The development of modern technologies such as the Internet of Things (IoT), cloud computing, and artificial intelligence (AI) resulted in a new era of industrial automation and data interchange, which is known as Industry 4.0. AI-based decision support systems (DSS) play a crucial role in this paradigm by enhancing the integration and processing of IoT and sensor data to optimize operations, improve productivity, and enable predictive maintenance. Machine learning models analyze production data and visual inspections to identify defects and ensure product quality. This review paper explores the transformative role of AI in enhancing DSS within Industry 4.0, highlighting key technologies including machine learning, deep learning, and natural language processing. It explores a number of applications, including supply chain optimization, energy management, predictive maintenance, quality control, and production planning, showing how AI-driven DSS can significantly boost operational dependability, cut costs, and improve efficiency. The article also discusses the AI-based DSS's architecture and implementation, with a focus on data management, user interface design, and IoT integration. Furthermore, it examines the challenges related to data quality, technical integration, and human factors, offering potential solutions and strategies for effective deployment. The study highlights the continued development of AI technologies and their potential to support autonomous decision-making in industrial settings by identifying new trends and areas for further research. This comprehensive review aims to provide valuable insights for researchers and practitioners, fostering a deeper understanding of the capabilities and future potential of AI-based DSS in Industry 4.0.
Stored only when both papers are in the corpus — 228 citations exist upstream.