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Implementation methodology of open source ERP in SME
Polák, Tomáš ; Gála, Libor (advisor) ; Lieb, Dušan (referee)
This paper deals with the implementation of open source ERP systems in the area of small and medium-sized enterprises. The main objective of this work is to define an open source ERP implementation methodology, suitable for small and medium enterprises. This objec-tive is fulfilled partially by defining of manifesto, as way of thinking about this area and analysis of current state of available implementation methodologies. Newly described met-hodology is then verified by a case study. The main contribution of this work is the defini-tion of implementation methodology for open source ERP systems in area of SME focusing on frequently occurring insufficiencies and leading to successful deployment of company IS.
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Clustering and regression analysis of micro panel data
Sobíšek, Lukáš ; Pecáková, Iva (advisor) ; Komárek, Arnošt (referee) ; Brabec, Marek (referee)
The main purpose of panel studies is to analyze changes in values of studied variables over time. In micro panel research, a large number of elements are periodically observed within the relatively short time period of just a few years. Moreover, the number of repeated measurements is small. This dissertation deals with contemporary approaches to the regression and the clustering analysis of micro panel data. One of the approaches to the micro panel analysis is to use multivariate statistical models originally designed for crosssectional data and modify them in order to take into account the within-subject correlation. The thesis summarizes available tools for the regression analysis of micro panel data. The known and currently used linear mixed effects models for a normally distributed dependent variable are recapitulated. Besides that, new approaches for analysis of a response variable with other than normal distribution are presented. These approaches include the generalized marginal linear model, the generalized linear mixed effects model and the Bayesian modelling approach. In addition to describing the aforementioned models, the paper also includes a brief overview of their implementation in the R software. The difficulty with the regression models adjusted for micro panel data is the ambiguity of their parameters estimation. This thesis proposes a way to improve the estimations through the cluster analysis. For this reason, the thesis also contains a description of methods of the cluster analysis of micro panel data. Because supply of the methods is limited, the main goal of this paper is to devise its own two-step approach for clustering micro panel data. In the first step, the panel data are transformed into a static form using a set of proposed characteristics of dynamics. These characteristics represent different features of time course of the observed variables. In the second step, the elements are clustered by conventional spatial clustering techniques (agglomerative clustering and the C-means partitioning). The clustering is based on a dissimilarity matrix of the values of clustering variables calculated in the first step. Another goal of this paper is to find out whether the suggested procedure leads to an improvement in quality of the regression models for this type of data. By means of a simulation study, the procedure drafted herein is compared to the procedure applied in the kml package of the R software, as well as to the clustering characteristics proposed by Urso (2004). The simulation study demonstrated better results of the proposed combination of clustering variables as compared to the other combinations currently used. A corresponding script written in the R-language represents another benefit of this paper. It is available on the attached CD and it can be used for analyses of readers own micro panel data.
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Míry podobnosti pro nominální data v hierarchickém shlukování
Šulc, Zdeněk ; Řezanková, Hana (advisor) ; Šimůnek, Milan (referee) ; Žambochová, Marta (referee)
This dissertation thesis deals with similarity measures for nominal data in hierarchical clustering, which can cope with variables with more than two categories, and which aspire to replace the simple matching approach standardly used in this area. These similarity measures take into account additional characteristics of a dataset, such as frequency distribution of categories or number of categories of a given variable. The thesis recognizes three main aims. The first one is an examination and clustering performance evaluation of selected similarity measures for nominal data in hierarchical clustering of objects and variables. To achieve this goal, four experiments dealing both with the object and variable clustering were performed. They examine the clustering quality of the examined similarity measures for nominal data in comparison with the commonly used similarity measures using a binary transformation, and moreover, with several alternative methods for nominal data clustering. The comparison and evaluation are performed on real and generated datasets. Outputs of these experiments lead to knowledge, which similarity measures can generally be used, which ones perform well in a particular situation, and which ones are not recommended to use for an object or variable clustering. The second aim is to propose a theory-based similarity measure, evaluate its properties, and compare it with the other examined similarity measures. Based on this aim, two novel similarity measures, Variable Entropy and Variable Mutability are proposed; especially, the former one performs very well in datasets with a lower number of variables. The third aim of this thesis is to provide a convenient software implementation based on the examined similarity measures for nominal data, which covers the whole clustering process from a computation of a proximity matrix to evaluation of resulting clusters. This goal was also achieved by creating the nomclust package for the software R, which covers this issue, and which is freely available.
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Data Mining for Effective Customer Communication
Madhi, Simona ; Šperková, Lucie (advisor) ; Novotný, Ota (referee)
The aim of this paper is to describe and illustrate benefits of using Data Mining for effective customer communication. The objective is to perform a Data Mining analysis in order to achieve results with potentially beneficial influence on the company s relationship with its customers, while using the KNIME Analytics Platform tool. The paper introduces the theoretical aspect of Customer Relationship Management, Data Mining and the opportunities of using Data Mining to improve CRM; followed by a market analysis of available Data Mining tools and the introduction of the KNIME Analysis Platform. Furthermore, the knowledge thus reached is used for the performance of real data analysis with the aim of reaching customer knowledge that would be appropriate to use within CRM strategy and finally to positively influence the value of customer relationships.
