Sushil Punia holds a Ph.D. degree in Management from IIT Delhi. Before that, he earned M.Tech in Industrial and Management Engineering from IIT Kanpur and B.Tech from NIT Kurukshetra. His areas of interests include operations research, business analytics, and operations and supply chain management. He focuses on developing advanced forecasting and data-driven optimization decision models and policy frameworks for tactical and operational planning for public sectors operations management, particularly, in healthcare and logistics areas.

Previously, Sushil was an Assistant Professor at VGSoM, IIT Kharagpur from 2021-2026. He has led several research projects funded by international and national agencies. He has also designed and coordinated several Management Development Programs for Public and Private organizations.

Sushil regularly publishes in top-tier research journals of his area. His work on pandemics, supply chain disruptions, and governmental policy decisions has received EURO 2023 Award for Best ‘Theory and Methodology’ Paper in EJOR journal. He has also received several other awards and fellowships in the past such as a postdoctoral fellowship from the University of Cambridge (UK), IIF-SAS Research Award, Springer Nature's Best Paper Award, the GoI MoUD research fellowship, among others.

Courses in the areas of or at intersections of operations research, business analytics, and operations and supply chain management

• Designing Vaccine allocation mechanisms in a multi-level planning system (ICSSR Major Research Project, 2023-25)
• Ridership Forecasting for Effective Operations Management in Urban Public Transit Systems (FSRG – IIT Kharagpur, 2023-25)

• EURO 2023 Award for Best ‘Theory and Methodology’ EJOR Paper
• Springer Nature’s Best Paper Award at XXIII Annual International SOM Conference
• Sumant Moolgaonkar Research Fellowship, MoUD (GOI) and IIT Delhi
• Emerald Literati Award, UK  
• Received a Post-doctoral position at the University of Cambridge (UK) (didn’t join) 2021
• Co-author of one of the Most Cited and Downloaded Articles in the EJOR journal, 2020-24
• Research Travel Awards 2017, 2018

Selected journal publications:

Punia, S. (2025). Medium- to Long-Term Demand Forecasting in Retail and Manufacturing Organizations: Integration of Machine Learning, Human Judgment, and Interval Variable. Journal of Forecasting, 45(1), 122–134. (ABDC- A)

Sarode, A. R., Rani, M. V., Das, B., S, U., & Punia, S. (2025). An AI-Based Framework for Interpretable Mental Health Literacy Segmentation and Decision Support. IEEE Journal of Biomedical and Health Informatics, 29(12), 8799–8806. (Q1)

Punia, S., & Shankar, S. (2022). Predictive analytics for demand forecasting: a deep learning-based decision support system. Knowledge-Based Systems, 258, 109956. (ABDC – A)

Sanguri, K., Shankar, S., Punia, S., & Patra, S. (2022). Hierarchical container throughput forecasting: The value of coherent forecasts in the management of ports operations. Computers & Industrial Engineering, 173, 108651. (ABDC – A)

Nikolopoulos, K., Punia, S., Schafers, A., Tsinopoulos, C., & Vasilakis, C. (2021). Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. European Journal of Operational Research, 290(1), 99-115. (ABDC – A*)

Punia, S., Singh, S., & Madaan, J. (2020). From predictive to prescriptive analytics: A data-driven multi-item newsvendor model. Decision Support Systems, 136, 113340. (ABDC – A*)

Punia, S., Singh, S. P., & Madaan, J. (2020). A cross-temporal hierarchical framework and deep learning for supply chain forecasting. Computers & Industrial Engineering, 149, 106796. (ABDC – A)

More details available at: https://scholar.google.com/citations?user=eMe-ddgAAAAJ

• Kundu, S., Punia, S., & Rani, M. V. (2025). Effective Operational Planning for Flood-Affected Areas Using Machine learning and Optimization. 2025 Eighth International Conference on Image Information Processing (ICIIP), 396–400.
• Punia, S. (2024). Passenger Flow Prediction in Urban Public Transit Systems for Effective Operations Management. 44th International Symposium on Forecasting, Dijon, France.
• Punia, S., Singh, S. P., & Madaan, J. K. (2019). Cross-temporal forecasting framework for supply chain forecasting. XXIII Annual International Conference of the Society of Operations Management (SOM 2019), IIT Kanpur, India.