David L. Olson – författare

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.

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.

The purpose of this work 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 US, solid waste management in Finland, pipeline location in India, and pipeline location in Russia. Methods covered are multi-attribute 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.

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.

Supply chain operations face many risks, including political, environmental, and economic. The past five years have seen major challenges, from pandemic, impacts of global warming, wars, and tariff impositions. In this rapidly changing world, risks appear in every aspect of operations. This book presents data mining and analytics tools with R programming as well as a brief presentation of Monte Carlo simulation that can be used to anticipate and manage these risks. RStudio software and R programming language are widely used in data mining. For Monte Carlo simulation applications we cover Crystal Ball software, one of a number of commercially available Monte Carlo simulation tools.Chapter 1 of this book deals with classification of risks. It includes a typical supply chain example published in academic literature. Chapter 2 gives a brief introduction to R programming. It is not intended to be comprehensive, but sufficient for a user to get started using this free open source and highly popular analytics tool. Chapter 3 discusses risks commonly found in finance, to include basic data mining tools applied to analysis of credit card fraud data. Like the other datasets used in the book, this data comes from the Kaggle.com site, a free site loaded with realistic datasets.The remainder of the book covers risk analytics tools. Chapter 4 presents R association rule modeling using a supply chain related dataset. Chapter 5 presents Monte Carlo simulation of some supply chain risk situations. Chapter 6 gives both time series and multiple regression prediction models as well as autoregressive integrated moving average (ARIMA; Box-Jenkins) models in SAS and R. Chapter 7 covers classification models demonstrated with credit risk data. Chapter 8 deals with fraud detection and the common problem of modeling imbalanced datasets. Chapter 9 introduces Naïve Bayes modeling with categorical data using an employee attrition dataset.Features:Overview of predictive analytics presented in an understandable mannerPresentation of useful business applications of predictive data miningCoverage of risk management in finance, insurance, and supply chain contextsPresentation of predictive modelsDemonstration of using these predictive models in RScreenshots enabling readers to develop their own modelsThe purpose of the book is to present tools useful to analyze risks, especially those faced in supply chain management and finance.

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.

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.

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.

Data mining has become the fastest growing topic of interest in business programs in the past decade. This book is intended to describe the benefits of data mining in business, the process and typical business applications, the workings of basic data mining models, and demonstrate each with widely available free software.

This book brings a unique combination of years of experience in academics research and studies in regards to “ERP systems” with years of experience from a practitioner’s perspective. Each year billions of dollars are spent by organizations to implement, manage, and maintain ERP systems. A simple browse through the Internet will demonstrate how challenging ERP implementations can be. Success rates are seen as quite low with time, costs, and effort typically being above planned and often significantly.Law firms make a living from ERP’s gone badly. Academia is investing more and more time and research into developing success models that not only attempt to objectively determine ERP success or failure but also attempt to be a proactive in that effort. But why? If ERP systems (and all their inherent functionality) can bring a true ROI to business, why are they so challenging? Why do they often deliver as advertised? And, why are they often seen as failing?

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 book is intended to present key concepts related to quantitative analysis in business. It is targeted to business students, undergraduate and graduate, taking an introductory core course. Topics covered include knowledge management, visualization, sampling and hypothesis testing, regression (simple, multiple, and logistic), as well as optimization modeling. It concludes with a brief overview of data mining. Concepts are demonstrated with worked examples.

This book addresses project management in the context of general project management.An introductory chapter discusses project features in general. Part I of the book focuses attention on the important human element in project management. Part II discusses two processes involved in the initial project definition stage, as well as covering estimation. Part III involves planning. Part III deals with project risk and implementation.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 relationship to the 2020 PMP Exam Outline.

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.

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.

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.

The objective of the book is to provide materials to demonstrate the development of TOPSIS and to serve as a handbook. 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 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.

Es bietet Beispiele für die Auswirkungen von Big Data und erweitert die Abdeckung von Assoziationsregeln und Clusteranalysen. Die Datensätze und die Software wurden so ausgewählt, dass sie für jeden Leser, der über einen Computeranschluss verfügt, weithin verfügbar und zugänglich sind.

This book presents data mining methods in the field of healthcare management in a practical way.

This book presents data mining methods in the field of healthcare management in a practical way.

The intent of this book is to describe some recent data mining tools that have proven effective in dealing with data sets which often involve unc- tain description or other complexities that cause difficulty for the conv- tional approaches of logistic regression, neural network models, and de- sion trees. Among these traditional algorithms, neural network models often have a relative advantage when data is complex. We will discuss methods with simple examples, review applications, and evaluate relative advantages of several contemporary methods. Book Concept Our intent is to cover the fundamental concepts of data mining, to dem- strate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. We have organized the material into three parts. Part I introduces concepts. Part II contains chapters on a number of different techniques often used in data mining. Part III focuses on business applications of data mining. Not all of these chapters need to be covered, and their sequence could be varied at instructor design. The book will include short vignettes of how specific concepts have been applied in real practice. A series of representative data sets will be generated to demonstrate specific methods and concepts. References to data mining software and sites such as www.kdnuggets.com will be provided. Part I: Introduction Chapter 1 gives an overview of data mining, and provides a description of the data mining process. An overview of useful business applications is provided.