METHODS OF HIGH-EFFICIENCY PROJECT PLANNING: TRADITIONAL APPROACHES AND MACHINE LEARNING

Abstract

In today’s world, efficiency and speed are the main success factors, so highly effective project planning is necessary for organizations in various fields of activity. The main task of planning is to develop and implement strategies and tactics aimed at optimizing the use of resources, reducing execution time, and improving the quality of the final result. This process requires a systematic approach, detailed analysis, consideration of risks, and flexible change management, making it one of the most important aspects of successful project management. Traditional project management methods include the critical path method (CPM), which allows you to identify the longest sequence of tasks that have a large impact on the overall project duration, and the programming network method (PERT), which takes into account uncertainty in the execution time of tasks. The decision tree method helps to depict possible ways of developing events and to choose the optimal option for decisions, taking into account potential risks and consequences. An iterative and incremental approach allows you to improve the product step by step, adapting it to new requirements and changes in the project. Modern technologies, in particular machine learning (ML) algorithms, open up new opportunities for optimizing project planning processes. The use of ML makes it possible to make accurate predictions regarding the timing of execution, risk assessment, and optimal allocation of resources. Machine learning helps automate the decision-making process, reducing risk and increasing project adaptability to change. For example, regression models allow for predicting time frames, and classification algorithms help assess possible risks in the early stages. The combination of traditional management methods with the latest technologies allows to creation more effective and adaptive project management systems, which contribute to achieving high results, subject to compliance with established deadlines and resource limitations.