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Optimization of Business Processes Using Business Intelligence Tools
Žáčiková, Erika ; Luhan, Jan (referee) ; Kříž, Jiří (advisor)
The final thesis deals with the optimization of business processes and the provision of a quality basis for managerial decision-making, which in itself represents a process. In later chapters, he presents the concept of Process Mining and, in the practical part, the application of acquired knowledge about process mining to the Purchase to Pay business process. The goal of this solution is to reveal weak links in the process flow, identify deviations from the reference process model and gain knowledge about real process performance. Based on this knowledge, aspects that can be optimized are proposed.
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Modelling and Analysis of Logistics Processes by Applying Process and Data Mining Techniques
Rudnitckaia, Julia ; Wang, Hao (referee) ; Zendulka, Jaroslav (referee) ; Hruška, Tomáš (advisor)
In this thesis, we propose an approach for modelling hidden and unknown processes and subprocesses in the example of a seaport logistics area. Having the underlying process model makes it possible to exploit more advanced algorithms since deviations and main paths are becoming visible and better controlled. The obtained model is the foundation for the core research of this work and will be enriched with key performing indicators and their forecast by applying advanced process mining, statistics, and machine learning techniques. The main difference of the approach is that we take as a target variable not any specific value, but the object - a process variant or a process type with a set of parameters. Bottleneck analysis, from one side, and predictive analysis, on the other hand, are enforced with context-aware information, especially with these additional objective process attributes. Furthermore, the support of the descriptive ("As is") current process model with certain notation and the integration with relevant bottleneck and predictive methods compromise the advantages of the approach. The work primarily focuses on the design of algorithms and methods for supporting logistics data analysis. However, it can be adjusted and applied to other areas accordingly, which makes the approach flexible and versatile. The result of the work is the framework for unstructured process modelling and the key process parameters predictive method. This analysis of processes with their attributes might be used for decision-making systems and process maps in future.
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Business Process Mining
Skácel, Jan ; Kreslíková, Jitka (referee) ; Bartík, Vladimír (advisor)
This thesis explains business process mining and it's principles. A substantial part is devoted to the problems of process discovery. Further, based on the analysis of specific manufacturing process are proposed three methods that are trying to identify shortcomings in the process. First discovers the manufacturing process and renders it into a graph. The second method uses simulator of production history to obtain products that may caused delays in the process. Acquired data are used to mine frequent itemsets. The third method tries to predict processing time on the selected workplace using asociation rules. Last two mentioned methods employ an algorithm Frequent Pattern Growth. The knowledge obtained from this thesis improve efficiency of the manufacturing process and enables better production planning.
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Business Process Analysis
Mička, David ; Burget, Radek (referee) ; Hruška, Tomáš (advisor)
The goal of this thesis is to design a method of business process analysis, with the focus on process optimalization by using Predictive Maintenance, creation of a suitable prototype for this method and evaluation of practical applicability. The basis of this thesis is the research of study group led by Prof.dr.ir. W.M.P. van der Aalst revolving around process mining and tools for his examination. The same study group also created the program ProM used in this thesis.
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Knowledge Discovery from Process Logs
Kluska, Martin ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
This Master's describes knownledge discovery from process logs by using process mining algorithms. Chosen algorithms are described in detail. These aim to create process model based on event log analysis. The goal is to design such components, which would be able to import the process and run the simulations. Results from components can be used for short term planning.
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Process Mining as a Service
Dobias, Ondrej ; MBA, Karel Fuksa, (referee) ; Luhan, Jan (advisor)
Softwérové a hardvérové aplikácie zaznamenávajú veľké množstvo informácií do protokolov udalostí. Každé dva roky sa množstvo zaznamenaných dát viac než zdvojnásobí. Dolovanie procesov je relatívne mladá disciplína, ktorá sa nachádza na rozmedzí strojového učenia a dolovania dát na jednej strane a modelovania a analýzy procesov na druhej strane. Cieľom dolovania procesov je popísať a analyzovať skutočné procesy extrahovaním znalostí z protokolov udalostí, ktoré sú v dnešných aplikáciách bežne dostupné. Táto práca mieri na spojenie obchodných príležitostí (organizácie bohaté na dáta; dopyt po službách BPM; limitácie na strane tradičnej dodávky BPM služieb) s technickými možnosťammi Dolovania procesov. Cieľom práce je návrh produktu, ktorý bude riešiť potreby zákazníkov a poskytovateľov služieb v oblasti Dolovania procesov lepšie než súčasné riešenie vybranej spoločnosti.
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Modelling and Analysis of Logistics Processes by Applying Process and Data Mining Techniques
Rudnitckaia, Julia ; Wang, Hao (referee) ; Zendulka, Jaroslav (referee) ; Hruška, Tomáš (advisor)
In this thesis, we propose an approach for modelling hidden and unknown processes and subprocesses in the example of a seaport logistics area. Having the underlying process model makes it possible to exploit more advanced algorithms since deviations and main paths are becoming visible and better controlled. The obtained model is the foundation for the core research of this work and will be enriched with key performing indicators and their forecast by applying advanced process mining, statistics, and machine learning techniques. The main difference of the approach is that we take as a target variable not any specific value, but the object - a process variant or a process type with a set of parameters. Bottleneck analysis, from one side, and predictive analysis, on the other hand, are enforced with context-aware information, especially with these additional objective process attributes. Furthermore, the support of the descriptive ("As is") current process model with certain notation and the integration with relevant bottleneck and predictive methods compromise the advantages of the approach. The work primarily focuses on the design of algorithms and methods for supporting logistics data analysis. However, it can be adjusted and applied to other areas accordingly, which makes the approach flexible and versatile. The result of the work is the framework for unstructured process modelling and the key process parameters predictive method. This analysis of processes with their attributes might be used for decision-making systems and process maps in future.
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Business Process Analysis
Mička, David ; Burget, Radek (referee) ; Hruška, Tomáš (advisor)
The goal of this thesis is to design a method of business process analysis, with the focus on process optimalization by using Predictive Maintenance, creation of a suitable prototype for this method and evaluation of practical applicability. The basis of this thesis is the research of study group led by Prof.dr.ir. W.M.P. van der Aalst revolving around process mining and tools for his examination. The same study group also created the program ProM used in this thesis.
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Knowledge Discovery from Process Logs
Kluska, Martin ; Burgetová, Ivana (referee) ; Bartík, Vladimír (advisor)
This Master's describes knownledge discovery from process logs by using process mining algorithms. Chosen algorithms are described in detail. These aim to create process model based on event log analysis. The goal is to design such components, which would be able to import the process and run the simulations. Results from components can be used for short term planning.
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