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Evaluation of Process Management in Production Systems
Juřica, Pavel ; Dupal, Andrej (referee) ; Lenort, Radim (referee) ; Urbánek, Jiří (referee) ; Jurová, Marie (advisor)
Business Process Management, the object of this thesis, has still growing importance in the functioning of the organization, and has a positive impact on its success. In the view of a dynamic market environment, Business Process Management has an inherent importance in the strategic business development. When an organization decides to implement process control and manage its activities, it should be also able to measure its processes and evaluate them. This ability gives the organization the possibility to efficiently and effectively manage processes and identify shortcomings for creation of business strategy on a competitive market. This dissertation presents a new evaluation model of a process control in the production systems, which organizations can use as a guide for further development. This work brings a new perspective in the field of process control and its evaluation of the production systems. The main objective of this dissertation is to design the assessment of the process control levels in the production systems on the basis of theoretical research and practical experience. The proposed solution was then verified by field research in selected organizations.
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Expert System for Decision-Making on Stock Markets Using Investor Sentiment
Janková, Zuzana ; Lenort, Radim (referee) ; Zinecker, Marek (referee) ; Chramcov, Bronislav (referee) ; Dostál, Petr (advisor)
The presented dissertation examines the potential of using the sentiment score extracted from textual data with historical stock index data to improve the performance of stock market prediction through the created model of the expert system. Given the large number of financial-related text documents published by both professional and amateur investors, not only on online social networks that could have an impact on real stock markets, but it is also crucial to analyze and in particular extract financial texts published by different users. investor sentiment. In this work, investor sentiment is obtained from online financial reports and contributions published on the financial social platform StockTwits. Sentiment scores are determined using a hybrid approach combining machine learning models with the teacher and neural networks, with multiple lexicons of positive and negative words used to classify sentiment polarity. The influence of sentiment score on the stock market through causality, cointegration and coherence is analyzed. The dissertation proposes a model of an expert system based on fuzzy logic methods. Fuzzy logic provides remarkable features when working with vague, inaccurate or unclear data and is able to deal with the chaotic environment of stock markets. In recent scientific studies, it has gained in popularity a higher level of fuzzy logic, which is referred to as type-2 fuzzy logic. Unlike the classic type-1 fuzzy logic, this higher type is able to integrate a certain level of uncertainty between the dual membership functions. However, this type of expert system is considerably neglected in the subject issue of stock market prediction using the extracted investor sentiment. For this reason, the dissertation examines the potential to use and the performance of type-2 fuzzy logic. Specifically, several type-2 fuzzy models are created. which are trained on historical stock index data and sentiment scores extracted from text data for the period 2018-2020. The created models are assessed to measure the prediction performance without sentiment and with the integration of investor sentiment. Subsequently, based on the created expert model, the investment strategy is determined, and its profitability is monitored. The prediction performance of fuzzy models is compared with the performance of several comparison models, including SVM, KNN, naive Bayes and others. It has been observed from experiments that fuzzy logic models are able to improve prediction by appropriate setting of membership and uncertainty functions contained in them and are able to compete with classical expert prediction models, which are standardly used in research studies. The created model should serve as a tool to support investment decisions for individual investors.
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The use of soft computing as support for business decision-making
Pekárek, Jan ; Jašek, Roman (referee) ; Lenort, Radim (referee) ; Zinecker, Marek (referee) ; Dostál, Petr (advisor)
The presented dissertation deals with the problem of deploying the charging infrastructure for electric vehicles in the Czech Republic. The core of the thesis is a mathematical optimization model, which is implemented in the language of MATLAB computing software. The model consists of several sub-units representing separate models of studied sub-problems. The individual chapters of the work describe successively these sub models. The sub models are: demand model of the charging service, model of charging supply, charging simulator model, optimization model and its resolving optimization method. The optimization model is accelerated by parallelization on the graphics card. The optimization method is designed as a case-specific implementation of genetic algorithms on a population of tree-structured individuals. The final chapter deals with an economic aspect of the problem under consideration, the implications of the findings and the role that the optimization model plays in the context under consideration. The main benefit of the work lies in the formulation of the problem as a mathematical model, the accompanying analyses and the provided justifications. Any user with updated data can then use this work along with the attached scripts to find answers to questions about the relationship between electromobility and the charging infrastructure.
