Digitising Enterprise in an Information Age is an effort that focuses on a very vast cluster of Enterprises and their digitising technology involvement and take us through the road map of the implementation process in them, some of them being ICT, Banking, Stock Markets, Textile Industry & ICT, Social Media, Software Quality Assurance, Information Systems Security and Risk Management, Employee Resource Planning etc. It delves on increased instances of cyber spamming and the threat that poses to e-Commerce and Banking and tools that help and Enterprise toward of such threats. To quote Confucius, "As the water shapes itself to the vessel that contains it, so does a wise man adapts himself to circumstances." And the journey of evolution and progression will continue and institutions and enterprises will continue to become smarter and more and more technology savvy. Enterprises and businesses across all genre and spectrum are trying their level best to adopt to change and move on with the changing requirements of technology and as enterprises and companies upgrade and speed up their digital transformations and move their outdate heirloom systems to the cloud, archaic partners that don't keep up will be left behind. Note: T&F does not sell or distribute the Hardback in India, Pakistan, Nepal, Bhutan, Bangladesh and Sri Lanka.

Convergenomics is about the megatrends that are shaping how people behave and organizations work. In this insightful analysis, Sang Lee and David Olson describe how globalization, digitization, changing demographics, changing industry mix, deregulation and privatization, commoditization of processes, new value chains, emerging new economies, deteriorating environment, and cultural conflicts have led to what they define as a convergence revolution. Lee and Olson discuss this convergence revolution from the perspectives of technology, industry, knowledge, open-source networking and bio-artificial convergence, and they explain how human systems are transformed by what they have named convergenomics. Understanding convergenomics can lead to innovative strategic approaches and, the authors contend, more agile businesses are already employing these approaches to become and remain competitive and to generate greater value in a world radically changed by e-commerce. Business leaders and 'students' of strategy at all levels will learn from this book how revolutionary developments can be embraced rather than feared, and how technology that is potentially frightening in its complexity can be harnessed and used to enable productive collaboration and gain competitive advantage.

Convergenomics is about the megatrends that are shaping how people behave and organizations work. In this insightful analysis, Sang Lee and David Olson describe how globalization, digitization, changing demographics, changing industry mix, deregulation and privatization, commoditization of processes, new value chains, emerging new economies, deteriorating environment, and cultural conflicts have led to what they define as a convergence revolution. Lee and Olson discuss this convergence revolution from the perspectives of technology, industry, knowledge, open-source networking and bio-artificial convergence, and they explain how human systems are transformed by what they have named convergenomics. Understanding convergenomics can lead to innovative strategic approaches and, the authors contend, more agile businesses are already employing these approaches to become and remain competitive and to generate greater value in a world radically changed by e-commerce. Business leaders and 'students' of strategy at all levels will learn from this book how revolutionary developments can be embraced rather than feared, and how technology that is potentially frightening in its complexity can be harnessed and used to enable productive collaboration and gain competitive advantage.

COVID-19 has spread around the world, causing tremendous structural change, and severely affecting global supply chains and financial operations. As such there is a need for analytic tools help deal with the impact of the pandemic on the world's economies; these tools are not panaceas and certainly won't cure the problems faced, but they offer a means to aid governments, firms, and individuals in coping with specific problems. This book provides an overview of the COVID-19 pandemic and evaluates its effect on financial and supply chain operations. It then discusses epidemic modeling, presenting sources of quantitative and text data, and describing how models are used to illustrate the pandemic impact on supply chains, macroeconomic performance on financial operations. It highlights the specific experiences of the banking system, which offers predictions of the impact on the Swedish banking sector. Further, it examines models related to pandemic planning, such as evaluation of financial contagion, debt risk analysis, and health system efficiency performance, and addresses specific models of pandemic parameters. The book demonstrates various tools using available data on the ongoing COVID-19 pandemic. While it includes some citations, it focuses on describing the methods and explaining how they work, rather than on theory. The data sets and software presented were all selected on the basis of their widespread availability to any reader with computer links.

