A Conceptual Framework for AI-Driven Predictive Optimization in Industrial Engineering: Leveraging Machine Learning for Smart Manufacturing Decisions

The integration of Artificial Intelligence (AI) in industrial engineering has revolutionized modern manufacturing through data-driven decision-making and intelligent automation. This paper proposes a conceptual framework for AI-driven predictive optimization in industrial engineering, focusing on leveraging machine learning (ML) algorithms to enhance smart manufacturing decisions. The proposed framework combines real-time data acquisition, advanced data preprocessing, predictive analytics, and optimization layers to enable proactive and adaptive decision-making in manufacturing processes. By incorporating supervised and unsupervised learning models, the framework facilitates the prediction of equipment failures, product quality deviations, and process inefficiencies, thereby minimizing downtime and improving operational performance. Central to the framework is the synergy between industrial Internet of Things (IIoT) technologies and AI-driven predictive models that extract actionable insights from vast, heterogeneous data sources. The proposed architecture emphasizes modularity, scalability, and interoperability, ensuring its applicability across diverse industrial domains and production environments. Furthermore, reinforcement learning components enable continuous improvement through feedback loops, aligning system performance with evolving operational goals and constraints. The framework supports multi-objective optimization by integrating predictive models with evolutionary algorithms and real-time simulations to optimize key performance indicators (KPIs) such as production throughput, energy efficiency, cost, and quality. Case illustrations and conceptual simulations highlight the potential of the framework to facilitate intelligent scheduling, dynamic resource allocation, and just-in-time maintenance planning. The integration of AI also addresses challenges related to uncertainty and variability in production systems, enhancing resilience and agility. This study contributes to the growing field of smart manufacturing by offering a structured approach to embedding AI and machine learning into core industrial engineering processes. It lays the foundation for future empirical studies and implementation strategies, with the ultimate aim of fostering autonomous, data-driven manufacturing ecosystems. The framework is particularly relevant for Industry 4.0 and future Industry 5.0 paradigms, where human-machine collaboration, sustainability, and adaptability are paramount.