Open Access

Internet of Things (IoT) technology transforms freight transport operations by adopting novel data-driven services and enables information sharing among actors involved in global transport chains. Mobile telematics represents emerging IoT technologies for global forwarding increasingly applied to full loads conveyed by freight transport assets (FTAs) (e.g., ISO containers) facilitating intelligent services. In this light, telematics-enabled FTAs support freight transport operations utilized by individual stakeholders in three overarching service dimensions: transport management, fleet management, and risk management. This topic is, however, understudied by information systems (IS) research and service science. For this reason, we establish a design science research project, conceptualize a shared Freight Service Intelligence Platform (FSIP), and introduce freight service intelligence as an interdisciplinary research field. To this aim, we first review related literature, interview 14 transport stakeholders, and theorize six meta-requirements. Second, we propose five design principles that indicate how the meta-requirements may be associated. Third, we develop a web-based prototype application to instantiate the proposed design principles comprising performance analytics, anomaly detection, risk assessment including prediction, data exchange, communication, and IS integration. Subsequently, we evaluate the application with six transport stakeholders and logistics software vendors. Finally, we conclude with a discussion on the implications of an emerging topic addressed by this paper.

Decarbonization initiatives are increasingly promoted by the emergence of digital platforms (DPs) and small and medium-sized road carriers (SMRCs) are urged to implement green services from DPs to ensure responsible management of their carbon footprint leading to sustainable efficiency gains, new revenue streams, and competitive advantages. However, platform-based decarbonization services are challenging for SMRCs due to scarce technology capabilities and it is necessary to identify green service offerings from existing DPs and to capture the dimensions for an adapted business model. This paper develops a Green Services Canvas (GSC) and provides a tool for SMRCs to use services from DPs for decarbonization. The conceptualized GSC is demonstrated by three use cases and evaluated by 10 digital experts. From the insights of this study, the authors explore (a) digital platforms for decarbonization in road transportation, and (b) the relevance of green service business models for SMRCs through DPs.

Reliable arrival and departure forecasts from transport service providers are now an important basis for the successful planning of operational work processes for many commercial enterprises. A lack of interoperability, data standards and interfaces for exchange on the one hand, and a lack of a basis for cooperation on the other, hinder the flow of data along the supply chain and thus the fulfillment of the customer's demand on transport service providers for reliable forecasts. The consortium research project "GAIA-X 4 ROMS" (Remote Operation for Automated and Connected Mobility Services), funded by the German Federal Ministry of Economics and Climate Protection, is developing the prototype of a novel value network for the intelligent fleet management of automated and connected vehicles in intermodal transport chains. Data and services, such as remote operation and planning services, are exchanged securely and sovereignly on the basis of a federated, decentrally organized data space. The innovative concept of Gaia-X for the design of federated data spaces will be used, which promises data sovereignty and standardized data exchange for all network participants. This paper presents the concept of the first Minimum Viable Demonstrator (MVD) of the research project. In the MVD, the principle functionality of the data space and the data exchange between participating network nodes via data connectors is to be demonstrated using the use case of arrival time forecasting for vehicle fleet operators. In the further course of the project, the forecast service developed for this purpose will form an elementary functional module for an AI-based planning tool for the dynamic real-time planning of road-based mobility services in public passenger transport as well as in commercial road freight transport. The paper concludes with the current challenges in implementing the innovative data space technologies in the MVD and the related outlook for the further course of the project.