Intelligent Project Tracking System

Abstract

Modern project management tools often present a fragmented view of data and reporting, leading to suboptimal decision-making and tracking inefficiencies. This dissertation introduces the "Intelligent Delivery Progress Tracking System," a comprehensive solution to enhance the tracking and management of software development projects. Integrating data from disparate project-planning tools into a centralized repository gives project managers a holistic view of progress and resource allocation, enabling more informed and strategic decision-making. Utilizing generative AI, the system navigates the complexity of project tasks and predicts potential risks, thereby refining estimation processes and improving resource allocation. This anticipatory approach to project management is expected to significantly reduce issue resolution times and enhance the overall quality of the software delivered. Predictive analytics embedded within the system provide foresight into project trajectories, allowing for preemptive adjustments and agile responses to emerging issues. The dissertation investigates the application of artificial intelligence in project management, demonstrating how AI can effectively supplement the project tracking domain. The anticipated outcome is a responsive and efficient project management system that not only streamlines project oversight but also advances the quality of software delivery. This has substantial implications, potentially revolutionizing project management by delivering an intelligent and intuitive system capable of handling the complexities of modern software development projects. The value of this research is in its potential to integrate the wealth of data from existing tools into a single, intelligent system that tracks, predicts, and advises, thereby adding a layer of predictive intelligence to the project management process. The main objectives of this thesis are to develop an intelligent tracking system/dashboard that provides comprehensive KPIs and visualizations for overall project tracking, and detailed feedback on specific issues, notably in the areas of task estimation and human resource allocation