@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}
}
@inproceedings{SchneidThoeneKuchen2023,
  author    = {Schneid, Konrad and Th{\"o}ne, Sebastian and Kuchen, Herbert},
  title     = {Modification-Impact based Test Prioritization for Process-Driven Applications},
  series = {2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)},
  booktitle = {2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)},
  organization    = {IEEE},
  doi       = {10.1109/ICSTW58534.2023.00068},
  pages     = {365 -- 372},
  year      = {2023},
  language  = {en}
}
@inproceedings{KindsgrabDircksenZadek2023,
  author    = {Kindsgrab, Kai and Dircksen, Michael and Zadek, Hartmut},
  title     = {Effects of CO2e measures for the transport logistics sector},
  series = {16th International Doctoral Students Workshop on Logistics, Supply Chain and Production Management},
  booktitle = {16th International Doctoral Students Workshop on Logistics, Supply Chain and Production Management},
  editor    = {Glistau, Elke and Trojahn, Sebastian},
  publisher = {Otto von Guericke University Library},
  address   = {Magdeburg},
  isbn      = {978-3-948749-37-8},
  doi       = {10.25673/103379},
  year      = {2023},
  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{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{MatthiesKochMaassenetal.2022,
  author    = {Matthies, Benjamin and Koch, Julian and Maassen, Kathrin and Coners, Andr{\´e}},
  title     = {A Curriculum Mining Method for Clustering Study Modules and Assessing their Uniqueness},
  series = {Proceedings of the 30th European Conference on Information Systems (ECIS)},
  booktitle = {Proceedings of the 30th European Conference on Information Systems (ECIS)},
  publisher = {Association for Information Systems (AIS)},
  year      = {2022},
  language  = {en}
}
@inproceedings{DamwerthBachBuchholz2022,
  author    = {Damwerth, Philipp and Bach, Norbert and Buchholz, Wolfgang},
  title     = {Ecosystem Emergence and Founding Conditions - Lessions Learned from an Imprinting Perspective},
  year      = {2022},
  abstract  = {The rise of ecosystem prominence has provided several definitions of how we understand ecosystems nowadays. In this context, several scholars have considered influencing factors for ecosystem emergence. This paper addresses this consideration and analyzes the salient characteristics of different ecosystem types and their potential persistence since ecosystem founding to improve the understanding of emergence. We applied a three-step approach (1) identifying ecosystem types based on bibliometric analysis, (2) exploring salient characteristics per ecosystem type using qualitative content analysis and (3) deriving founding conditions from the salient characteristics following a conceptual approach. Based on a bibliometric analysis, we identified business/innovation, entrepreneurial and service ecosystems. In a second step, we developed salient characteristics within the themes of structure, power constellation/interdependencies and governance by inductive coding. As we identified a significant difference in alignment structure, we analyzed if alignment structure persists since ecosystem origin and explains why ecosystems differ. We analyzed potential pairings between alignment structure and their respective founding condition for every ecosystem type. With the alignment structures' persistence, we can better understand why ecosystem types differ.},
  language  = {en}
}
@inproceedings{SchneidThoeneHerbert2022,
  author    = {Schneid, Konrad and Th{\"o}ne, Sebastian and Herbert, Kuchen},
  title     = {Semi-automated Test Migration for BPMN-Based Process-Driven Applications},
  series = {Enterprise Design, Operations, and Computing. Hrsg. Jo{\~a}o Paulo A. Almeida, Dimka Karastoyanova, Giancarlo Guizzardi, Marco Montali, Fabrizio Maria Maggi, Claudenir M. Fonseca},
  booktitle = {Enterprise Design, Operations, and Computing. Hrsg. Jo{\~a}o Paulo A. Almeida, Dimka Karastoyanova, Giancarlo Guizzardi, Marco Montali, Fabrizio Maria Maggi, Claudenir M. Fonseca},
  publisher = {Springer International Publishing},
  address   = {Cham},
  isbn      = {978-3-031-17603-6},
  issn      = {0302-9743},
  doi       = {10.1007/978-3-031-17604-3_14},
  pages     = {237 -- 254},
  year      = {2022},
  abstract  = {Automated regression tests are a key enabler for applying popular continuous software engineering techniques. This paper focuses on testing BPMN-based Process-Driven Applications (PDA). When evolving PDAs, the affected test cases must be identified and co-evolved as well. In this process, affected test cases can be overlooked, misunderstandings may occur during communication between different roles involved, and implementation errors can arise. Regardless of possible error sources, the entire test migration process is time-consuming. This paper presents a new semi-automated test migration process for PDAs. The concept builds on previous work on creating regression tests using a no-code approach. Our approach identifies the modifications of the PDA and classifies their impact on previously defined tests. The classification indicates whether existing test code can be migrated automatically or whether a manual revision becomes necessary. During an AB/BA experiment, the concept and the developed prototype proved a more efficient test migration process and a higher test quality.},
  language  = {en}
}
@inproceedings{WesbuerKurzhalsUude2022,
  author    = {Wesbuer, Annika and Kurzhals, Kerstin and Uude, Katrin},
  title     = {Framework for university-society co-creation.},
  series = {Continuous Innovation Network Conference},
  booktitle = {Continuous Innovation Network Conference},
  address   = {Pisa, Italy},
  year      = {2022},
  language  = {en}
}