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Evaluating AI Tools for Enhancing Production Quality in Manufacturing Supply Chains: A Hybrid Fuzzy Delphi–AHP–TOPSIS Approach
Assia BILAD1 , Mounia Zaim2 , Faical Zaim3
1- LASTIMI Laboratory, Higher School of Technology, Mohammed V University, Avenue Prince Héritier -BP 227 Sale Médina, Sale, 11000, Morocco , assia_bilad@um5.ac.ma
2- LASTIMI Laboratory, Higher School of Technology, Mohammed V University, Avenue Prince Héritier -BP 227 Sale Médina, Sale, 11000, Morocco
3- REMTEX & CELOG Laboratory, School of textile and clothing industries, Hassan II University, Route El Jadida Km 8, BP: 7731, Quartier Laymoune, Casablanca, 20190, Morocco
Abstract:   (63 Views)
The increasing adoption of artificial intelligence (AI) tools in manufacturing supply chains has intensified competition and highlighted the need for effective approaches to improve production quality. However, selecting the most appropriate AI tools remains challenging due to multiple evaluation criteria and uncertainty in expert judgments. This study proposes a hybrid fuzzy multi-criteria decision-making framework combining Fuzzy Delphi, Fuzzy Analytic Hierarchy Process (FAHP), and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) to assess the impact of AI tools on production quality. The Fuzzy Delphi method is used to achieve expert consensus on relevant quality criteria, FAHP determines their relative importance, and Fuzzy TOPSIS ranks AI tools according to their performance. The results reveal that quality control and process performance criteria are the most influential in evaluating production quality. Predictive maintenance is identified as the most effective AI tool for enhancing production quality, followed by computer vision and machine learning applications. A case study conducted on Moroccan manufacturing firms further confirms the positive role of AI adoption in improving production quality across the supply chain. This research provides a practical decision-support framework for managers and contributes to the literature by offering a structured and robust approach for evaluating AI tools under uncertainty. 
Keywords: Artificial intelligence, Delphi method, AHP, TOPSIS, MCDM, Fuzzy set theory.
     
Type of Study: Research | Subject: Logistic & Apply Chain
Received: 2025/04/9 | Accepted: 2026/02/8
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.