. More iformatio about data miig for marketig ca be see i (Berry & Lioff, 2004). Some of them are coected to data miig techiques ad kowledge discovery process, while others are related to kowledge maagemet. Data research through data miig techiques is a iteractive process of learig similar to other processes of acquirig kowledge, like scietific research. Selectio of data miig algorithms, hypothesis formig, model evaluatio ad remodelig are the key compoets of the research process. Sice the cycle of attempts ad failures for progressive adoptig are made of the most valuable kowledge through data miig, the aspect of learig through experimets ca be suitable for that. Oe of the research challeges is to make sure that this process is multi-structured, ad therefore to icrease the productivity of data miig trials. Furthermore, it is eeded to maage the kowledge i the sese that it outlies orgaizatioal borders ad further distributes towards the other parters. Aother challege is multiple classificatios whe the customer belogs to more tha oe category. There is the case of web miig.the Iteret is gettig the primate as a ew chael for the goods distributio, product promotio, trasactio maagemet ad coordiatio of busiess processes ad it becomes a valuable ad suitable source of data about customers. More iformatio o maagemet ca be see i (Draker, 2003). However, multiple formats of data ad distributive ature of kowledge o the Web are the challege for collectig, revealig, orgaizig ad kowledge maagemet i the way that is suitable for providig support to busiess decisio makig. More iformatio o decisio support system ca be see i (Kotsiatis, 2011). GROUPS OF DATA THAT WILL BE TESTED There is the table with three types of data from differet sources. Those data are the processed ad real ad importat iformatio for the defied problem is obtaied. By applicatio of associatio rules, kowledge is the fial outcome of the decisio makig process. The structure ad size of research data, as well as the proper use of busiess itelligece tools ifluece the creatio of the improved model of busiess decisio makig. More iformatio o busiess itelligece ca be foud i (Guster & Brow, 2012). Goal Creatio of model by system of associatio rules Class Decisio 1 Decisio 2 Decisio 3 Attributes Data 1 Data 2 Data 3 Sub-attributes Complexity Use Precisio Accuracy Credibility Importace Size of sample Certaity of source Possibility of processig Appropriateess Cofidece Up-to-date Compact Availability Correctess Completeess Actuality Visibility Table 1. Suggestio of groups of data for testig Outcome Improved model of busiess decisio makig Accordig to (Suković, 2001), there are three dimesios that determie the complete developmet of moder decisio makig. These are: qualitative, quatitative aspect ad iformatio-commuicatio aspect. Research of all three aspects is the most optimal. Data will be show i quatitative aspect, as give i the table, ad will be processed by umerical 47

5 values. Busiess itelligece methods ad techiques will be applied. Attributes ad sub-attributes will be described, as show i the table, i order to esure the qualitative aspect of moder decisio makig. Descriptio of the results will esure the qualitative aspect. The applicatio of software ad visual overview of data will show the iformatio-commuicatio aspect of moder decisio makig. Table 1 shows the groups of test data with three classes ad attributes, each of which cotais six sub-attributes. Durig the research, it is possible to modify the umber of classes, attributes ad sub-attributes (Mihailović, 2004). Depedig o the umber of data ad relevat criteria, the fial data structure of research will be defied. Sub-attributes are values that relate to attributes ad are defied by maagers based o the sigificace of certai segmets eeded for coductig research. Sub-attributes i Table 1 are ot of the same importace, ad the way of structurig of multiple problem active maager participatio i the process of its solvig, are i the model of defiig the weight of users