Business mathematics and systems Books

Book SynopsisYou can get there Where do you want to go? You might already be working in a business setting. You may be looking to expand your skills. Or, you might be setting out on a new career path. Wherever you want to go, Business Math will help you get there. Easy-to-read, practical, and up-to-date, this text not only helps you learn fundamental mathematical concepts needed for business, it also helps you master the core competencies and skills you need to succeed in the classroom and beyond. The book''s brief, modular format and variety of built-in learning resources enable you to learn at your own pace and focus your studies. With this book, you will be able to: * Understand the business uses of percent calculations. * Solve business problems using algebraic equations. * Learn why stores markup and markdown their inventory. * Calculate different types of discounts. * Examine different banking options. * Compare personTrade Review"...the book is particularly effective at showing that several basic aspects of business can be well understood...by using elementary mathematics." (Journal of the Operational Research Society, 58/2007)Table of ContentsPart I: Math Principles 1 Whole Numbers, Decimals, and Negative Numbers 1 2 Fractions 15 3 Calculating Percents 30 4 Using Algebraic Equations to Solve Business Problems 47 Part II: Retailing Applications 5 Markup, Markdown, and Inventory Management 66 6 Discounts 85 Part III: General Business Applications 7 Banking and Insurance 101 8 Taxes 131 9 Calculating Interest 149 10 Loans and Consumer Credit 177 11 Depreciation 193 12 Financial Statements 209 13 Business Statistics 244 Glossary 263 Index 275

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Book SynopsisAn Authoritative Introduction to a Major Subject in Systems Engineering and Management This important volume fills the need for a textbook on the fundamentals of economic systems analysis and assessment, illustrating their vital role in systems engineering and systems management. Providing extensive coverage on key topics, it assumes no prior background in mathematics or economics in order to comprehend the material. The book is comprised of five major parts: Microeconomics: a concise overview that covers production and the theory of the firm; theory of the consumer; market equilibria and market imperfections; and normative or welfare economics, including imperfect competition effects and consumer and producer surplus Program Management Economics: discusses economic valuation of programs and projects, including investment rates of return; cost-benefit and cost-effectiveness analysis; earned value management; cost structures and estimTable of ContentsPREFACE. Chapter 1 Introduction to Economic Systems Analysis and Assessment: Cost, Value, and Competition in Information and Knowledge Intensive Systems, Organizations, and Enterprises. 1.1 Inroduction. 1.2 A Framework for Systems Engineering and Management. 1.3 Theory of the Firm. 1.4 Theory of the Consumer. 1.5 The Interaction of the Theories of the Firms and Consumers: Microeconomic Models of Economic Activity. 1.6 Welfare or Normative Economics. 1.7 Program and Program Management Economics. 1.8 Contemporary Issues Concerning Information and Information Technology Economic. 1.9 Economic Pitfalls in the Engineering of Systems. 1.10 Summary. Chapter 2 Production and the Theory of the Firm. 2.1 Introduction. 2.2 The Production Function. 2.3 Multiproduct Firms and Multiproduct Production Functions. 2.4 Classical Theory of the Firm. 2.5 Imperfect Competition. 2.6 Summary. Chapter 3 The Theory of the Consumer. 3.1 Introduction. 3.2 Economic Utility Theory and Its Axioms. 3.3 Property of Utility Functions. 3.4 The Fundamental Problem of the Consumer. 3.5 Sensitivity and Substitution Effects. 3.6 Summary. Chapter 4 Supply-Demand Equilibria and Microeconomic Systems Analysis and Assessment Models. 4.1 Introduction. 4.2 Basic Supply-Demand Equilibrium for a Single Good. 4.3 General Supply-Demand Equilibrium Conditions. 4.4 Extensions to General Equilibrium to Include Market Interdependencies. 4.5 Microeconomics Models. 4.6 Summary. Chapter 5 Normative or Welfare Economics, Decisions and Games, and Behavioral Economics. 5.1 Introduction. 5.2 Pareto Optimality Under Perfect Competitive Conditions. 5.3 External Effects and Imperfect Competition-Public Goods. 5.4 External Effects and Imperfect Competition-Nonindependent Production and Consumption. 5.5 Welfare Maximization and Social Choice. 5.6 Summary. 5.7 Selected Bibliography. Chapter 6 Cost-Benefit and Cost-Effectiveness Analysis and Assessments. 6.1 Introduction. 6.2 The Time Value of Money. 6.3 Identification of Costs and Benefits. 6.4 The Identification and Quantification of Effectiveness. 6.5 A Guide to Cost-Benefit Analysis. Chapter 7 Cost Assessment. 7.1 Model-Based Parameters for Productivity. 7.2 Early Models for Estimation of Costs, Including Effort and Schedule. 7.3 The Constructive Cost Model. 7.4 Summary. CHAPTER 8 APPROACHES TO INVESTMENT VALUATION. 8.1. Microeconomics. 8.2. The Investment Problem. 8.3. Investment Valuation. 8.4. Summary. CHAPTER 9 REAL OPTIONS FOR INVESTMENT VALUATION. 9.1. Nature of Options. 9.2. Option Pricing Theory. 9.3. Option Calculator. 9.4. Strategic Metrics. 9.5. Investment Decision Making. 9.6. Value-Centered R&D. 9.7. Summary. CHAPTER 10 CONTEMPORARY PERSPECTIVES. 10.1. Introduction. 10.2. Evolutionary Economics. 10.3. Path Dependence and Network Effects. 10.4. Intellectual Capital. 10.5. Value of Information. 10.6. Investing in Humans. 10.7. Summary. INDEX.

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Book SynopsisElectronic Health Records: An Audit and Internal Control Guide describes the infrastructure of electronic health records and the impact that the government''s new criteria will have on the private and public marketplace. Understand what to look for in a health care record management system and find tips and helpful guidance for implementation. If you are trying to facilitate an audit of a health record management system, you can apply the example described in the model, which will serve as a timely model and invaluable resource.Table of ContentsPreface. About the Author. Acknowledgments. Introduction. Chapter 1. Market Background. E-Health. How is Electronic Information Created? Information Technology Considerations. How is Health Information Created? Review of Primary HCC Market Players. Patients. Providers. Third Party Vendors. Payers. Review of HCC Secondary Market Players. Major Initiative for E-Health. Audit Implication Overview. Chapter 2. Industry Application. Public Uses. Private Uses. Information Continuum. Market Standards and Initiatives. Agency for Healthcare Research and Quality. Health Level Seven. Certification Commission for Healthcare Information Technology. Department of Defense Records Management Program. Association of Records Managers and Administrators. Audit Implication Overview. Chapter 3. Impact of E-Health on Case Management. Financial Picture. Hospital-Based FCM Application. Background Information and Provider Perspective. Problem: Getting Paid Correctly for Services Provided. Findings. Additional Findings. Summary. Consumer-Based FCM Application. Market Problems: The Industry as It Operates Today. Consumer FCM Model. Healthcare Portfolio Application. Virtual Case Management. VCM Payer Model. VCM Patient Model. VCM Hospital Model. VCM Physician Model. VCM Allied Health Services. VCM Nontraditional Health Services Model. VCM Other Business Services Model. Audit Implication Overview. Chapter 4. Data in an E-Health Environment. Data Library. Data Intelligence. New Data. More New Data. Processed Data. Data Warehouse. Audit Implication Overview. Chapter 5. Algorithms. Background. Understanding Algorithms. Data Elements. Case Study. Algorithm Selection Auditor Implication Overview. Chapter 6. Data-Driven Health Decisions in an E-Health Environment. Knowledge Models. Primary Healthcare Continuum. Secondary Healthcare Continuum. Information Continuum. Third-Party Vendor Knowledge Model. Knowledge Models For White-Collar and Organized Crime. Sample Identity Theft Case. Medical Identity Theft. Medical Identity Theft – Definition. How Medical Identity Theft Occurs. Damages To Primary Victims. Medical Identity Theft From A Consumer Perspective. When The Consumer Is Not Aware. When The Consumer Is Involved. When An Individual Wants Products Or Services. Damages To Secondary Victims. Medical Identity Theft From An Entity’s Perspective. Auditor Considerations. Sample Fraud Case. Sample Pharmaceutical Fraud Case. Audit Implication Overview. Examples Of Worldwide Activity. Chapter 7. Analytic Tools and Audit Checklists. E-Health And Healthcare Business Processes. Patient Business Process. Problem #1: Financial Case Management Advocacy. Problem #2: Clinical Case Management Advocacy. Provider Business Process. Problem #1: Lack Of Electronic Internal Controls. Problem #2: Lack Of Internal Controls With User Identity. Problem #3: Lack Of Internal Controls For Services Provided And Charged. Payer Business Process. Problem #1: Use And Loss Of Health Information – Handling Subcontracted Vendors. Problem #2: Lack Of Insurance – Processing Fraudulent Claims For Enrolled Beneficiaries. Plan Sponsor Business Process. Problem #1: Employee Working Environments. Problem #2: Employer Increase In Healthcare Expenditures. Third-Party Vendor Business Process. Problem: Increase In Pharmaceutical Expenditures. Audit Implication Overview. Chapter 8. Electronic Health Records. Current E-Health Offerings. Market Evolution. E-Health Content Standards. E-Health Offering Vulnerabilities. Audit Implication Overview. Chapter 9. Healthcare Portfolio. Health Infomediary Support. PHR Attributes. Future Considerations. Major Market Activity. Audit Implication Overview. Chapter 10. Conclusions. Market Overview. Market Standards. Market Conflicts. Market Intelligence. Market Audits. Market Directions. Consumer Response to PHRs. Audit Implication Overview. Index.

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Book SynopsisIn this book, authors Dalton Cervo and Mark Allen show you how to implement Master Data Management (MDM) within your business model to create a more quality controlled approach. Focusing on techniques that can improve data quality management, lower data maintenance costs, reduce corporate and compliance risks, and drive increased efficiency in customer data management practices, the book will guide you in successfully managing and maintaining your customer master data. You''ll find the expert guidance you need, complete with tables, graphs, and charts, in planning, implementing, and managing MDM.Table of ContentsForeword xiii Preface xvii Acknowledgments xxi Introduction 1 Part I: Planning Your Customer MDM Initiative 7 Chapter 1: Defining Your MDM Scope and Approach 9 MDM Approaches and Architectures 9 Analytical MDM 11 Operational MDM 14 Enterprise MDM 18 Defining the Business Case 20 Cost Reduction 21 Risk Management 22 Revenue Growth 23 Selecting the Right MDM Approach 23 Data Management Maturity Level 24 Addressing the ROI Question 27 Summary 27 Note 28 Chapter 2: Establishing Effective Ownership 29 The Question of Data Ownership 29 Executive Involvement 31 MDM with Segmented Business Practices 31 A Top-Down and Bottom-Up Approach 32 Creating Collaborative Partnerships 33 Can Your Current IT and Business Model Effectively Support MDM? 33 The Acceptance Factor 34 Business Access to Data 35 Coordination of MDM Roles and Responsibilities 36 Summary 38 Notes 38 Chapter 3: Priming the MDM Engine 39 Introduction 39 Positioning MDM Tools 40 Data Integration and Synchronization 42 Data Profiling 43 Data Migration 46 Data Consolidation and Segmentation 55 Reference Data 57 Metadata 60 Summary 63 Notes 63 Part II: The Implementation Fundamentals 65 Chapter 4: Data Governance 67 Initiating a Customer Data Governance Model 67 Planning and Design 69 Establishing the Charter 70 Policies, Standards, and Controls 78 Implementation 85 Process Readiness 85 Implement 88 Maintain and Improve 91 Summary 93 Notes 94 Chapter 5: Data Stewardship 95 From Concept to Practice 95 People 96 MDM Process Core Team 97 Operational Process Areas 102 Processes 107 Data Caretaking 108 Summary 109 Chapter 6: Data Quality Management 111 Implementing a Data Quality Model 111 A Process for Data Quality 114 Drivers 115 Data Quality (DQ) Forum 117 Controls/Data Governance 119 Data Analysts 120 Design Team 123 IT Support/Data Stewards 125 Metrics 126 Establishing a Data Quality Baseline 127 Context 127 Data Quality Dimensions 129 Entities and Attributes 129 Putting It All Together 132 Data Alignment and Fitness Assessment 136 Data Correction Initiatives 137 Summary 140 Note 140 Chapter 7: Data Access Management 141 Creating the Business Discipline 141 Beyond the System Administrator 142 Creating the Right Gatekeeper Model 144 Preparing 145 Employee Data 146 Access Management Requirements 146 Add User Group Names 148 Map Privileges to Requirement Categories 149 Profiling the Data 150 Implementing and Managing the Process 152 Testing and Launching the Process 157 Resolve Issues Immediately 157 Auditing and Monitoring 158 Segregation of Duty (SoD) Management 159 Summary 161 Notes 161 Part III: Achieving a Steady State 163 Chapter 8: Data Maintenance and Metrics 165 Data Maintenance 165 Specify, Profile, and Analyze 167 Improve 167 Data Quality Metrics 184 Monitors 185 Scorecards 187 Summary 189 Note 190 Chapter 9: Maturing Your MDM Model 191 How to Recognize and Gauge Maturity? 191 Data Governance Maturity 193 Data Stewardship Maturity 194 Data Quality Maturity 195 Data Access Management Maturity 197 Summary 198 Notes 199 Part IV: Advanced Practices 201 Chapter 10: Creating the Customer 360 View 203 Introduction 203 Hierarchy Management (HM) 206 Operational versus Analytical Hierarchies 207 Single versus Multiple Hierarchies 208 Number of Levels in the Customer Hierarchy 209 Virtual versus Physical Customer Records 211 Legal versus Non-Legal Hierarchies 212 The Elusive, yet Achievable, 360 Customer View 213 Summary 213 Chapter 11: Surviving Organizational Change 215 How Adaptable is Your Customer Master Data? 215 Data Quality Factors 216 Data Completeness 217 Data Consistency 217 Data Integrity 218 The Change Management Challenge 219 Data Governance Can Greatly Assist a Transitioning State 220 Leveraging the Data Stewards and Analysts 220 Adopting Best Practices 222 Summary 222 Chapter 12: Beyond Customer MDM 225 The Leading and Lagging Ends 225 Technology’s Influence on MDM 226 Overcoming the IT and Business Constraints 228 Achieving an Effective Enterprise-Wide MDM Model 230 Where Does MDM Lead? 233 Summary 235 Note 236 Recommended Reading 237 About the Authors 239 Index 241

