The Production & Quantitative Methods (P&QM) area engages in interdisciplinary research, teaching and consulting relating to scientific methodologies in Operations Management, Operations Research and Statistics. The goal is to design, influence and enable good management practices and strengthen policies by providing the necessary strategic thinking, tools and techniques for evidence-based decision-making towards improving organizational performance in an increasingly data intensive world.
Faculty research interests in operations management are on strategic or operational issues related to manufacturing and service planning, supply chain coordination, shop floor scheduling and improving productivity of plant operations, design of operations, technological change and innovation, R&D capabilities, economics of flexible operations and process planning and in the area of Public sector and socially responsible operations.
In operations research, faculty members have expertise in linear and integer programming, large scale optimization, combinatorial optimization, revenue management and network optimization. Interest in this area is both in modeling as well as in development of algorithms and heuristics for such problems. Typical application areas for research include finance, logistics, and the process industry.
Faculty research interests in statistics include modeling discrete and financial data, survey sampling, finite population inference, biostatistics, longitudinal and survival analysis, Bayesian inference, reliability analysis, time series analysis, statistical genetics, directional statistics, functional data analysis and stochastic processes.
The area offers several courses in Operations management, Operations research, and Statistics across all the programmes of the institute. In addition, faculty members offer a wide array of Executive Education Programmes, both on-campus and online, that cater to a large number of working professionals from diverse backgrounds. The complete list of courses on offer with a brief summary of each is provided here
The area welcomes applications from candidates who have demonstrated strong research capabilities in carrying out interdisciplinary work, especially those that can influence management, theory, practice and policy, as well as methodological developments in any of the core fields, namely operations management, operations research or statistics. The following areas will be of special interest: Machine learning and Artificial Intelligence, Causal Inference, logistics and transportation, sustainable operations, empirical operations management, big data analytics, high dimensional data analysis, managing platforms and sharing economy, Industry 4.0, smart mobility.