The subject of the master’s thesis is a risk analysis in transport infrastructure projects. In the theoretical part, I deal with public sector, life cycle of the project, evaluation of public projects, conception of risk, clasification of risk, risk analysis and valuation of the risk. In the practical section I process risk analysis of the project in transport infrastucture.

Master’s thesis focused on the risk identification with application of selected strategic and economic analyses of the company Daikin Device Czech Republic s.r.o. External and internal analysis will be performed by PESTLE analysis, Porter's Five Forces, McKinsey 7S analysis, selected financial analyses indicators, SWOT analysis and risk analysis by RIPRAN analysis. Risks will be evaluated from the results of analyses. For this risks will be suggested recommendations, which should lead to their decrease and improve the situation in the company.

The thesis contains risk analysis of the investment construction projects. The theoretical part is focused on risk definition, types of risks and risk management methods. Further there is various risk analysis in investment projects described. In the practical part is the evaluation of economic efficiency including the risk assessment in development project of the company Prologis.

Diploma thesis is focused on risk management of Obchodní společnost Slokov, a.s. which is engaged in production of heating equipment. Thesis contains issues of risk management, risk analysis and proposal of measures which will eliminate the result of identified risks. The first part of thesis is focused on theoretical background, which describes the analytical methods and basics of risk management. The second part includes application of the selected methods. Risk identification of company is performed. Risks were found by internal and external analysis and method of risk analysis. In the conclusion of thesis are recommendation to help minimize risks in company on preventive or corrective level.

The thesis deals with risk management in the company Energopoint Ltd. The theoretical part explains important concepts related to the issue. This primarily involves the identification, analysis and follow-up methods of risk reduction. In the analytical part are identified all possible risks a company that has been detected by analysis of macro environment, micro environment and internal analysis of the company. To evaluate the significance of risks is used scoring method. Based on the findings of the analytical part I proposed measures to reduce risks. The contribution of the work is to gain the advantages of introducing risk management in the company.

The thesis deals with risk management of business entity, which provides services in field of education officials. Risk management proces consists of several parts, such as identification, analysis and risks reduction. All of these parts, including basic concepts, are described in the theoretical part. In the analytical part there are methods, which are using for identification potential risks. It include SLEPT analysis, Porter analysis and 7S model, then comes SWOT analysis. Based on these analyzes are proposed measures and recommendations that will help reduce risks.

This diploma thesis deals with risk management of a selected company. The described company is called PLASTIC BOX CZ, Ltd. and it is engaged in the manufacture of plastic products. The theoretical part contains the definition of components, which are mainly identification, analysis and methods to reduce individual risks. Appropriate analytical methods related to the risk management process are subsequently defined. In the analytical part, risks of the business entity are identified using selected methods. Risk analysis is performed by the FMEA analysis and elaborated in the specific process of running the company. The last part of this thesis includes the design of a risk management system of operating the company PLASTIC BOX CZ, Ltd., which is meant to ensure minimization and prevention of possible risks.

Machine Learning has significantly influenced development across domains by leveraging incoming and existing data. However, despite its advancements, criticism persists regarding its failure to adequately address real-world problems, with the construction domain being an example. Construction sector is crucial for global economic growth, yet it remains largely unexplored, lacking sufficient research and technological utilization of its extensive data. Despite increasing publications on adapting technological advancements, the primary focus is on urging industry innovation through digitization. Recently, adopting Machine Learning to address operational challenges has gained attention. While some studies have explored potential ML integration opportunities in construction, there is a gap in understanding the factors and barriers hindering its adoption across projects. This study investigates the factors restricting organizations from implementing ML in construction projects and their consequent operational impacts. This study employs a comprehensive literature review of ML concepts and identifies gaps in construction data. Qualitative interviews have been conducted in a semi-structured manner with five industry professionals offering practical insights, preceding a thematic analysis of interview data. Themes are analyzed and discussed in relation to theoretical material to identify connections. Finally, a risk assessment based on identified risks is evaluated through a risk matrix. The results of this study discuss the challenges and potential benefits of implementing ML within the construction industry. The study further emphasizes the necessity of knowledge to understand project-specific datasets. With a primary focus on unstructured text and image data, the study uncovers challenges related to data inconsistency that affect data reliability. While recognizing ML’s potential to streamline construction operations, the study underscores challenges such as data security and digitalization. In summary, this study emphasizes the importance of data quality, security, and cultural transformation in harnessing ML’s capabilities to improve construction project management and operations.

A public-private partnership (PPP) is a collaborative partnership between the publicsector and a private entity. The adoption of PPPs has risen throughout the years, utilizedfor developing cultural infrastructure. With the increase in opting for PPPs, the numberof failed PPPs has shown to be due to inefficient risk management procedures. This thesisdelves into the realm of public-private partnerships (PPPs) and examines the integrationof stakeholder management in the risk identification process, aiming to answer thefollowing research question: How do the public sector and private sector in a public-private partnership respectivelyconsider stakeholder management when identifying risks? Through a qualitative single case study of a PPP project in Sweden, the research aims toprovide insights into how key actors in PPPs consider stakeholders when identifyingrisks. The study emphasizes the importance of stakeholder theory in enhancing riskidentification practices within the context of collaborative ventures. By exploring thecomplexities of PPPs and the dynamics of stakeholder engagement, this researchcontributes to the existing literature on effective partnership arrangements and riskmanagement in public-private partnerships (PPPs). The findings highlight thesignificance of incorporating stakeholder perspectives in risk identification strategies tofoster successful outcomes in PPP projects. To answer the research question, semi-structured interviews were conducted with thepublic and private entity involved in the PPP chosen for the case study. This study notonly offers theoretical implications for understanding the interplay between stakeholdertheory and risk management but also provides practical insights for organizations seekingto establish and maintain successful PPPs. The research underscores the critical role ofclear project objectives, defined partnership responsibilities, communication, andcollaboration in sustaining long-term partnerships. By bridging the gap betweenstakeholder theory and risk management, the study offers a theoretical contribution forunderstanding how stakeholder engagement can enhance risk identification strategies in PPP projects. Overall, this thesis contributes to advancing knowledge on stakeholder considerations inPPPs and offers recommendations for enhancing risk identification measures incollaborative ventures.

This master's thesis focuses on the knowledge of risk management. The core of this issue contains risk management process, risk identification, monitoring and risk analysis. The text in the first part explains stages of identifying the risks and the methodology used. The aim was to create, after a thorough analysis, the detailed design, then proceed to implementing individual parts of the system and to create a functional system for the support of identification of risks in IT projects. The system was implemented in PHP programming language, working with the MySQL database system on the database level. In the end there is an assessment and discussion of possible expansion.