Raghu Nandan Sengupta – författare

This handbook is an endeavour to cover many current, relevant, and essential topics related to decision sciences in a scientific manner. Using this handbook, graduate students, researchers, as well as practitioners from engineering, statistics, sociology, economics, etc. will find a new and refreshing paradigm shift as to how these topics can be put to use beneficially. Starting from the basics to advanced concepts, authors hope to make the readers well aware of the different theoretical and practical ideas, which are the focus of study in decision sciences nowadays. It includes an excellent bibliography/reference/journal list, information about a variety of datasets, illustrated pseudo-codes, and discussion of future trends in research. Covering topics ranging from optimization, networks and games, multi-objective optimization, inventory theory, statistical methods, artificial neural networks, times series analysis, simulation modeling, decision support system, data envelopment analysis, queueing theory, etc., this reference book is an attempt to make this area more meaningful for varied readers. Noteworthy features of this handbook are in-depth coverage of different topics, solved practical examples, unique datasets for a variety of examples in the areas of decision sciences, in-depth analysis of problems through colored charts, 3D diagrams, and discussions about software.

This handbook is an endeavour to cover many current, relevant, and essential topics related to decision sciences in a scientific manner. Using this handbook, graduate students, researchers, as well as practitioners from engineering, statistics, sociology, economics, etc. will find a new and refreshing paradigm shift as to how these topics can be put to use beneficially. Starting from the basics to advanced concepts, authors hope to make the readers well aware of the different theoretical and practical ideas, which are the focus of study in decision sciences nowadays. It includes an excellent bibliography/reference/journal list, information about a variety of datasets, illustrated pseudo-codes, and discussion of future trends in research. Covering topics ranging from optimization, networks and games, multi-objective optimization, inventory theory, statistical methods, artificial neural networks, times series analysis, simulation modeling, decision support system, data envelopment analysis, queueing theory, etc., this reference book is an attempt to make this area more meaningful for varied readers. Noteworthy features of this handbook are in-depth coverage of different topics, solved practical examples, unique datasets for a variety of examples in the areas of decision sciences, in-depth analysis of problems through colored charts, 3D diagrams, and discussions about software.

This handbook is an endeavour to cover many current, relevant, and essential topics related to decision sciences in a scientific manner. Using this handbook, graduate students, researchers, as well as practitioners from engineering, statistics, sociology, economics, etc. will find a new and refreshing paradigm shift as to how these topics can be put to use beneficially. Starting from the basics to advanced concepts, authors hope to make the readers well aware of the different theoretical and practical ideas, which are the focus of study in decision sciences nowadays. It includes an excellent bibliography/reference/journal list, information about a variety of datasets, illustrated pseudo-codes, and discussion of future trends in research. Covering topics ranging from optimization, networks and games, multi-objective optimization, inventory theory, statistical methods, artificial neural networks, times series analysis, simulation modeling, decision support system, data envelopment analysis, queueing theory, etc., this reference book is an attempt to make this area more meaningful for varied readers. Noteworthy features of this handbook are in-depth coverage of different topics, solved practical examples, unique datasets for a variety of examples in the areas of decision sciences, in-depth analysis of problems through colored charts, 3D diagrams, and discussions about software.

This edited volume is an in-depth collation of the usage of different quantitative decision making techniques in practical areas such as lean & green supply chain, reverse logistics, perishable logistics, closed loop supply chain, sustainable project management, retail management, block chain applications, optimal supplier selection problem, demand/supply modelling, forecasting under uncertainties, scheduling & sequencing, resource constraint logistics, dynamic network supply chain, risk evaluation, and so on. Additionally, the book also solves these issues in theoretical and practical context using innovative mathematical tools. Consisting of selected papers from the 23rd Annual International Conference of the Society of Operations Management, this book's highlight is not only the coverage of interesting topics, but also how these topics are dealt with, such that post-graduate students as well as researchers and industry personnel working in areas like engineering, economics, social sciences, management, mathematics, etc., can derive the maximum benefit by reading or referring to this book. Apart from the emphasis on new mathematical, operations research, operations management, and statistical techniques, the authors also ensure that all the concepts are made clear by highlighting their practical significance in different areas of applications of operations management. By using novel presentation methods, the book offers a good practical flavor of all the different topics relevant to operations management in the coming decades.

This edited volume is an in-depth collation of the usage of different quantitative decision making techniques in practical areas such as lean & green supply chain, reverse logistics, perishable logistics, closed loop supply chain, sustainable project management, retail management, block chain applications, optimal supplier selection problem, demand/supply modelling, forecasting under uncertainties, scheduling & sequencing, resource constraint logistics, dynamic network supply chain, risk evaluation, and so on. Additionally, the book also solves these issues in theoretical and practical context using innovative mathematical tools. Consisting of selected papers from the 23rd Annual International Conference of the Society of Operations Management, this book''s highlight is not only the coverage of interesting topics, but also how these topics are dealt with, such that post-graduate students as well as researchers and industry personnel working in areas like engineering, economics, social sciences, management, mathematics, etc., can derive the maximum benefit by reading or referring to this book. Apart from the emphasis on new mathematical, operations research, operations management, and statistical techniques, the authors also ensure that all the concepts are made clear by highlighting their practical significance in different areas of applications of operations management. By using novel presentation methods, the book offers a good practical flavor of all the different topics relevant to operations management in the coming decades. 

This edited volume is an in-depth collation of the usage of different quantitative decision making techniques in practical areas such as lean & green supply chain, reverse logistics, perishable logistics, closed loop supply chain, sustainable project management, retail management, block chain applications, optimal supplier selection problem, demand/supply modelling, forecasting under uncertainties, scheduling & sequencing, resource constraint logistics, dynamic network supply chain, risk evaluation, and so on. Additionally, the book also solves these issues in theoretical and practical context using innovative mathematical tools. Consisting of selected papers from the 23rd Annual International Conference of the Society of Operations Management, this book's highlight is not only the coverage of interesting topics, but also how these topics are dealt with, such that post-graduate students as well as researchers and industry personnel working in areas like engineering, economics, social sciences, management, mathematics, etc., can derive the maximum benefit by reading or referring to this book. Apart from the emphasis on new mathematical, operations research, operations management, and statistical techniques, the authors also ensure that all the concepts are made clear by highlighting their practical significance in different areas of applications of operations management. By using novel presentation methods, the book offers a good practical flavor of all the different topics relevant to operations management in the coming decades.

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.