(392n) AI-Driven Risk-Aware Blockchain for Shared and Toll Manufacturing Under Uncertainty: A Tokenized Cooperative Framework for Decentralized Industrial Ecosystems

The rise of Industry 4.0 and the increasing demand for electric vehicles (EVs) have prompted a paradigm shift in how industrial assets and supply chains are managed. Emerging models such as shared manufacturing, Machine-as-a-Service (MaaS), and circular economy principles necessitate scalable, secure, and transparent infrastructures capable of handling dynamic supply-demand fluctuations and lifecycle complexities [1]. However, conventional centralized systems are ill-equipped to address the growing challenges of transparency, risk asymmetry, and real-time adaptability under uncertainty. In response, this study proposes a novel AI-driven, risk-aware blockchain architecture tailored for shared and toll manufacturing systems, with a special focus on battery recycling and reverse logistics in decentralized industrial ecosystems [2].

Our proposed framework integrates Inverse Physics-Informed Neural Networks (iPINNs), Gradient Boosting ensemble models, and blockchain smart contracts to create a cohesive decision-making environment. This hybrid model—termed BRAIL (Blockchain-enabled Reverse AI Logistics)—is engineered to facilitate real-time visibility, predictive analytics, and consensus-based governance across complex value chains. By tokenizing manufacturing assets and embedding smart contracts into a blockchain infrastructure, the framework supports fractional ownership, pay-per-use MaaS models, and secure, traceable reverse logistics for EV batteries. Decentralized Autonomous Organizations (DAOs) further enhance stakeholder coordination, enabling automated dispute resolution, contract enforcement, and governance via programmable consensus [3].

The AI component—anchored in iPINNs—translates resilience goals and recycling performance targets into physically informed, operationally feasible constraints. iPINNs, trained on detailed battery health data (e.g., charge cycles, voltage, temperature exposure), are used to inversely design recycling pathways and predict the remaining useful life of returned batteries. Complementary Gradient Boosting models enhance predictive accuracy, especially under noisy or incomplete data scenarios. These models enable shared risk analysis and dynamic stress testing of the reverse logistics network, ensuring operational decisions are not only data-driven but also physically grounded and economically viable [4].

Blockchain’s decentralized ledger provides immutable tracking of battery materials from production to end-of-life, addressing longstanding concerns of counterfeit components, inconsistent labeling, and unauthorized resales [5, 6]. Tokenized digital twins of batteries allow for seamless traceability across forward and reverse logistics, improving auditability and environmental compliance. Smart contracts automate material handoffs, quality checks, and revenue distribution, reducing transaction friction and fraud. Cross-chain interoperability protocols ensure that information flows seamlessly across diverse platforms, eliminating siloed operations and enabling a unified supply chain view.

A case study on EV battery recycling demonstrates the effectiveness of the BRAIL framework. Compared to traditional server-based systems, the proposed architecture improves traceability, optimizes asset utilization, and enhances stakeholder trust. Simulation results reveal that the system can dynamically adapt to stochastic demand spikes, unplanned disruptions, and supplier delays, achieving up to a 30% improvement in recycling efficiency and a significant reduction in misallocated resources. The DAO-based consensus protocol supports equitable decision-making and incentivizes sustainable behavior across the value chain. Furthermore, the integration of real-time analytics and shared governance transforms battery recycling from a fragmented and opaque process into a transparent, resilient, and data-intelligent operation [7].

In sum, this work establishes a comprehensive, scalable, and interdisciplinary framework that bridges AI, blockchain, and game-theoretic modeling to manage shared and toll manufacturing systems under uncertainty. By addressing existing research gaps in reverse logistics, tokenized asset governance, and interoperable data exchange, the proposed BRAIL architecture paves the way for secure, cooperative, and sustainable circular industrial ecosystems.

References

[1] A. Samadhiya, A. Kumar, R. Agrawal, Y. Kazancoglu, R. Agrawal, Reinventing reverse logistics through blockchain technology: a comprehensive review and future research propositions, SUPPLY CHAIN FORUM 24(1) (2023) 81-102.

[2] M. Hrouga, A. Sbihi, M. Chavallard, The potentials of combining Blockchain technology and Internet of Things for digital reverse supply chain: A case study, JOURNAL OF CLEANER PRODUCTION 337 (2022).

[3] K. Muduli, S. Luthra, J.A. Garza-Reyes, D. Huisingh, Application of blockchain technology for addressing reverse logistics challenges: current status and future opportunities, SUPPLY CHAIN FORUM 24(1) (2023) 1-6.

[4] X.G. Zhang, S.L. Zhu, S.Q. Dai, Z.G. Jiang, Q.S. Gong, Y. Wang, Optimization of third party take-back enterprise collection strategy based on blockchain and remanufacturing reverse logistics, COMPUTERS & INDUSTRIAL ENGINEERING 187 (2024).

[5] J.S. Wu, Sustainable development of green reverse logistics based on blockchain, ENERGY REPORTS 8 (2022) 11547-11553.

[6] M.S. Sangari, A. Mashatan, A data-driven, comparative review of the academic literature and news media on blockchain-enabled supply chain management: Trends, gaps, and research needs, COMPUTERS IN INDUSTRY 143 (2022).

[7] K. Bajar, A. Kamat, S. Shanker, A. Barve, Blockchain technology: a catalyst for reverse logistics of the automobile industry, SMART AND SUSTAINABLE BUILT ENVIRONMENT 13(1) (2024) 133-178.