Artificial Intelligence in Collaborative Data Sharing for Intra-Company Logistics
6.2 VALUE CREATION AND VALUE CAPTURE ACROSS ECOSYSTEM PARTICIPANTS
The benefits of collaborative data sharing are jointly created, but they are not necessarily realized in the same form or at the same time by all participants. The industrial service provider contributes integration capabilities and can benefit through improved service delivery and datadriven services. The sensor technology partner contributes sensing capabilities and gains insights into technology performance in a multi-vendor environment. The equipment partner contributes asset data and can benefit from improved utilization, maintenance planning, and lifecycle information.
At ecosystem level, these complementary contributions create a shared data asset that no individual organization could generate independently. Clearly defined roles and responsibilities are therefore central to institutionalized analytic data sharing [ 8 ]. They allow distributed data and expertise to be combined while preserving organizational boundaries and data sovereignty. The resulting value includes operational improvements and opportunities for service development, technology validation, and expanded Digital Twin applications.
This distribution creates a potential tension between value creation and value capture. A participant bearing early integration costs may not receive the largest immediate benefit, while commercial opportunities may emerge only after additional applications are developed. Sustained participation therefore depends on a credible relationship between contributions, risks, and expected benefits. Trust, transparency, and a jointly understood value proposition support this alignment [ 9 ]. Visible analytical benefits can encourage continued data contribution, which in turn improves subsequent analytics and reinforces the collaborative arrangement.
6.3 CONDITIONS AND TRADE-OFFS FOR SUSTAINED ECOSYSTEM VALUE CREATION
Technically enabled benefits do not automatically become durable ecosystem value. Their realization depends on the continuity and quality of the shared data foundation. Reliable availability, consistent semantics, timely updates, and stable partner contributions are necessary for operational applications and for keeping Digital Twins synchronized with physical assets and processes. Additional participants and data sources make these requirements more demanding by increasing heterogeneity and coordination complexity.
Value realization also depends on organizational integration. Analytical outputs must be incorporated into operational workflows, and responsibilities for interpreting and acting on AIgenerated insights must remain clear. Human acceptance, oversight, and clear decision authority become particularly important when recommendations affect cross-organizational processes. Rules for derived data, analytical outputs, and infrastructure costs must therefore evolve alongside the technical system.
Horizontal expansion consequently involves a trade-off. New assets, partners, and applications can increase the scope and reuse value of the collaborative infrastructure, but they also create additional interfaces, governance requirements, and potentially divergent incentives. Research
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