David L. Olson

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 editionprovides 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.

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.

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.

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.

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. The book focuses on demonstrating common business data mining applications. It provides exposure to the data mining process, to include problem identification, data management, and available modeling tools. The book takes the approach of demonstrating typical business data sets with open source software. KNIME is a very easy-to-use tool, and is used as the primary means of demonstration. R is much more powerful and is a commercially viable data mining tool. We also demonstrate WEKA, which is a highly useful academic software, although it is difficult to manipulate test sets and new cases, making it problematic for commercial use.

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?

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.

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.

Risk is inherent in business. Without risk, there would be no motivation to conduct business. But a key principle is that organizations should accept risks that they are competent enough to deal with, and “outsource” other risks to those who are more competent to deal with them (such as insurance companies). Enterprise Risk Management (2nd Edition) approaches enterprise risk management from the perspectives of accounting, supply chains, and disaster management, in addition to the core perspective of finance. While the first edition included the perspective of information systems, the second edition views this as part of supply chain management or else focused on technological specifics. It discusses analytical tools available to assess risk, such as balanced scorecards, risk matrices, multiple criteria analysis, simulation, data envelopment analysis, and financial risk measures.

Information Systems Project Management addresses project management in the context of information systems. It deals with general project management principles, with focus on the special characteristics of information systems. It is based on an earlier text, but shortened to focus on essential project management elements.This updated version presents various statistics indicating endemic problems in completing information system projects on time, within budget, at designed functionality. While successful completion of an information systems project is a challenge, there are some things that can be done to improve the probability of project success. This book reviews a number of project management tools, including, developing organizational ability to work on projects, better systems analysis and design, project estimation, and project control and termination.

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.

A step by step proven turnkey system that will walk you through buying and selling residential real estate in today's turbulent economy at FULL MARKET VALUE. Great for first time investors and seasoned professionals. Our system is unique for today's market. We have the secrets to tapping into a huge population of families that the real estate world does not know how to service. Become part of the changing of an industry while building incredible wealth.

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.

This book analyzes various aspects of enterprise information systems (EIS), including enterprise resource planning, customer relationship management, supply chain management systems, and business process reengineering. It describes the evolution and functions of these systems, focusing on issues related to their implementation and upgrading. Enhanced with pedagogical features, the book can be read by graduate and undergraduate students, as well as senior management and executives involved in the study and evaluation of EIS.

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.