Generative Artificial Intelligence as a Strategic Engine for Manufacturing Execution System Optimization and Sustainable Digital Enterprise Transformation
Keywords:
Generative artificial intelligence, Manufacturing Execution Systems, digital manufacturing, enterprise systemsAbstract
The rapid evolution of artificial intelligence has fundamentally redefined the way organizations conceptualize, design, and execute digital transformation strategies. Among the most consequential developments in this domain is the emergence of generative artificial intelligence, particularly large language models, which extend beyond traditional predictive analytics toward creative, adaptive, and context-aware decision support. In manufacturing-intensive enterprises, the integration of generative artificial intelligence into Manufacturing Execution Systems represents a critical inflection point in the broader journey toward intelligent, autonomous, and sustainable production. This research article develops a comprehensive theoretical and empirical synthesis of how generative artificial intelligence can be used to optimize Manufacturing Execution Systems through digital manufacturing configuration recommendation mechanisms, building directly on the conceptual and operational foundations articulated by Chowdhury, Pagidoju, and Lingamgunta (2025). By situating their model within a wider body of interdisciplinary scholarship on artificial intelligence, digital business models, strategic management, sustainability, and organizational governance, this study constructs a holistic framework for understanding how language-model-driven systems can reshape production planning, resource allocation, workflow orchestration, and strategic alignment.
The article argues that Manufacturing Execution Systems are no longer merely operational tools for shop-floor control but have evolved into strategic platforms that mediate between enterprise planning systems, supply chain networks, and real-time production intelligence. Generative artificial intelligence enables these platforms to move from reactive monitoring to proactive configuration, learning from historical data, contextual signals, and strategic objectives to generate optimal manufacturing scenarios. Drawing on theoretical insights from artificial intelligence research, organizational theory, sustainability governance, and business model innovation, this study examines how generative artificial intelligence supports dynamic reconfiguration, reduces uncertainty, enhances customer responsiveness, and improves environmental performance. It also interrogates the ethical, managerial, and institutional challenges associated with delegating configuration authority to algorithmic agents.
Using a qualitative, literature-grounded methodological approach, this research synthesizes evidence from existing studies to develop interpretive results regarding performance gains, strategic flexibility, and governance implications of generative artificial intelligence in Manufacturing Execution Systems. These results demonstrate that generative artificial intelligence, when embedded into digital manufacturing infrastructures, transforms enterprises into adaptive systems capable of aligning operational decisions with long-term strategic and sustainability goals. The discussion advances a critical perspective on how these technologies alter power relations, knowledge production, and managerial accountability, offering a roadmap for future research and practice. Ultimately, this study contributes to both the artificial intelligence and operations management literatures by providing a theoretically rich and practically relevant understanding of generative artificial intelligence as a central pillar of intelligent manufacturing transformation.
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