Intelligent Decision Support for Logistics and Supply Chain Management

Permanent URI for this collection

https://hdl.handle.net/10125/109874

Browse

Recent Submissions

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    On the Relevance of Demand Pattern Categorization
    (2025-01-07) Grimm, Florian; Straub, Tim; Bitsch, Günter; Van Dinther, Clemens
    The application of transfer learning to predict sales demand is an emerging topic that has been attracting more and more attention recently. However, the selection of data to be used for the learning process is not trivial. Data resources are usually scarce and often anonymized to a certain extent, so their usability for successful training is not guaranteed. One solution is to use already developed categorization schemes that group time series based on certain calculated parameters, but the derived categories do not necessarily capture the process of time series formation. This research addresses the question of whether categorization schemes are beneficial for transfer learning approaches by conducting an experiment in which Syntetos’, Boylan’s and Croston’s categorization scheme is used in combination with two deep learning architectures for the transfer learning process. The results show that similar patterns are indeed beneficial for prediction, but that models using all available data perform quite similarly.
  • Thumbnail Image
    Item
    Towards a Taxonomy of Digitalization Technologies in Road Freight Transportation Logistics Business Processes
    (2025-01-07) Kasher, John-Dean; Sardarabady, Navid Julian; Durst, Susanne
    This study proposes a taxonomy of digitalization technologies to be used in road freight transportation logistics, employing the taxonomy development methods by Nickerson et al. (2013) and the revised approach by Kundisch et al. (2021). Based on a literature review and a case study analysis, key dimensions for classifying digitalization technologies are identified. The taxonomy provides a framework for decision-makers to promote strategic technology adoption and operational efficiency. This taxonomy offers practical insights, enhancing the strategic selection of digital solutions in road freight transportation logistics and contributing to academic discourse by addressing a notable research gap in the field of information systems.
  • Thumbnail Image
    Item