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Prof. Sushil Punia

Prof. Sushil Punia

Ph.D. (Thesis Defended), IIT Delhi, M.Tech (Industrial and Management Engineering), IIT Kanpur, B.Tech (Industrial Engineering and Management), NIT Kurukshetra


Contact: 011-46485504

Total Years of Experience


Brief description of Experience

Sushil Punia is a Ph.D. (Thesis Defended) from Indian Institute of Technology (IIT) Delhi, India, and an M.Tech from Indian Institute of Technology (IIT) Kanpur, India. His research interests lie at the intersection of operations management and applied data science in healthcare, energy, and manufacturing industries. He also works on the assessment of governmental policies in his research areas. His research has appeared in leading management and data sciences journals, including the European Journal of Operational Research (EJOR), Decision Support Systems (DSS), International Journal of Production Research (IJPR), and presented at international conferences like INFORMS 2017, and SOM 2019. He received Emerald Best Reviewer Award 2018 and Springer’s Best Paper Award at Annual International SOM 2019.

Research Interest Areas

Operations and Supply Chain Analytics in Healthcare, Energy, and Industry 4.0

Consulting Interest Areas

Operations and Supply Chain Analytics, Healthcare Analytics, and Energy Analytics

Selected Publications
  1. Punia, S., Singh, S., & Madaan, J. (2020). From predictive to prescriptive analytics: A data-driven multi-item newsvendor model. Decision Support Systems, 136, 113340.
  2. Punia, S., Singh, S. P., & Madaan, J. K. (2020). A cross-temporal hierarchical framework and deep learning for supply chain forecasting. Computers & Industrial Engineering, 149, 106796.
  3. Nikolopoulos, K., Punia, S., Schafers, A., Tsinopoulos, C., & Vasilakis, C. (2020). Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. European Journal of Operational Research. (names are in alphabetical order)
  4. Punia, S., Nikolopoulos, K., Singh, S., Madaan, J., & Litsiou, K. (2020). Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail. International Journal of Production Research, 58(16), 4964–4979.
  5. Shankar, S., Ilavarasan, P. V., Punia, S., & Singh, S. P. (2019). Forecasting container throughput with long short-term memory networks. Industrial Management & Data Systems, 120(3), 425–441.

Executive Education/MDPs

FORE School of Management has been designing, developing and conducting innovative Executive Education (EE)/ Management Development Programmes (MDPs) for working executives in India for over three decades.