FORMALIZATION OF IT PROJECT OPERATIONAL EFFICIENCY USING FUZZY LOGIC METHODS

Abstract

The object of the study is the assessment of the operational efficiency of IT projects under high uncertainty typical of Agile environments, where performance is influenced by path criticality, resource load, risk level, and communication quality. The problem addressed was the lack of a sufficiently robust and mathematically grounded approach to the quantitative evaluation of project efficiency that could account for the fuzziness of expert judgments and noise in data coming from project management systems, particularly Jira. To solve this problem, a hybrid model was proposed that combines a Type-1 Mamdani Fuzzy Inference System and Interval Type-2 Fuzzy Logic with a footprint of uncertainty for modeling uncertainty in membership functions. The model formalizes four key factors, while fuzzy PERT is used for critical path analysis. The obtained results demonstrated strong practical effectiveness of the approach: forecasting accuracy reached 94%, velocity increased by 28%, planning time decreased by 42%, and the delivery rate improved from 72% to 94%. Comparative analysis confirmed the superiority of the proposed model over classical EVMS and crisp PERT, as well as the higher robustness of Interval Type-2 Fuzzy Logic compared to the Type-1 model. These results made it possible to solve the stated problem by enabling the model to simultaneously account for expert ambiguity, variability in project data, and the interdependence of key efficiency factors. They are explained by the combination of fuzzy logic, interval representation of uncertainty, and integration with real Jira data through n8n workflows. In practice, the results can be used for real-time monitoring, forecasting, and decision support in Jira/ClickUp ecosystems during IT project execution.