Artificial Intelligence in Collaborative Data Sharing for Intra-Company Logistics
Collectively, these three dimensions position AI as a powerful scaling mechanism for shared value creation in logistics ecosystems. By enhancing transparency, interoperability, and resilience while enabling adaptive responses to dynamic conditions. This premise is consistent with broader empirical findings on( inter-) organizational integration [ 4 ] and the role of intelligent automation in strengthening supply chain efficacy and resilience [ 5 ].
3 OPERATIONAL INTEGRATION THROUGH INTELLIGENT DATA PROVISIONING
The operational integration dimension is grounded in the cooperative IoT development method and data-sharing architecture implemented in the ICL Testbed. Building on core principles of effective data sharing across organizational boundaries [ 6 ], the partners followed a structured three-phase trajectory, Design, Build, and Evolve. They moved from defining joint value propositions, data requirements, interoperability standards, and governance mechanisms to the technical realization of sensor and IoT integration, secure data pipelines, and cross-boundary sharing protocols [ 2, 7 ].
Example
In the ICL Testbed, operational integration relies on a live IoT data-sharing system connecting 15 forklifts operating continuously on-site. Each asset streams geolocation, loaded / unloaded status, on-duty indicators and synchronized timestamps every two seconds. An enhanced configuration adds RFID-based detection of individual loaded goods together with real-time weight measurements. The resulting high-frequency data feed is made interoperable through jointly defined models, allowing all consortium partners to consume the same live operational picture without custom transformations. The system automatically flags outliers that typically indicate broken or miscalibrated sensors – most commonly missing or misplaced coordinates and inconsistent loading states. The outcome is a practical foundation for collaborative Track and Trace and process optimization: partners receive not only visibility across the fleet but also pre-validated data that reduces manual reconciliation effort and accelerates the transition from raw sensor input to actionable, crossorganizational insights.
In practice, data sharing architecture integrates heterogeneous sensor technologies and data sources from independent partners into a unified, real-time Track and Trace capability. Interoperability is achieved through negotiated standards and secure sharing mechanisms that respect organizational boundaries while enabling ecosystem-level visibility [ 3 ]. Weber et al. [ 8 ] also offer a particularly instructive role-based perspective on the institutional foundations of analytic data sharing. Their analysis shows that successful institutionalization depends on dedicated governance structures, clearly defined partner roles and responsibilities, and explicit mechanisms for managing data scope, volume, quality, and, most critically, inter-organizational trust. These elements create a stable framework [ 9 ] that sustains operational integration and
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