Mahmoud Elsayed | Digital Transformation | Best Researcher Award

Published on by Awards and Recognitions

Mr. Mahmoud Elsayed | Digital Transformation | Best Researcher Award

Mr. Mahmoud Elsayed | Digital Transformation | PhD Student at University of Pittsburgh | United States

Mr. Mahmoud Ashraf is an emerging scholar whose research bridges the fields of supply chain management, blockchain, digital twin technologies, and machine learning. He has established himself as a dedicated researcher with a growing academic impact, reflected through citations and recognition within the global research community. His work is characterized by innovation, depth of analysis, and a strong focus on solving real-world challenges faced by modern supply chains. Through collaborations with international institutions and contributions to high-quality journals, Mr. Mahmoud Ashraf has positioned himself as a promising leader in the research landscape.

Academic Profile:

ORCID

Scopus

Google Scholar

Education:

Mr. Mahmoud Ashraf is currently pursuing his doctoral studies at the University of Pittsburgh, where his academic journey is dedicated to developing robust and intelligent solutions for supply chain disruptions. His education has provided him with the technical expertise and research capabilities to explore complex problems in logistics, operations, and digital innovation. His doctoral training emphasizes interdisciplinary learning, combining engineering principles with computational modeling, artificial intelligence, and operations research. This solid academic foundation has enabled him to contribute meaningfully to both theoretical advancements and applied projects in his domain.

Experience:

Mr. Mahmoud Ashraf has accumulated valuable experience through active involvement in international research collaborations and joint academic initiatives. His collaborative projects with the Egypt-Japan University of Science and Technology and other globally recognized institutions showcase his commitment to advancing the supply chain and operations research field. He has participated in studies addressing project completion time prediction, disruption detection, and recovery planning in complex, technology-enabled environments. These projects highlight his capacity to contribute to multidisciplinary teams and deliver impactful outcomes. His experience also extends to presenting research at scholarly forums and disseminating findings in peer-reviewed journals and conferences indexed in Scopus and IEEE.

Research Interest:

Mr. Mahmoud Ashraf’s research interests lie at the intersection of supply chain resilience, artificial intelligence, and digital innovation. He is particularly focused on blockchain-enabled supply chain systems, cognitive digital supply chain twins, and hybrid deep learning approaches for disruption detection and recovery strategies. His work contributes to advancing knowledge on predictive modeling, time-to-recovery analysis, and cognitive systems that strengthen the efficiency and adaptability of global supply chains. By integrating machine learning with supply chain modeling, he seeks to create intelligent frameworks that not only forecast disruptions but also provide actionable solutions for recovery. His interests extend to promoting sustainability, agility, and competitiveness in supply chains, reflecting his forward-looking approach to research.

Award:

Mr. Mahmoud Ashraf has demonstrated significant academic promise and leadership, making him a strong candidate for recognition in international research awards. His work has contributed to the body of knowledge in operations research and emerging technologies, with publications that have gained citations and visibility among peers. This recognition underscores his ability to conduct impactful research that resonates with both academics and practitioners. His academic trajectory reflects qualities aligned with award-winning scholarship, including innovation, collaboration, and commitment to societal impact.

Selected Publication:

  • Evaluation of project completion time prediction accuracy in a disrupted blockchain-enabled project-based supply chain (2023, 14 citations)

  • Disruption detection for a cognitive digital supply chain twin using hybrid deep learning (2024, 13 citations)

  • Time-To-Recovery Prediction in a Disrupted Three-Echelon Supply Chain Using LSTM (2022, 6 citations)

  • A Hybrid Deep Learning-Based Approach for Disruption Detection and Recovery Planning in a Prototype Cognitive Digital Supply Chain Twin (2025, citations pending)

Conclusion:

In conclusion, Mr. Mahmoud Ashraf is a highly motivated researcher whose contributions to supply chain resilience, blockchain systems, and machine learning reflect both academic rigor and practical relevance. His scholarly achievements, combined with his collaborative spirit and growing international profile, make him an exceptional candidate for recognition. His publications have already established a foundation of academic influence, while his ongoing research promises to address pressing global challenges in supply chain disruptions and digital transformation. With a trajectory marked by innovation, dedication, and leadership potential, Mr. Mahmoud Ashraf is strongly deserving of this award nomination.