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Book SynopsisAn insightful, hands-on focus on the statistical methods used by compensation and human resources professionals in their everyday work Across various industries, compensation professionals work to organize and analyze aspects of employment that deal with elements of pay, such as deciding base salary, bonus, and commission provided by an employer to its employees for work performed. Acknowledging the numerous quantitative analyses of data that are a part of this everyday work, Statistics for Compensation provides a comprehensive guide to the key statistical tools and techniques needed to perform those analyses and to help organizations make fully informed compensation decisions. This self-contained book is the first of its kind to explore the use of various quantitative methodsfrom basic notions about percents to multiple linear regressionthat are used in the management, design, and implementation of powerful compensation strategies. Drawing upon his exteTrade Review“As an experienced compensation manager for a publicly traded Fortune 500 company, I have found this book to be an all-inclusive, highly useful and infor­mative desk reference. It certainly has been extremely valuable in helping me to contribute to successful strategic decisions at my company.” (Workspan, 1 January 2013) "The book can serve as a text for students specializing in compensation or human resources, or as a reference for practitioners. He provides worked examples throughout." (Booknews, 1 June 2011) Table of ContentsPreface xiii Chapter 1 Introduction 1 1.1 Why do Statistical Analysis? 2 Example Analysis 3 1.2 Statistics 5 1.3 Numbers Raise Issues 6 1.4 Behind Every Data Point, There Is a Story 8 1.5 Aggressive Inquisitiveness 9 1.6 Model Building Framework 9 Example Model 10 1.7 Data Sets 10 1.8 Prerequisites 11 Chapter 2 Basic Notions 13 2.1 Percent 14 Graphical Displays of Percents 16 2.2 Percent Difference 21 2.3 Compound Interest 23 Future Value 24 Present Value 26 Translating 27 Practice Problems 28 Chapter 3 Frequency Distributions and Histograms 31 3.1 Definitions and Construction 41 Rules for Categories 43 3.2 Comparing Distributions 48 Absolute Comparison and Relative Comparison 48 Comparing More Than Two Distributions 50 3.3 Information Loss and Comprehension Gain 51 3.4 Category Selection 51 3.5 Distribution Shapes 54 Uniform Distribution 55 Bell-Shaped Distribution 55 Normal Distribution 56 Skewed Distribution 59 Bimodal Distribution 60 Practice Problems 62 Chapter 4 Measures of Location 67 4.1 Mode 67 4.2 Median 68 4.3 Mean 70 4.4 Trimmed Mean 73 4.5 Overall Example and Comparison 73 Comparison 75 4.6 Weighted and Unweighted Average 76 Which Measure to Use? 78 Application of Weighted Averages to Salary Increase Guidelines 80 4.7 Simpson’s Paradox 82 4.8 Percentile 85 Reverse Percentile 88 4.9 Percentile Bars 90 Practice Problems 92 Chapter 5 Measures of Variability 95 5.1 Importance of Knowing Variability 95 5.2 Population and Sample 96 Examples of Populations 96 Examples of Samples and Populations 96 5.3 Types of Samples 97 5.4 Standard Deviation 98 Interpretations and Applications of Standard Deviation 100 5.5 Coefficient of Variation 107 Interpretations and Applications of Coefficient of Variation 108 5.6 Range 109 Interpretations and Applications of Range 109 5.7 P90/P10 110 Interpretations and Applications of P90/P10 111 5.8 Comparison and Summary 112 Practice Problems 115 Chapter 6 Model Building 119 6.1 Prelude to Models 119 6.2 Introduction 120 6.3 Scientific Method 122 6.4 Models 123 6.5 Model Building Process 126 Plotting Points 128 Functional Forms 132 Method of Least Squares 136 Practice Problems 138 Chapter 7 Linear Model 141 7.1 Examples 141 7.2 Straight Line Basics 143 Interpretations of Intercept and Slope 144 Using the Equation 145 7.3 Fitting the Line to the Data 147 What We Are Predicting 148 Interpretations of Intercept and Slope 149 7.4 Model Evaluation 149 Appearance 150 Coefficient of Determination 150 Correlation 152 Standard Error of Estimate 154 Common Sense 154 7.5 Summary of Interpretations and Evaluation 155 7.6 Cautions 155 7.7 Digging Deeper 158 7.8 Keep the Horse before the Cart 160 Practice Problems 164 Chapter 8 Exponential Model 167 8.1 Examples 167 8.2 Logarithms 168 Antilogs 170 Scales 170 Why Logarithms? 171 8.3 Exponential Model 172 8.4 Model Evaluation 176 Appearance 176 Coefficient of Determination 177 Correlation 177 Standard Error of Estimate 177 Common Sense 178 Summary of Evaluation 178 Practice Problems 178 Chapter 9 Maturity Curve Model 181 9.1 Maturity Curves 181 9.2 Building the Model 184 Cubic Model 184 Cubic Model Evaluation 186 Spline Model 187 Spline Model Evaluation 188 9.3 Comparison of Models 190 Practice Problems 190 Chapter 10 Power Model 193 10.1 Building the Model 193 10.2 Model Evaluation 197 Appearance 197 Coefficient of Determination 198 Correlation 198 Standard Error of Estimate 198 Common Sense 199 Summary of Evaluation 199 Practice Problems 200 Chapter 11 Market Models and Salary Survey Analysis 201 11.1 Introduction 201 11.2 Commonalities of Approaches 203 11.3 Final Market-Based Salary Increase Budget 205 Initial Market-Based Salary Increase Budget and Market Position 205 Final Market-Based Salary Increase Budget 206 Raises Given Throughout the Year 206 Raises Given on a Common Date 208 11.4 Other Factors Influencing the Final Salary Increase Budget Recommendation 210 Assumptions 211 11.5 Salary Structure 211 Practice Problems 213 Chapter 12 Integrated Market Model: Linear 215 12.1 Gather Market Data 215 12.2 Age Data to a Common Date 217 12.3 Create an Integrated Market Model 217 Interpretations 219 12.4 Compare Employee Pay with Market Model 222 Practice Problems 228 Chapter 13 Integrated Market Model: Exponential 233 Practice Problems 246 Chapter 14 Integrated Market Model: Maturity Curve 251 Practice Problems 261 Chapter 15 Job Pricing Market Model: Group of Jobs 265 Practice Problems 272 Chapter 16 Job Pricing Market Model: Power Model 277 Practice Problems 280 Chapter 17 Multiple Linear Regression 283 17.1 What It Is 283 17.2 Similarities and Differenceswith Simple Linear Regression 284 17.3 Building the Model 285 First x-Variable 292 Second x-Variable 295 Standardized Coefficient 298 Third x-Variable 300 Multicollinearity 301 17.4 Model Evaluation 305 Regression Coefficients 305 Standardized Coefficients 306 Coefficient of Determination 306 Standard Error of Estimate 306 Multicollinearity 306 Simplicity 307 Common Sense 307 Acceptability 307 Reality 307 Decision 307 17.5 Mixed Messages in Evaluating A Model 308 r2 Versus Common Sense 308 r2 Versus Simplicity 308 Simplicity Versus Acceptability 308 17.6 Summary of Regressions 308 17.7 Digging Deeper 310 Summary 315 Practice Problems 317 Appendix 319 A.1 Value Exchange Theory 319 Achieving Organization Goals 319 Value Exchange 319 A Fair Value Exchange Is a Good Deal 320 A.2 Factors Determining a Person’s Pay 321 System Factors 322 Individual Factors 323 A.3 Types of Numbers 324 Definitions and Properties 324 Histograms with All Four Types of Measurements 327 A.4 Significant Figures 330 A.5 Scientific Notation 331 A.6 Accuracy and Precision 332 Which Is More Important? 333 A.7 Compound Interest–Additional 333 Other Formulas 333 A.8 Rule of 72 334 Derivation of the Rule of 72 335 A.9 Normal Distribution 336 Central Limit Theorem 337 Distribution of Salary Survey Data 338 A.10 Linear Regression Technical Note 338 A.11 Formulas for Regression Terms 340 A.12 Logarithmic Conversion 340 A.13 Range Spread Relationships 340 Overlap 343 A.14 Statistical Inference in Regression 344 t-Statistic and Its Probability 347 F-Statistic and Its Probability 348 Mixed Messages in Evaluating a Model 349 A.15 Additional Multiple Linear Regression Topics 349 Adjusted r2 349 Coding of Indicator Variables 350 Interaction Terms 351 GLOSSARY 357 REFERENCES 369 ANSWERS TO PRACTICE PROBLEMS 371 INDEX 433

