Investigating Generative AI Solutions in Supply Chain with Hesitant Fuzzy DEMATEL-ARAS Methods
Journal of Systems Science and Systems Engineering, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s11518-026-5767-z
- Dergi Adı: Journal of Systems Science and Systems Engineering
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Compendex, INSPEC, Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: ARAS, DEMATEL, generative AI, hesitant fuzzy sets, multi-criteria decision-making, supply chain
- Galatasaray Üniversitesi Adresli: Evet
Özet
The COVID-19 pandemic has caused a change in the supply chain landscape, requiring a shift toward resilience and flexibility. This study is motivated by the profound changes in supply chain dynamics brought about by the COVID-19 epidemic. This disruption has led to an increased emphasis on resilience and flexibility within supply chains, thereby creating an urgent need for organizations to explore generative AI (Artificial Intelligence) solutions. The study seeks to illuminate the critical factors organizations must consider when evaluating these solutions, emphasizing the importance of understanding both benefits and costs. Additionally, it aims to identify the most appropriate applications of generative AI across supply chain functions, including planning, sourcing, manufacturing, and logistics, ultimately assisting organizations in navigating the complexities of modern supply chain management. The study aims to provide valuable insights into the factors to consider when evaluating generative AI solutions, the relative importance of benefits and costs, and the most appropriate applications of generative AI in supply chain planning, sourcing, manufacturing, and logistics by addressing three main research questions. The research methodology combines the hesitant fuzzy DEMATEL (The Decision Making Trial and Evaluation Laboratory) and ARAS (Additive Ratio ASsessment) methods to investigate the complicated interrelationships between the indicated components comprehensively. The findings show that practitioners should prioritize staff resistance, cost savings, decision support, sustainability, green efforts, and ethical concerns when investing in generative AI solutions. This study assists organizations in navigating the opportunities and challenges posed by disruptive innovations by introducing a structured and actionable framework and a novel decision-support tool for evaluating generative AI solutions and by offering practitioners a practical tool to guide decision-making.