Production planning and control (PPC) processes are increasingly subject to uncertainty due to growing system complexity, variable demand, and the integration of data-driven decision-support tools. Artificial intelligence (AI)-supported tools provide significant conveniences in managing this uncertainty, but also give rise to new risks that are difficult to control. However, the literature mainly addresses the risks posed by AI in PPC in a scattered manner through different concepts, and comprehensive approaches that holistically assess these risks remain limited. This study aims to address this gap in the literature by assessing the risks arising from the use of AI in PPC applications using Fuzzy Failure Mode and Effects Analysis (Fuzzy FMEA). In this context, the nine risk factors within the scope of the study were analysed using linguistic assessments by 14 academics specialising in PPC, with respect to probability, severity, and detectability. The analysis results identified cybersecurity, lack of explainability, and algorithmic bias as the most significant risks associated with AI use in PPC applications. The study offers a structured risk-prioritisation approach under uncertainty, providing a guiding framework for a more controlled and sustainable integration of AI into PPC processes.
Zafer Duran (Wed,) studied this question.