This book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R') and WEKA. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on prescriptive analytics. The book seeks to provide simple explanations and demonstration of some descriptive tools. This second edition provides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic data types. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling. Chapter 5 demonstrates regression tree modeling. Chapter 6 presents autoregressive/integrated/moving average models, as well as GARCH models. Chapter 7 covers the set of data mining tools used in classification, to include special variants support vector machines, random forests, and boosting. Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links.

This book is a comprehensive guide to several aspects of risk, including information systems, disaster management, supply chain and disaster management perspectives. A major portion of this book is devoted to presenting a number of operations research models that have been (or could be) applied to enterprise supply risk management, especially from the supply chain perspective. Each chapter of this book can be used as a unique module on a different topics with dedicated examples, definitions and discussion notes. This book comes at a time when the world is increasingly challenged by different forms of risk and how to manage them. Events of the 21st Century have made enterprise risk management even more critical. Risks such as suspicions surrounding top-management structures, financial and technology bubbles (especially since 2008), as well as the demonstrated risk from terrorism, such as the 9/11 attack in the U.S. as well as more recent events in France, Belgium, and other locations in Europe, have a tremendous impact on many facets of business. Businesses, in fact, exist to cope with risk in their area of specialization.

This book offers an overview of knowledge management. It starts with an introduction to the subject, placing descriptive models in the context of the overall field as well as within the more specific field of data mining analysis. Chapter 2 covers data visualization, including directions for accessing R open source software (described through Rattle). Both R and Rattle are free to students. Chapter 3 then describes market basket analysis, comparing it with more advanced models, and addresses the concept of lift. Subsequently, Chapter 4 describes smarketing RFM models and compares it with more advanced predictive models. Next, Chapter 5 describes association rules, including the APriori algorithm and provides software support from R. Chapter 6 covers cluster analysis, including software support from R (Rattle), KNIME, and WEKA, all of which are open source. Chapter 7 goes on to describe link analysis, social network metrics, and open source NodeXL software, and demonstrates link analysis application using PolyAnalyst output. Chapter 8 concludes the monograph. Using business-related data to demonstrate models, this descriptive book explains how methods work with some citations, but without detailed references. The data sets and software selected are widely available and can easily be accessed.

This book reviews forecasting data mining models, from basic tools for stable data through causal models, to more advanced models using trends and cycles. These models are demonstrated on the basis of business-related data, including stock indices, crude oil prices, and the price of gold. The book's main approach is above all descriptive, seeking to explain how the methods concretely work; as such, it includes selected citations, but does not go into deep scholarly reference. The data sets and software reviewed were selected for their widespread availability to all readers with internet access.

One of the most important tasks faced by decision-makers in business and government is that of selection. Selection problems are challenging in that they require the balancing of multiple, often conflicting, criteria. In recent years, a number of interesting decision aids have become available to assist in such decisions.
The aim of this book is to provide a comparative survey of many of the decision aids currently available. The first chapters present general ideas which underpin the methodologies used to design these aids. Subsequent chapters then focus on specific decision aids and demonstrate some of the software which implement these ideas. A final chapter provides a comparative analysis of their strengths and weaknesses.

1 Facility Location Problems The location problem has been with humans for all of their history. In the past, many rulers had the decision of locating their capital. Reasons for selecting various locations included central location, transportation benefits to foster trade, and defensibility. The development of industry involved location problems for production facilities and trade outlets. Obvious th criteria for location ofbusiness facilities includedprofit impact. In the 19 century, there seemed to be a focus on the cost of transporting raw materials versus the cost of transporting goods to consumers. Location decisions were made considering all potential gains and expenses. Some judgment was required, because while most benefits and costs could be measured accurately, not all could be. Successful business practice depended on the soundjudgment of the decision-maker in solvinglocation problems. Each of these enterprises produced some wastes. Finding a location to dispose of these wastes was not a difficult task. In less-enlightened times, governments resorted to fiat and land-condemnationto take the sites needed th for disposal. In the 19 century, industry grew rapidly in Great Britain and elsewhere as mass production served expanding populations of consumers. The by-products of mass-production were often simply discarded in the most expeditious manner. There are still mountains in the United States Introduction 2 with artificial facades created from the excess material discarded from mining activity. We have developed the ability to create waste of lethal toxicity

