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Title:Uporaba orodij poslovne inteligence v diagnostično analitskem centru družbe ELES
Authors:ID Kozjek, Denis (Author)
ID Kofjač, Davorin (Mentor) More about this mentor... New window
ID Kerin, Uroš (Comentor)
Files:.pdf MAG_Kozjek_Denis_2019.pdf (5,70 MB)
MD5: 1463F94EB334B8D6E698EB1F4D806143
PID: 20.500.12556/dkum/bbdbf206-41b3-42f2-844b-312bae979e6c
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FOV - Faculty of Organizational Sciences in Kranj
Abstract:V podjetju ELES, d.o.o. se je zaradi vse večje količine in kompleksnosti podatkov ter prepoznanem potencialu odkrivanja informacij, ki jih področje poslovne inteligence ponuja, vodstvo odločilo za izgradnjo diagnostično analitskega centra. Center bo v podjetju predstavljal središče tehnično poslovne inteligence. Podjetje ELES, d.o.o., se je s poslovno inteligenco že srečevalo, vendar celovite rešitve še ne uporabljajo. V magistrskem delu smo preučili in opisali področje poslovne inteligence ter različno programsko opremo oziroma orodja, ki se na tem področju uporabljajo. Z namenom izbire najprimernejšega orodja smo na podlagi zahtev, potreb in predlogov podjetja ter raziskav trga izvedli večparametrsko analizo izbranih orodij. Poleg analize smo vsako orodje tudi praktično preizkusili. Na podlagi rezultatov smo izbrali najprimernejše orodje za uporabo pri izgradnji nadzornih plošč. Namestitev in uporabo izbranega orodja smo v magistrskem delu tudi podrobneje opisali. Poleg tega je podrobno opisana tudi izgradnja in delovanje nadzornih plošč, ustvarjenih za potrebe diagnostično analitskega centra. Nadzorne plošče so osredotočene na visokonapetostne naprave. Omogočajo spremljanje številnih parametrov stanja in vzdrževanja visokonapetostnih naprav, z namenom zagotavljanja ažurnih ter kakovostnih informacij za sprejemanje poslovnih odločitev.
Keywords:poslovna inteligenca, večparametrska analiza, odločitveni modeli, DEXi, nadzorne plošče, Microsoft Power BI, elektroenergetski sistemi
Place of publishing:Maribor
Year of publishing:2019
PID:20.500.12556/DKUM-75429 New window
COBISS.SI-ID:8163091 New window
NUK URN:URN:SI:UM:DK:WMAVFTBD
Publication date in DKUM:15.01.2020
Views:1724
Downloads:174
Metadata:XML DC-XML DC-RDF
Categories:FOV
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:13.11.2019

Secondary language

Language:English
Title:Application of business intelligence tools in the ELES diagnostic analytical center
Abstract:Due to the ever increasing quantity, complexity and the recognized potential of structured data mining and analysis Slovenian transmission system operator ELES, ltd., decided to develop and implement a diagnostics and analytics center. The center aims at establishing a central point for technical business intelligence in the company. The company already uses strategies that can be related to business intelligence as such, however is not relying on any of the commercially available software in doing that. In this master thesis we analyzed the concept of business intelligence in a corporate environment and software tools developed for that purpose. To find the most suitable tool to use for the development in use of dashboards, we conducted a multiparameter analysis of a set of tools, selected with regard to the requirements of business intelligence in the power industry. In addition to the analysis we thoroughly tested each tool. Based on the test results we selected the most adequate tool and used it to develop various information visualizations. The installation and use of the selected tool is described in detail in this thesis, including detail description of the development and operation of dashboards. The dashboards contain information on high voltage devices and enable users to monitor multiple parameters of the devices’ condition and maintenance logs, in order to assure up-to-date and quality information which is the corner stone for making fact-based business decisions.
Keywords:business intelligence, multiparameter analysis, decision models, DEXi, dashboards, Microsoft Power BI, electric power system


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