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Book SynopsisIndustrial Statistics with MINITAB demonstrates the use of MINITAB as a tool for performing statistical analysis in an industrial context. This book covers introductory industrial statistics, exploring the most commonly used techniques alongside those that serve to give an overview of more complex issues.Table of ContentsPreface xiii Part One Introduction and Graphical Techniques 1 1 A First Look 3 1.1 Initial Screen 3 1.2 Entering Data 4 1.3 Saving Data: Worksheets and Projects 5 1.4 Data Operations: An Introduction 5 1.5 Deleting and Inserting Columns and Rows 7 1.6 First Statistical Analyses 8 1.7 Getting Help 10 1.8 Personal Configuration 12 1.9 Assistant 13 1.10 Any Difficulties? 14 2 Graphics for Univariate Data 15 2.1 File ‘PULSE’ 15 2.2 Histograms 16 2.3 Changing the Appearance of Histograms 17 2.4 Histograms for Various Data Sets 21 2.5 Dotplots 23 2.6 Boxplots 24 2.7 Bar Diagrams 25 2.8 Pie Charts 27 2.9 Updating Graphs Automatically 28 2.10 Adding Text or Figures to a Graph 29 3 Pareto Charts and Cause–Effect Diagrams 31 3.1 File ‘DETERGENT’ 31 3.2 Pareto Charts 32 3.4 Cause-and-Effect Diagrams 35 4 Scatterplots 37 4.1 File ‘pulse’ 37 4.2 Stratification 38 4.3 Identifying Points on a Graph 39 4.4 Using the ‘Crosshairs’ Option 45 4.5 Scatterplots with Panels 46 4.6 Scatterplots with Marginal Graphs 48 4.7 Creating an Array of Scatterplots 50 5 Three Dimensional Plots 52 5.1 3D Scatterplots 52 5.2 3D Surface Plots 55 5.3 Contour Plots 58 6 Part One: Case Studies – Introduction and Graphical Techniques 62 6.1 Cork 62 6.2 Copper 68 6.3 Bread 73 6.4 Humidity 76 Part Two Hypothesis Testing. Comparison of Treatments 79 7 Random Numbers and Numbers Following a Pattern 81 7.1 Introducing Values Following a Pattern 81 7.2 Sampling Random Data from a Column 83 7.3 Random Number Generation 83 7.4 Example: Solving a Problem Using Random Numbers 85 8 Computing Probabilities 87 8.1 Probability Distributions 87 8.2 Option ‘Probability Density’ or ‘Probability’ 88 8.3 Option ‘Cumulative Probability’ 89 8.4 Option ‘Inverse Cumulative Probability’ 89 8.5 Viewing the Shape of the Distributions 92 8.6 Equivalence between Sigmas of the Process and Defects per Million Parts Using ‘Cumulative Probability’ 92 9 Hypothesis Testing for Means and Proportions. Normality Test 95 9.1 Hypothesis Testing for One Mean 95 9.2 Hypothesis Testing and Confidence Interval for a Proportion 99 9.3 Normality Test 100 10 Comparison of Two Means, Two Variances or Two Proportions 103 10.1 Comparison of Two Means 103 10.2 Comparison of Two Variances 107 10.3 Comparison of Two Proportions 109 11 Comparison of More than Two Means: Analysis of Variance 110 11.1 ANOVA (Analysis of Variance) 110 11.2 ANOVA with a Single Factor 110 11.3 ANOVA with Two Factors 114 11.4 Test for Homogeneity of Variances 119 12 Part Two: Case Studies – Hypothesis Testing. Comparison of Treatments 120 12.1 Welding 120 12.2 Rivets 124 12.3 Almonds 126 12.4 Arrow 127 12.5 U Piece 131 12.6 Pores 133 Part Three Measurement Systems Studies and Capability Studies 137 13 Measurement System Study 139 13.1 Crossed Designs and Nested Designs 139 13.2 File ‘RR_CROSSED’ 140 13.3 Graphical Analysis 140 13.4 R&R Study for the Data in File ‘RR_CROSSED’ 141 13.5 File ‘RR_NESTED’ 147 13.6 Gage R&R Study for the Data in File ‘RR_NESTED’ 147 13.7 File ‘GAGELIN’ 148 13.8 Calibration and Linearity Study of the Measurement System 148 14 Capability Studies 151 14.1 Capability Analysis: Available Options 151 14.2 File ‘VITA_C’ 152 14.3 Capability Analysis (Normal Distribution) 152 14.4 Interpreting the Obtained Information 152 14.5 Customizing the Study 154 14.6 ‘Within’ Variability and ‘Overall’ Variability 155 14.7 Capability Study when the Sample Size is Equal to One 158 14.8 A More Detailed Data Analysis (Capability Sixpack) 161 15 Capability Studies for Attributes 163 15.1 File ‘BANK’ 163 15.2 Capability Study for Variables that Follow a Binomial Distribution 163 15.3 File ‘OVEN_PAINTED’ 166 15.4 Capability Study for Variables that Follow a Poisson Distribution 166 16 Part Three: Case Studies – R&R Studies and Capability Studies 168 16.1 Diameter_measure 168 16.2 Diameter_capability_1 173 16.3 Diameter_capability_2 174 16.4 Web_visits 176 Part Four Multi-Vari Charts and Statistical Process Control 181 17 Multi-Vari Charts 183 17.1 File ‘MUFFIN’ 183 17.2 Multi-Vari Chart with Three Sources of Variation 184 17.3 Multi-Vari Chart with Four Sources of Variation 186 18 Control Charts I: Individual Observations 188 18.1 File ‘CHLORINE’ 188 18.2 Graph of Individual Observations 188 18.3 Customizing the Graph 191 18.4 I Chart Options 192 18.5 Graphs of Moving Ranges 196 18.6 Graph of Individual Observations – Moving Ranges 197 19 Control Charts II: Means and Ranges 198 19.1 File ‘VITA_C’ 198 19.2 Means Chart 199 19.3 Graphs of Ranges and Standard Deviations 200 19.4 Graphs of Means-Ranges 201 19.5 Some Ideas on How to Use Minitab as a Simulator of Processes for Didactic Reasons 201 20 Control Charts for Attributes 204 20.1 File ‘MOTORS’ 204 20.2 Plotting the Proportion of Defective Units (P) 204 20.3 File ‘CATHETER’ 205 20.4 Plotting the Number of Defective Units (NP) 206 20.5 Plotting the Number of Defects per Constant Unit of Measurement (C) 208 20.6 File ‘FABRIC’ 210 20.7 Plotting the Number of Defects per Variable Unit of Measurement (U) 210 21 Part Four: Case Studies – Multi-Vari Charts and Statistical Process Control 212 21.1 Bottles 212 21.2 Mattresses (1st Part) 217 21.3 Mattresses (2nd Part) 221 21.4 Plastic (1st Part) 223 21.5 Plastic (2nd Part) 224 Part Five Regression and Multivariate Analysis 231 22 Correlation and Simple Regression 235 22.1 Correlation Coefficient 235 22.2 Simple Regression 238 22.3 Simple Regression with ‘Fitted Line Plot’ 239 22.4 Simple Regression with ‘Regression’ 244 23 Multiple Regression 247 23.1 File ‘CARS2’ 247 23.2 Exploratory Analysis 247 23.3 Multiple Regression 249 23.4 Option Buttons 250 23.5 Selection of the Best Equation: Best Subsets 252 23.6 Selection of the Best Equation: Stepwise 254 24 Multivariate Analysis 256 24.1 File ‘LATIN_AMERICA’ 256 24.2 Principal Components 257 24.3 Cluster Analysis for Observations 263 24.4 Cluster Analysis for Variables 266 24.5 Discriminant Analysis 267 25 Part Five: Case Studies – Regression and Multivariate Analysis 272 25.1 Tree 272 25.2 Power Plant 278 25.3 Wear 285 25.4 TV Failure 290 Part Six Experimental Design and Reliability 293 26 Factorial Designs: Creation 295 26.1 Creation of the Design Matrix 295 26.2 Design Matrix with Data Already in the Worksheet 301 27 Factorial Designs: Analysis 303 27.1 Calculating the Effects and Determining the Significant Ones 303 27.2 Interpretation of Results 308 27.3 A Recap with a Fractional Factorial Design 310 28 Response Surface Methodology 313 28.1 Matrix Design Creation and Data Collection 313 28.2 Analysis of the Results 317 28.3 Contour Plots and Response Surface Plots 322 29 Reliability 325 29.1 File 325 29.2 Nonparametric Analysis 326 29.3 Identification of the Best Model for the Data 329 29.4 Parametric Analysis 330 29.5 General Graphical Display of Reliability Data 333 30 Part Six: Case Studies – Design of Experiments and Reliability 335 30.1 Cardigan 335 30.2 Steering wheel – 1 340 30.3 Steering Wheel – 2 343 30.4 Paper Helicopters 345 30.5 Microorganisms 349 30.6 Jam 359 30.7 Photocopies 365 Appendices 371 A1 Appendix 1: Answers to Questions that Arise at the Beginning 373 A2 Appendix 2: Managing Data 377 A2.1 Copy Columns with Restrictions (File: ‘PULSE’) 377 A2.2 Selection of Data when Plotting a Graph 381 A2.3 Stacking and Unstacking of Columns (File ‘BREAD’) 382 A2.4 Coding and Sorting Data 386 A3 Appendix 3: Customization of Minitab 390 A3.1 Configuration Options 390 A3.2 Use of Toolbars 392 A3.3 Add Elements to an Existing Toolbar 392 A3.4 Create Custom Toolbars 393 Index 397

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Book SynopsisThis book teaches how to calculate retail prices. It provides formulas for calculating markon, markup, and markdowns; planning net sales, purchases, stock turnover, expenses, gross margin; and it covers transportation terms, discounts, and all other financial considerations relevant to cost of goods and pricing.Table of ContentsPartial table of contents: PURCHASING AND PRICING. Transportation Terms and Charges. Terms and Dating. Markon Based on Selling Price. Markon Based on Cost Price. Average Markon. COMPUTING AND COVERING EXPENSES. Gross Margin. Expenses. Initial Markon. Cumulative Markon. Markdown. Stock Turnover. SELLING. The Merchandise Plan. Planning Sales. Planning Stocks. Planning Purchases: Open-to-Buy. The Selling Cost Percent. RECORD KEEPING. Inventory Valuation. Comparing Departments. The Income Statement. Appendix. Index.

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Book SynopsisFor all their remarkable benefits, the arsenal of electronic technologies found in most offices can lose allure in the face of a disorganized environment. Now, efficiency expert Kerry Gleeson has adapted the techniques of his internationally acclaimed Personal Efficiency Program (PEP) to the electronic office.Table of ContentsPrinciples of Work. Organizing Your Work Space. Organizing the Computer. Organizing Yourself with a Computer: Time and Money. Road Warrior Wisdom. The Internet and World Wide Web. Groupware. Epilogue. Glossary. References. Index. About the Institute for Business Technology.

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Book SynopsisPRAISE FOR ACHIEVING BUSINESS VALUE FROM TECHNOLOGY Clearly, IT investments have never before played such a critical part in business growth. The book addresses the weakness existing in most management systems involving the lack of a systematic process to realize the economic benefits of the IT investment and provides a clear A-Z methodology for business to bridge this gap. This book is clearly written for all levels and backgrounds in business management and is a must-do for those whose business involves IT, is considering IT, or would like to significantly tailor IT investments for their economic advantage. Professor Richard P. Wool, University of Delaware, President and CEO, Cara Plastics Inc. Tony Murphy addresses the difficult question of the value of IT investments head on. He translates an elegant theory into effective practice. The case studies in the book effectively reinforce his key messages. Dr. Dermot Moynihan, Senior Vice PresideTable of ContentsIntroduction xi Chapter 1: Industrial Age Thinking for Information Age Problems 1 Business Value from Information Technology (BVIT)—A Large and Growing Problem 2 Learning from Case Studies 3 What Do These Case Studies Have in Common? 11 Some Historical Perspective 12 Drawing Conclusions from These Changes 23 Revisiting the Case Studies 27 Industrial Age Thinking for Information Age Problems 30 The Limitations of Financial Metrics 30 The Value of Financial Techniques for IT Investment Appraisal 36 What Else Do We Need? 36 Summary 38 Chapter 2: The Five Pillars of Benefits Realization 39 Pillar 1: Strategic Alignment 42 Pillar 2: Business Process Impact 45 Pillar 3: Architecture 52 Pillar 4: Direct Payback 61 Pillar 5: Risk 66 Summary 76 Chapter 3: P2—Process 77 Step 1: Setting the Ground Rules 81 Step 2: Defining IT Value Standards 90 Step 3: IT Value Analysis 95 Step 4: IT Value Project Management 113 Step 5: IT Value Achievement 118 Step 6: IT Value Management 127 Selecting Specific Products and Vendors 140 Refinements 142 Sensitivity Analysis 146 Investment Scope 147 Summary 148 Chapter 4: P3—People 151 Roles—A Foundation Stone of Effective Governance 153 The IT Council 155 The IT Investment Board 157 The Office of Architecture and Standards 159 The Project Office 163 Summary and Next Steps 165 Appendix A: Sample Value Standards 169 Strategic Alignment 169 Payback 171 Architecture 173 Business Process Impact 174 Risk 174 Appendix B: Sample Value Standards Scoring Metrics 177 Business Process Impact 177 Architecture 180 Risk 181 Appendix C: Completed Weightings 183 Appendix D: From the Field—Investment Case Worked Example 185 Appendix E: Quantifying End-User Benefits 201 Preliminary Information 201 Opening the Meeting 202 Resolution of Inconsistencies 202 Quantifying the Benefits 203 Refine Quantification 205 Cross-Verification 205 Appendix F: Gartner Recommended Approach to Risk Management 207 Collaborative Risk Management 207 Risk Assessment Processes 208 Predictability and Complexity of Risks 215 Summary 216 Appendix G: Analytical Hierarchy Process/Analysis 217 Appendix H: Probability Analysis (Monte Carlo Simulation) 227 Appendix I: Gartner Approach to Change Management: Strategic Planning Assumption 231 The Need for Change Management 231 The Human Response to Change 233 Appendix J: Financial Metrics 239 Net Present Value (NPV) 239 Internal Rate of Return (IRR) 239 Index 243

