TY - CHAP A1 - Dobhan, Alexander A1 - Eberhardt, Lars A1 - Haseneder, Markus A1 - Raab, Heiko A1 - Rabenstein, Steffen A1 - Treutlein, Axel A1 - Wahyudi, Vincent A1 - Storath, Martin A1 - Storath, Martin T1 - Digitalization in Small-Load-Carrier Management T2 - Proceedings of the 27th International Conference on Enterprise Information Systems Y1 - 2025 U6 - https://doi.org/10.5220/0013363200003929 SP - 848 EP - 855 PB - SCITEPRESS - Science and Technology Publications ER - TY - JOUR A1 - Wahyudi, Vincent A1 - Ziegler, Cedric C. A1 - Frieß, Matthias A1 - Schramm, Stefan A1 - Lang, Constantin A1 - Eberhardt, Lars A1 - Freund, Fabian A1 - Dobhan, Alexander A1 - Storath, Martin T1 - A computer vision system for recognition and defect detection for reusable containers JF - Machine Vision and Applications N2 - Abstract Small load carriers (SLCs) are standardized reusable containers used to transport and protect customer goods in many manufacturers. Throughout the life cycle of the SLCs, they will be collected, manually checked for defects (wear, cracks, and residue on the surface), and cleaned by specialized logistic companies. Human operators in small to medium-sized companies manually evaluate the defects due to the variety and degree of possible defects and varying customer needs. This manual evaluation is not scalable and prone to errors. This work aims to fill this gap by proposing a computer vision system that can recognize the SLC type for inventory management and perform defect detection automatically. First, we develop a camera portal, consisting of standard components, that capture the relevant surfaces of the SLC. A labeled dataset of 17,530 images of 34 different SLCs with their defect status was recorded using this camera portal. We trained a classification model (ConvNeXt) using our dataset to predict the different types of SLCs achieving 100% class prediction accuracy. For defect detection, we explore eight state-of-the-art (SOTA) anomaly detection models that achieved high rankings in the MVTec industrial anomaly detection benchmark. These models are trained using default hyperparameters and the two highest-scoring models were chosen and fine-tuned. The best-fine-tuned models based on “Area under the Receiver Operating Characteristic Curve (AUROC)” are PatchCore (0.811) and DRAEM (0.748). These results indicate that there is still potential for improvement in the automation of defect detection of SLCs. Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:863-opus-57430 SN - 0932-8092 VL - 36 IS - 2 PB - Springer Science and Business Media LLC ER - TY - CHAP A1 - Müller, Jonas A1 - Eberhardt, Lars A1 - Wahyudi, Vincent A1 - Storath, Martin A1 - Dobhan, Alexander T1 - Towards an Approach on Location Data Analysis for Reusable Small-load Carriers T2 - Proceedings of the 58th Hawaii International Conference on System Sciences Y1 - 2025 UR - https://hdl.handle.net/10125/109488 ER - TY - CHAP A1 - Schneider, Stanislav A1 - Eberhardt, Lars A1 - Dobhan, Alexander A1 - Schwindl-Braun, Kurt T1 - Human Factors Affecting a Strategic Marketing Decision: An Experimental Research Design T2 - Eurasian Studies in Business and Economics Y1 - 2025 SN - 9783032033208 U6 - https://doi.org/10.1007/978-3-032-03321-5_10 SN - 2364-5067 SP - 191 EP - 209 PB - Springer Nature Switzerland CY - Cham ER - TY - JOUR A1 - Eberhardt, Lars A1 - Markert, Robert A1 - Dobhan, Alexander T1 - Towards an approach for strategic IoT architecture decision processes in SMEs JF - Information Systems and e-Business Management N2 - Research in this article refers to the influencing factors during Strategic IoT architecture decisions in small and medium-sized enterprises. Our results reveal the importance of each factor and its influence on other factors. A selection of influencing factors - mainly from literature - is examined for their significance and relations using Interpretive Structural Modeling. The data for the Interpretive Structural Modeling comes from an interview study with high-quality experts from the German-speaking area. Based on the results, a decision environment for practical decision-makers is built up from the influencing factors found. Subsequently, we provide a tactic to manage the decision-making process via a guide for practitioners. The outcome of our research can help SME practitioners control influencing factors on strategic decisions about the IoT architecture to improve the decision outcome. The authors are unaware of any literature source that provides an approach to handling the influencing factors of strategic IoT architecture decisions in small and medium-sized enterprises. Y1 - 2025 U6 - https://doi.org/10.1007/s10257-025-00712-7 SN - 1617-9846 PB - Springer Science and Business Media LLC ER -