Browsing by Author "Balitkaia, Valeria"
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- Process Mining and Power BI for KPI Monitoring in Higher Education InstitutionsPublication . Balitkaia, Valeria; Malheiro, Ricardo Manuel da SilvaThe increasing digitalization of administrative activities in Higher Education Institutions (HEIs) has generated large volumes of process data stored in several information systems. While these systems ensured traceability and accessibility of information, they often lacked systematic monitoring of business process performance, making it difficult to identify inefficiencies, deviations or bottlenecks. This gap makes it difficult for stakeholders to make data-driven decisions and limits the opportunities for continuous improvement. This work addressed the problem of limited visibility over the execution and efficiency of business processes within a HEI by applying Process Mining techniques combined with Business Intelligence (BI) visualizations. The aim was to extract, process and analyze process-related data from the institution’s management information systems, identify relevant performance indicators and provide decision-makers with actionable insights through interactive dashboards. The solution involved process Key Performance Indicators (KPI) definitions, which were grouped into temporal, volume-based, cost efficiency and compliance categories, i.e., KPIs that measure whether the real execution of a process followed the intended process model. Using Process Mining, KPIs related process metrics were discovered and calculated, enabling the identification of deviations and potential optimization points. Beforehand, a validation session with institutional stakeholders occurred, where the practical value of the KPIs for supporting operational and strategic decisions was confirmed. Additionally, the process mining analysis revealed patterns that were previously unknown to managers, reinforcing the benefits of integrating analytical techniques into daily process monitoring. For visualization purposes, three interactive dashboards were developed in Microsoft Power BI, presenting process execution times, workload distribution, cost indicators and other relevant metrics. The developed solution successfully provided a clear, data-driven overview of the institution’s business processes, highlighting areas of inefficiency such as excessive idle times, which affect the costs of each process, and frequent process deviations. In conclusion, the project demonstrated that the combination of Process Mining and Business Intelligence tools can effectively enhance process transparency and performance monitoring in HEIs.