Artificial Intelligence in Collaborative Data Sharing for Intra-Company Logistics
Related research on inter-organizational AI in supply chains [ 4 ] similarly notes that sophisticated, context-specific governance mechanisms are still evolving.
While the architecture supports the three AI value dimensions, current implementation focuses more on data provisioning and initial analytics than on advanced, high-autonomy AI applications. Achieving fuller potential will require larger longitudinal datasets, rigorous cross-partner model validation, and careful attention to explainability and liability when AI outputs influence decisions that span organizational boundaries.
Evidence of business impact remains uneven. The longitudinal study provides rich process insights and indicates immediate operational benefits, including improved visibility and reduced reconciliation effort, but longer-term strategic and cumulative effects are still emerging. Credible baselines and clearer attribution of the AI layers remain necessary for assessing the value discussed in Section 6 and building replicable business cases.
Finally, context matters. The German industrial manufacturing setting, with its existing patterns of collaboration and trust in research intermediaries, may not transfer directly to other regions or sectors. Role definitions, development phases, and scaling practices will need adaptation for different industry structures or SME-dominated environments.
7.2 LESSONS LEARNED AND IMPLICATIONS FOR AI APPLICATION AND DIGITAL TWINS
Several practical lessons emerge from the Testbed experience.
First, collaborative data sharing should be treated as evolving strategic infrastructure rather than as a short-term visibility project. Governance structures [ 8 ] and the phased development approach [ 2 ] must continue to develop as data sources, applications, and participating organizations change.
Second, AI and Digital Twin applications should be considered at ecosystem level. Shared multipartner data increases the volume, variety, and contextual relevance available for analytics, but also means that contributions, risks, and benefits may differ among participants. Technical and ecosystem design therefore need to be addressed together.
Third, scaling AI capabilities and Digital Twin coverage is as much an organizational and relational challenge as a technical one. Additional partners and use cases increase reuse potential but also introduce new interfaces, incentives, and governance requirements. Trust, incentive alignment, and adaptive coordination must develop alongside the technology, consistent with insights from [ 3 ].
Fourth, a clear, jointly owned value proposition creates motivation for sustained data contribution and iterative improvement [ 9 ]. The initial use case should provide immediate operational relevance while establishing data and capabilities for later applications.
For practitioners, the ICL Testbed shows that cooperative data-sharing architectures can support AI applications and Digital Twin use cases whose value no single company could achieve independently, provided that technical integration, operational adoption, and partner incentives
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