Vol. 14 No. 1 (2026): Business & Management Studies: An International Journal
Articles

Analysis of risks related to the use of artificial intelligence in production planning and control processes using fuzzy FMEA

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  • Zafer Duran
Zafer Duran
Asst. Prof. Dr., Alanya Alaaddin Keykubat University, Antalya, Türkiye

Published 2026-03-25

Keywords

  • Production Planning and Control, Artificial Intelligence Risks, Fuzzy FMEA
  • Üretim Planlama ve Kontrol, Yapay Zekâ Riskleri, Bulanık FMEA

How to Cite

Analysis of risks related to the use of artificial intelligence in production planning and control processes using fuzzy FMEA. (2026). Business & Management Studies: An International Journal, 14(1), 312-328. https://doi.org/10.15295/bmij.v14i1.2705

Copyright (c) 2026 Zafer Duran

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

Analysis of risks related to the use of artificial intelligence in production planning and control processes using fuzzy FMEA. (2026). Business & Management Studies: An International Journal, 14(1), 312-328. https://doi.org/10.15295/bmij.v14i1.2705

Abstract

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.

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