Springer Decision Sciences for Quality and Productivity Improvement: Towards Operational and Business Excellence

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.