Kailash Choudhary | Sustainable Supply Chain | Editorial Board Member

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Dr. Kailash Choudhary | Sustainable Supply Chain | India

Indian Institute of Management Shillong | India

Dr. Kailash Choudhary is a leading researcher at the Indian Institute of Management Shillong whose expertise spans operations management, quantitative techniques, sustainability analytics, and green supply chain optimization. His work integrates lean principles, quality management frameworks, and environmental performance metrics to advance sustainable industrial and supply chain systems. With influential contributions in life cycle assessment, green supply chain pressures, sustainable production strategies, and performance modeling, he significantly enhances cleaner manufacturing, resource efficiency, and evidence-based decision-making. His empirical investigations in ceramics manufacturing, studies on sustainable materials, and critical reviews of green supply chain mechanisms demonstrate strong methodological depth and high industry relevance. Supported by 1,024 citations, 18 research documents, and an h-index of 11, he has established a robust global research footprint, shaping the evolution of sustainable operations and fostering greener transitions across manufacturing, construction, and logistics ecosystems.

Profile: Scopus | Orcid

Featured Publications

Choudhary, K., Gill, G., Sangwa, N. R., & Sarna, B. (2025). Utilising copper tailings as a sustainable alternative to conventional structural filling materials: LCA analysis. Built Environment Project and Asset Management.

Choudhary, K., Sangwa, N. R., & Sangwan, K. S. (2023). Comparative life cycle assessment of flooring options for sustainable buildings. Construction Innovation.

Gill, G., Choudhary, K., Mittal, R. K., Sangwan, K. S., & Arya, A. (2023). Efficient utilization of tire chip reinforced sand under footings subjected to purely inclined loads—An experimental and life cycle investigation. Environmental Science and Pollution Research.

Choudhary, K., Jakhar, S., Gakkhar, N., & Sangwan, K. S. (2022). Comparative life cycle assessments of photovoltaic thermal systems with earth water heat exchanger cooling. Procedia CIRP.

Choudhary, K., & Sangwan, K. S. (2022). Green supply chain management pressures, practices and performance: A critical literature review. Benchmarking: An International Journal.

Sebastian Paull| Supply Chain | Best Researcher Award

Published on by Global Academic Awards

Mr. Sebastian Paull| Supply Chain | Best Researcher Award

Mr. Sebastian Paull, University of Mannheim, Germany

Sebastian Paull is a highly qualified researcher in supply chain science, with expertise in probabilistic forecasting, stochastic optimization, and big data analytics. His research has practical relevance, offering decision-making improvements beyond traditional forecasting methods. His industry experience and leadership further validate his contributions. However, enhancing publication records and expanding interdisciplinary impact would further strengthen his candidacy.

Publication Profile: 

Orcid

Education & Training 🎓

Sebastian Paull is currently a doctoral candidate at the University of Mannheim (2020 – Present), focusing on integrating machine learning-based probabilistic forecasting and stochastic optimization into supply chain planning. He holds a Master of Science in Business Informatics (1.9) from DHBW Karlsruhe & EnBW (2016 – 2018) and a Bachelor of Science in Business Informatics (2.2) from the same institution (2012 – 2015).

 

Work Experience 💼

Sebastian has extensive experience in supply chain analytics, optimization, and IT risk assurance. Currently, he is a Lead Supply Chain Scientist at Aioneers (October 2023 – Present), where he leads an international data engineering team, facilitates cross-regional collaboration, and develops technology solutions for supply chain optimization. Previously, he worked as a Senior Supply Chain Scientist (April 2020 – September 2023), conducting demand forecasting, cluster analysis, and production optimization. His expertise in dashboarding and supply chain data modeling has helped organizations improve inventory management and reduce operational costs.,Earlier, he served as a Supply Chain Analyst at EY Mannheim (2020 – Present), focusing on demand forecasting, portfolio analysis, and IT risk assessment. Before that, as an IT Risk Assurance Consultant at EY Frankfurt (2018 – 2020), he optimized IT processes, ensured regulatory compliance, and advised businesses on secure technology adoption.

Research Focus 🔬🧪

Sebastian’s research bridges theory and practice in supply chain management, emphasizing ML-based probabilistic forecasting and stochastic optimization to address demand uncertainties. His work demonstrates the advantages of neural networks over traditional point forecasts, particularly in managing complex demand patterns. He provides practical, adaptable approaches to supply chain practitioners, advocating for a gradual transition from simpler models to probabilistic forecasting techniques to improve real-world decision-making.

Awards and Recognitions:

Sebastian has contributed to multiple high-impact projects in supply chain optimization and data science. His work has been recognized for enhancing supply chain performance, improving IT risk assurance, and developing advanced forecasting methodologies. His academic research and professional contributions position him as a leader in data-driven supply chain optimization.

Skills : 

Sebastian specializes in probabilistic forecasting, stochastic optimization, and big data processing. He has extensive experience with PySpark on Databricks, Pyomo for optimization modeling, and machine learning techniques for demand forecasting. His expertise extends to dashboarding and Business Intelligence tools, enabling data-driven decision-making in supply chain management.,Additionally, he is fluent in German (native), English (native), Swedish (C1), Spanish (B1), and French (A2).

 

Publication Top Notes

Machine Learning for Master Production Scheduling: Combining probabilistic forecasting with stochastic optimisation

Spinning gold from straw – evaluating the flexibility of data centres on power markets