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Book SynopsisThe sign of a smart decision about information systems isn't based on technical details alone; it's based on how well that decision contributes to the overall success of the business. To obtain pay off, from investing in information systems, requires a truly managerial perspective about IS.In Paul Gray's Manager's Guide to Making Decisions About IS you'll learn how IS can help the organization as a whole, and how to make key decisions on whether to undertake, upgrade, or outsource large software systems. You'll also learn about how IS is used for all aspects of a businessfrom recording individual transactions to gaining competitive intelligence to business strategy. The Manager's Guide to Making Decisions About IS first focuses on big picture issues, such as hardware, software, and the Internet; strategic uses of IS; aligning IS with the business; types of applications; and inter-organizational systems. Gray then provides you with essential knowledge that will help Table of ContentsChapter 1 What Is An Information System? 1 Managerial Questions 1 Introduction 1 What is an Information System? 2 Components of an Information System 2 Hardware 3 Personal Computers 3 Mainframe Computers 3 Client–Server Computers 4 Remote Computing Using Application Service Providers, Web Services, and Grid Computing 4 Software 5 Operating Systems 5 Applications Programs 5 Electronic Commerce (Chapter 3) 7 ERP (Enterprise Requirements Planning) (Chapter 4) 7 Data Warehousing (Chapter 5) 8 Customer Relationship Management (Chapter 6) 8 Knowledge Management (Chapter 7) 9 Business Intelligence (Chapter 8) 10 The Internet 11 Websites 11 Connectivity 11 Finding Things Using Search Engines 12 Content Management 12 Intranet/Extranet 12 Privacy 12 Security 12 Corporate Portals 13 Website Development 13 Website Traffic Analysis 13 Answers to Managerial Questions 14 Chapter 2 the Big Picture: It and Business 15 Managerial Questions 15 Introduction 15 Strategic Advantage Versus Strategic Necessity 15 Continual Improvement 17 Introducing Strategic Information Systems 17 Risks 19 Business–it Alignment 20 Assessing the Alignment Maturity Level 21 Alignment Considerations 21 Alignment when Strategy Changes 21 Information Economics 23 The Search for Productivity 23 Why Invest? 25 Organizational Change 25 Some Practical Problems 26 Does Information Technology Matter? 26 Carr’s Argument 26 The Response to Carr 27 This Book’s Position 27 The Arguments for Carr 27 The Arguments against Carr’s Position 28 Types of Information Systems 29 Transaction Processing Systems 29 Management Information Systems 30 Decision Support Systems 30 Knowledge-Based and Other Systems 30 Interorganizational Computing 30 Replacing Intermediation through Electronic Markets 31 Key Issues 32 Application and Technology Developments 34 Answers to Managerial Questions 35 Problems 36 Chapter 3 Electronic Commerce 37 Managerial Questions 37 Introduction 37 What is Involved in E-commerce? 39 Some E-commerce Statistics 39 Retail 40 Business to Business (B2B) 40 Technology 40 Benefits and Costs 41 Benefits 41 Costs 41 Business to Consumer (B2C) 43 Infrastructure 43 Electronic Retail Business Model 44 Information Transactions 45 Advertising 45 Impact on Retail Distribution 46 B2C in Service Industries 46 Cybermalls and Metamalls 47 Business to Business (B2B) 47 Structure of the E-marketplace 48 Electronic Data Interchange (EDI) 48 Moving to the Internet 48 Interorganizational Systems 48 Electronic Markets 50 Consumer to Consumer (C2C): The Case of Ebay 52 Consumer to Government (C2G): The Case of Wireless and 911 53 E-commerce Strategies 53 Conclusions 55 Answers to Managerial Questions 55 Problems 56 Chapter 4 Enterprise Requirements Planning 58 Managerial Questions 58 Introduction 58 How Sap Works 59 Cultural Problems of Implementation 61 ERP Vendors 62 Costs 62 Benefits 64 The Markus and Tanis View of the ERP Experience 64 Integration 65 Packages 65 Best Practices 65 Some Assembly Required 65 Reasons for Adopting or Not Adopting ERP 66 Adopting ERP 66 Not Adopting ERP 66 The ERP Team 67 The Future: ERP II 71 Final Thoughts 71 Answers to Managerial Questions 72 Problems 73 Chapter 5 Data Warehousing 74 Managerial Questions 74 Introduction 74 Definition of a Data Warehouse 75 Characteristics of a Data Warehouse 75 Subject Orientation 76 Data Integration 77 Time 78 Nonvolatile Data 78 Structure of the Data Warehouse 79 Metadata 80 Flow of Data 81 Why a Separate Warehouse? 81 Other Forms of Data Warehouses 82 Application: Online Analytic Processing 84 Application: Data Mining 86 Application: Customer Relationship Management 87 Application: Business Intelligence 88 Strategic Use 89 Managing the Data Warehouse 90 Why Warehouse Projects Can Fail 92 Data Warehousing Costs 93 The Data Warehousing Industry 94 Benefits and Problems 94 Answers to Managerial Questions 94 Problems 95 Chapter 6 Customer Relationship Management 96 Managerial Questions 96 Introduction 96 History of the CRM Market 97 The CRM Approach 99 Basic Assumptions of CRM 99 Customer 100 Relationship 100 Management 100 Drivers for CRM Applications 102 Revenue and Cost Goals 103 Principles of CRM 103 Technology and Service 104 Information Technologies for CRM 104 Key Tasks 104 IT Factors of CRM 105 Consultants 106 Return on Investment of Implementation 106 Cost and Time 106 Benefits 107 ROI of CRM Project 108 CRM Issues 109 Customer Privacy 109 Technical Immaturity 109 CRM Myths 110 The Importance of Channels 111 Vendors 111 Management Risks 112 Conclusions 115 Answers to Managerial Questions 115 Problems 115 Chapter 7 Knowledge Management 117 Managerial Questions 117 Introduction 117 Claims 118 Role of Information Technology 118 Principles of Knowledge Management 119 Knowledge Strategies 120 Leverage 120 Fragmentation, Leakage, Refreshment 120 Uncertain Value 120 Uncertainty in Value Sharing 121 Implications 121 Tacit Versus Explicit Knowledge 121 A Hierarchy of Knowledge 123 Generating Knowledge 123 Knowledge Acquisition 124 Dedicated Resources 125 Fusion 126 Adaptation 127 Networks 127 Transferring Knowledge 127 Personnel Issues 128 Km as an Industry 129 Technology 130 When Knowledge Management Makes Sense 134 Answers to Managerial Questions 135 Problems 136 Chapter 8 Business Intelligence 137 Managerial Questions 137 Introduction 137 Definition 137 Relation to other Software 138 What Can BI Do? 138 BI Technology 140 OLAP 144 Other Data Sources 147 Example: Competitive Intelligence 148 Example: Financial Analytics 150 Potential and Shortcomings 150 Return on Investment 151 Building a Business Intelligence System 152 The Business Intelligence Industry 153 Emerging Ideas in Business Intelligence 155 BI Everywhere 155 Business Process Management and Business Activity Monitoring 156 Answers to Managerial Questions 158 Problems 159 Chapter 9 Outsourcing and Its Variations: Letting Someone else Do the Work 161 Managerial Questions 161 Introduction 161 Origins of Outsourcing 162 Offshore Outsourcing 162 Outsourcing Considerations 164 A Typical Domestic Large Outsourcing Project 164 The Size of the Outsourcing Market 165 Why Outsource? 165 The Central Role of the Contract 166 Managing the Outsourcer Once the Contract Is Signed 167 Controls 169 Bottom Line on Outsourcing 170 Outsourcing the Infrastructure: Web Hosting Service Providers, Web Services, and Utility Computing 170 The Outsourcers 171 Web Hosting 171 Service Providers (xSPs) 171 Rationale 172 Should You Hire an xSP? 173 Web Services 173 Definition and Implication of Web Services 174 Running Computing as a Utility 176 Why Do It? 176 Implementing the Utility Approach 176 Stages of Utility Computing 176 Alternative Approaches 177 Risks of ASPs and Utilities 177 Answers to Managerial Issues 180 Problems 181 Chapter 10 Working Together: Systems Integration, Interorganizational Systems, Alliances, and Globalization 182 Managerial Questions 182 Introduction 182 Systems Integration 183 Definition 183 Objective 184 The Concept 184 Integrating the Enterprise 184 Integrating the Technology 185 Integration of Data, Application, and Processes 185 Aspects of Systems Integration 188 Enterprise Computing 188 Lessons Learned from Experience 189 The Systems Integration Industry 189 Management Strategy for Integration 190 Interorganizational Systems 191 Readiness 192 Control 192 Who Benefits? 194 Alliances 194 Globalization 194 Bringing in Immigrants, Moving Work Abroad 196 Privacy Rules and Transborder Data Flows 196 Answers to Managerial Questions 198 Problems 199 Chapter 11 Is in Managing Business Operations: the Supply Chain 200 Managerial Questions 200 Introduction 200 The Supply Chain 201 Software’s Role 202 Why Install Supply Chain Software? 203 Cooperation 203 Manufacturing 204 Overview 204 Cad 204 Cam 205 Just-in-Time Manufacturing 205 Inventory 206 Role of ERP 207 Role of Data Warehousing 207 Challenges to Implementation 208 Other Problems Along the Supply Chain 209 Demand Forecasting 209 What Is the Payoff From Information Systems In the Supply Chain? 210 Returns 213 The Supply Chain Market 213 Major Firms in the Supply Chain Market 214 Answers to Managerial Questions 215 Problems 215 Chapter 12 The Chief Information Officer, People Issues, Project Management, Change Management 216 Managerial Questions 216 Introduction 216 Chief Information Officer 217 Managing Expectations 217 Chief Technology Officer 218 Governance and Steering Committees 219 What Is People Need to Know 219 Create Programs 220 Manage Vendors and Outsourcers 220 Customize Programs to the Firm’s Needs 220 Know the Needs of the Business 220 Business Processes 220 Communicating with the User Community 221 Maintaining the Corporate Data 221 Maintaining Corporate Telecommunications 221 Integrating Systems 221 Managing E-mail and Internet Usage 222 Managing the Firm’s Web Presence 222 Managing Computer Security 222 Documentation, Updates, and Training 222 Maintaining and Running the Computers 223 Help Desk 223 Role of Educational Institutions 224 Project Management 224 The Project Manager 226 Change Management 227 Conclusions 229 Answers to Managerial Questions 229 Problems 230 Chapter 13 Information Systems in Mergers and Acquisitions 231 Managerial Questions 231 Introduction 231 Why Merge? 231 Effect of Merger on Information Systems 232 Merger Principles 233 The Merger Process 233 Security Issues 234 Cultural Considerations 235 Implications of Cases 237 Answers to Managerial Questions 237 Problems 238 Chapter 14 Work Systems and Infrastructure 239 Managerial Questions 239 Introduction 239 Work System 240 Infrastructure 241 What Is Infrastructure? 241 The Infrastructure Portfolio 243 Infrastructure Architecture 244 Infrastructure Investment Strategies 244 Making Infrastructure Investment Decisions 245 Infrastructure Capabilities 246 Work Systems 246 Fundamental Concepts of Work Systems 247 Relations among the Concepts 249 Technical Considerations for IS Work System Projects 253 Answers to Managerial Questions 254 Problems 255 Chapter 15 Privacy, Security, Copyright, Patents, and other Legal And Ethical Issues 256 Managerial Questions 256 Introduction 256 Privacy 257 Privacy Statements 258 Surveillance 259 Privacy versus Convenience 261 CRM, Data Warehousing, and Other Large Databases 261 Security 261 Controls 262 New Technologies 264 The Risk of Not Investing 264 Risk Management 264 HIPAA: the Intersection of Security and Privacy 265 Announcing Security Failures 266 Disaster Recovery and Business Continuity 266 An Example 267 Who Is Responsible? 267 The Threats 267 Some IT Measures 267 Some Lessons 268 Legal Issues 268 Privacy 269 Taxation of E-commerce 269 UCITA 270 Copyright and Patents 271 Copyright 271 Patents 272 Ethics 272 Answers to Managerial Questions 274 Appendix: the Computer Industry 277 Managerial Questions 277 Definition of the Information Industry 277 U.S. Census Bureau 278 Shapiro and Varian 278 Hoover’s Online 278 A Framework for Analyzing the Information Industry— The Houghton Map 279 Computer and Computer-related Segments 281 In-house Computer Shops 282 Employment 283 Outside Shops 285 Application Service Providers 285 Web Services 287 Outsourcers 287 Hardware Manufacturers 290 Retailers and Middlemen 291 Pricing and Maintenance 291 Open Source 292 Computing for Nonbusiness Applications 292 Telecommunications and Networks 292 Search Engines 293 New Technologies 293 Wi-Fi 294 RFID 294 Finding Out More About the Computer Industry and Computer Applications: the Computer Press and the Industry Observers 295 Conclusion 296 Answers to Managerial Questions 296 Glossary 299 Index 313

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Book SynopsisHave you been asked to perform an information systems audit and don't know where to start? Examine a company's hardware, software, and data organization and processing methods to ensure quality control and security with this easy, practical guide to auditing computer systems--the tools necessary to implement an effective IS audit.Table of ContentsPreface. Acknowledgments. PART ONE: CORE CONCEPTS. Chapter 1. Basics of Computing Systems. Chapter 2. Identifying Computer Systems. PART TWO: STANDARD INFORMATION SYSTEMS AUDIT APPROACH. Chapter 3. Information Systems Audit Program. Chapter 4. Information Systems Security Policies, Standards, and/or Guidelines. Chapter 5. Auditing Service Organization Applications. Chapter 6. Assessing the Financial Stability of Vendor Organizations, Examining Vendor Organization Contracts, and Examining Accounting Treatment of Computer Equipment and Software. Chapter 7. Physical Security. Chapter 8. Logical Security. Chapter 9. Information Systems Operations. PART THREE: CONTEMPORARY INFORMATION SYSTEMS AUDITING CONCEPTS. Chapter 10. Control Self-Assessment and an Application in an Information Systems Environment. Chapter 11. Encryption and Cryptography. Chapter 12. Computer Forensics. Chapter 13. Other Contemporary Information Systems Auditing Challenges. Chapter 14. Humanistic Aspects of Information Systems Auditing. Chapter 15. Information Systems Project Management Audits. Chapter 16. Conclusion. Appendix A: Professional Auditing Associations and Other Organizations Related to Information Systems Auditing and Computer Security. Appendix B: Common Criteria for Information Technology Security Evaluation. Appendix C: The International Organization for Standardization: Seven-Layer Open Systems Interconnection Reference Model. Selected References. Glossary. Index.