Risk management has become a critical part of doing business in the twenty-first century. This book is a collection of material about enterprise risk management, and the role of risk in decision making. Part I introduces the topic of enterprise risk management. Part II presents enterprise risk management from perspectives of finance, accounting, insurance, supply chain operations, and project management. Technology tools are addressed in Part III, including financial models of risk as well as accounting aspects, using data envelopment analysis, neural network tools for credit risk evaluation, and real option analysis applied to information techn- ogy outsourcing. In Part IV, three chapters present enterprise risk management experience in China, including banking, chemical plant operations, and information technology. Lincoln, USA David L. Olson Toronto, Canada Desheng Wu February 2008 v Contents Part I Preliminary 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 David L. Olson & Desheng Wu 2 The Human Reaction to Risk and Opportunity . . . . . . . . . . . . . . . . . . . 7 David R. Koenig Part II ERM Perspectives 3 Enterprise Risk Management: Financial and Accounting Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Desheng Wu & David L. Olson 4 An Empirical Study on Enterprise Risk Management in Insurance . . 39 Madhusudan Acharyya 5 Supply Chain Risk Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 David L. Olson & Desheng Wu 6 Two Polar Concept of Project Risk Management. . . . . . . . . . . . . . . . . . 69 Seyed Mohammad Seyedhoseini, Siamak Noori & Mohammed AliHatefi Part III ERM Technologies 7 The Mathematics of Risk Transfer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Marcos Escobar & Luis Seco 8 Stable Models in Risk Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Risk management has become a critical part of doing business in the twenty-first century. This book is a collection of material about enterprise risk management, and the role of risk in decision making. Part I introduces the topic of enterprise risk management. Part II presents enterprise risk management from perspectives of finance, accounting, insurance, supply chain operations, and project management. Technology tools are addressed in Part III, including financial models of risk as well as accounting aspects, using data envelopment analysis, neural network tools for credit risk evaluation, and real option analysis applied to information techn- ogy outsourcing. In Part IV, three chapters present enterprise risk management experience in China, including banking, chemical plant operations, and information technology. Lincoln, USA David L. Olson Toronto, Canada Desheng Wu February 2008 v Contents Part I Preliminary 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 David L. Olson & Desheng Wu 2 The Human Reaction to Risk and Opportunity . . . . . . . . . . . . . . . . . . . 7 David R. Koenig Part II ERM Perspectives 3 Enterprise Risk Management: Financial and Accounting Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Desheng Wu & David L. Olson 4 An Empirical Study on Enterprise Risk Management in Insurance . . 39 Madhusudan Acharyya 5 Supply Chain Risk Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 David L. Olson & Desheng Wu 6 Two Polar Concept of Project Risk Management. . . . . . . . . . . . . . . . . . 69 Seyed Mohammad Seyedhoseini, Siamak Noori & Mohammed AliHatefi Part III ERM Technologies 7 The Mathematics of Risk Transfer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Marcos Escobar & Luis Seco 8 Stable Models in Risk Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . .

This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. The book is organized in three parts. Part I introduces concepts. Part II describes and demonstrates basic data mining algorithms. It also contains chapters on a number of different techniques often used in data mining. Part III focuses on business applications of data mining.