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Machine learning in customer churn prediction
Fridrich, Martin ; Chramcov, Bronislav (referee) ; Lenort, Radim (referee) ; Šimberová, Iveta (referee) ; Dostál, Petr (advisor)
The dissertation examines customer churn prediction in e-commerce retail settings, presenting the current research landscape, analyzing key trends, and pinpointing opportunities for further investigation. The literature review is conducted using language processing. The study aims to develop, implement, and evaluate a machine learning system for predicting customer churn in the e-commerce environment, considering the economic implications of retention efforts, and facilitating a deeper understanding of the modeled phenomenon. The solution is organized into sections covering problem definition, data comprehension and processing, model development, evaluation, interpretation, and deployment. The author extends the traditional concept of customer churn as the lack of a transaction in a future period with a novel idea of the incremental economic impact of a retention campaign. The notions are validated using two datasets. The modeling framework incorporates GLM, SVM, ANN, decision trees, and meta-algorithms. Bayesian optimization estimates external parameters related to data processing and model building. The understanding of the phenomena is enhanced using SHAP tools, which are improved in terms of computation and visual representation. From the perspective of natural prediction performance, random forests and gradient boosting dominate; in the original task, ANN also performs well. When considering the financial results of the retention campaign, the novel approach functions excellently, mainly when coupled with decision trees or meta-learning. Recency and frequency representations of interactions and transactions are identified as key features; the feature importance of customer value emerges in the novel approach. Identifying and comprehending customer segments to target directly supports subsequent retention initiatives. In summary, the thesis offers an extensive overview of novel methods and tools for predicting customer churn, which can be valuable for future research and practical applications in business or educational settings.
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Design of a method for measuring the leanness level of production processes
Medonos, Michal ; Blecha, Petr (referee) ; Čambál, Miloš (referee) ; Lenort, Radim (referee) ; Jurová, Marie (advisor)
Focus of this dissertation thesis is to find suitable methodology for measuring the leanness of the production process. The history of approaches to the optimization of production systems is outlined in the literature search. Furthermore, the methodology of lean production is described, the concept of leanness of the production process is defined and also the current approaches and methods of measuring the leanness of the production process are summarized. The next section presents developed Lead Time Leanness Indicator as a proposal for a suitable way to measure the leanness of the production process. Within the primary research, the necessary data are obtained from production companies using a questionnaire survey to test the usability of this indicator in practice. Based on the evaluation of the results of this survey, all established hypotheses are confirmed, and it can be stated that the defined indicator and its methodology is a suitable tool for measuring the leanness of the production process. It is further confirmed that this indicator can be used to set goals for the implementation of the lean production methodology also for a mutual evaluation and comparison of the efficiency of production processes of different companies. Another benefit obtained from the questionnaire survey is the mapping of the current state of the level of leanness of production processes and the intensity of the use of lean production tools in production companies not only in the Czech Republic but also abroad. At the end of the work, a case study is presented, which demonstrates the use of the indicator to determine the current state of leanness of the production process and to define any potential for improvement in a particular manufacturing company.
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The use of convolutional neural networks for predicting the financial failure of a company
Šebestová, Monika ; Chramcov, Bronislav (referee) ; Lenort, Radim (referee) ; Režňáková, Mária (referee) ; Dostál, Petr (advisor)
The doctoral thesis deals with the use of convolutional neural networks for predicting the financial failure of companies. A bibliometric analysis was used during the processing of the literature review, which enabled a better orientation in scientific works oriented to the methods and approaches used in the past to predict the financial failure of companies. On the basis of the obtained knowledge, a deep learning model based on the GoogLeNet architecture was proposed, with inputs consisting of financial and macroeconomic indicators of companies. The modeling was based on the transfer learning method, in which it is possible to fine-tune the parameters of the pre-established networks to accelerate the learning process of the convolutional neural network. The initial set of financial and macroeconomic indicators was compiled from the variables that were most often used in business failure prediction models in scientific papers. Appropriate statistical methods were used for the specific selection of indicators from which the model is built. Since convolutional neural networks work best with image processing, the quantitative values of the input indicators were graphically interpreted and it was investigated which type of graphical processing is most suitable for predicting the failure of companies. Due to the existence of an unbalanced data set, the effect of the SMOTE method on the accuracy of the model's prediction was analyzed in the thesis. The method was used to increase the number of samples of the minority class of firms. To model the prediction of financial default, several variants of models were proposed, which differed in the form of input data. It was tested how the removal of outliers from the data set, the point in time from which the data come or the method of predictor selection will affect the accuracy of the prediction. The parameters of the resulting model were further fine-tuned so that it was able to classify businesses from new real data. The research conducted showed that using the right type of graphical processing of input data, SMOTE technique and appropriate parameter settings, convolutional neural networks can predict the financial failure of companies with high accuracy.