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Book SynopsisRussell Ackoff is a very special management thinker. As an architect, city planner, doctor of philosophy, behavioral scientist, trailblazer in the fields of organizational, operations, and systems theory, bestselling author, distinguished Wharton School professor, and head of his own management education and consulting firm, he qualifies, as do few others in this century, for the title of Renaissance Man. Fortunately, he makes up for this grievous shortcoming by also being an outrageously funny observer of homo commercium. Now, Ackoff''s Best offers you an opportunity to become acquainted with this irreverent genius who, over the past forty years, has done so much to shape our understanding of the modern business organization. Compiled by the author, Ackoff''s Best encapsulates the author''s most controversial, influential, and wittiest work to appear since the 1970s. Ackoff''s groundbreaking exploration of systems theory and its effect on business provides the backbonTrade Review"Russ Ackoff uses words that cut through the familiar and open doors in one's brain."-Arie de Geus, author of The Living CompanyTable of ContentsSYSTEMS. Our Changing Concept of the World. Reflections on Systems and Their Models. Growth versus Development. Toward a System of Systems Concepts. Beating the System. PLANNING. The Nature of Planning. Alternative Types of Planning. Problem Treatments. Mess Management. Ends Planning.@aMission Statements. Creativity and Constraints. APPLICATIONS. Consumer Design. Education.@aNever Let Your Schooling Interfere with Your Education. Crime. The Effect of Advertising on Sales: A Study of Relations. On Pairs and Trios: The Smallest Social Systems. Why People Drink: Toward Understanding Objectives. Corporate Perestroika: The Internal Market Economy. Design of Management Systems. SCIENCE. The Nature of Science and Methodology. Objectivity. Rationality. The Future of Operational Research Is Past. Epilogue: The Role of Business in a Democratic Society. Index.

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Book SynopsisThe complete guide to choosing and using GIS in business Over the last few years, Geographical Information Systems (GIS)have become less expensive and easier to use, and the tremendouspotential of GIS to boost business productivity is finally beingrealized. Incorporating the latest developments in GIS technologyand applications, this book explores what GIS has to offercompanies in many different areas of industry today and how it canbe successfully integrated into existing business operations. Building on the success of its predecessor, this Second Editioncovers every key aspect of using GIS in business. It explains whatGIS is and helps readers gain a clear understanding of the costsand benefits of moving to a GIS. New case studies from both themanufacturing and service sectors illustrate how GIS can supporttactical and strategic business decision-making, and the book''sunbiased coverage of commercially available GIS software isinvaluable for anyone involved in selectTrade Review"...a useful introduction to GIS in business for those who have not yet convinced management of its importance..." (GI News, July 2000)Table of ContentsUsing Geographical Knowledge in Business. THE MANAGEMENT OF GEOGRAPHICAL INFORMATION. What Is a Geographical Information System? GIS and Information Strategies. The Nature of Geographical Data. THE ORGANIZATIONAL CHALLENGE. GIS for Decision Support. Making the Business Case for Investment in GIS. Implementation of the GIS Project. USING GEOGRAPHICAL INFORMATION IN BUSINESS. Application Capabilities. Operational Applications. Tactical Operations. Strategic Applications. CONCLUSION. An Integrated Part of the Business. Index.

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Book SynopsisTargeted for junior/senior/grad level students majoring in Information Systems, Enterprise Integration combines the basic concepts of integrated systems with practical experience and their use in a business environment. Understanding integration requires an understanding of the business processes within a firm and between a firm and its external business partners. The book will help students understand conceptually how linkages form between business processes and how these linkages are supported by ERP systems. Case studies are included throughout to provide a balance of theory and practice.Table of ContentsForeword vii Preface ix Part One: An Introduction To Enterprise Systems 1. Information Systems, Organizations, and Integration 3 2. Silos, Mousetraps, and Islands: A Chronicle of Information Systems in Organizations 13 3. The Challenge of Integration 37 Part Two: A New Environment For Enterprise Systems 4. Let's Get Horizontal: Toward a Process View of Organization 51 5. The Relentless Distribution of Information Technology 67 6. Data at the Core of the Enterprise 87 7. The Architecture of an Enterprise System 121 Part Three: Building Enterprise Systems 8. Planning for Enterprise Systems 137 9. The Design of Enterprise Systems 153 10. Realizing and Operating Enterprise Systems 173 11. People in Enterprise Systems 189 Part Four: Extending Enterprise Systems 12. Integrating Backward: Extending the Supply Chain 203 13. Integrating Forward: Meeting Demand and Managing Customers 223 14. Integrating Upward: Supporting Managers and Executives 241 Glossary 255 Index 267

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Book Synopsis Discusses all types of corporate risks and practical means of defending against them. Security is currently identified as a critical area of Information Technology management by a majority of government, commercial, and industrial organizations. Offers an effective risk management program, which is the most critical function of an information security program. Trade Review"Throughout, practical examples are included from various healthcare, manufacturing, and retail industries that demonstrate key concepts, implementation guidance to get started, as well as tables of risk indicators and metrics, physical structure diagrams, and graphs". (PR-Inside.com, 29 March 2011)Table of ContentsPreface xiii About the Authors xv Part I Industry Practices in Risk Management 1 1. Information Security Risk Management Imperatives and Opportunities 3 1.1 Risk Management Purpose and Scope 3 1.1.1 Purpose of Risk Management 3 1.1.2 Text Scope 17 References 24 Appendix 1A: Bibliography of Related Literature 25 2. Information Security Risk Management Defined 33 2.1 Key Risk Management Definitions 33 2.1.1 Survey of Industry Definitions 33 2.1.2 Adopted Definitions 37 2.2 A Mathematical Formulation of Risk 40 2.2.1 What is Risk? A Formal Definition 44 2.2.2 Risk in IT Environments 44 2.2.3 Risk Management Procedures 49 2.3 Typical Threats/Risk Events 56 2.4 What is an Enterprise Architecture? 61 References 65 Appendix 2A: The CISSPforum/ISO27k Implementers Forum Information Security Risk List for 2008 66 Appendix 2B: What is Enterprise Risk Management (ERM)? 71 3. Information Security Risk Management Standards 73 3.1 ISO/IEC 13335 77 3.2 ISO/IEC 17799 (ISO/IEC 27002:2005) 78 3.3 ISO/IEC 27000 SERIES 78 3.3.1 ISO/IEC 27000, Information Technology—Security Techniques—Information Security Management Systems—Fundamentals and Vocabulary 79 3.3.2 ISO/IEC 27001:2005, Information Technology—Security Techniques—Specification for an Information Security Management System 79 3.3.3 ISO/IEC 27002:2005, Information Technology—Security Techniques—Code of Practice for Information Security Management 84 3.3.4 ISO/IEC 27003 Information Technology—Security Techniques—Information Security Management System Implementation Guidance 90 3.3.5 ISO/IEC 27004 Information Technology—Security Techniques—Information Security Management—Measurement 91 3.3.6 ISO/IEC 27005:2008 Information Technology—Security Techniques—Information Security Risk Management 92 3.4 ISO/IEC 31000 92 3.5 NIST STANDARDS 94 3.5.1 NIST SP 800-16 96 3.5.2 NIST SP 800-30 99 3.5.3 NIST SP 800-39 101 3.6 AS/NZS 4360 105 References 106 Appendix 3A: Organization for Economic CoOperation and Development (OECD) Guidelines for the Security of Information Systems and Networks: Toward a Culture of Security 107 4. A Survey of Available Information Security Risk Management Methods and Tools 111 4.1 Overview 111 4.2 Risk Management/Risk Analysis Methods 114 4.2.1 Austrian IT Security Handbook 114 4.2.2 CCTA Risk Assessment and Management Methodology (CRAMM) 115 4.2.3 Dutch A&K Analysis 117 4.2.4 EBIOS 117 4.2.5 ETSI Threat Vulnerability and Risk Analysis (TVRA) Method 119 4.2.6 FAIR (Factor Analysis of Information Risk) 122 4.2.7 FIRM (Fundamental Information Risk Management) 124 4.2.8 FMEA (Failure Modes and Effects Analysis) 125 4.2.9 FRAP (Facilitated Risk Assessment Process) 128 4.2.10 ISAMM (Information Security Assessment and Monitoring Method) 129 4.2.11 ISO/IEC Baselines 130 4.2.12 ISO 31000 Methodology 130 4.2.13 IT-Grundschutz (IT Baseline Protection Manual) 136 4.2.14 MAGERIT (Metodologia de Analisis y Gestion de Riesgos de los Sistemas de Informacion) (Methodology for Information Systems Risk Analysis and Management) 137 4.2.15 MEHARI (Méthode Harmonisée d’Analyse de Risques—Harmonised Risk Analysis Method) 142 4.2.16 Microsoft’s Security Risk Management Guide 146 4.2.17 MIGRA (Metodologia Integrata per la Gestione del Rischio Aziendale) 152 4.2.18 NIST 153 4.2.19 National Security Agency (NSA) IAM / IEM / IA-CMM 153 4.2.20 Open Source Approach 155 4.2.21 PTA (Practical Threat Analysis) 158 4.2.22 SOMAP (Security Officers Management and Analysis Project) 160 4.2.23 Summary 161 References 162 5. Methodologies Examples: Cobit and Octave 164 5.1 Overview 164 5.2 COBIT 166 5.2.1 COBIT Framework 172 5.2.2 The Need for a Control Framework for IT Governance 173 5.2.3 How COBIT Meets the Need 175 5.2.4 COBIT’s Information Criteria 175 5.2.5 Business Goals and IT Goals 176 5.2.6 COBIT Framework 177 5.2.7 IT Resources 178 5.2.8 Plan and Organize (PO) 180 5.2.9 Acquire and Implement (AI) 180 5.2.10 Deliver and Support (DS) 180 5.2.11 Monitor and Evaluate (ME) 181 5.2.12 Processes Need Controls 181 5.2.13 COBIT Framework 181 5.2.14 Business and IT Controls 184 5.2.15 IT General Controls and Application Controls 185 5.2.16 Maturity Models 187 5.2.17 Performance Measurement 194 5.3 OCTAVE 205 5.3.1 The OCTAVE Approach 205 5.3.2 The OCTAVE Method 208 References 210 Part II Developing Risk Management Teams 211 6. Risk Management Issues and Organization Specifics 213 6.1 Purpose and Scope 213 6.2 Risk Management Policies 216 6.3 A Snapshot of Risk Management in the Corporate World 219 6.3.1 Motivations for Risk Management 224 6.3.2 Justifying Risk Management Financially 225 6.3.3 The Human Factors 230 6.3.4 Priority-Oriented Rational Approach 232 6.4 Overview of Pragmatic Risk Management Process 234 6.4.1 Creation of a Risk Management Team, and Adoption of Methodologies 234 6.4.2 Iterative Procedure for Ongoing Risk Management 236 6.5 Roadmap to Pragmatic Risk Management 236 References 239 Appendix 6A: Example of a Security Policy 239 7. Assessing Organization and Establishing Risk Management Scope 243 7.1 Assessing the Current Enterprise Environment 244 7.2 Soliciting Support From Senior Management 248 7.3 Establishing Risk Management Scope and Boundaries 259 7.4 Defining Acceptable Risk for Enterprise 260 7.5 Risk Management Committee 263 7.6 Organization-Specific Risk Methodology 264 7.6.1 Quantitative Methods 265 7.6.2 Qualitative Methods 267 7.6.3 Other Approaches 269 7.7 Risk Waivers Programs 272 References 274 Appendix 7A: Summary of Applicable Legislation 275 8. Identifying Resources and Implementing the Risk Management Team 280 8.1 Operating Costs to Support Risk Management and Staffing Requirements 281 8.2 Organizational Models 286 8.3 Staffing Requirements 287 8.3.1 Specialized Skills Required 290 8.3.2 Sourcing Options 291 8.4 Risk Management Tools 295 8.5 Risk Management Services 296 8.5.1 Alerting and Analysis Services 296 8.5.2 Assessments, Audits, and Project Consulting 296 8.6 Developing and Implementing the Risk Management/Assessment Team 298 8.6.1 Creating Security Standards 298 8.6.2 Defining Subject Matter Experts 300 8.6.3 Determining Information Sources 300 References 301 Appendix 8A: Sizing Example for Risk Management Team 302 Appendix 8B: Example of Vulnerability Alerts by Vendors and CERT 331 Appendix 8C: Examples of Data Losses—A One-Month Snapshot 336 9. Identifying Assets and Organization Risk Exposures 338 9.1 Importance of Asset Identification and Management 338 9.2 Enterprise Architecture 340 9.3 Identifying IT Assets 346 9.4 Assigning Value to IT Assets 353 9.5 Vulnerability Identification/Classification 354 9.5.1 Base Parameters 360 9.5.2 Temporal Parameters 362 9.5.3 Environmental Parameters 363 9.6 Threat Analysis: Type of Risk Exposures 367 9.6.1 Type of Risk Exposures 368 9.6.2 Internal Team Programs (to Uncover Risk Exposures) 371 9.7 Summary 371 References 371 Appendix 9A: Common Information Systems Assets 372 10. Remediation Planning and Compliance Reporting 377 10.1 Determining Risk Value 377 10.2 Remediation Approaches 380 10.3 Prioritizing Remediations 384 10.4 Determining Mitigating Timeframes 385 10.5 Compliance Monitoring and Security Metrics 387 10.6 Compliance Reporting 390 References 391 Basic Glossary of Terms Used in This Text 392 Index 415

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Book SynopsisConceptual model building is accepted as a key phase in Soft Systems Methodology. Despite the recognition of the importance of the SSM, students are still experiencing difficulty with the basic process of conceptual model building. This book addresses that issue.Table of ContentsForeword by Mike Duffy. Preface. Preamble. Models and Methodology. Basic Principles of HAS Modelling. Selection of Relevant Systems. Business Process Re-engineering. The Consensus Primary Task Model. CPTMFormulation Using Wider-system Extraction. CPTMAssembly Using the Enterprise Model. Application to Training Strategy and HR. Generic Model Building. Conclusions. Appendix 1: The Albion Case. Appendix 2: Exercises. Appendix 3: The Development of the United Kingdom's Single Army Activity Model and Associated Information Needs and its Relationship to Command and Control. Appendix 4: An Overview of Soft Systems Methodology. Appendix 5: Example of Applying Information Analysis Method to Airspace Control Function. Appendix 6: Examples of Product to Information Category Mapping. References. Index.

