Inteligência artificial na gestão de operações industriais: revisão sistemática da literatura e proposição do modelo conceitual MEDI 4.0

Abstract

This study aimed to analyze the application of artificial intelligence (AI) in industrial operations management and to propose a conceptual model to support data-driven decision-making. To this end, a qualitative and exploratory Systematic Literature Review (SLR) was conducted, guided by the PRISMA protocol. Data collection was carried out in the Scopus database, filtering articles published between 2020 and 2026, which resulted in a final sample of 40 eligible studies, of which 34 were fully assessed. The systematization of the literature, organized into six thematic groups, showed that AI acts as an analytical assistant that expands human capacity, while maintaining the human-in-the-loop principle, and contributes to optimizing operational efficiency and resilience. It was also found that technological barriers are the most frequently reported in the literature, while the lack of top management support stands out as the most causally critical factor in studies that use formal multi-criteria prioritization methods. Based on this evidence, the study proposes a progressive conceptual model composed of four layers of AI adoption, in which a methodological gap is identified between operational application (Layer 2) and managerial decision support (Layer 3). This gap is addressed through the proposition and theoretical grounding of MEDI 4.0, the Industrial Decision Engineering Model, an eight-step pipeline that acts as an integrating mechanism between operational events, analytical inference, and managerial action, thereby forming a future research agenda aimed at industrial environments with intermediate digital maturity.