TY - JOUR A1 - Woltering, Tim A1 - Sardoux Klasen, André A1 - Feldmann, Carsten T1 - The Economic Value Added of Augmented Reality in the Packing Process JF - Journal of Applied Business and Economics Y1 - 2020 U6 - http://dx.doi.org/10.33423/jabe.v22i5.3051 VL - 22 IS - 5 SP - 88 EP - 96 ER - TY - CHAP A1 - Woltering, Tim A1 - Sardoux Klasen, Andre A1 - Feldmann, Carsten ED - Freitag, Michael ED - Haasis, Hans-Dietrich ED - Kotzab, Herbert ED - Pannek, Jürgen T1 - Augmented Reality in the Packing Process A Model for Analyzing Economic Efficiency T2 - Dynamics in Logistics. LDIC 2020. Lecture Notes in Logistics. N2 - The use of augmented reality (AR) in outbound logistics is associated with potentially strong stimuli for cost savings and throughput time. Nevertheless, the benefits of AR compared to conventional methods require a holistic analysis for investment decision making. Until now, research has only assessed case-study-related potentials and selected aspects of the technology. This paper answers the following research questions: How can the economic efficiency of AR in the packing process be quantified by utilizing a holistic model of value drivers? How can AR be technically implemented for packing processes in outbound logistics? What economic profit results from the use of AR technology in a case company’s packing process? The presented model enables the investment decision to be supported based on economic value added (EVA), thereby providing an assessment of value drivers in packing systems. Cost drivers are identified on the basis of the Supply Chain Operations Reference (SCOR) process model. The technical and economic validation of the model was carried out by means of an empirical study: Expert interviews were conducted for validating the model elements. Data collection by a prototype at a mechanical-engineering company was used to calculate the value contribution. The mapping of cause-effect relationships within the framework of EVA driver trees has proven itself in both the expert interviews and the prototype validation. The field experiment at the case company demonstrated a positive value contribution of AR, in particular regarding employee productivity, length and variance of throughput time, quality aspects, volume utilization, and quantity of packing material used. KW - Augmented Reality KW - Cost drivers KW - Packaging KW - Economic value added KW - Value contribution Y1 - 2020 UR - https://link.springer.com/chapter/10.1007%2F978-3-030-44783-0_46 SN - 978-3-030-44783-0 U6 - http://dx.doi.org/https://doi.org/10.1007/978-3-030-44783-0_46 SP - 493 EP - 503 PB - Springer CY - Cham 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 - Wiethölter, Jost A1 - Salingré, Jan A1 - Feldmann, Carsten A1 - Schwanitz, Johannes A1 - Niessing, Joerg T1 - Exploring Customer Journey Mining and RPA: Prediction of Customers’ Next Touchpoint T2 - Business Process Management: Blockchain, Robotic Process Automation and Educators Forum. BPM 2023. Lecture Notes in Business Information Processing, vol 491. J. Köpke (ed.) Y1 - 2023 SN - 978-3-031-43432-7 U6 - http://dx.doi.org/10.1007/978-3-031-43433-4_12 SP - 181 EP - 196 PB - Springer CY - Cham ER - TY - JOUR A1 - Werther, Eike A1 - Feldmann, Carsten ED - Huss, Wolfgang ED - Seebauer, Petra T1 - Auf der Überholspur: Digitale Speditionen JF - Logistik Heute Y1 - 2020 SN - 0173-6213 VL - 42 IS - 7-8 SP - 58 EP - 59 ER - TY - CHAP A1 - Thesing, Theo A1 - Feldmann, Carsten A1 - Burchardt, Martin T1 - Agile versus Waterfall Project Management: Decision Model for Selecting the Appropriate Approach to a Project T2 - ProjMAN – International Conference on Project Management 2020, Procedia Computer Science 181 (2021) Y1 - 2021 U6 - http://dx.doi.org/10.1016/j.procs.2021.01.227 SP - 746 EP - 756 ER - TY - CHAP A1 - Tackenberg, Sven A1 - Jungkind, Wilfried A1 - Feldmann, Carsten A1 - Appelfeller, Wieland ED - Padoano, Elio ED - Villmer, Franz-Josef T1 - Digital Transformation of Companies: Experience Gained in the Implementation of an IoT Check T2 - 9th International Conference on Production Engineering and Management (PEM) 2019, Proceedings Y1 - 2019 SN - 978-3-946856-04-7 SP - 281 EP - 290 CY - Triest, Lemgo ER - TY - JOUR A1 - Tackenberg, Sven A1 - Jungkind, Wilfried A1 - Feldmann, Carsten A1 - Appelfeller, Wieland T1 - Digitale Transformation von Unternehmen: Erfahrungen aus der Durchführung eines Digitalisierungs-Checks JF - ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb Y1 - 2019 U6 - http://dx.doi.org/10.3139/104.112190 SN - 0032-678X VL - 114 IS - 11 SP - 771 EP - 775 ER - TY - CHAP A1 - Stahl, Maximilian A1 - Feldmann, Carsten A1 - Dircksen, Michael T1 - Straßengüterverkehr der Zukunft T2 - Jahrbuch Logistik Y1 - 2022 SN - 978-3-947135-09-7 SP - 14 EP - 18 ER - TY - RPRT A1 - Sossna, David A1 - Sardoux Klasen, André A1 - Feldmann, Carsten T1 - Antworten made in münsterLAND: Ergebnisbericht der DigiTrans@KMU Online-Umfrage Y1 - 2020 UR - www.digitalradar-muensterland.de ER -