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Book SynopsisThis book is about helping managers and decision makers to choose and use the most appropriate problem solving approaches available for managing the complexity and diversity of the difficulties that they face. By describing and analysing a range of different approaches, it investigates the strengths and weaknesses of each and provides its own approach for the complementary and integrated use of different system methodologies.Table of ContentsThe Nature of Systems Thinking. A System of Systems Methodologies. The Logic and Process of Total Systems Intervention (TSI). System Dynamics (SD). Viable System Diagnosis (VSD). Strategic Assumption Surfacing and Testing (SAST). Interactive Planning (IP). Soft Systems Methodology (SSM). Critical Systems Heuristics (CSH). Total Systems Intervention (TSI) Revisited. Index.

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Book SynopsisIncluding contributions from leading experts in the field, this book covers applications and developments of heuristic search methods for solving complex optimization problems. The book covers various local search strategies including genetic algorithms, simulated annealing, tabu search and hybrids thereof.Table of ContentsPartial table of contents: Modern Heuristic Techniques. TECHNIQUES. Localized Simulated Annealing in Constraint Satisfaction andOptimization. Observing Logical Interdependencies in Tabu Search: Methods andResults. Reactive Search: Toward Self-Tuning Heuristics. Integrating Local Search into Genetic Algorithms. CASE STUDIES. Local Search for Steiner Trees in Graphs. Local Search Strategies for the Vehicle Fleet Mix Problem. A Tabu Search Algorithm for Some Discrete-Continuous SchedulingProblems. The Analysis of Waste Flow Data from Multi-Unit IndustrialComplexes Using Genetic Algorithms. The Evolution of Solid Object Designs Using GeneticAlgorithms. The Convoy Movement Problem with Initial Delays. A Brief Comparison of Some Evolutionary Optimization Methods. Index.

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Book SynopsisAdvanced Credit Analysis presents the latest and most advanced modelling techniques in the theory and practice of credit risk pricing and management. The book stresses the logic of theoretical models from the structural and the reduced-form kind, their applications and extensions. It shows the mathematical models that help determine optimal collateralisation and marking-to-market policies. It looks at modern credit risk management tools and the current structuring techniques available with credit derivatives.Trade Review"... an ambitious, well-researched book with probably the most comprehensive review of the credit-risk-modelling literature.... I eagerly await the next edition." (Quantitative Finance, March 2001)Table of ContentsAcknowledgements. Introduction. CREDIT RISK PRICING. Introduction to Modern Credit Risk Pricing. Merton's Approach: The Intuition Behind Structural Models. Subsequent Financial Engineering. Stochastic Interest Rates and Credit Risk. Advanced Considerations on Bankruptcy Endogeneity. Reduced-Form/Mixed Approaches. CREDIT RISK OF DERIVATIVES. Swap Credit Risk Pricing. Credit Risk in Options: Vulnerable Options. THEORETICAL WRAP-UP AND EMPIRICAL EVIDENCE. Introduction. Literature Wrap-Up. Empirical Evidence. A PROPOSITION FOR A STRUCTURAL MODEL. Introduction. The Pricing Model. Comparative Statics. The Practical Implementation and Final Issues. COLLATERALIZATION, MARKING-TO-MARKET, AND THEIR IMPACT ON CREDIT RISK. Introduction. A Structural Methodology for Haircut Determination and the Pricing of Credit Risk with Risky Collateral. Credit Risk Collateral Control as an Impulse Control Problem. MANAGEMENT OF CREDIT RISK. Advanced Management Tools. Financial Structuring with Credit Derivatives. Appendix A: Itô's Lemma. Appendix B: A Review of Interest Rate Models. General Bibliography. Index.

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Book SynopsisProviding an introduction to mathematical analysis as it applies to economic theory and econometrics, this book bridges the gap that has separated the teaching of basic mathematics for economics and the increasingly advanced mathematics demanded in economics research today. Dean Corbae, Maxwell B. Stinchcombe, and Juraj Zeman equip students with the knowledge of real and functional analysis and measure theory they need to read and do research in economic and econometric theory. Unlike other mathematics textbooks for economics, An Introduction to Mathematical Analysis for Economic Theory and Econometrics takes a unified approach to understanding basic and advanced spaces through the application of the Metric Completion Theorem. This is the concept by which, for example, the real numbers complete the rational numbers and measure spaces complete fields of measurable sets. Another of the book''s unique features is its concentration on the mathematical foundations of econometrics. To illustrate difficult concepts, the authors use simple examples drawn from economic theory and econometrics. Accessible and rigorous, the book is self-contained, providing proofs of theorems and assuming only an undergraduate background in calculus and linear algebra. Begins with mathematical analysis and economic examples accessible to advanced undergraduates in order to build intuition for more complex analysis used by graduate students and researchers Takes a unified approach to understanding basic and advanced spaces of numbers through application of the Metric Completion Theorem Focuses on examples from econometrics to explain topics in measure theory Trade Review"I've struggled in teaching a math for economics course for several years without an appropriate text. This book will remedy this problem and, more generally, will fill a gap that has existed in the profession for at least a decade."—L. Joe Moffitt, University of Massachusetts"This book will prove extremely useful for anyone who wants to learn mathematical economics in an accessible and intuitive fashion, while still tackling advanced concepts. The range of topics is impressive, with many illuminating examples. An excellent text!"—Jaksa Cvitanic, California Institute of Technology"This book makes accessible an extraordinary amount of mathematics used in economics and carries it to a high level. By means of illustrative examples, the authors succeed in explaining most of the main ideas of economic theory. This is an important resource for economists and an excellent text for mathematics courses for economic graduate students."—Truman F. Bewley, Yale University"A much-needed textbook for graduate students and a useful desk reference for researchers, this book is of tremendous value to the economics profession because it bridges abstract mathematics and concrete economic applications. Given the current technical level required in research, knowledge of materials covered in this book is indispensable for graduate students."—Han Hong, Stanford University"Without ever sacrificing rigor, the authors have a style that will help students trying to decipher arcane mathematical ideas. I recommend this book to students."—Richard P. McLean, Rutgers University

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Book SynopsisTony Plummer is a director of Helmsman Economics Ltd, and is a former director of Guinness Flight Hambro Global Fund Managers Ltd, Hambros Fund Management PLC, and Hambros Bank Ltd. He carries out independent research into the patterns and rhythms of global markets as well as giving global lectures on crowd psychology and technical analysis.Trade Review"This book will entertain and intrigue keen investors." Financial Times "As clear cut and easy-to-read an introduction as one could want." The IndependentTable of Contents Chapter - 00: Introduction; Section - ONE: The logic of non-rational behaviour in financial markets; Chapter - 01: Wholly individual or indivisibly whole; Chapter - 02: Two’s a crowd; Chapter - 03: The individual in the crowd; Chapter - 04: The systems approach to crowd behaviour; Chapter - 05: Cycles in the crowd; Chapter - 06: Approaches to forecasting crowd behaviour; Section - TWO: The dynamics of the bull–bear cycle; Chapter - 07: The stock market crowd; Chapter - 08: The shape of the bull–bear cycle; Chapter - 09: Energy gaps and pro-trend shocks; Chapter - 10: The spiral and the golden ratio; Chapter - 11: The mathematical basis of price movements; Chapter - 12: The shape of things to come; Section - THREE: Forecasting turning points; Chapter - 13: The phenomenon of cycles; Chapter - 14: The threefold nature of cycles; Chapter - 15: Economic cycles; Chapter - 16: Recurrence in economic and financial activity; Chapter - 17: Integrating the cycles; Chapter - 18: Forecasting with cycles; Chapter - 19: Price patterns in financial markets; Chapter - 20: The Elliott wave principle; Chapter - 21: Information shocks and corrections; Chapter - 22: The confirmation of buy and sell signals; Section - FOUR: The psychology of trading; Chapter - 23: The psychology of fear; Chapter - 24: The troubled trader; Chapter - 25: The psychology of success; Chapter - 26: Summary and conclusions

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Book SynopsisIT solutions from the leading edge Information technology promises much but, as many businesses arefinding, it often fails to deliver. Representing the new wave ofthinking about I.T., this thought-provoking collection assemblesleading researchers from four continents, including Dan Robey,Robert Zmud, Claudio Ciborra and Robert Benjamin. Writing with deepknowledge of both I.T. and business, they persuasively argue forthe integration of the core business unit and the I.T. function,advocate a new role for I.T. professionals, stress the importanceof managing outcomes rather than process, and provide practicalguidelines for turning new ideas into new management practices.Trade Review"An important book for those executives looking to transition theirorganizations into the 21st Century...especially those users andproviders of information technology services. The IT-basedtransformation is the wave of the next millennium." --Carl C.Williams, Vice President of Information Technology, AmocoCorporation "Fresh approaches to some of the most vexing issues facingorganizations today....A powerful argument for a new view of therole of information technology within the business organization ofthe future." --Michael Vitale, Professor and Head, Dept. ofInformation Systems, University of Melbourne and former VicePresident, Information Technology and Corporate Services,Prudential Insurance (1988-92) "[Steps to the Future] helps us approach the impAnding third waveof major social change since farming and the industrial revolution-- namely IT&T. Not only are the difficulties and risks offailure analyzed, but ideas for new methods of organizationalapproach and finding new success parameters are documented toensure that we move toward this change with enthusiasm and hope."--Steve Burdon, Managing Director, British Telecommunications, AsiaPacific "Executives who adopt the new ideas advanced in this book willunderstand why IT must be an integral part of their organizationand how to act decisively to capture maximum business value fromit." --Neville J. Roach, Managing Director, Fujitsu AustraliaLtd. Endorsement from Tim Besley to come. Ask Nathalie to have itemailed to her. --Tim Besley, Chairman, Commonwealth Bank ofAustraliaTable of ContentsPreface. The Authors. 1. The Right Stuff: An Introduction to New Thinking About ITManagement. (Christopher Sauer, Phillp W. Yetton) Part One: The Traditional Solutions 2. False Prophecies, Successful Practice, and Future Directions inIT Management. (Phillp W. Yetton) 3. A Professional Balancing Act: Walking the Tightrope of StrategicAlignment. (Janice M. Burn) 4. The Pathology of Strategic Alignment. (Christopher Sauer, JaniceM. Burn) Part Two: Competencies of IT-Enabled Organizational Change. 5. IT-Enabled Organizational Change: New Developments of ITSpecialists. (M. Lynne Markus, Robert Benjamin) 6. At the Heart of Success: Organizationwide ManagementCompetencies. (V. Sambamurthy, Robert W. Zmund) Part Three: Process Change. 7. Against Obliteration: Reducing Risk in Business Process Change.(Robert D. Galliers) 8. The Real Event of Reengineering. (Jane Craig, Phillp W.Yetton) Part Four: New Interpretations. 9. The Paradoxes of Transformation. (Daniel Robey) 10. Joint Outcomes: The Coproduction of IT and OrganizationalChange. (Rod Coombs) 11. Improvising in the Shapeless Organization of the Future.(Claudio U. Ciborra) 12. The Paths Ahead. (Christopher Sauer, Phillp W. Yetton) Index.

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Book SynopsisThis is the sixth volume in a series dealing with such topics as information systems practice and theory, information systems and the accounting/auditing environment, and differing perspectives on information systems research.