criteria. More iformatio ca be see i (Čupić & Suković, 2008). Aggregatio of criteria (sub-attributes) has bee coducted based o the aalysis of various curret research, opiios from data miig experts ad umerous discussios i order to geerate the most relevat criteria for data values whose iflueces caot be eglected: complexity, accuracy, size of the sample, etc. (table 1). For determiatio of values of criteria i multicriteria decisio makig, defiig of vector coefficiets has bee used. Criteria defied i the form of vector of coefficiets represet the exchage or mutual relatio betwee the criteria. Certai hierarchical structure of problem of compariso of criteria betwee each other is the matrix of evaluatio. For the purpose of testig the group, the software made at the Faculty of Orgaizatioal Scieces i Belgrade has bee used. More iformatio o software ca be foud o the web page of the Cetre for busiess decisio makig. Table 2 shows the compariso of the criteria subattributes from the example of future research. Matrix of evaluatio is square dimesios, equal to umber of criteria i model. There is o evaluatio of criteria betwee each other ad items are visible at the mai diagoal. The other values are imported as a result of compariso of each criterio separately. Oly direct values are imported, while iverted values are defied o their ow, i this example through software. Solutio to the problem has the followig steps: Matrix of compariso should be adjusted i pairs, so as to put the uit i the field where each criteria is compared to each other. I order to cout the sum of elemets i each colum of adjusted matrix, with followig equatio: i lj = å P, j=1, 1 ij (1) = Table 2. Matrix of evaluatio i pairs of criteria 48

6 Lis the sum of elemets of adjusted colum, P is the value of the criteria. To divide each elemet of the colum with the sum of values of that colum, with the followig equatio: h = P l, i=1,, j=1, (2) ij ij j Coutig the sum of elemets ad the determie the arithmetic mea of each row. Colum that has the middle values is actually the ormalized vector. Wi = å h, =1, 1 ij i (3) = j W is the colum of values of criteria elemets i row, H is the value of criteria. Middle value is determied by data i colum W that splits with umber of criteria ad is show i row t, because it is a square matrix. EJAE (2) Figure 1 shows the iitial step of defiig the optios for creatig the level ad attributes iside each level of the defied model. It is defied which method of multicriteria decisio makig will be used i research (Promethee, Electre or AHP). For realizatio of this example, the method of aalytical-hierarchical process (AHP) has bee used. The method of the aalytical-hierarchical process (AHP) was defied by Thomas Saaty i the 1970s. AHP represets the tool ad the aalysis of decisio makig, created with the help of maagers i solvig the complex problems with multiple criteria ad greater umber of maagers. AHP is based o the cocept of balace used for determiatio of the overall relative sigificace of the group of attributes, activities or criteria, ad it relates to the aalyzed problem of decisio makig. t = w, j=1, j j (4) I this maer, the value of each criterio i the model is calculated. Vector of coefficiets is ormalized ad the sum of elemets is equal to oe. More iformatio about the process of defiig the vector of coefficiets ca be foud i (Čupić & Suković, 2008). Figure 1. Dialog box for defiig the level ad attributes of model Figure 2. Dialog box for defiig the vector of coefficiets Figure 2 shows the dialog box for assigig the sigificace of each criterio related to others i the predefied table. The predefied table with ie optios is Saaty s most famous scale for assigig the weights that is cofidet i solvig real problems of busiess systems. Saaty s scale for coversio of liguistic statemets while comparig the sigificace of pairs of criteria is put as a stadard due to its simplicity. The maager most ofte defies the relatios betwee the criteria, ad therefore the subjective aspect of the decisio makig is apparet. Figure 3 provides a overview for the aalysis of hierarchy betwee the defied levels of the model. There is the ifluece ad iterdepedece of the defied criteria of busiess decisio makig. Visual overview eables data miig to the user ad adaptatio of the existig elemets of the model. More iformatio o data miig treds ca be see i (Kriegel et al., 2007; Krüger et al., 2010; Bramer, 2007). There is the ifluece of all sub-attributes o all data, the ifluece of all three types of data o three models of 49

