TY - CHAP A1 - Wiethölter, Jost A1 - Kühl, Linus A1 - Feldmann, Carsten ED - Wimmer, Maria A. ED - Räckers, Michael ED - Hünemohr, Holger T1 - AI-based chatbots as enabler for efficient external knowledge management in public administration T2 - 7. Fachtagung Rechts- und Verwaltungsinformatik (RVI 2024): Neue Wege der Zusammenarbeit und Vernetzung für digitale Transformation und Verwaltungsmodernisierung. Hrsg. Maria A. Wimmer, Michael Räckers, Holger Hünemohr N2 - This study addresses the pressing issue of staff shortages in German public administrations through the lens of digitalization, focusing on the potential of AI-based chatbots to solve this problem by replacing human labour. Employing a Design Science Research Process (DSRP) methodology, the research synthesizes theoretical foundations and regulatory frameworks to develop a robust chatbot concept. The artifact presented is a comprehensive architectural framework integrating user-centric design, linguistic processing, and regulatory compliance. The proposed artifact navigates complex federal structures and diverse IT infrastructures, promoting accessibility and inclusivity. Implications suggest enhanced efficiency and accessibility in public service delivery for potentially increasing citizen satisfaction and decreasing employee workload. The study underscores the importance of legal compliance and the evolving regulatory landscape in AI deployment. Future research will involve prototyping and evaluating the artifact's performance and applicability throughout the course of the DSRP, thus contributing to the advancement of digital transformation in public administrations. KW - Artificial Intelligence KW - Generative AI KW - Large Language Models KW - Chatbot KW - DSRP Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:836-opus-181972 SN - 978-3-88579-745-6 SP - 149 EP - 159 PB - Gesellschaft für Informatik e.V. CY - Bonn ER - TY - CHAP A1 - Eschenbächer, Jens A1 - Wiethölter, Jost A1 - Kühl, Linus T1 - Data-driven supply chain analysis: Development and potential analysis of a model-based damage prediction approach and its integration into SCM T2 - Proceedings of the 27th International Symposium on Logistics N2 - Against the setting of an increasing need for innovation and low margins, companies in the logistics sector are facing highly competitive pressure. One field with high potential for optimization lies within damage quotas. The use of big data analytics or data mining represents a promising approach to face this challenge. However, within supply chain management, data mining is hardly being researched on regarding damage quotas and thus not being utilized to its full possible extend. At the current time it seems to predominantly be used for route and utilization optimization while the analysis of delivery damages is hardly considered. The aim of this research is therefore to showcase an initial approach for data mining in logistics to predict delivery damage probabilities and to validate this by means of a multiple case study research. To create a sound basis for evaluation, the groundwork is laid out based on CRISP-DM by the analysis of reference data (German road-cargo market). As a central result it is noted that data mining can systematically be used to help reducing the damages by forecasting the probabilities of damages occurring during transport in dependence of different factors. The approach can be utilized across different markets as long as sufficient data tracking delivery damages is being collected within a company. Challenges arise in the field of air- and sea-freight. Y1 - 2023 UR - https://www.islconf.org/wp-content/uploads/2023/07/ISL_2023_Final_Proceedings.pdf SN - 13 978-0-85358-352-3 SP - 136 EP - 144 ER - TY - CHAP A1 - Kühl, Linus A1 - Wiethölter, Jost A1 - Dircksen, Michael T1 - Enhancing Supply Chain Risk Identification: Analyzing the Impact of LLM Parameters for precise Classification T2 - Building sustainable connectivity through logistics and supply chains : proceedings of the 28th International Symposium on Logistics (ISL 2024) : 07-10th July 2024 N2 - This study investigates the impact of Large Language Model (LLM) parameters, specifically temperature and top P, on Supply Chain Risk Detection (SCRD). With a heightened focus on Supply Chain Risk Management (SCRM) using AI, the research employs a Design of Experiments (DoE) approach. The results reveal optimal temperature values for valid assessments in SCRD applications. The study emphasizes the importance of tailored LLM