|
|
International Journal of Engineering، جلد ۳۹، شماره ۳، صفحات ۵۸۵-۶۰۵
|
|
|
| عنوان فارسی |
|
|
| چکیده فارسی مقاله |
|
|
| کلیدواژه‌های فارسی مقاله |
|
|
| عنوان انگلیسی |
AI-Driven Strategies for Supply Chain Resilience: A Review of Challenges and Solutions During Pandemics |
|
| چکیده انگلیسی مقاله |
The COVID-19 pandemic exposed critical vulnerabilities in global supply chains, underscoring the urgent need for adaptive, data-driven strategies. While Artificial Intelligence (AI) and Machine Learning (ML) are recognized as enablers of resilience, existing literature lacks a systematic synthesis of their pandemic-specific applications across industries. This review addresses this gap by analyzing how AI/ML technologies mitigated disruptions, their sector-specific effectiveness, and implementation challenges. Through a systematic examination of available studies, we evaluate applications spanning blockchain-enabled traceability in healthcare supply chains to deep learning models for demand volatility in retail. Key contributions include: (1) a Resilience-Adaptation Framework aligning AI/ML solutions with disruption severity and organizational agility, (2) empirical evidence of AI/ML’s role in enhancing transparency and reducing bottlenecks in critical sectors like pharmaceuticals, and (3) identification of ethical risks, including biases in crisis-driven decision-making. The study further proposes integrating AI/ML with IoT and digital twins to future-proof supply chains against systemic shocks. By consolidating fragmented insights and offering strategic pathways, this review advances scholarly discourse and equips practitioners with actionable strategies to transform supply chains into proactive, crisis-ready systems. |
|
| کلیدواژه‌های انگلیسی مقاله |
Supply chain management,COVID-19,Artificial intelligence,Machine Learning,Disruption management |
|
| نویسندگان مقاله |
A. Salehi |
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
A. Babaei |
Department of Industrial Engineering, Information Technology Engineering Group, K. N. Toosi University of Technology, Tehran, Iran
H. Hamidi |
Department of Industrial Engineering, Information Technology Engineering Group, K. N. Toosi University of Technology, Tehran, Iran
|
|
| نشانی اینترنتی |
https://www.ije.ir/article_218046_7c4d2457f80bd42f17563ab6f5180381.pdf |
| فایل مقاله |
فایلی برای مقاله ذخیره نشده است |
| کد مقاله (doi) |
|
| زبان مقاله منتشر شده |
en |
| موضوعات مقاله منتشر شده |
|
| نوع مقاله منتشر شده |
|
|
|
|
برگشت به:
صفحه اول پایگاه |
نسخه مرتبط |
نشریه مرتبط |
فهرست نشریات
|