AI-Driven Collaborative Innovation Model for Logistics Supply Chains in an E-commerce Environment

Abstract

The expansion of e-commerce has increased the frequency of demand changes, shortened order-response windows, and distributed logistics decisions across platforms, sellers, warehouses, carriers, and service partners. These conditions expose the limits of collaboration based on periodic data exchange and bilateral contracts. Artificial intelligence offers a different coordination mechanism by converting operational data into shared forecasts, allocation recommendations, exception signals, and learning routines. Yet the use of isolated forecasting or routing tools does not by itself produce supply-chain synergy. This paper examines how AI can support collaborative innovation across e-commerce logistics supply chains and develops a model that links data-sharing, joint decision processes, operational applications, and governance controls. Literature synthesis, comparative analysis, and inductive reasoning are used to define the model, compare conventional and AI-supported collaboration, distinguish three implementation modes, and identify recurring barriers. The analysis indicates that fragmented data rights, incompatible semantics, uneven analytical capability, weak incentive alignment, and limited accountability prevent firms from converting technical output into coordinated action. The proposed model therefore combines a federated data foundation, shared prediction and optimization services, cross-organizational decision protocols, application modules for forecasting, inventory, fulfillment, transport, and returns, and a governance layer covering access, model performance, risk, and value allocation. Its operating logic follows a repeated cycle in which partners sense demand and capacity conditions, formulate joint plans, execute distributed tasks, evaluate outcomes, and update models and rules. The paper also specifies differentiated adoption paths for platform-led networks, focal-firm networks, and federated ecosystems. Each path assigns data control, integration responsibility, and approval authority in a form consistent with the network's power structure and information sensitivity. The model treats AI as an interorganizational coordination capability rather than a stand-alone automation tool. It provides a structured basis for designing data agreements, allocating decision rights, selecting operational use cases, and evaluating collaborative value in e-commerce logistics networks.