parameter settings, contributing insights for future research and practical applications in enhancing supply chain resilience. Suggestions for incorporating Response Surface Methodology (RSM) and refining the process are proposed for further investigation. KW - Artificial Intelligence KW - Large Language Model KW - Data Analytics KW - Design of Experiments KW - Logistics Y1 - 2024 UR - https://www.islconf.org/wp-content/uploads/2024/07/ISL_2024_Proceedings_Final.pdf SN - 978-0-85358-354-7 SP - 197 EP - 205 PB - Centre for Concurrent Enterprise, Nottingham University Business School CY - Nottingham, UK ER - TY - CHAP A1 - Wiethölter, Jost A1 - Salingré, Jan A1 - Feldmann, Carsten A1 - Schwanitz, Johannes A1 - Niessing, Jörg ED - Köpke, Julius ED - Plattfaut, Ralf ED - Gdowska, Katarzyna ED - Munoz-Gama, Jorge ED - van der Werf, Jan Martijn ED - López-Pintado, Orlenys ED - Rehse, Jana-Rebecca ED - Gonzalez-Lopez, Fernanda ED - Smit, Koen T1 - Exploring Customer Journey Mining and RPA: Prediction of Customers’ Next Touchpoint T2 - Business Process Management: Blockchain, Robotic Process Automation and Educators Forum N2 - In-depth analysis of customer journeys to broaden the understanding of customer behaviors and expectations in order to improve the customer experience is considered highly relevant in modern business practices. Recent studies predominantly focus on retrospective analysis of customer data, whereas more forward-directed concepts, namely predictions, are rarely addressed. Additionally, the integration of robotic process automation (RPA) to potentially increase the efficiency of customer journey analysis is not discussed in the current field of research. To fill this research gap, this paper introduces “customer journey mining”. Process mining techniques are applied to leverage digital customer data for accurate prediction of customer movements through individual journeys, creating valuable insights for improving the customer experience. Striving for improved efficiency, the potential interplay of RPA and customer journey mining is examined accordingly. The research methodology followed is based on a design science research process. An initially defined customer journey mining artifact is operationalized through an illustrative case study. This operationalization is achieved by analyzing a log file of an online travel agency functioning as an orientation for researchers and practitioners while also evaluating the initially defined framework. The data is used to train seven distinct prediction models to forecast the touchpoint a customer is most likely to visit next. Gradient-boosted trees yield the highest prediction accuracy with 43.1%. The findings further indicate technical suitability for RPA implementation, while financial viability is unlikely. KW - Customer Journey Mining KW - Customer Journey Mapping KW - Robotic Process Automation KW - Process Mining KW - Prediction Y1 - 2023 UR - https://link.springer.com/chapter/10.1007/978-3-031-43433-4_12#Abs1 SN - 978-3-031-43432-7 U6 - http://dx.doi.org/https://doi.org/10.1007/978-3-031-43433-4 SN - 1865-1348 SP - 181 EP - 196 PB - Springer ER - TY - CHAP A1 - Eschenbächer, Jens A1 - Dircksen, Michael A1 - Kühl, Linus A1 - Wiethölter, Jost T1 - Initial approach for AI-based real time global risk assessment in SCM T2 - Proceedings of the 27th International Symposium on Logistics Y1 - 2023 UR - https://www.islconf.org/wp-content/uploads/2023/07/ISL_2023_Final_Proceedings.pdf SN - 13 978-0-85358-352-3 SP - 75 EP - 76 ER - TY - CHAP A1 - Eschenbächer, Jens A1 - Kühl, Linus A1 - Wiethölter, Jost T1 - Initial Approach for Data Mining in Logistics: software supported prognosis exemplified by delivery damage probabilities depending on different factors T2 - Proceedings of the 26th International Symposium on Logistics Y1 - 2022 UR - https://www.islconf.org/wp-content/uploads/2022/08/ISL-2022-PROCEEDINGS-1.pdf SN - 13 978-0-85358-350-9 SP - 32 EP - 32 ER - TY - CHAP A1 - Wiethölter, Jost A1 - Kühl, Linus T1 - Supply Network Mapping: Development of a region-centric Approach T2 - Building sustainable connectivity through logistics and supply chains : proceedings of the 28th International Symposium on Logistics (ISL 2024) : 07-10th July 2024 KW - Mapping KW - Analytics KW - Logistics Y1 - 2024 UR - https://www.islconf.org/wp-content/uploads/2024/07/ISL_2024_Proceedings_Final.pdf SN - 978-0-85358-354-7 SP - 101 EP - 102 PB - Centre for Concurrent Enterprise, Nottingham University Business School CY - Nottingham, UK ER -