Artificial Intelligence in Collaborative Data Sharing for Intra-Company Logistics
above the core data infrastructure. This layer can host reusable, containerized services( dashboards, machine learning models, and GenAI components) and expose them through standardized, secure interfaces. New use cases can then“ plug in” to existing data streams and models without heavy custom development or risk to the stable Track and Trace core. An AIaugmented orchestration layer, drawing on Shen et al.’ s framework [ 11 ], can further support the five orchestration practices, for example by helping partners model expanded value propositions, run shared scenario simulations, or co-create new analytic objectives.
In short, the ICL Testbed shows how AI, when built on a well-governed and interoperable datasharing foundation, can move beyond initial visibility and local optimization to become a driver of scalable, combinatorial value creation. The“ One Step Further” expansion, supported by collaborative innovation affordances and deliberate orchestration practices, directly contributes to the smart and adaptive supply chains.
6 BUSINESS BENEFITS AND ECOSYSTEM IMPACT
The ICL Testbed shows how business value can emerge when Artificial Intelligence is integrated into a collaborative multi-partner data-sharing architecture for intra-company logistics. Sections 3 to 5 examined the capabilities created through intelligent data provisioning, AI-supported analytics, and horizontal expansion. This section focuses on their benefits, distribution among ecosystem participants, and conditions for sustained realization.
6.1 OPERATIONAL AND STRATEGIC VALUE REALIZATION
The most immediate benefits arise from harmonized, interoperable, and continuously updated operational data. Integrating vehicle telematics, environmental sensors, and proprietary partner systems improves Track and Trace accuracy and timeliness across organizational boundaries. It reduces manual reconciliation and creates a shared operational picture that supports faster coordination between the participating companies.
Further value emerges when the shared data is incorporated into analytical and operational routines. Predictive maintenance, anomaly detection, and data-supported capacity planning can reduce downtime, improve utilization and scheduling, and support earlier responses to bottlenecks. The common effect is a shift from reactive and experience-driven decision-making toward more proactive and data-augmented processes, consistent with empirical findings on AI in supply chain management [ 4 ]. These benefits nevertheless depend on sufficiently mature datasets and the integration of analytical outputs into established workflows.
Longer-term value lies in reusing the established data, governance, and service infrastructure. Additional assets, data streams, and applications can build on capabilities created for the initial Track and Trace use case. The modular development approach [ 2 ] therefore creates cumulative value: the initial application generates direct benefits while also enabling further AI services, more comprehensive Digital Twins, and adjacent collaborative scenarios. Its significance consequently extends to the strategic options created for subsequent development.
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