Application of a Probabilistic Schedule Estimation Model for Planning Software Development Projects

In the context of increasing complexity, rapidly changing requirements, and high uncertainty in the implementation of modern IT projects, traditional deterministic methods of network-based scheduling demonstrate low accuracy in deadline forecasting and limited applicability for managerial decision support. This article presents the results of applying a probabilistic project duration estimation model using a real-world IT project as a case study, with an explicitly defined task structure and resource constraints. The aim of the study is to assess the applicability and effectiveness of a probabilistic approach forimproving the accuracy of task duration estimates in network scheduling. The researchemploys probabilistic modeling and simulation analysis using the Monte Carlo method, which makes it possible to account for the stochastic nature of task durations and the cascading effects of delays. The results of the validation show that percentile-based planning provides more robust and realistic estimates of project completion times, enables the identification of critical zones of uncertainty, and supports the development of schedules with a predefined acceptable level of risk. The findings confirm the practical value of probabilistic models as a decision-support tool in IT project management.