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Amine Belhadi
Researcher at Cadi Ayyad University
Publications - 59
Citations - 2312
Amine Belhadi is an academic researcher from Cadi Ayyad University. The author has contributed to research in topics: Supply chain & Computer science. The author has an hindex of 14, co-authored 36 publications receiving 613 citations.
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Journal ArticleDOI
Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries
Amine Belhadi,Sachin S. Kamble,Charbel José Chiappetta Jabbour,Angappa Gunasekaran,Nelson Oly Ndubisi,Mani Venkatesh +5 more
TL;DR: The authors' findings indicate that the automobile industry perceived that the best strategies to mitigate risks related to COVID-19, were to develop localized supply sources and use advanced industry 4.0 (I4.0) technologies, and Big Data Analytics (BDA) to play a significant role by providing real-time information on various supply chain activities to overcome the challenges posed by CO VID-19.
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Agriculture supply chain risks and COVID-19: mitigation strategies and implications for the practitioners
TL;DR: The agricultural supply chains (ASCs) are exposed to unprecedented risks following COVID-19 and it is necessary to investigate the impact of risks and to create resilient ASC organizations as discussed by the authors.
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Investigating the effects of renewable energy on international trade and environmental quality.
TL;DR: The results indicate that renewable energy consumption improved to environmental quality and policies to promote renewables can provide for economic growth and environmental sustainability and ensure crucial sustainable development goals.
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The integrated effect of Big Data Analytics, Lean Six Sigma and Green Manufacturing on the environmental performance of manufacturing companies: The case of North Africa
TL;DR: In this article, a two-stage hybrid Factorial Analysis - Structural Equation Modeling (FAEM) is used to draw insights from 201 industry practitioners from North African companies.
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A machine learning based approach for predicting blockchain adoption in supply Chain
TL;DR: A decision support system for managers to predict an organization's probability of successful blockchain adoption using a machine learning technique and identifies competitor pressure, partner readiness, perceived usefulness, and perceived ease of use as the most influencing factors for blockchain adoption.