This edited volume explores decision science theory-based approaches for enhancing the quality and productivity of products and processes, particularly in the fields of manufacturing, services, healthcare, banking, environment, agriculture, education, digital technology, and information technology. The decision science theories are drawn from various areas of management science, economics, operations research, statistical methods, machine learning, data mining, artificial intelligence, behavioural decision making and cognitive psychology. The book offers a unique platform to address various real-life problems and scenarios related to quality and productivity improvement, as well as operations excellence. The new concepts, varied solution methods, diverse research implications, industry case studies, comparative analysis of relevant approaches, in-depth literature review, and future research scopes discussed in the articles will certainly provide food for thought to researchers, decision-makers, and practitioners working in the domain of quality, productivity, and operations excellence. These theme-based book chapters demonstrate the immense potential of decision science theories to develop novel ideas that can support scientific decision-making, thereby improving the operations, quality, and productivity of any organisation.
Table of Contents:
Quality Management.- Chapter 1 Conversational Chatbot for Enhancing Healthcare Services.- Chapter 2 Current and future trends in healthcare quality control and improvement.- Chapter 3 The adaptation of Industry 4.0 for Total Quality Management (TQM): Empirical observations from the banking service sector.- Chapter 4 Integrating decision science and fuzzy logic to evaluate and improve water quality: A pathway to operational excellence in environmental management.- Chapter 5 A Solution Framework to Address Model Parameter Uncertainties in ANN-based Response Surface Models for Multivariate Process Quality Control.
About the Author :
Indrajit Mukherjee is Professor at Shailesh J. Mehta School of Management, IIT Bombay. He did his Ph.D. from the Department of Industrial Engineering and Management, Indian Institute of Technology, Kharagpur. He did his Master's degree in Quality, Reliability & Operations Research from Indian Statistical Institute, Kolkata, India. He has authored and co-authored several research papers published in refereed and reputed journals. His research contributions are published in the European Journal of Operational Research, Annals of Operations Research, Journal of Manufacturing Systems, Quality and Reliability Management, Quality Engineering, Computers & Industrial Engineering. His primary research interest is in multivariate quality control, quality management, applied operations research, and sourcing in the supply chain.
Raghu Nandan Sengupta is Professor, Department of Industrial & Management Engineering, Indian Institute of Technology Kanpur. He obtained his PhD from Indian Institute of Management Calcutta and PDF from Princeton University, USA. His research interests are in areas of sequential estimation, statistical and mathematical reliability theory, risk analysis, optimization techniques in finance, meta heuristic techniques, reliability based optimization, robust optimization. His research work has been published in EJOR, QF, CSDA, Communication in Statistics, Journal of Applied Statistics, Metrika, Statistics, Marketing Intelligence and Planning, Journal of Marketing Theory and Practice, Annals of Operations Research. He has also edited two books titled (i) Decision Sciences: Theory and Practice, (2016); and (ii) Studies in Quantitative Decision Making (2022), the latter being published by Springer.
Bhaskar Basu is Professor of Information Systems at Xavier Institute of Management, XIM University, Bhubaneswar. He obtained his PhD from Indian Institute of Technology, Kharagpur (India) and is a University Gold Medallist (Jadavpur University, India). He also has a Post Graduate Diploma in Business Management (equivalent to MBA) from Indian Institute of Management, Calcutta. His research interests are in the areas of knowledge management, AI applications in business and sports management. His research work has been published in journals like VINE, Journal of Modeling in Management, IJITDM etc. He has also edited a book titled “Organizational Learning-Perspectives and Practices” (2006) and another one on sports management (co-edited) with Springer (2023).
Jitendra Kumar Jha is working as a Professor in the Department of Industrial and Systems Engineering at IIT Kharagpur. He obtained his PhD from IIT Kanpur. He has received several scholarships and awards from DRPG IIT Kanpur, BITSAA of NorthAmerica, SJ Jindal Trust New Delhi, IIT Kharagpur. His main areas of teaching and research include operations research, statistical decision modeling, facility planning, supply chain and logistics planning, and inventory control. He has published/presented more than sixty papers in international journals and conferences, and his publications have appeared in the reputed journals like Journal of the Operational Research Society, Journal of Manufacturing Systems, Applied Mathematical Modelling, Computers & Industrial Engineering, International Journal of Production Research, and other leading journals of Industrial Engineering. He is serving as an editorial board member of International Journal of Industrial Engineering: Theory, Applications and Practice.
