Engineering AI-Native Decision Support Systems for Industry 4.0 Environments

Abstract

Industry 4.0 environments increasingly rely on artificial intelligence to support complex operational decisions across manufacturing, logistics, and industrial control systems. Traditional decision support systems struggle to operate effectively under the scale, heterogeneity, and real time demands of modern industrial platforms. This study presents a systems engineering approach to AI native decision support systems designed specifically for Industry 4.0 contexts. The proposed framework integrates machine learning models, data pipelines, and governance mechanisms into a unified decision architecture. Empirical evaluation demonstrates that AI native decision support systems improve responsiveness, scalability, and robustness while maintaining operational stability. The findings highlight the importance of treating decision intelligence as an integrated system capability rather than an isolated analytical component.