Artificial Intelligence Applications in Industrial Engineering: A Structured Framework for Decision Support and Optimization

Abstract

Artificial intelligence is increasingly influencing how industrial systems are planned, optimized, and managed. Industrial engineering has traditionally relied on analytical models, optimization techniques, and deterministic assumptions to support decision-making. As industrial systems grow in scale, complexity, and uncertainty, these approaches often face limitations in adaptability and robustness. Data-driven and learning-based methods offer new opportunities to address these challenges, yet their adoption in Industrial Engineering remains uneven due to fragmented methodologies, limited interpretability, and insufficient validation. This study presents a structured framework for applying artificial intelligence to industrial engineering decision support and optimization. The methods used in this study integrate disciplined problem definition, systematic data preparation, appropriate model selection, and rigorous validation within a unified methodological process. The framework is designed to support core industrial engineering activities, including prioritization, planning, optimization, and system evaluation, while maintaining alignment with established engineering decision processes and systems engineering principles. The key findings indicate that artificial intelligence enhances decision quality and operational relevance most effectively when employed as a decision support mechanism rather than as an automated replacement for engineering judgment. Structured integration improves transparency, traceability, and consistency, enabling artificial intelligence outputs to remain explainable, reproducible, and suitable for real industrial environments. The novelty of this work lies in providing a clear, methodologically grounded, and practically applicable framework that bridges data-driven techniques with disciplined engineering decision logic. The contributions of this study include formalizing a repeatable integration structure that supports responsible artificial intelligence adoption while preserving engineering rigor, accountability, and decision confidence in complex industrial engineering systems.