Umang Soni



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  • Profile
  • QualificationsB.E (Mechanical), M.E. (BITS Pilani), Phd (IIT Delhi)
  • DesignationAssistant Professor
  • DivisionManufacturing Processes and Automation Engineering
  • Contact No.011-25000072
  • E-Mailumangsoni.1@gmail.com, umangsoni.iitd@gmail.com

Biosketch

B.E. (Mechanical Engineering)-RGPV University

M.E (Manufacturing Systems & engineering)- BITS Pilani

Ph.D. (Industrial Engg)- IIT Delhi (submiited)

Umang Soni is assistant Professor at Division of manufacturing process and automation engineering at NSIT. He has 8+ years of Industrial, research and teaching experience, which includes organizations like Wipro technologies (Bangalore), Bharat Forge (Pune). His area of expertise includes supply chain management, operations management and risk management.

Areas of Interest
  • Operations Management
  • Supply Chain Management
  • Artificial Intelligence
  • Risk Management
Publications (Click to expand)

List of Publications:

  1. Kumar, G., Jain, V., & Soni, U.(2019) Modelling and simulation of repairable  mechanical systems reliability and availability.  International Journal of System Assurance Engineering and Management, 1-13.
  2. Gupta, S., Soni, U., Kumar, G. and Gupta A.(2019).Supplier selection using multi-criterion decision-making under fuzzy environment: A case study in automotive industry. Computers & Industrial Engineering
  3. Soni, U., Roy, A., Verma, A., & Jain, V. (2019). Forecasting municipal solid waste generation using artificial intelligence models—a case study in India. SN Applied Sciences, 1(2), 162.
  4. Chawla, A., Singh, A., Lamba, A., Gangwani, N., & Soni, U. (2019). Demand Forecasting Using Artificial Neural Networks—A Case Study of American Retail Corporation. In Applications of Artificial Intelligence Techniques in Engineering (pp. 79-89). Springer, Singapore.
  5. Soni, U. (2019). Analyzing Risk in Dynamic Supply Chains Using Hybrid Fuzzy AHP Model. In Applications of Artificial Intelligence Techniques in Engineering (pp. 411-420). Springer, Singapore.
  6. Singh, S., & Soni, U. (2019). Predicting Order Lead Time for Just in Time production system using various Machine Learning Algorithms: A Case Study. In 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 422-425). IEEE.
  7. Seth, G., Prithvi, K. A., Paruthi, A., Jain, S., & Soni, U. (2019). Prediction of PhotoVoltaic Power Generation Using Monte Carlo Simulation. In Data and Communication Networks (pp. 279-289). Springer, Singapore.
  8. Jain, S., Agrawal, S., Paruthi, A., Trivedi, A., & Soni, U. (2019). Neural Networks for Mobile Data Usage Prediction in Singapore. In International Conference on Innovative Computing and Communications (pp. 349-357). Springer, Singapore.
  9. Gautam, A., Prakash, S., & Soni, U. (2018). Supply chain risk management and quality: a case study and analysis of Indian automotive industry. International Journal of Intelligent Enterprise, 5(1-2), 194-212.
  10. Soni, U., Singh, N., Swami, Y., & Deshwal, P. (2018, September). A Comparison Study between ANN and ANFIS for the Prediction of Employee Turnover in an Organization. In 2018 International Conference on Computing, Power and Communication Technologies (GUCON) (pp. 203-206). IEEE.
  11. Trivedi, A., Deshwal, P., Soni, U., & Mani, N. (2018). Demographic variables and Online Customer Experience of Educational Websites users. Procedia computer science, 132, 965-970.
  12. Jain, V., Kumar, S., Soni, U., & Chandra, C. (2017). Supply chain resilience: model development and empirical analysis. International Journal of Production Research, 55(22),6779-6800.
  13. Soni, Umang, Vipul Jain, and Sameer Kumar. "Measuring supply chain resilience using a deterministic modeling approach." Computers & Industrial Engineering 74 (2014).
  14. Soni, Umang, Vipul Jain, and M. Paz Salmador. "Coping with uncertainties via resilient supply chain framework." International Journal of Procurement Management 8.1-2 (2014): 182-201

    15. Soni, U., & Jain, V. (2011, December). Minimizing the vulnerabilities of supply chain: A new framework for enhancing the resilience. In 2011 IEEE                    International Conference on Industrial Engineering and Engineering Management (pp. 933-939). IEEE