Agentic AI and Ambient Intelligence in Sustainable Supply Chain Management: A Framework for Autonomous Sustainability Decision-Making
DOI:
https://doi.org/10.64229/9kzvxq77Keywords:
Agentic Artificial Intelligence, Ambient Intelligence, Sustainable Supply Chain Management, Autonomous Decision-Making, Circular EconomyAbstract
Sustainable Supply Chain Management (SSCM) faces unprecedented challenges in achieving real-time sustainability decision-making across complex global networks. While 67% of Chief Supply Chain Officers are accountable for environmental and social sustainability KPIs, current approaches lack autonomous decision-making capabilities that can respond dynamically to sustainability imperatives. This research addresses critical gaps in integrating agentic artificial intelligence with ambient intelligence technologies for autonomous sustainability management.
The study identifies significant limitations in existing SSCM frameworks: predominant focus on deterministic approaches (missing real-time adaptability), insufficient integration of emerging AI technologies for sustainability decisions, and lack of comprehensive frameworks combining multiple Industry 4.0 technologies for circular economy implementation. These gaps prevent supply chains from achieving the autonomous sustainability operations that leading organizations require.
This research aims to develop and validate an integrated framework leveraging agentic AI and ambient intelligence for autonomous sustainability decision-making in supply chains, examine the synergistic effects of these technologies on triple bottom line performance, and establish implementation guidelines for practitioners across different industry sectors. The methodology provides comprehensive theoretical and practical validation through qualitative analyses including systematic literature review, case study analysis, and interviews with sustainability leaders from Gartner's Top 25 global supply chains.
Findings reveal that integrated agentic AI-ambient intelligence systems can enhance sustainability performance by 34% while reducing decision-making time by 67%. The developed framework provides actionable guidance for autonomous sustainability implementation, contributing to SSCM theory while addressing urgent industry needs for real-time sustainable operations.
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