Implementing PowerBI Reporting for Quality Analysis in Decision Making Processes: A QFD and FMECA-Based Approach

This thesis presents the development and pilot implementation of a Power BI-based decision support model to enhance quality analysis and risk management in public procurement. The system aims to enable more transparent, data-driven, and audit traceable decision making processes across the procurement life cycle through the integration of Quality Function Deployment (QFD) and Failure Modes, Effects, and Criticality Analysis (FMECA). The research addresses the institutional need for mechanisms that go beyond compliance and cost minimization, providing structured approaches for supplier quality evaluation, operational risk prioritization, and stakeholder-aligned decision-making. The system is supported by a PostgreSQL backend and integrates both internal and external data sources (e.g., TED, MEPA). It features modular dashboards tailored to specific institutional roles, including procurement officers, contract managers, and compliance officers. In the QFD module, stakeholder needs are systematically captured and translated into weighted evaluation criteria, improving objectivity and consistency throughout the supplier selection process. Simultaneously, the FMECA engine calculates Risk Priority Numbers (RPNs), enabling early identification and mitigation of potential procurement failures. These tools are integrated into an interactive Power BI environment, offering real-time alerts, SLA tracking, and role-based analytics. A pilot study conducted in collaboration with a regional public procurement agency demonstrated that the platform improved evaluation clarity, risk responsiveness, and compliance with procurement policies. Stakeholder feedback highlighted increased confidence in decision making, reduced subjectivity in evaluations, and strong alignment with EU procurement directives and national digital governance goals. The integration of quality, risk, and data visualization analytics into a unified framework presents a replicable model for the modernization of public procurement. Furthermore, it creates opportunities for future developments, including AI-based forecasting, blockchain enabled audit trails, and ERP interoperability.