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Methods of Competitive Intelligence for chemical industrial companies
Lisová, Martina ; Molnár, Zdeněk (advisor) ; Kocánek, Marek (referee)
The content of this thesis is the introduction of Competitive Intelligence and its methods to companies in the chemical industry. The main objective of this work is to create particular solution of Competitive Intelligence for Lovochemie, a.s., i.e. to propose competent employee who will be periodically monitor the selected information resources using Competitive Intelligence software tools. The analyses were used to achieve this objective. At first analysis of the chemical industry was conducted in the Czech Republic and also in the world and analysis of the selected company has been created. Information resources that the company should follow through Competitive Intelligence tools were selected on the basis of this information. The first part deals with the introduction of Competitive Intelligence, intelligence cycle CI, strategic analysis methods and Competitive Intelligence tools for searching and monitoring of information on the Internet. The second part is devoted to the aforementioned analysis. At first the chemical industry is analysed and the world's biggest fertilizers producers are described. This section also introduces the company called Lovochemie, a.s., its competitors, suppliers and customers. In the last part of this thesis, the information resources available to the company and selected Competitive Intelligence tools are described. Finally, the particular solution for Lovochemie is created.
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Possibilities of Big Data use for Competitive Intelligence
Verníček, Marek ; Molnár, Zdeněk (advisor) ; Šperková, Lucie (referee)
The main purpose of this thesis is to investigate the use of Big Data for the methods and procedures of Competitive Intelligence. Among the goals of the work is a toolkit for small and large businesses which is supposed to support their work with the whole process of Big Data work. Another goal is to design an effective solution of processing Big Data to gain a competitive advantage in business. The theoretical part of the work processes available scientific literature in the Czech Republic and abroad as well as describes the current state of Competitive Intelligence, and Big Data as one of its possible sources. Subsequently, the work deals with the characteristics of Big Data, the differences from working with common data, the need for a thorough preparation and Big Data applicability for the methods of Competitive Intelligence. The practical part is focused on analysis of Big Data tools available in the market with regard to the whole process from data collection to the analysis report preparation and integration of the entire solution into an automated state. The outcome of this part is the Big Data software toolkit for small and large businesses based on their budget. The final part of the work is devoted to the classification of the most promising business areas, which can benefit from the use of Big Data the most in order to gain competitive advantages and proposes the most effective solution of working with Big Data. Among other benefits of this work are expansion of the range of resources for Competitive Intelligence and in-depth analysis of possibilities of Big Data usage, designed to help professionals make use of this hitherto untapped potential to improve market position, gain new customers and strengthen the existing user base.
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Interaction of apoprotein with heme side chains: A comparison of cytochromes P450 and NO-synthases
Fastová, Dagmar ; Hudeček, Jiří (advisor) ; Šulc, Miroslav (referee)
Cytochromes P450 and NO-synthases are two classes of heme-thiolate proteins, participating in catalysis of important biochemical reactions. In this work we analysed and compared both enzyme groups from the viewpoint of torsional angles of vinyl side chains of heme in positions 2 and 4. We used the Yasara programme to analyse the available crystalographic data. Whereas the torsional angle distributions for both vinyl groups in NO-synthases are pointing to a rather "conservative" conformations of both vinyls (between -30ř and 90ř for position 2, -150ř to 120ř for position 4, resp.) with little differences for individual forms of these enzymes, the mammalian cytochromes P450 display considerable differences between different families. In accordance with previous analyses is the distribution of torsional angles of 2-vinyls in most cases narrower (-120ř to 150ř) compared to 4-vinyls, which display more conformational flexibility (with torsional angles between -30ř and 180ř). In the second part of the Thesis we analysed the interaction of cytochrome P450 with its reaction partner, cytochrome P450 reductase. On the basis of crystalographic data we tried to prove, if the spatial positions of basic aminoacids, corresponding in the primary structure of various mammalian forms the those in the isoform 2B4...
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The influence of nutrient loading, meteorological and hydrological conditions and operating manipulations on phytoplankton in the water suply reservoir Římov.
Hejzlar, Josef ; Jarošík, Jiří ; Nedoma, Jiří ; Seďa, Jaromír ; Znachor, Petr
Analysis of data collected during long-term and complex limnologic monitoring of the Římov reservoir in the period 1983 – 2015, which depict the development of physico-chemical conditions, hydrology and hydrodynamics of the reservoir with links to biological data on phytoplankton, zooplankton etc. showed that the concentration and species composition of phytoplankton depend on the supply and availability of nutrients, but are also influenced by climatic and hydrological conditions and water management operation of the reservoir.\n
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Measurement of \kur{in situ}Phasporus Availability in Acidified Soils using Iron-Infused Resin soils.
ČAPEK, Petr
A hybrid anion resin was tested for in situ phosphorus (P) availability measurement in soils of two stands recovering from acidification and having different P-sorption characteristics. The phosphate (P-PO4) sorption capacity of the resin (before saturation) was 48 mol g-1. Sorption and elution were tested under P-PO4 concentrations common in acidic soils (0 0.42 mmol l-1) either with or without the presence of sulfate (0.2 mmol l-1). The efficiency of P-PO4 sorption was independent of the sulfate and was 100 +/- 0.2% (n = 56, +/- SD). The P-PO4 recovery stabilized after six elution steps (each: 50 ml of 0.5 M sodium hydroxide, resin/solution 5:1). The efficiency of P-PO4 recovery was 80 +/- 7% and was used to evaluate field measurements. We determined the amount of P-PO4 in the field using resin bags in three consecutive years. The results indicate that bioavailable P is negatively related to the soil ability to retain P.
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