https://hdl.handle.net/10419/182045 2026-11-06T08:11:25Z 2026-11-06T08:11:25Z Stadlthanner, Dominik Steinkellner, Harald Landschützer, Christian Kaever, Domenik https://hdl.handle.net/10419/333431 2025-12-06T02:06:39Z 2024-01-01T00:00:00Z

Title: A hierarchical density-based clustering method applied to mixed-mail in Austria Authors: Stadlthanner, Dominik; Steinkellner, Harald; Landschützer, Christian; Kaever, Domenik Abstract: As the courier, express and parcel (CEP) market has grown rapidly in recent years, shipment packaging has also shifted from classic cuboid cardboards to mixed-mail, typically with flexible plastic or paper packaging. Despite being cost-effective and spaceefficient, the physical characteristics of mixed-mail items vary greatly, resulting in substantial difficulties when utilizing existing automated material handling technology in logistics distribution centers. Developing new material handling technologies that meet the requirements of mixed-mail is challenging due to the heterogeneity of the physical properties of mixedmail, making it difficult to find suitable specimens for testing. To address this issue, this study categorizes mixed-mail based on common combinations of physical characteristics using density-based cluster analysis. The physical characteristics of >400 mixed-mail items were recorded at an Austrian distribution center. The resulting dataset is of the mixed-variable type, meaning that it features both numerical and categorical variables. To homogenize the data for clustering, different methods are available. We compared four homogenization approaches using a benchmark study featuring simulated mixed-variable datasets with varying properties. The approach based on Gower's distance in combination with the clustering algorithm Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) showed the best results over a wide range of different dataset properties. We then use this approach to cluster the Mixed-Mail dataset, resulting in two different clustering solutions based on different hyperparameter settings, with a total of six and eight clusters, respectively.

2024-01-01T00:00:00Z Nitsche, Anna-Maria Franczyk, Bogdan Schumann, Christian-Andreas Reuther, Kevin https://hdl.handle.net/10419/333432 2025-12-06T02:06:34Z 2024-01-01T00:00:00Z

Title: A decade of Artificial Intelligence for supply chain collaboration: Past, present, and future research agenda Authors: Nitsche, Anna-Maria; Franczyk, Bogdan; Schumann, Christian-Andreas; Reuther, Kevin Abstract: Due to its transformative potential, artificial intelligence is considered to be of growing importance for supply networks. As supply chain management faces a multitude of challenges, innovative organizational and technological concepts are required. As the application of artificial intelligence for supply chain collaboration has increased over the last years, a comprehensive overview of past and current developments in this heterogeneous and fragmented field could provide relevant insights into the main research streams and their developments. This paper presents a systematic literature review comprising 83 articles to assess the last decade of research and application of artificial intelligence for supply chain collaboration. The review adds clarity and richness to the field by enabling researchers and practitioners to navigate this topic and by facilitating the identification of future research avenues. The review outcomes are summarized in a conceptual framework of artificial intelligence inspired supply chain collaboration and a research agenda is derived.

2024-01-01T00:00:00Z Gruchmann, Tim Grenzfurtner, Wolfgang Salzmann, Axel https://hdl.handle.net/10419/333428 2025-12-06T02:06:38Z 2024-01-01T00:00:00Z

Title: From inside the cabin: Truck drivers' technology acceptance of driving assistance systems Authors: Gruchmann, Tim; Grenzfurtner, Wolfgang; Salzmann, Axel Abstract: Driving assistants offer opportunities for innovation in transportation logistics by preventing accidents and improving workers' well-being. The related transition towards technological interaction, however, changes the spectrum of job tasks and the drivers' perceptions of their workplace. In this vein, driving assistants are not always viewed positively and deactivated. Using a quantitative research approach of conducting online surveys among truck drivers in Germany (N=142), a theoretical framework based on the Technology Acceptance Model and Innovation Diffusion Theory is tested with PLS-SEM and mediation analyses. As a result, the use of assistance systems and its acceptance is mainly driven by social norms, functionality, and trialability. The research contributes to the behavioral operations management discourse concerned by studying of how behavioral factors, such as cognitive biases and social preferences, influence and impede the interaction with assistance systems. Managerial and policy recommendations are further provided for improving the job design in transport logistics and related incentives for advanced use.

2024-01-01T00:00:00Z Papert, Marcel Reich, Daniel Fischer, Isabel Goertler, Thomas https://hdl.handle.net/10419/333430 2025-12-06T02:06:40Z 2024-01-01T00:00:00Z

Title: Where do we go from here? A bibliometric analysis identifying implications for future research in Circular Economy and Supply Chain Management Authors: Papert, Marcel; Reich, Daniel; Fischer, Isabel; Goertler, Thomas Abstract: The Circular Economy (CE) relies heavily on Supply Chain Management (SCM) as it involves managing the entire value chain and coordinating flows. Despite significant contributions, research on SCM for CE remains fragmented, which hinders a comprehensive exploration. This study presents a bibliometric analysis of 281 publications to examine the intersection between CE and SCM. The main research topics are identified and future directions are suggested. The findings contribute to the literature on CE and SCM by providing a comprehensive overview of the current state of knowledge and the barriers. Valuable implications for future research directions are also provided. Additionally, the implications can guide practitioners, institutions, and policymakers in shaping their SCM strategies and contributing to the ongoing transition to a CE.

2024-01-01T00:00:00Z