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Expert System for Decision-Making on Stock Markets Using Investor Sentiment
Janková, Zuzana ; Lenort, Radim (referee) ; Zinecker, Marek (referee) ; Chramcov, Bronislav (referee) ; Dostál, Petr (advisor)
The presented dissertation examines the potential of using the sentiment score extracted from textual data with historical stock index data to improve the performance of stock market prediction through the created model of the expert system. Given the large number of financial-related text documents published by both professional and amateur investors, not only on online social networks that could have an impact on real stock markets, but it is also crucial to analyze and in particular extract financial texts published by different users. investor sentiment. In this work, investor sentiment is obtained from online financial reports and contributions published on the financial social platform StockTwits. Sentiment scores are determined using a hybrid approach combining machine learning models with the teacher and neural networks, with multiple lexicons of positive and negative words used to classify sentiment polarity. The influence of sentiment score on the stock market through causality, cointegration and coherence is analyzed. The dissertation proposes a model of an expert system based on fuzzy logic methods. Fuzzy logic provides remarkable features when working with vague, inaccurate or unclear data and is able to deal with the chaotic environment of stock markets. In recent scientific studies, it has gained in popularity a higher level of fuzzy logic, which is referred to as type-2 fuzzy logic. Unlike the classic type-1 fuzzy logic, this higher type is able to integrate a certain level of uncertainty between the dual membership functions. However, this type of expert system is considerably neglected in the subject issue of stock market prediction using the extracted investor sentiment. For this reason, the dissertation examines the potential to use and the performance of type-2 fuzzy logic. Specifically, several type-2 fuzzy models are created. which are trained on historical stock index data and sentiment scores extracted from text data for the period 2018-2020. The created models are assessed to measure the prediction performance without sentiment and with the integration of investor sentiment. Subsequently, based on the created expert model, the investment strategy is determined, and its profitability is monitored. The prediction performance of fuzzy models is compared with the performance of several comparison models, including SVM, KNN, naive Bayes and others. It has been observed from experiments that fuzzy logic models are able to improve prediction by appropriate setting of membership and uncertainty functions contained in them and are able to compete with classical expert prediction models, which are standardly used in research studies. The created model should serve as a tool to support investment decisions for individual investors.
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The use of soft computing as support for business decision-making
Pekárek, Jan ; Jašek, Roman (referee) ; Lenort, Radim (referee) ; Zinecker, Marek (referee) ; Dostál, Petr (advisor)
The presented dissertation deals with the problem of deploying the charging infrastructure for electric vehicles in the Czech Republic. The core of the thesis is a mathematical optimization model, which is implemented in the language of MATLAB computing software. The model consists of several sub-units representing separate models of studied sub-problems. The individual chapters of the work describe successively these sub models. The sub models are: demand model of the charging service, model of charging supply, charging simulator model, optimization model and its resolving optimization method. The optimization model is accelerated by parallelization on the graphics card. The optimization method is designed as a case-specific implementation of genetic algorithms on a population of tree-structured individuals. The final chapter deals with an economic aspect of the problem under consideration, the implications of the findings and the role that the optimization model plays in the context under consideration. The main benefit of the work lies in the formulation of the problem as a mathematical model, the accompanying analyses and the provided justifications. Any user with updated data can then use this work along with the attached scripts to find answers to questions about the relationship between electromobility and the charging infrastructure.
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Evaluation of Process Management in Production Systems
Juřica, Pavel ; Dupal, Andrej (referee) ; Lenort, Radim (referee) ; Urbánek, Jiří (referee) ; Jurová, Marie (advisor)
Business Process Management, the object of this thesis, has still growing importance in the functioning of the organization, and has a positive impact on its success. In the view of a dynamic market environment, Business Process Management has an inherent importance in the strategic business development. When an organization decides to implement process control and manage its activities, it should be also able to measure its processes and evaluate them. This ability gives the organization the possibility to efficiently and effectively manage processes and identify shortcomings for creation of business strategy on a competitive market. This dissertation presents a new evaluation model of a process control in the production systems, which organizations can use as a guide for further development. This work brings a new perspective in the field of process control and its evaluation of the production systems. The main objective of this dissertation is to design the assessment of the process control levels in the production systems on the basis of theoretical research and practical experience. The proposed solution was then verified by field research in selected organizations.
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