Artificial Intelligence in Collaborative Data Sharing for Intra-Company Logistics
planned based on actual condition rather than fixed schedules. This reduces unplanned downtime and improves spare-parts logistics, all without installing new sensors or renegotiating data rights.
A second extension is dynamic fleet orchestration and collaborative optimization. Real-time position and status data from the 15 assets, enriched with external data, enable the system to suggest better task allocation and routing across partners. This builds directly on the optimization work in Section 4 but adds new objectives that only become visible when data is shared and analyzed across organizational boundaries. The orchestration practices map directly onto what is needed: defining the expanded value proposition, maintaining alignment on benefits and data rules, integrating operational data with predictive models, extending the existing technical stack, and running joint experiments with new goals( including sustainability metrics).
Example
Enhancing the previous example of data-driven capacity planning, the integration of external traffic data allows the system to generate proactive prognosis of truck arrival delays. By fusing live traffic feeds with historic arrival patterns and current forklift utilization data, the platform could anticipate delays and automatically adjust recommended fleet sizes in advance; preventing both under-utilization during late arrivals and bottlenecks when multiple delayed trucks converge.
Further opportunities arise when additional assets or external data sources are brought into the ecosystem. The modular architecture designed for iterative growth [ 2 ] makes it relatively straightforward to connect more equipment or contextual feeds such as weather or site conditions. This can support joint emission-reduction initiatives. Here the collaborativeinnovation mechanism [ 10 ] becomes concrete: AI can help domain experts co-create new offerings such as sustainability dashboards or multi-objective optimizers that balance efficiency, resilience, and environmental impact.
Example
Orchestration via MCP( Model Context Protocol, see Annex A1) adapters for AI applications extends the existing IoT and analytics foundation into a fully flexible, on-demand analysis environment. Rather than being restricted to prebuilt dashboards or scheduled reports, logistics managers can initiate ad-hoc prompts that combine real-time forklift capacity and utilization data with previously unintegrated sources, such as an urgent e-mail from the logistics manager containing changed priorities, truck arrival updates or tacit constraints. These adapters interpret the natural-language request, dynamically retrieve and fuse the relevant structured sensor data with the unstructured e-mail content, and return actionable insights within minutes, without requiring new data pipelines or IT development cycles.
Realizing these extensions at scale while maintaining governance integrity requires some targeted architectural improvements. The Testbed already benefits from a Service Layer sitting
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