Artificial Intelligence and Machine Learning Framework for Smart Manufacturing Optimization

This systematic review examines how artificial intelligence (AI) and machine learning (ML) frameworks drive competitive advantage and operational excellence within smart manufacturing enterprises. The review proposes the AI-Enabled Smart Manufacturing Optimization (AISMO) Framework, a theory-driven model elucidating how Artificial Intelligence Competencies, mediated by Digital Data Architecture and Sociotechnical Embeddedness, yield quantifiable Manufacturing Process Efficacy and Firm-Level Performance Outcomes, governed throughout by AI Governance Mechanisms. Findings demonstrate that deep learning, reinforcement learning, graph neural networks, and digital twin integration deliver measurable business value reducing operational costs, compressing lead times, elevating product quality, and strengthening supply chain resilience when embedded within robust data infrastructure and responsible governance frameworks. Algorithm selection trade-offs are critically examined, enabling informed investment decisions across regulated and high-complexity manufacturing contexts. Critical business risks are identified, encompassing cybersecurity vulnerabilities, data governance constraints, model