Artificial Intelligence in Collaborative Data Sharing for Intra-Company Logistics
technical substrate required for the horizontal expansion dynamic. The insights and reusable analytical components developed here provide the foundation for extending AI value creation to neighboring assets, additional data streams, and novel collaborative scenarios, the precise mechanism examined under the“ One Step Further” logic in Section 5. In this way, the analytics dimension simultaneously strengthens smart operations( through pervasive AI-driven intelligence) and adaptive processes through enhanced resilience, scalability, and the capacity for continuous, data-informed evolution.
5 EXTENSION TO ADJACENT SCENARIOS(“ ONE STEP FURTHER”)
The ICL Testbed’ s Track and Trace system creates a strong foundation for the third and most powerful dimension of AI value: horizontal expansion. This is what we call the“ One Step Further” logic. Instead of stopping once basic visibility and local optimization are achieved, the same data foundation, models, digital twins, and governance arrangements can be extended to new assets, new data sources, and entirely new collaborative use cases.
This expansion does not happen by accident. Three perspectives help explain how it works in practice.
First, generative AI on digital platforms creates value through collaborative innovation [ 10 ]. Rather than acting only as a passive analysis tool, GenAI becomes an active co-creator together with human experts. This leads to unexpected, combinatorial innovations – new solutions that recombine existing data, models, and partner capabilities in ways that were not planned upfront. In the logistics ecosystem, this is visible when core Track and Trace data and predictive models are reused for purposes that go beyond the original scope.
Second, successful ecosystem expansion requires deliberate orchestration [ 11 ]. The authors identify five key practices, strategic design, relational work, resource integration, technological leveraging, and innovation practices, that industrial firms use to scale collaborative initiatives while keeping partners aligned. These practices provide a practical checklist for how the ICL Testbed can grow its AI capabilities without damaging the trust and clear boundaries that make the collaboration work.
Third, AI systems shape what becomes possible by creating new affordances [ 12 ]. Once highquality data and models are available across partners, certain actions become visible and realistic that were previously out of reach. In a logistics setting, this means the platform can support proactive intervention, exploratory scenario planning, and cross-partner optimization – practices that go well beyond the reactive alerts of conventional Track and Trace systems.
Concrete examples already emerging in the Testbed show how this works in practice.
The first clear extension is predictive maintenance. The sensor streams( vibration, temperature, usage intensity) and digital twin components already in place, combined with the secure datasharing protocols agreed by the three partners, provide a ready foundation. The anomaly detection and predictive models developed for daily operations( Section 4) can be reused to forecast component wear weeks in advance. When the system predicts bearing degradation on a specific forklift, it can automatically alert the service provider partner so maintenance can be
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