I work in the areas of operations and supply chain management, decision analytics, and public sector operations research. I have specific research interests in building forecasting and optimization (a.k.a. predictive and prescriptive analytics) decision models using data-driven optimization and machine (and deep) learning. Lately, I have worked on building decision models for healthcare operations management and policy interventions for COVID-19. A research article based on that work is now amongst the most downloaded and most cited journal articles in the European Journal of Operational Research. During the first wave of COVID-19, it was also covered by international media (including Daily Mail, UK).
My present focus is to address contemporary strategic and operational issues of renewable energy supply chain and smart manufacturing (Industry 4.0 & 5.0), in addition to public healthcare delivery.
Read more: https://sites.google.com/view/spunia-iitdelhi
From predictive to prescriptive analytics: A data-driven multi-item newsvendor model by Punia S., Singh S. P., Madaan J. Decision Support Systems 136 - (2020)
A cross-temporal hierarchical framework and deep learning for supply chain forecasting by Punia S., Singh S. P., Madaan J. Computers & Industrial Engineering 149 - (2021)
Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail by Punia S., Nikolopoulos K. , Singh S. P., Madaan J. , Litsiou K. International Journal of Production Research 58 4964-4979 (2020)
Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions by Nikolopoulos K., Punia S. , Schäfers A. , Tsinopoulos C. , Vasilakis C. European Journal of Operational Research 290 99-115 (2021)
Area of Research: Operations Management and Analytics