Grafelmann, Michaela Zlotos, Constatin Lange, Ann-Kathrin Jahn, Carlos
Year of Publication:
[Editor:] Jahn, Carlos [Editor:] Kersten, Wolfgang [Editor:] Ringle, Christian M. [Title:] Data Science in Maritime and City Logistics: Data-driven Solutions for Logistics and Sustainability. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 30 [ISBN:] 978-3-7531-2347-9 [Year:] 2020 [Pages:] 159-179
Purpose: A wide range of customer relationships, services and organizational interfaces characterizes inland intermodal terminals, which are hubs of combined transport. The purposes of this paper are twofold. The first is to highlight challenges of small and medium-sized enterprises (SME) at the time of digitalization. Secondly, approaches to illustrate the IT and business landscape are presented. Methodology: This paper is based on a literature analysis as well as interviews and identifies aspects of SME- and branch-specific IT and business process landscapes of inland terminals. Moreover, approaches to visualize those landscapes are high-lighted and a distinction is made between different software map types. Findings: Inland intermodal terminals often use a variety of different small, sometimes self-developed IT solutions. Findings show a lack of means of communications and IT equipment as well as the interlinking of systems, which lead to media breaks and inefficient information flow. Therefore, approaches to visualize relevant processes and their application landscapes are presented. Originality: Most literature focuses on larger terminals, which use terminal operating systems (TOS) to manage and link computerized applications efficiently. Due to the effort required to adapt TOS to operational conditions as well as resulting costs, these are often not an option for small and medium-sized terminals. This paper pro-vides a basis for SMEs to systematically visualize and improve their IT and process landscape.
Logistics Industry 4.0 Supply Chain Management Sustainability City Logistics Maritime Logistics Data Science