7 Figure 3. Overview of segmets of the defied model hierarchy decisios, ad their overall ifluece o the goal of the model creatig the improved model of busiess decisio makig. After data processig by AHP method, there is the graphic with defied vectors of weight of criteria coefficiet. Based o the graphical overview of the model, it could be easy to see the criteria that has high vector of coefficiets ad, therefore, bigger value. Based o the give example, it is easy to see the sigificace of data (with defied vector 0.14), accuracy (0.122) ad possibility of processig (0.121). The pla is to iclude the axiom of trasitivity i future research. Accordig to (Čupić & Suković, 2008), the axiom of trasitivity states: if A is bigger tha B, ad B is bigger tha C, tha it follows that A is bigger tha C. A > B ad B > C => A > C As could be see i Table 1, axiom of trasitivity could be applied to the choice of class with the most suitable dataset accordig to the defied criteria by most optimal decisio. If the class "Decisio 1" is more sigificat (acceptable, umerically bigger) tha class "Decisio 2", ad class Decisio 2 is more sigificat that Decisio 3, it follows that class Decisio 1 is more sigificat tha Decisio 3. I this maer, the rakig of the fial results ad classes would be easier, which would lead to the solutio to the defied model of busiess decisio makig. More iformatio o classes ca be foud i (Gupta et al., 2010). If the axiom of trasitivity is successfully applied to the example of classes ad attributes, ad if the applicatio is scietifically verified, it follows that it ca be successfully used for choosig ad comparig the sub-attributes. Accordig to Figure 4, deductive method leads to the followig coclusio: if sub-attribute accuracy has bigger umerical value Figure 4. Criteria levels defied by AHP method 50

8 Figure 5. Graphic of criteria level for the most optimal decisio of the model (defied accordig to the vector of coefficiets) tha sub-attribute "precisio", ad "precisio" has bigger value tha sub-attribute "source cofidece" tha it follows that sub-attribute "accuracy" has bigger value tha sub-attribute "source cofidece". More iformatio ca be see i (Istrat, 2014). Figure 5 shows the defied vector of coefficiets at the decisio level. Decisio 2 has the biggest vector (0.717) ad the biggest value for the goal of the model. Here, the axiom of trasitivity ca be applied: if the vector of Decisio 1 is bigger tha the vector of Decisio 2, ad the vector of Decisio 2 is bigger tha vector of Decisio 3, the it follows that the vector of the Decisio 1 is bigger that the vector of the Decisio 3. Decisio 1 is of greater importace tha the Decisio 3. CONCLUSION Based o the curret research of domestic ad foreig experts i the field of busiess itelligece, it has bee show that this area has vast potetial but is still relatively urevealed i some segmets of applicatio. Therefore, it is the challege for the researchers ad the area where sigificat scietific ad professioal beefits ca be provided. Iovative research approach to busiess itelligece is characterized by kowledge ad creativity, as well as the use of moder data miig software. Moder scietific methods aalyze the results ad offer recommedatios ad guidelies for further research. The primary motivatio for developmet of this model is to show the sigificace that busiess itelligece has i the process of decisio makig for moder maagemet. The aim is to create the busiess decisio makig model that ca be applicable to busiess systems of differet