Klassen, GerhardBauer, Luca T.Fritzsche, RobinKordyaka, BastianWeber,SebastianNiehaves, BjörnWimmer, Maria A.Räckers, MichaelHünemohr, Holger2024-09-232024-09-232024978-3-88579-745-6https://dl.gi.de/handle/20.500.12116/44613Public procurement in Germany, accounting for 15% of GDP, is plagued by inefficiencies, high costs, and lack of transparency. This study investigates how Open Data can enhance competitive bidding and streamline the identification of suitable companies. Using the German public procurement market, we propose a web-based portal employing machine learning to automate tender and bidder matchmaking. Our methodology includes data collection, company profiling, and NLPbased similarity searches. Results indicate that integrating Open Data can increase competition, improve bid quality, and enhance procurement efficiency. This research provides a scalable framework for more transparent and effective public procurement practices, with potential applications in other regions and sectors.enOpen DataProcurementMachine LearningArtificial IntelligenceText/Research Paper10.18420/rvi2024-111617-5468