-The information technology industry cannot be imagined without large-or small-scale projects. They are implemented to develop systems enabling key business processes and improving performance and enterprise resource management. However, projects often experience various difficulties during their execution. These problems are usually related to the three objectives of the project -costs, quality and deadline. A way these challenges can be solved is project risk management. However, not always the main problems and their influencing factors can be easily identified. Usually there is a need for a more profound analysis of the problem situation. In this paper, we propose the use of a Bayesian Network concept for quantitative risk management in agile projects. The Bayesian Network is explored using a case study focusing on a project that faces difficulties during the software delivery process. We explain why an agile risk analysis is needed and assess the potential risk factors, which may occur during the project. Thereafter, we design the Bayesian Network to capture the actual problem situation and make suggestions how to improve the delivery process based on the measures to be taken to reduce the occurrence of project risks.
Development of Enterprise Resource Planning (ERP) systems has become an independent industry for the improvement of information systems. It can be stated that ERP systems are designed to support the operation of a company. The fundamental objective of the system is to create a business value that aims at reducing the time and costs of the business while increasing the profit of the company. Although the adaptation and deployment of the ERP system, in general, are complex and protracted processes that require a lot of resources, the obtained results sometimes differ from the expected results of users. Mostly for that reason, there are incomplete internal enterprise business processes and software requirements analysis and development. Therefore, there is a need to determine the compliance of the main enterprise requirements and business processes with the ERP system. To choose the most appropriate ERP system, it is necessary to identify all possible methods of input data for the fit-gap analysis method. Thus, the main aim of the present study is to identify possible input data for the fit-gap analysis method, which can be used for the selection of the most appropriate ERP system.
In software development projects, managers still have to face a variety of organisational and technical limitations despite the development of technology and approaches to improve the project management process. Projects, Human Resources and Costs are planned for a specific period of time. However, in the progression of project execution, there is a need to make various decisions and to dynamically adjust the work plan during the project in order to conform to its evolution. Thus, there is a need for a method that employs the latest technology to support the project management decision-making process.The aim and the expected result of the article are to identify and collect available information in the scientific literature to answer the following questions: (1) Which challenges of project management have been addressed using genetic algorithms? (2) What are the opportunities and limitations of genetic algorithms in the project management decision-making process? (3) What are the potential solutions to the identified genetic algorithm problems?
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