aspects ad for commercial purposes. The sigificace of busiess itelligece for creatig the model is show, which ca icrease the effectiveess of the decisio makig process i maagemet. REFERENCES Berry, M.J.A., & Lioff, G. (2004). Data miig techiques: For marketig, sales, ad customer relatioship maagemet. Idiaapolis: Wiley. Bramer, M. (2007). Priciples of Data Miig. Spriger: Lodo. Cetre for busiess decisio makig. (2015). Decisio-makig. Retrieved May 5, 2015, from Čupić, M, & Suković, M. (2008). Odlučivaje. Beograd: FON. I Serbia. Draker, P. (2003). Moj pogled a meadžmet : izbor iz dela o meadžmetu Pitera Drakera. Novi Sad: Adizes. I Serbia. Grabmeier, J.L., & Lambe, L.A. (2007). Decisio trees for biary classificatio variables grow equally with the Gii impurity measure ad Pearso s chi-square test. Iteratioal Joural of Busiess Itelligece ad Data Miig, 2(2), doi: /ijbidm Gupta, A., Li, J., Coradi, R., Røeberg, H., & Ladre, E. (2010). Chage profiles of a reused class framework vs. two of its applicatios. Iformatio ad Software Techology, 52(1), doi: /j.ifsof Guruler, H., Istabullu, A., & Karahasa, M. (2010). A ew studet performace aalyzig system usig kowledge discovery i higher educatioal databases. Computers & Educatio, 55(1), doi: /j.compedu

9 Guster, D., & Brow, C.G. (2012). The Applicatio of Busiess Itelligece to Higher Educatio: Techical ad Maagerial Perspectives. Joural of Iformatio Techology Maagemet, 23(2), Horick, M.F., Marcadé, E., & Vekayala, S. (2007). Java data miig: Strategy, stadard, ad practice: a practical guide for architecture, desig, ad implemetatio. Amsterdam: Elsevier-Morga Kaufma. Istrat, V. (2014). Pristupi rad doktorske disertacije, Uapredjeje modela poslovog odlučivaja sistemom asocijativih pravila. Beograd: FON. I Serbia. Johasso, U., Nilsso, L., & Koeig, R. (2004). Accuracy vs. comprehesibility i data miig models. Proceedigs of the Seveth Iteratioal Coferece o Iformatio Fusio (pp ). Retrieved May 5, 2015, from Kotsiatis, S.B. (2011). Use of machie learig techiques for educatioal proposes: a decisio support system for forecastig studets grades. Artificial Itelligece Review, 37(4), doi: /s x. Kriegel, H., Borgwardt, K.M., Kröger, P., Pryakhi, A., Schubert, M., & Zimek, A. (2007). Future treds i data miig. Data Miig ad Kowledge Discovery, 15(1), doi: /s Krüger, A., Mercero, A., & Wolf, B.A. (2010). Data Model to Ease Aalysis ad Miig of Educatioal Data. Retrieved May 5, 2015, from EDM2010/uploads/proc/edm2010_submissio_9.pdf Mihailović, D. (2004). Metodologija aučih istraživaja. Beograd: FON. I Serbia. Suković, M. (2001). Razvoj metodologije podrške grupom odlučivaju. Doktorska disertacija, Fakultet orgaizacioih auka, Beograd. I Serbia Suković, M., & Delibašić, B. (2010). Poslova iteligecija i sistemi za podršku odlučivaju. Beograd: FON. I Serbia. Verhoef, P.C., & va Door, J., & Dorotic, M. (2007). Customer value maagemet: A overview ad research ageda. Joural of Research i Maagemet, 2, Verhoef, P.C., Vekatesa, R., McAlister, L., Malthouse, E.C., Krafft, M., & Gaesa, S. (2010). CRM i Data-Rich Multichael Retailig Eviromets: A Review ad Future Research Directios. Joural of Iteractive Marketig, 24(2), doi: /j.itmar ULOGA POSLOVNE INTELIGENCIJE U SAVREMENOM ODLUČIVANJU Rezime: Odlučivaje je veoma začaja i kompleksa fukcija meadžmeta koja zahteva metode i tehike koje olakšavaju proces izbora jede od više pouđeih alterativa. U savremeom poslovaju veliki izazov za meadžmet jeste proaći alterative ačie za poboljšaje procesa poslovog odlučivaja. Odlučivaje utiče direkto a stvaraje profita i pozicioiraje kompaija a tržištu. Čijeica je da su se u svakoj fazi razvoja društva ljudi bavili feomeom odlučivaja, što uslovljava šireje zaja o ovom procesu. Glavi doprios ovog rada jeste da ukaže a začaj alata i tehika poslove iteligecije kao podrške meadžmetu u procesu odlučivaja. Rezultati ukazuju a ogroma začaj poslove iteligecije u savremeom odlučivaju. Ključe reči: poslova iteligecija, odlučivaje, odluka. Received: May 6, Correctio: Ju 8, Accepted: Ju 15,