The purpose of Multiple Criteria Analysis in Strategic Siting Problems is to demonstrate how multiple criteria can be used in analysis of facility location problems. The book begins with an overview, explains the internationally most popular multiple objective analysis methods, and demonstrates their applications on real problems. Siting problems reviewed include nuclear waste disposal in the U.S., solid waste management in Finland, pipeline location in India, and pipeline location in Russia. Methods covered are multiattribute utility analysis, analytic hierarchy process, the ELECTRE outranking method, and verbal decision analysis. The book concludes with a comparative review of methods. The book uses the multi-attribute, multi-party framework of Kunreuther to present the decision context, to include parties with interests in the decisions, as well as the sequence of project events. This perspective is valuable in identifying the qualitative backgrounds of siting problems that need to be considered. The book demonstrates the importance of multiple criteria in hazardous facility site selection. It also shows how each of the four methodologies covered operate, both in terms of demonstration problems worked with numbers, and how these methods have been applied in the real applications. The real applications were taken from refereed journal documentation, with the exception of Russian pipeline analysis decisions in which Professor Larichev participated. The book is recommended for those interested in decision-making involving problems with social import. This includes environmental aspects, as well as international aspects of decision making.

One of the most important tasks faced by decision-makers in business and government is that of selection. Selection problems are challenging in that they require the balancing of multiple, often conflicting, criteria. In recent years, a number of interesting decision aids have become available to assist in such decisions.
The aim of this book is to provide a comparative survey of many of the decision aids currently available. The first chapters present general ideas which underpin the methodologies used to design these aids. Subsequent chapters then focus on specific decision aids and demonstrate some of the software which implement these ideas. A final chapter provides a comparative analysis of their strengths and weaknesses.

Enterprise Risk Management in Finance is a guide to measuring and managing Enterprise-wide risks in financial institutions. Financial institutions operate in a unique manner when compared to other businesses. They are, by the nature of their business, highly exposed to risk at every level, and indeed employ their own risk management functions to manage many of these risks. However, financial firms are also highly exposed at enterprise level. Traditional approaches and frameworks for ERM are flawed when applied to banks, asset managers or insurance houses, and a different approach is needed. This new book provides a comprehensive, technical guide to ERM for financial institutions. Split into three parts, it first sets the scene, putting ERM in the context of finance houses. It will examine the financial risks already inherent in banking, and then insurance operations, and how these need to be accounted for at a floor and enterprise level. The book then provides the necessary tools to implement ERM in these environments, including performance analysis, credit analysis and forecasting applications. Finally, the book provides real life cases of successful and not so successful ERM in financial institutions. Technical and rigorous, this book will be a welcome addition to the literature in this area, and will appeal to risk managers, actuaries, regulators and senior managers in banks and financial institutions.

This book presents data mining methods in the field of healthcare management in a practical way. Healthcare quality and disease prevention are essential in today’s world. Healthcare management faces a number of challenges, e.g. reducing patient growth through disease prevention, stopping or slowing disease progression, and reducing healthcare costs while improving quality of care. The book provides an overview of current healthcare management problems and highlights how analytics and knowledge management have been used to better cope with them. It then demonstrates how to use descriptive and predictive analytics tools to help address these challenges. In closing, it presents applications of software solutions in the context of healthcare management. Given its scope, the book will appeal to a broad readership, from researchers and students in the operations research and management field to practitioners such as data analysts and decision-makers who work in the healthcare sector.

The objective of the book is to provide materials to demonstrate the development of TOPSIS and to serve as a handbook. It contains the basic process of TOPSIS, numerous variant processes, property explanations, theoretical developments, and illustrative examples with real-world cases. Possible readers would be graduate students, researchers, analysts, and professionals who are interested in TOPSIS, a distance-based algorithm, and who would like to compare TOPSIS with other MCDM methods. The book serves as a research reference as well as a self-learning book with step-by-step illustrations for the MCDM community.

This book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R') and WEKA. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on prescriptive analytics. The book seeks to provide simple explanations and demonstration of some descriptive tools. This second edition provides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic data types. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling. Chapter 5 demonstrates regression tree modeling. Chapter 6 presents autoregressive/integrated/moving average models, as well as GARCH models. Chapter 7 covers the set of data mining tools used in classification, to include special variants support vector machines, random forests, and boosting. Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links.

