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.

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 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 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.

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. . . . . . . . . . . . . . . . . . . . . . . . . . . . .