A project report submitted in partial fulfillment of the requirements for the Degree of Master of Science (Professional) in Data Science and Artificial Intelligence Applications

Project - Master of Science (Professional) - Asian Institute of Technology, 2025

This research addresses the limitations of the current Productivity and Quality Measurement System (NSCL) at VNPT Hanoi{u2019}s Information Operation Center (IOC), which relies on a legacy SQL-based database. The existing system suffers from rigid data structures, oversized and inconsistent job catalogs, lack of task segmentation, manual data entry for high-frequency operations, and the absence of real-time dashboards. These weaknesses result in unfair employee evaluations, operational inefficiencies, and limited decision-making support.The study aims to design and implement an improved, task-segmented evaluation framework that ensures fairness, consistency, and transparency. Using 2024 operational records from six departments, the research employs a mixed-methods approach: quantitative data analysis, qualitative interviews, and benchmarking against international standards such as ISO 9001, Lean IT, and TM Forum eTOM. The proposed system integrates MongoDB for flexible data modeling, task segmentation for fair scoring, and Business Intelligence dashboards for real- time KPI monitoring.Expected contributions include a standardized job catalog, a scalable database architecture, automation of repetitive tasks, and a unified KPI evaluation model that combines organizational objectives with task-based performance. The framework also establishes data governance mechanisms to enhance data integrity and proposes predictive analytics as a future extension to identify low performance early.