Artificial Intelligence in Collaborative Data Sharing for Intra-Company Logistics
remain aligned. For researchers, the case demonstrates the value of longitudinal action research for examining how governance, value realization, and scaling interact. Future work should extend these principles to larger Data Space environments and develop transferable patterns for role definition, data-quality assurance, AI model governance, and responsibility allocation in multiparty Digital Twins.
8 CONCLUSIONS & OUTLOOK
The ICL Testbed demonstrates that AI can generate observable operational benefits in collaborative logistics data-sharing environments when it rests on a stable foundation of shared, high-quality data. The three dimensions explored combine raw IoT streams, analytical capabilities, and horizontal expansion to support improved visibility, earlier responses to disruptions, and more resilient coordination across three independent companies. The live system with 15 connected assets shows that these capabilities do not require a single proprietary platform or replacement of existing tools. They emerge when partners agree on governance, data ownership, and interoperable connection standards.
For supply chain and operations teams, the practical lesson is to begin with one narrow, highimpact use case while designing the underlying data foundation for reuse. This staged approach lowers risk, builds trust, and enables later applications to draw on capabilities established during earlier development. It also requires clarity about how contributions, costs, and benefits are distributed. The Testbed underlines that governance, data quality, and operational integration matter more than sophisticated algorithms during the early stages.
Looking ahead, the next steps are to onboard additional asset types and partners, test predictive maintenance and automated scheduling, and examine alignment with emerging European Data Spaces. These extensions should assess whether longer-term and cumulative value persists as the ecosystem becomes more diverse. The case indicates that the scaling logic can support expansion, but its continued effectiveness will depend on joint value definition, clear data ownership, interoperable standards, and governance arrangements that evolve with the ecosystem.
9 REFERENCES
[ 1 ] Rowley, J.( 2007). The wisdom hierarchy: representations of the DIKW hierarchy. Journal of Information Science, 33( 2), 163 – 180. https:// doi. org / 10.1177 / 0165551506070706
[ 2 ] Kurrle, S.( 2026). Implementing cooperative IoT systems: A product development method. 59th Hawaii International Conference on System Sciences( HICSS-59)
[ 3 ] Würthner, T., & Weber, P.( 2026). Corporate ecosystem start-ups: An organizational approach for successfully scaling data ecosystems. 59th Hawaii International Conference on System Sciences( HICSS-59)
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