QUANTUM AI-DRIVEN BUSINESS INTELLIGENCE FOR CARBON-NEUTRAL SUPPLY CHAINS: REAL-TIME PREDICTIVE ANALYTICS AND AUTONOMOUS DECISION-MAKING IN COMPLEX ENTERPRISES

Abstract

Amid growing global pressures to combat climate change, enterprises are reengineering their supply chains to align with carbon-neutral objectives while maintaining operational agility and competitiveness. This study explores the transformative potential of integrating Quantum Artificial Intelligence (QAI), Business Intelligence (BI), and autonomous decision-making technologies in building intelligent, sustainable supply chains capable of minimizing environmental impact. By leveraging the computational advantages of quantum algorithms, machine learning, real-time analytics, and decentralized control systems, organizations can address the increasing complexity of emissions management, logistics optimization, and sustainability forecasting. A comprehensive systematic literature review was conducted following the PRISMA 2020 guidelines, encompassing 97 peer-reviewed articles published between 2010 and 2024 across fields including supply chain management, artificial intelligence, quantum computing, and sustainability analytics. The review reveals that QAI significantly enhances the efficiency of solving combinatorial problems such as routing, scheduling, and emissions prediction, outperforming classical AI in both speed and scalability. BI platforms have evolved from retrospective reporting tools to intelligent systems that facilitate real-time carbon monitoring, dynamic scenario modeling, and sustainability-focused KPI visualization. In parallel, the deployment of autonomous systems—supported by IoT, RFID, edge computing, and AI agents—has enabled decentralized, self-optimizing decision-making across manufacturing, logistics, and procurement functions. Real-world case studies from industry leaders like Siemens, IBM, Honeywell, and John Deere illustrate the tangible impact of these technologies in achieving emissions reductions and improving system-wide sustainability performance. This study provides a comprehensive understanding of how the convergence of QAI, BI, and autonomous systems is shaping the future of carbon-conscious supply chains, offering both theoretical advancement and practical relevance for businesses committed to environmental responsibility and technological innovation.