Balster, Andreas, Ole Hansen, Hanno Friedrich and André Ludwig (2020): An ETA Prediction Model for Intermodal Transport Networks Based on Machine Learning, Business & Information Systems Engineering.
Abstract: Transparency in transport processes is becoming increasingly important for transport companies to improve internal processes and to be able to compete for customers. One important element to increase transparency is reliable, up-to-date and accurate arrival time prediction, commonly referred to as estimated time of arrival (ETA). ETAs are not easy to determine, especially for intermodal freight transports, in which freight is transported in an intermodal container, using multiple modes of transportation. This computational study describes the structure of an ETA prediction model for intermodal freight transport networks (IFTN), in which schedule-based and non-schedule-based transports are combined, based on machine learning (ML). For each leg of the intermodal freight transport, an individual ML prediction model is developed and trained using the corresponding historical transport data and external data. The research presented in this study shows that the ML approach produces reliable ETA predictions for intermodal freight transport. These predictions comprise processing times at logistics nodes such as inland terminals and transport times on road and rail. Consequently, the outcome of this research allows decision makers to proactively communicate disruption effects to actors along the intermodal transportation chain. These actors can then initiate measures to counteract potential critical delays at subsequent stages of transport. This approach leads to increased process efficiency for all actors in the realization of complex transport operations and thus has a positive effect on the resilience and profitability of IFTNs.
Grieger, Marcus and André Ludwig (2019): On the move towards customer-centric business models in the automotive industry - a conceptual reference framework of shared automotive service systems, Electronic Markets, 29 (3): 473-500.
Abstract: Digitalization drives automotive original equipment manufacturers (OEMs) to change their value propositions and open-up towards greater collaboration and customer integration. The shift towards services implies a transformational change from product- towards customer-centricity. This study proposes a conceptual reference framework (CRF) out of a business model perspective to systematize automotive service systems. The CRF presents relevant dimensions and dependencies between the involved stakeholders and the necessary infrastructures in order to facilitate digital service conceptualization in the early phases of the service design. The artifact is developed based on a literature review and conceptual modeling, then iteratively evaluated by means of guideline-supported interviews from three different perspectives and applied to a real problem statement within a case workshop. The results suggest value creation for automotive services occurs in shared mobility networks among interdependent stakeholders in which customers play an integral role during the service life-cycle. Additionally, the results deepen the understanding of service business model development under consideration of industry-specific aspects and suggest the framework to be a beneficial structuring tool that can save resources and specify solution finding.