@inproceedings{GerschLindertHewing2010,
  author    = {Gersch, Martin and Lindert, Ralf and Hewing, Michael},
  title     = {AAL-business models: Different prospects for the successful implementation of innovative services in the first and second healthcare market},
  series = {Proceedings of the AALIANCE European Conference on AAL},
  booktitle = {Proceedings of the AALIANCE European Conference on AAL},
  year      = {2010},
  language  = {en}
}
@inproceedings{WiethoelterSalingreFeldmannetal.2023,
  author    = {Wieth{\"o}lter, Jost and Salingr{\´e}, Jan and Feldmann, Carsten and Schwanitz, Johannes and Niessing, J{\"o}rg},
  title     = {Exploring Customer Journey Mining and RPA: Prediction of Customers' Next Touchpoint},
  series = {Business Process Management: Blockchain, Robotic Process Automation and Educators Forum},
  booktitle = {Business Process Management: Blockchain, Robotic Process Automation and Educators Forum},
  editor    = {K{\"o}pke, Julius and Plattfaut, Ralf and Gdowska, Katarzyna and Munoz-Gama, Jorge and van der Werf, Jan Martijn and L{\´o}pez-Pintado, Orlenys and Rehse, Jana-Rebecca and Gonzalez-Lopez, Fernanda and Smit, Koen},
  publisher = {Springer},
  isbn      = {978-3-031-43432-7},
  issn      = {1865-1348},
  doi       = {https://doi.org/10.1007/978-3-031-43433-4},
  pages     = {181 -- 196},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}
@inproceedings{EschenbaecherKuehlWiethoelter2022,
  author    = {Eschenb{\"a}cher, Jens and K{\"u}hl, Linus and Wieth{\"o}lter, Jost},
  title     = {Initial Approach for Data Mining in Logistics: software supported prognosis exemplified by delivery damage probabilities depending on different factors},
  series = {Proceedings of the 26th International Symposium on Logistics},
  booktitle = {Proceedings of the 26th International Symposium on Logistics},
  isbn      = {13 978-0-85358-350-9},
  pages     = {32 -- 32},
  year      = {2022},
  language  = {en}
}
@inproceedings{EschenbaecherWiethoelterKuehl2023,
  author    = {Eschenb{\"a}cher, Jens and Wieth{\"o}lter, Jost and K{\"u}hl, Linus},
  title     = {Data-driven supply chain analysis: Development and potential analysis of a model-based damage prediction approach and its integration into SCM},
  series = {Proceedings of the 27th International Symposium on Logistics},
  booktitle = {Proceedings of the 27th International Symposium on Logistics},
  isbn      = {13 978-0-85358-352-3},
  pages     = {136 -- 144},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}
@inproceedings{EschenbaecherDircksenKuehletal.2023,
  author    = {Eschenb{\"a}cher, Jens and Dircksen, Michael and K{\"u}hl, Linus and Wieth{\"o}lter, Jost},
  title     = {Initial approach for AI-based real time global risk assessment in SCM},
  series = {Proceedings of the 27th International Symposium on Logistics},
  booktitle = {Proceedings of the 27th International Symposium on Logistics},
  isbn      = {13 978-0-85358-352-3},
  pages     = {75 -- 76},
  year      = {2023},
  language  = {en}
}
@inproceedings{AppelhansFeldmannBorgmann2024,
  author    = {Appelhans, Hendrik and Feldmann, Carsten and Borgmann, Christopher},
  title     = {Sensor-Based Analysis of Manual Processes in Production and Logistics: Motion-Mining versus Lean Tools},
  series = {International Conference on Dynamics in Logistics. Michael Freitag, Aseem Kinra, Herbert Kotzab, Nicole Megow (Eds.)},
  booktitle = {International Conference on Dynamics in Logistics. Michael Freitag, Aseem Kinra, Herbert Kotzab, Nicole Megow (Eds.)},
  publisher = {Springer Nature Switzerland},
  address   = {Cham},
  isbn      = {978-3-031-56826-8},
  doi       = {10.1007/978-3-031-56826-8_18},
  pages     = {235 -- 248},
  year      = {2024},
  language  = {en}
}