Artificial Intelligence in Collaborative Data Sharing for Intra-Company Logistics
This contribution examines the role of Artificial Intelligence( AI) in a data sharing framework for intra-company logistics, with a focus on Track and Trace enabled through multi-partner collaboration. Intra-company logistics constitutes a critical operational subset of multi-partner supply-chain networks; the present contribution focuses on the former while remaining embedded in the latter. Using the Intra-Company Logistics( ICL) Testbed as a real-world reference case, the paper explores how AI can enhance transparency, interoperability, and resilience across logistics operations while supporting broader digital transformation.
The ICL Testbed originated from a 41-month longitudinal study inspired by Action Design Research in which teams from three large industrial companies collaboratively developed and implemented a cooperative IoT system for intralogistics applications. This ecosystem evolved from an initial concept of shared value to a live operational system connecting assets with realtime data exchange across organizational boundaries. This cooperative approach orchestrates existing products, sensors, and data sources from independent companies, including an industrial service provider, a sensor technology manufacturer, and a heavy equipment manufacturer, to generate novel ecosystem-level value.
The paper argues that AI creates value in collaborative logistics data sharing across three dimensions: intelligent data provisioning and sensor data preparation for operational integration, AI-based analytics for decision support and process optimization, and horizontal expansion of use cases toward adjacent assets, data sources, and business scenarios. Together, these dimensions position AI as a scaling mechanism for shared value creation in logistics ecosystems, contributing to smart operations, adaptive, resilient, and scalable processes.
Analysis of the Testbed’ s data-sharing architectures and early prototypes demonstrates that data sharing together with cooperative AI-assisted insights improves operational transparency, strengthens interoperability between partners, and increases resilience in logistics processes. It enables enhanced decision support, more efficient process optimization, and the development of complementary data-driven services that create mutual value across the supply network.
Early prototype results indicate that AI can extend the value of logistics data sharing architectures beyond visibility alone toward adaptive, scalable, and collaborative optimization, thereby strengthening supply-chain resilience and supporting circular material and data flows. The paper concludes that AI adoption in logistics ecosystems is most effective when embedded in interoperable data sharing structures that support both current operational goals and future ecosystem-wide innovation.
1 MOTIVATION
Value stream directors and digital-transformation leads in logistics deal with a persistent problem: critical information about assets stays locked inside each company’ s own systems. A logistics service provider needs to see in real time where a partner’ s excavator sits on the yard and whether it is ready for the next job. The equipment manufacturer holds the asset records. The sensor partner can stream location, fuel level, and vibration data. Yet turning those separate streams into a single, trusted and unified shared view across organisational boundaries still requires manual workarounds and custom interfaces. This intra-company logistics challenge is
EDM Association – Journal of Innovation 127