The Use of Artificial Intelligence in Supply Chain Controlling: A Systematic Literature Review and Content Analysis of Controlling Task Areas and AI Techniques

Artificial intelligence is increasingly regarded as an important driver of innovation in supply chain management and supply chain controlling. Although academic and practical interest in this topic has grown significantly recently, research on the specific use of artificial intelligence in supply chain controlling remains fragmented and comparatively limited. This study therefore examines which controlling tasks within supply chains are currently supported by artificial intelligence and which technological approaches are most frequently applied. The analysis is based on a systematic literature review and content analysis of 19 academic publications published between 2020 and 2025. The results indicate that artificial intelligence is mainly applied in performance controlling as well as risk and resilience controlling, while sustainability-oriented controlling tasks are receiving increasing but still comparatively limited attention. Machine learning and predictive analytics represent the most frequently used technological approaches, whereas methods such as deep learning, natural language processing, and reinforcement learning are considerably less established in current research. The findings further indicate that artificial intelligence applications are particularly concentrated in data-intensive, forecasting-oriented, and decision-support-related controlling tasks. In addition, the study identifies several thematic research clusters focusing on resilience, performance improvement, and organizational integration. Overall, the analysis provides a structured overview of the current research landscape and highlights important areas for future research and practical implementation.