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Book SynopsisIn recent years, there has been a growing debate, particularly in the UK and Europe, over the merits of using discrete-event simulation (DES) and system dynamics (SD); there are now instances where both methodologies were employed on the same problem.Table of ContentsPreface xv List of contributors xvii 1 Introduction 1Sally Brailsford, Leonid Churilov and Brian Dangerfield 1.1 How this book came about 1 1.2 The editors 2 1.3 Navigating the book 3 References 9 2 Discrete-event simulation: A primer 10 Stewart Robinson 2.1 Introduction 10 2.2 An example of a discrete-event simulation: Modelling a hospital theatres process 11 2.3 The technical perspective: How DES works 12 2.3.1 Time handling in DES 14 2.3.2 Random sampling in DES 15 2.4 The philosophical perspective: The DES worldview 21 2.5 Software for DES 23 2.6 Conclusion 24 References 24 3 Systems thinking and system dynamics: A primer 26 Brian Dangerfield 3.1 Introduction 26 3.2 Systems thinking 28 3.2.1 ‘Behaviour over time’ graphs 28 3.2.2 Archetypes 29 3.2.3 Principles of influence (or causal loop) diagrams 30 3.2.4 From diagrams to behaviour 32 3.3 System dynamics 34 3.3.1 Principles of stock–flow diagramming 34 3.3.2 Model purpose and model conceptualisation 35 3.3.3 Adding auxiliaries, parameters and information links to the spinal stock–flow structure 36 3.3.4 Equation writing and dimensional checking 37 3.4 Some further important issues in SD modelling 40 3.4.1 Use of soft variables 40 3.4.2 Co-flows 42 3.4.3 Delays and smoothing functions 43 3.4.4 Model validation 46 3.4.5 Optimisation of SD models 48 3.4.6 The role of data in SD models 49 3.5 Further reading 49 References 50 4 Combining problem structuring methods with simulation: The philosophical and practical challenges 52 Kathy Kotiadis and John Mingers 4.1 Introduction 52 4.2 What are problem structuring methods? 53 4.3 Multiparadigm multimethodology in management science 54 4.3.1 Paradigm incommensurability 55 4.3.2 Cultural difficulties 57 4.3.3 Cognitive difficulties 58 4.3.4 Practical problems 59 4.4 Relevant projects and case studies 60 4.5 The case study: Evaluating intermediate care 62 4.5.1 The problem situation 62 4.5.2 Soft systems methodology 64 4.5.3 Discrete-event simulation modelling 66 4.5.4 Multimethodology 67 4.6 Discussion 68 4.6.1 The multiparadigm multimethodology position and strategy 68 4.6.2 The cultural difficulties 70 4.6.3 The cognitive difficulties 70 4.7 Conclusions 72 Acknowledgements 72 References 72 5 Philosophical positioning of discrete-event simulation and system dynamics as management science tools for process systems: A critical realist perspective 76 Kristian Rotaru, Leonid Churilov and Andrew Flitman 5.1 Introduction 76 5.2 Ontological and epistemological assumptions of CR 80 5.2.1 The stratified CR ontology 80 5.2.2 The abductive mode of reasoning 81 5.3 Process system modelling with SD and DES through the prism of CR scientific positioning 82 5.3.1 Lifecycle perspective on SD and DES methods 84 5.4 Process system modelling with SD and DES: Trends in and implications for MS 90 5.5 Summary and conclusions 97 References 99 6 Theoretical comparison of discrete-event simulation and system dynamics 105 Sally Brailsford 6.1 Introduction 105 6.2 System dynamics 106 6.3 Discrete-event simulation 108 6.4 Summary: The basic differences 110 6.5 Example: Modelling emergency care in Nottingham 112 6.5.1 Background 112 6.5.2 The ECOD project 113 6.5.3 Choice of modelling approach 114 6.5.4 Quantitative phase 114 6.5.5 Model validation 116 6.5.6 Scenario testing and model results 116 6.5.7 The ED model 118 6.5.8 Discussion 119 6.6 The $64 000 question: Which to choose? 120 6.7 Conclusion 123 References 123 7 Models as interfaces 125 Steffen Bayer, Tim Bolt, Sally Brailsford and Maria Kapsali 7.1 Introduction: Models at the interfaces or models as interfaces 125 7.2 The social roles of simulation 126 7.3 The modelling process 129 7.4 The modelling approach 131 7.5 Two case studies of modelling projects 134 7.6 Summary and conclusions 137 References 138 8 An empirical study comparing model development in discrete-event simulation and system dynamics 140 Antuela Tako and Stewart Robinson 8.1 Introduction 140 8.2 Existing work comparing DES and SD modelling 142 8.2.1 DES and SD model development process 143 8.2.2 Summary 146 8.3 The study 146 8.3.1 The case study 146 8.3.2 Verbal protocol analysis 147 8.3.3 The VPA sessions 149 8.3.4 The subjects 149 8.3.5 The coding process 150 8.4 Study results 151 8.4.1 Attention paid to modelling topics 152 8.4.2 The sequence of modelling stages 154 8.4.3 Pattern of iterations among topics 155 8.5 Observations from the DES and SD expert modellers’ behaviour 158 8.6 Conclusions 160 Acknowledgements 162 References 162 9 Explaining puzzling dynamics: A comparison of system dynamics and discrete-event simulation 165 John Morecroft and Stewart Robinson 9.1 Introduction 165 9.2 Existing comparisons of SD and DES 166 9.3 Research focus 169 9.4 Erratic fisheries – chance, destiny and limited foresight 170 9.5 Structure and behaviour in fisheries: A comparison of SD and DES models 173 9.5.1 Alternative models of a natural fishery 174 9.5.2 Alternative models of a simple harvested fishery 178 9.5.3 Alternative models of a harvested fishery with endogenous ship purchasing 184 9.6 Summary of findings 192 9.7 Limitations of the study 193 9.8 SD or DES? 194 Acknowledgements 196 References 196 10 DES view on simulation modelling: SIMUL8 199 Mark Elder 10.1 Introduction 199 10.2 How software fits into the project 200 10.3 Building a DES 202 10.4 Getting the right results from a DES 208 10.4.1 Verification and validation 210 10.4.2 Replications 211 10.5 What happens after the results? 212 10.6 What else does DES software do and why? 212 10.7 What next for DES software? 213 References 214 11 Vensim and the development of system dynamics 215 Lee Jones 11.1 Introduction 215 11.2 Coping with complexity: The need for system dynamics 216 11.3 Complexity arms race 219 11.4 The move to user-led innovation 221 11.5 Software support 222 11.5.1 Apples and oranges (basic model testing) 223 11.5.2 Confidence 224 11.5.3 Helping the practitioner do more 237 11.6 The future for SD software 245 11.6.1 Innovation 245 11.6.2 Communication 245 References 247 12 Multi-method modeling: AnyLogic 248 Andrei Borshchev 12.1 Architectures 249 12.1.1 The choice of model architecture and methods 251 12.2 Technical aspect of combining modeling methods 252 12.2.1 System dynamics ® discrete elements 252 12.2.2 Discrete elements ® system dynamics 253 12.2.3 Agent based « discrete event 255 12.3 Example: Consumer market and supply chain 257 12.3.1 The supply chain model 257 12.3.2 The market model 258 12.3.3 Linking the DE and the SD parts 259 12.3.4 The inventory policy 260 12.4 Example: Epidemic and clinic 262 12.4.1 The epidemic model 262 12.4.2 The clinic model and the integration of methods 264 12.5 Example: Product portfolio and investment policy 267 12.5.1 Assumptions 268 12.5.2 The model architecture 270 12.5.3 The agent product and agent population portfolio 271 12.5.4 The investment policy 274 12.5.5 Closing the loop and implementing launch of new products 275 12.5.6 Completing the investment policy 277 12.6 Discussion 278 References 279 13 Multiscale modelling for public health management: A practical guide 280 Rosemarie Sadsad and Geoff McDonnell 13.1 Introduction 280 13.2 Background 281 13.3 Multilevel system theories and methodologies 281 13.4 Multiscale simulation modelling and management 283 13.5 Discussion 289 13.6 Conclusion 290 References 290 14 Hybrid modelling case studies 295 Rosemarie Sadsad, Geoff McDonnell, Joe Viana, Shivam M. Desai, Paul Harper and Sally Brailsford 14.1 Introduction 295 14.2 A multilevel model of MRSA endemicity and its control in hospitals 296 14.2.1 Introduction 296 14.2.2 Method 296 14.2.3 Results 297 14.2.4 Conclusion 302 14.3 Chlamydia composite model 302 14.3.1 Introduction 302 14.3.2 Chlamydia 302 14.3.3 DES model of a GUM department 303 14.3.4 SD model of chlamydia 304 14.3.5 Why combine the models 304 14.3.6 How the models were combined 305 14.3.7 Experiments with the composite model 305 14.3.8 Conclusions 307 14.4 A hybrid model for social care services operations 308 14.4.1 Introduction 308 14.4.2 Population model 308 14.4.3 Model construction 309 14.4.4 Contact centre model 310 14.4.5 Hybrid model 311 14.4.6 Conclusions and lessons learnt 313 References 316 15 The ways forward: A personal view of system dynamics and discrete-event simulation 318 Michael Pidd 15.1 Genesis 318 15.2 Computer simulation in management science 319 15.3 The effect of developments in computing 320 15.4 The importance of process 324 15.5 My own comparison of the simulation approaches 324 15.5.1 Time handling 324 15.5.2 Stochastic and deterministic elements 326 15.5.3 Discrete entities versus continuous variables 327 15.6 Linking system dynamics and discrete-event simulation 328 15.7 The importance of intended model use 329 15.7.1 Decision automation 330 15.7.2 Routine decision support 331 15.7.3 System investigation and improvement 331 15.7.4 Providing insights for debate 332 15.8 The future? 333 15.8.1 Use of both methods will continue to grow 333 15.8.2 Developments in computing will continue to have an effect 334 15.8.3 Process really matters 335 References 335 Index 337

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Book SynopsisLearn to maintain and update scorecards and dashboards with Excel Balanced Scorecards and operational dashboards measure organizational performance and Microsoft Excel is the tool used worldwide to create these scorecards and dashboards. This book covers time-proven step-by-step processes on how to guide executive teams and managers in creating scorecards and dashboards. It then shows Excel developers how to create those scorecards and dashboards. This is the only book that converts theory into practice. The author addresses the people and processes you need to identify strategy and operational metrics and then implement them in dashboards in three versions of Excel. You''ll learn how balanced scorecards help organizations translate strategy into action and the ways that performance dashboards enable managers monitor operations. Covers Excel 2010 back to Excel 2003 Shows how to develop consensus on strategy and operational plans with the executiveTable of ContentsIntroduction xxvii Part I Strategic Performance with Balanced Scorecards 1 Chapter 1 Accelerating Strategic Performance 3 Chapter 2 Developing Your Strategic Foundation 17 Chapter 3 Preparing to Build Your Balanced Scorecard 31 Chapter 4 Step-by-Step to Building Your Strategy Map 47 Chapter 5 Step-by-Step from Strategy to Action 61 Chapter 6 Step-by-Step to Selecting Metrics and Setting Targets 71 Chapter 7 Step-by-Step to Developing Your Implementation Plan 85 Chapter 8 Step-by-Step to Rollout and Strategic Reviews 91 Part II Operational Performance with Dashboards 101 Chapter 9 Developing Executive and Operational Dashboards 103 Chapter 10 Mapping Your Operational Processes 109 Chapter 11 Identifying Critical Metrics and Key Performance Indicators 121 Part III Building Maps, Scorecards, and Dashboards 133 Chapter 12 Creating Dashboards for Decision-Making 135 Chapter 13 Drawing Process and Strategy Maps 147 Chapter 14 Using Microsoft Excel for Balanced Scorecards and Dashboards 157 Chapter 15 Text-Based Dashboards 167 Chapter 16 Custom Labels and Formatting 183 Chapter 17 Working with Data That Changes Size 207 Chapter 18 Retrieving Data from Lists and Tables of Data 229 Chapter 19 Creating Miniature Charts and Tables 243 Chapter 20 Controlling Charts with Menus, Combo Boxes, and Buttons 267 Chapter 21 Working with PivotTables 283 Chapter 22 Working with PowerPivot 297 Chapter 23 Smoothing Data and Forecasting Trends 317 Chapter 24 Identifying Targets and Displaying Alerts 331 Chapter 25 Building Powerful Decision-Making Charts 347 Chapter 26 Drilling to Detail 371 Chapter 27 Using Excel Add-ins for Extra Capabilities 385 Chapter 28 Finishing Touches 395 Chapter 29 Data Integration Methods 405 Chapter 30 Publishing Balanced Scorecards and Dashboards 425 Index 441

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Book SynopsisA wealth of international case studies illustrating current issues and emerging best practices in enterprise risk management Despite enterprise risk management''s relative newness as a recognized business discipline, the marketplace is replete with guides and references for ERM practitioners. Yet, until now, few case studies illustrating ERM in action have appeared in the literature. One reason for this is that, until recently, there were many disparate, even conflicting definitions of what, exactly ERM is and, more importantly, how organizations can use it to utmost advantage. With efforts underway, internationally, to mandate ERM and to standardize ERM standards and practices, the need has never been greater for an authoritative resource offering risk management professionals authoritative coverage of the full array of contemporary ERM issues and challenges. Written by two recognized international thought leaders in the field, ERM-Enterprise Risk Management prTable of ContentsContributor List vii About the Editors ix Acknowledgements x Introduction xi ISO 31000 and Guide 73: 2009 Select Terms and Their Definitions xvii Part I Erm Articles 1 1 Establishing the Internal and External Contexts 3 1.1 Managing Risks to Enable Strategy 3 Jean-Paul Louisot and Christopher Mandel 1.2 Strategy, Constraint, Risk Management and the Value Chain 12 Christopher Ketcham and Kevin W. Knight 1.3 The Risk of Group Decision Making within Organizations: A Synthesis 19 Daniel A. Gaus 2 Risk Assessment 41 2.1 Risk Quantification: Cornerstone for Rational Risk Management 41 Jean-Paul Louisot, Laurent Condamin and Patrick Naim 2.2 Brief Overview of Cindynics 48 Georges-Yves Kervern and Jean-Paul Louisot 2.3 Risk Assessment or Exposure Diagnostic 56 Jean-Paul Louisot 2.4 Managing the Collection of Relevant Data for an ERM Program: The Importance of Efficient and Neutral Questionnaires 84 Sophie Gaultier-Gaillard 2.5 Enterprise Risk Analytics Systems 96 Richard Connelly and Jean-Paul Louisot 2.6 Emerging Enterprise Risks Facing the US Healthcare Industry 103 Robert L. Snyder 3 Select and Implement the Appropriate Risk Management Technique 109 3.1 Risk to Reputation 109 Sophie Gaultier-Gaillard, Jean-Paul Louisot and Jenny Rayner 3.2 Disturbance Management 123 Jean-Paul Louisot 4 Monitor Results and Revise 135 4.1 Business Ethics and Risk Management 135 Marc Ronez 4.2 Governance, Risk, Compliance: The New Paradigm of Risk Management 146 Jean-Paul Louisot 5 Communicate and Consult 155 5.1 Communication as a Risk Mitigation Tool 155 Jean-Paul Louisot Part II Case Studies 163 6 Case Study Protocol 165 7 Case Study: Risk Management Implementation in China 167 Duojia (Doug) Lu 8 Case Study: Agreeing Upon the Scope of the Project and the Job of the ERM Risk Manager 187 Christopher Ketcham 9 Case Study: Wellcome Trust 191 Fiona Davidge Interviewed by Jean-Paul Louisot 10 Case Study: Large Health Insurer in the US 199 Anonymous Interviewed by Christopher Ketcham 11 Case Study: Three Steps for Bringing Risk Management Back in House 217 Renee Reimer Interviewed by Christopher Ketcham 12 Case Study: University of California 229 Grace Crickette Interviewed by Christopher Ketcham 13 Case Study: Managing Risk at the OPAC du Rhône 241 Samiha Viand Interviewed by Jean-Paul Louisot ERM References for Practitioners 249 Further Reading 253 Index 255

