TY - CHAP A1 - Gersch, Martin A1 - Lindert, Ralf A1 - Hewing, Michael T1 - AAL-business models: Different prospects for the successful implementation of innovative services in the first and second healthcare market T2 - Proceedings of the AALIANCE European Conference on AAL Y1 - 2010 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 - 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 - 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 - 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 - Appelhans, Hendrik A1 - Feldmann, Carsten A1 - Borgmann, Christopher T1 - Sensor-Based Analysis of Manual Processes in Production and Logistics: Motion-Mining versus Lean Tools T2 - International Conference on Dynamics in Logistics. Michael Freitag, Aseem Kinra, Herbert Kotzab, Nicole Megow (Eds.) Y1 - 2024 SN - 978-3-031-56826-8 U6 - http://dx.doi.org/10.1007/978-3-031-56826-8_18 SP - 235 EP - 248 PB - Springer Nature Switzerland CY - Cham ER -