This book provides an overview of data mining methods demonstrated by software. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Diagnostic analytics can apply analysis to sensor input to direct control systems automatically. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on descriptive analytics. The book seeks to provide simple explanations and demonstration of some descriptive tools. This second edition provides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic software support to data visualization. Chapter 3 covers fundamentals of market basket analysis, and Chapter 4 provides demonstration of RFM modeling, a basic marketing data mining tool. Chapter 5 demonstrates association rule mining. Chapter 6 is a more in-depth coverage of cluster analysis. Chapter 7 discusses link analysis. Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links.

Dieses Buch bietet einen UEberblick uber Data-Mining-Methoden, die durch Software veranschaulicht werden. Beim Wissensmanagement geht es um die Anwendung von menschlichem Wissen (Erkenntnistheorie) mit den technologischen Fortschritten unserer heutigen Gesellschaft (Computersysteme) und Big Data, sowohl bei der Datenerfassung als auch bei der Datenanalyse. Es gibt drei Arten von Analyseinstrumenten. Die deskriptive Analyse konzentriert sich auf Berichte uber das, was passiert ist. Bei der pradiktiven Analyse werden statistische und/oder kunstliche Intelligenz eingesetzt, um Vorhersagen treffen zu koennen. Dazu gehoert auch die Modellierung von Klassifizierungen. Die diagnostische Analytik kann die Analyse von Sensoreingaben anwenden, um Kontrollsysteme automatisch zu steuern. Die praskriptive Analytik wendet quantitative Modelle an, um Systeme zu optimieren oder zumindest verbesserte Systeme zu identifizieren. Data Mining umfasst deskriptive und pradiktive Modellierung. Operations Research umfasst alle drei Bereiche. Dieses Buch konzentriert sich auf die deskriptive Analytik. Das Buch versucht, einfache Erklarungen und Demonstrationen einiger deskriptiver Werkzeuge zu liefern. Es bietet Beispiele fur die Auswirkungen von Big Data und erweitert die Abdeckung von Assoziationsregeln und Clusteranalysen. Kapitel 1 gibt einen UEberblick im Kontext des Wissensmanagements. Kapitel 2 eroertert einige grundlegende Softwareunterstutzung fur die Datenvisualisierung. Kapitel 3 befasst sich mit den Grundlagen der Warenkorbanalyse, und Kapitel 4 demonstriert die RFM-Modellierung, ein grundlegendes Marketing-Data-Mining-Tool. Kapitel 5 demonstriert das Assoziationsregel-Mining. Kapitel 6 befasst sich eingehender mit der Clusteranalyse. Kapitel 7 befasst sich mit der Link-Analyse. Die Modelle werden anhand geschaftsbezogener Daten demonstriert. Der Stil des Buches ist beschreibend und versucht zu erklaren, wie die Methoden funktionieren, mit einigen Zitaten, aber ohne tiefgehende wissenschaftliche Referenzen. Die Datensatze und die Software wurden so ausgewahlt, dass sie fur jeden Leser, der uber einen Computeranschluss verfugt, weithin verfugbar und zuganglich sind.

This book presents key concepts related to quantitative analysis in business. It is targeted at business students (both undergraduate and graduate) taking an introductory core course. Business analytics has grown to be a key topic in business curricula, and there is a need for stronger quantitative skills and understanding of fundamental concepts. This second edition adds material on Tableau, a very useful software for business analytics. This supplements the tools from Excel covered in the first edition, to include Data Analysis Toolpak and SOLVER.

This book addresses the use of quantitative tools to support general project management. Part I of the book deals with critical path modeling. Part II discusses risk modeling tools to include Program Evaluation and Review Technique (PERT), critical chain modeling, and agile/scrum approaches. Project control through earned value analysis is also covered. Part III is a Microsoft Project orientation. A feature of the book is an effort to tie content to that of the Project Management Body of Knowledge (PMBOK). Each chapter includes reference to how each chapter relates to the PMBOK structure and its relationship to the 2020 Project Management Professional (PMP) Exam Outline.