@inproceedings{DobhanEberhardtHasenederetal., author = {Dobhan, Alexander and Eberhardt, Lars and Haseneder, Markus and Raab, Heiko and Rabenstein, Steffen and Treutlein, Axel and Wahyudi, Vincent and Storath, Martin and Storath, Martin}, title = {Digitalization in Small-Load-Carrier Management}, series = {Proceedings of the 27th International Conference on Enterprise Information Systems}, booktitle = {Proceedings of the 27th International Conference on Enterprise Information Systems}, publisher = {SCITEPRESS - Science and Technology Publications}, doi = {10.5220/0013363200003929}, pages = {848 -- 855}, language = {en} } @article{WahyudiZieglerFriessetal., author = {Wahyudi, Vincent and Ziegler, Cedric C. and Frieß, Matthias and Schramm, Stefan and Lang, Constantin and Eberhardt, Lars and Freund, Fabian and Dobhan, Alexander and Storath, Martin}, title = {A computer vision system for recognition and defect detection for reusable containers}, series = {Machine Vision and Applications}, volume = {36}, journal = {Machine Vision and Applications}, number = {2}, publisher = {Springer Science and Business Media LLC}, issn = {0932-8092}, doi = {10.1007/s00138-024-01636-x}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:863-opus-57430}, abstract = {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.}, language = {en} } @inproceedings{MuellerEberhardtWahyudietal., author = {M{\"u}ller, Jonas and Eberhardt, Lars and Wahyudi, Vincent and Storath, Martin and Dobhan, Alexander}, title = {Towards an Approach on Location Data Analysis for Reusable Small-load Carriers}, series = {Proceedings of the 58th Hawaii International Conference on System Sciences}, booktitle = {Proceedings of the 58th Hawaii International Conference on System Sciences}, pages = {5330 - 5339}, language = {en} } @inproceedings{SchneiderEberhardtDobhanetal., author = {Schneider, Stanislav and Eberhardt, Lars and Dobhan, Alexander and Schwindl-Braun, Kurt}, title = {Human Factors Affecting a Strategic Marketing Decision: An Experimental Research Design}, series = {Eurasian Studies in Business and Economics}, booktitle = {Eurasian Studies in Business and Economics}, publisher = {Springer Nature Switzerland}, address = {Cham}, isbn = {9783032033208}, issn = {2364-5067}, doi = {10.1007/978-3-032-03321-5_10}, pages = {191 -- 209}, language = {en} } @article{EberhardtMarkertDobhan, author = {Eberhardt, Lars and Markert, Robert and Dobhan, Alexander}, title = {Towards an approach for strategic IoT architecture decision processes in SMEs}, series = {Information Systems and e-Business Management}, journal = {Information Systems and e-Business Management}, publisher = {Springer Science and Business Media LLC}, issn = {1617-9846}, doi = {10.1007/s10257-025-00712-7}, abstract = {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.}, language = {en} }