Sustainable Manufacturing through AI-Governed Capacity Sharing and Cooperative Resource Utilization
Keywords:
sustainable manufacturing, capacity sharing, cooperative resource utilization, artificial intelligence, governance, circular economy, industrial symbiosisAbstract
The transition toward sustainable manufacturing demands a systemic departure from isolated, overbuilt production capacities toward orchestrated, cooperative resource networks. This paper presents a comprehensive analysis of how artificial intelligence (AI) can govern capacity sharing across heterogeneous manufacturing firms, enabling collaborative utilization of machinery, logistics assets, and human skill pools while advancing environmental and economic sustainability goals. We examine the structural trade-offs between centralized coordination and decentralized autonomy, and argue that AI-driven governance architectures must balance efficiency with fairness, transparency, and long-term relational trust. The discussion spans multi-agent system design, trust and reciprocity dynamics, incentive alignment through smart contracts, algorithmic fairness, and the integration of digital twins with industrial data spaces. Particular attention is given to how AI governance can incorporate behavioral insights, ensuring that capacity-sharing ecosystems remain robust against strategic manipulation, supply disruptions, and evolving regulatory landscapes. The paper further explores deployment challenges at scale, the role of interoperability standards, and the policy frameworks necessary for fostering cross-sector circular manufacturing networks without undermining competition or data sovereignty. By synthesizing insights from operations management, artificial intelligence, industrial ecology, and socio-technical systems research, we outline a forward-looking research agenda that positions AI-governed capacity sharing as a foundational pillar of regenerative industrial systems.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
