Indrajit Mukherjee – författare

This book explores how optimization and data science significantly impact the operations of supply chain and logistics and investigates them extensively for additional benefits. It demonstrates the concept and implementation of data science methods, advanced analytics, optimization, metaheuristics, and machine learning algorithms developed over the years for critical decision-making in warehousing, inventory, and transportation in supply chain management that facilitates the optimization of supply chain and logistics operations. It covers the following topics with an emphasis on their applications in areas like supply chain coordination, pricing and revenue management, warehousing, reverse logistics, facility location and supply chain network design, transportation and routing. The book is a great resource for senior undergraduate students intending to get themselves acquainted with interesting future trends in the topic, postgraduate students curious to augment their knowledge in the detailed theory and applications of the topic, PhD and postdoctoral students who want to focus in-depth on the stimulating research areas on these issues, and practitioners from industry who wish to explore fruitful applications and their future trends in supply chain and logistics.

This edited volume collates discussions on latest challenges, opportunities, trends and practices applicable to two major business sectors -- retail and financial services -- and chapters are segregated accordingly. It explores how these sectors are increasingly becoming data-intensive, and how ways of handling, manipulating, exploiting and evaluating data continues to change in this era of big data. Part A of the book looks into better understanding of marketing metrics/ strategy/ theory and practice using retail analytics. Topics being explored are: inventory, assortment, and promotion effectiveness; pricing strategies; brand/category switching and customer loyalty; customer profitability; customer engagement; cross-buying and up-buying; synergistic effects of online and offline channels and retail innovations; effectiveness of different type of distribution channels; customer experience; service excellence/failure/recovery; digitalizationin retailing; consumer behaviour and marketing in omni-channel retail environments. In Part B, topics of relevance include financial aspects of data analytics such as: behavioral data finance; Big data and credit risk; data analytics and financial econometrics; financial sentiment and news analysis; information and evidence in digital age; policy-making mechanisms and modelling approaches; stock market manipulation detection; stock market trading strategies using data analytics; high frequency/ algorithmic trading with AI; financial forecasting including interest rate/ old price/ crude oil/ macroeconomic variables/ FOREX/ cryptocurrency price; deep learning for finance and insurance applications. The book covers diverse and advanced concepts with practical examples to supplement and complement varied topics of interest within the domain. Thus students, researchers, and practitioners will undoubtedly find novel and new ideas for data analytics in the realm of two critical domain areas: retail and financial services.

This edited volume collates discussions on latest challenges, opportunities, trends and practices applicable to two major business sectors -- retail and financial services -- and chapters are segregated accordingly. It explores how these sectors are increasingly becoming data-intensive, and how ways of handling, manipulating, exploiting and evaluating data continues to change in this era of big data. Part A of the book looks into better understanding of marketing metrics/ strategy/ theory and practice using retail analytics. Topics being explored are: inventory, assortment, and promotion effectiveness; pricing strategies; brand/category switching and customer loyalty; customer profitability; customer engagement; cross-buying and up-buying; synergistic effects of online and offline channels and retail innovations; effectiveness of different type of distribution channels; customer experience; service excellence/failure/recovery; digitalizationin retailing; consumer behaviour and marketing in omni-channel retail environments. In Part B, topics of relevance include financial aspects of data analytics such as: behavioral data finance; Big data and credit risk; data analytics and financial econometrics; financial sentiment and news analysis; information and evidence in digital age; policy-making mechanisms and modelling approaches; stock market manipulation detection; stock market trading strategies using data analytics; high frequency/ algorithmic trading with AI; financial forecasting including interest rate/ old price/ crude oil/ macroeconomic variables/ FOREX/ cryptocurrency price; deep learning for finance and insurance applications. The book covers diverse and advanced concepts with practical examples to supplement and complement varied topics of interest within the domain. Thus students, researchers, and practitioners will undoubtedly find novel and new ideas for data analytics in the realm of two critical domain areas: retail and financial services.

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.

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.