The objective of the project is to analyse the information requirements and define the purpose, ensuring a clear understanding of what data is needed. A review of the technical possibilities for determining the key figures is required to provide informed decision-making based on available data. An ideal scenario would involve minimizing or eliminating the dependency on OIZ, although the final solution will depend on further analysis. Throughout this process, the embeddings in the Management Information System (MIS) must be taken into consideration.

The thesis begins with a literature review on payment deadline management and public sector reporting, complemented by expert interviews for practical insights. The existing SKWF accounts payable workflow is examined, and XML files are analyzed to extract relevant payment data. Using this information, a mockup of a Power BI dashboard is created to visualize deadlines, payment statuses, and trends. The prototype aims to improve transparency, reduce manual effort, and support efficient financial management in the public sector.

The thesis concludes that integrating SKWF data into payments deadline reporting provides a significant improvement in process reliability and transparency. However, the implementation also requires close collaboration with IT, data owners, and business users to ensure data quality and system compatibility. It is recommended that further development include automation of report generation and testing in a live environment before full deployment. This project contributes a practical and scalable solution to enhance accounts payable management and supports organisations in strengthening their financial operations through improved reporting practices. The proposed reporting system leverages Power BI for dynamic data visualisation and interactive dashboards, providing real-time insights into payment deadlines. Additionally, as the second solution, Python is utilized to automate data extraction, transformation, and integration processes, ensuring the efficient handling of SKWF data and improving the overall reliability and accuracy of the reporting framework.