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Book SynopsisEmbrace data and use it to sell and market your products Data is everywhere and it keeps growing and accumulating. Companies need to embrace big data and make it work harder to help them sell and market their products.Table of ContentsIntroduction 1 Part I: Getting Started with Data Driven Marketing 5 Chapter 1: Data Driven Marketing 101: It’s All About the Customer 7 Chapter 2: Communicating Directly with Your Customers 19 Chapter 3: The Forest for the Trees: Where Is the Customer in All That Data? 33 Chapter 4: Using and Managing Your Customer Contact Information 51 Chapter 5: Getting Your Message Out: Marketing Campaign Basics 61 Part II: Digging Deeper into Your Data: Analytics 71 Chapter 6: You’re Going to Need a Geek: Introduction to Analyzing Data 73 Chapter 7: Birds of a Feather Buy Together: Segmenting Your Customers 89 Chapter 8: Getting the Most from Your Transaction Data 105 Chapter 9: The Good, the Bad, and the Ugly: Understanding Customer Profitability 119 Part III: Putting Your Data to Work 129 Chapter 10: The Tactical Advantage: Designing Data Driven Marketing Campaigns 131 Chapter 11: From the Window to the Counter: Getting Shoppers to Buy 151 Chapter 12: Crafting Your Marketing Message 163 Chapter 13: Using Customer Data Online 175 Part IV: The Feedback Cycle: Learning from Experience 189 Chapter 14: Learning Curve: Setting Up a Testing Plan 191 Chapter 15: Getting to the Bottom Line: Tracking and Measuring Your Campaigns 207 Chapter 16: Putting Your Geek to Work: Analyzing Campaign Results 223 Chapter 17: Sharing Customer Data Throughout Your Enterprise 243 Part V: The Part of Tens 259 Chapter 18: Ten (or So) Ways to Capture Customer Data 261 Chapter 19: Ten Resources for Information and Assistance 269 Index 277

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Book SynopsisInsightful modelling of dynamic systems for better business strategy The business environment is constantly changing and organisations need the ability to rehearse alternative futures. By mimicking the interlocking operations of firms and industries, modelling serves as a dry run' for testing ideas, anticipating consequences, avoiding strategic pitfalls and improving future performance. Strategic Modelling and Business Dynamics is an essential guide to credible models; helping you to understand modelling as a creative process for distilling and communicating those factors that drive business success and sustainability. Written by an internationally regarded authority, the book covers all stages of model building, from conceptual to analytical. The book demonstrates a range of in-depth practical examples that vividly illustrate important or puzzling dynamics in firm operations, strategy, public policy, and everyday life. This updated new edition alsTable of ContentsAbout the Author xvii Foreword by Peter Checkland xix Preface to the Second Edition xxi Preface from the First Edition xxv How to Use This Book xxxv Chapter 1 The Appeal and Power of Strategic Modelling 1 Introduction 1 A New Approach to Modelling 5 The Puzzling Dynamics of International Fisheries 7 Model of a Natural Fishery 10 Simulated Dynamics of a Natural Fishery 12 Operating a Simple Harvested Fishery 13 Harvesting in Bonavista, Newfoundland – A Thought Experiment 15 A Start on Analysing Dynamics and Performance Through Time 17 Saving Bonavista – Using Simulation to Devise a Sustainable Fishery 20 Dynamic Complexity and Performance Through Time 20 Cunning Fish – A Scenario with Reduced Dynamic Complexity 23 Preview of the Book and Topics Covered 25 Appendix – Archive Materials from World Dynamics 27 References 28 Chapter 2 Introduction to Feedback Systems Thinking 31 Ways of Interpreting Situations in Business and Society 31 Event-oriented Thinking 32 Feedback Systems Thinking – An Illustration 34 A Shift of Mind 37 The Invisibility of Feedback 38 A Start on Causal Loop Diagrams 39 Structure and Behaviour Through Time – Feedback Loops and the Dynamics of a Slow-to-Respond Shower 41 Processes in a Shower ‘System’ 44 Simulation of a Shower and the Dynamics of Balancing Loops 45 From Events to Dynamics and Feedback – Drug-related Crime 47 A Feedback View 48 Scope and Boundary of Factors in Drug-related Crime 50 An Aside – More Practice with Link Polarity and Loop Types 51 Purpose of Causal Loop Diagrams – A Summary 52 Feedback Structure and Dynamics of a Technology-based Growth Business 52 Causal Loop Diagrams – Basic Tips 55 Picking and Naming Variables 55 Meaning of Arrows and Link Polarity 56 Drawing, Identifying and Naming Feedback Loops 57 Causal Loop Diagram of Psychological Pressures and Unintended Haste in a Troubled Internet Start-Up 58 References 61 Chapter 3 Modelling Dynamic Systems 63 Asset Stock Accumulation 63 Accumulating a ‘Stock’ of Faculty at Greenfield University 65 Asset Stocks in a Real Organisation – BBC World Service 69 The Coordinating Network 70 Modelling Symbols in Use: A Closer Look at Drug-related Crime 72 Equation Formulations 75 Drug-related Crime 76 Funds Required to Satisfy Addiction 77 Street Price and Price Change 78 Allocation of Police 79 Experiments with the Model of Drug-related Crime 80 A Tour of the Model 80 Escalating Crime – The Base Case 82 Drilling Down to the Equations 84 Anomalous Behaviour Over Time and Model Boundary 86 Benefits of Model Building and Simulation 89 References 90 Chapter 4 World of Showers 91 Getting Started 91 Taking a Shower in World of Showers A 92 Taking a Shower in World of Showers B 95 Redesigning Your World of Showers 96 Reflections on the World of Showers 98 Metaphorical Shower Worlds in GlaxoSmithKline, IBM and Harley-Davidson 100 Inside World of Showers 102 A Tour of Formulations in the Comfort-seeking Loop of the Hidden Shower 102 Interdependence of Showers – Coupling Formulations 105 Simulations of World of Showers B 105 References 107 Chapter 5 Cyclical Dynamics and the Process of Model Building 109 An Overview of the Modelling Process 109 Dynamic Hypothesis and Fundamental Modes of Dynamic Behaviour 111 Team Model Building 112 Employment and Production Instability – Puzzling Performance Over Time 117 Dialogue About Production Control 120 Thought Experiment: A Surprise Demand Increase in an Ideal Factory 122 Equation Formulations and Computations in Production Control 124 Forecasting Shipments – Standard Formulations for Information Smoothing 126 Inventory Control – Standard Formulations for Asset Stock Adjustment 127 Desired Production 128 The Computations Behind Simulation 129 Modelling Workforce Management and Factory Production Dynamics 133 Dialogue About Workforce Management 133 Operating Constraint Linking Workforce to Production 135 Simulation of the Complete Model: A Surprise Demand Increase in a Factory Where Production is Constrained by the Size of the Workforce 136 Pause for Reflection 140 Equation Formulations in Workforce Management 140 Departure Rate – Standard Formulation for Stock Depletion 141 Hiring – Standard Formulations for Asset Stock Replacement and Adjustment 142 Workforce Planning 144 Chronic Cyclicality in Employment and Production and How to Cure It 145 The Curious Effect of Random Variations in Demand 145 Industry Cyclicality and Business Cycles 147 Policy Formulation and What-ifs to Improve Factory Performance 148 Modelling for Learning and Soft Systems 152 A Second Pause for Reflection: System Dynamics and Soft Systems 153 A Link to Soft Systems Methodology 156 Alternative Views of a Radio Broadcaster 159 Appendix 1: Model Communication and Policy Structure Diagrams 162 Appendix 2: The Dynamics of Information Smoothing 164 References 166 Chapter 6 The Dynamics of Growth from Diffusion 169 Stocks and Flows in New Product Adoption – A Conceptual Diffusion Model 171 The Bass Model – An Elegant Special Case of a Diffusion Model 172 The Dynamics of Product Adoption by Word-of-mouth 175 The Need to Kick-start Adoption 177 The Complete Bass Diffusion Model With Advertising 177 The Dynamics of Product Adoption by Word-of-mouth and Advertising 179 A Variation on the Diffusion Model: The Rise of Low-cost Air Travel in Europe 182 easyJet – A Bright Idea, but Will it Work? 182 Visualising the Business: Winning Customers in a New Segment 183 Visualising Retaliation and Rivalry 186 Feedback Loops in the easyJet Model 188 Strategy and Simulation of Growth Scenarios 189 Using the Fliers Simulator to Create Your Own Scenarios 193 Simulation, Predictions and Scenarios 194 Conclusion 194 Appendix: More About the Fliers Model 195 Back to the Future – From easyJet to People Express and Beyond 197 References 199 Chapter 7 Managing Business Growth 201 A Conceptual Model of Market Growth and Capital Investment 203 Background to the Case 203 Adopting a Feedback View 204 Formulation Guidelines for Portraying Feedback Structure 206 Review of Operating Policies and Information Flows in the Market Growth Model 209 Customer Ordering 209 Sales Force Expansion 210 Budgeting 211 Capital Investment 212 Goal Formation 214 An Information Feedback View of Management and Policy 215 Information Available to Decision Makers and Bounded Rationality 217 Nature of Decision Making and the Decision Process 220 Policy Structure and Formulations for Sales Growth 222 Sales Force Hiring – Standard Stock Adjustment Formulation 223 Sales Force Budgeting – Revenue Allocation and Information Smoothing 223 Order Fulfilment – Standard Stock Depletion Formulation 225 Customer Ordering 226 Policy Structure and Formulations for Limits to Sales Growth 226 Customer Response to Delivery Delay – Non-linear Graphical Converter 228 Customers’ Perception of Delivery Delay – Information Smoothing 229 Order Fulfilment and Capacity Utilisation 229 Policy Structure and Formulations for Capital Investment 231 Assessment of Delivery Delay 232 Goal Formation – Weighted Average of Adaptive and Static Goals 232 Capacity Expansion – Fractional Asset Stock Adjustment 233 Production Capacity – Two-Stage Stock Accumulation 236 Simulation Experiments 237 Simulation of Sales Growth Loop 238 Strength of Reinforcing Loop 241 Simulation of Sales Growth and Customer Response Loops 242 Simulation of the Complete Model with all Three Loops Active – The Base Case 246 Redesign of the Investment Policy 250 Top Management Optimism in Capital Investment 251 High and Unyielding Standards – A Fixed Operating Goal for Delivery Delay 253 Policy Design, Growth and Dynamic Complexity 256 Conclusion 257 Overview of Policy Structure 257 Growth and Underinvestment at People Express? 261 More Examples of Growth Strategies that Failed or Faltered – and One that Succeeded 262 Growth Strategy for New Products and Services in a Competitive Industry 264 Appendix – Gain of a Reinforcing Loop 266 References 268 Chapter 8 Industry Dynamics – Oil Price and the Global Oil Producers 271 Problem Articulation – Puzzling Dynamics of Oil Price 272 Towards a Dynamic Hypothesis 274 Model Development Process 275 A Closer Look at the Stakeholders and Their Investment Decision Making 278 Investment by the Independent Producers 279 Development Costs 280 Policy Structure and Formulations for Upstream Investment – Fractional Asset Stock Adjustment 282 Oil Price and Demand 284 The Swing Producer 286 Quota Setting 288 The Opportunists 290 The Rise of Russian Oil – Incorporating Unforeseen Political Change 291 The Shale Gale – Incorporating Unforeseen Technological Change 292 Connecting the Pieces – A Feedback Systems View 294 Two Invisible Hands and More 295 The Visible Hand of OPEC 297 Webs of Intrigue – Inside OPEC’s Opulent Bargaining Rooms 297 A Simple Thought Experiment: Green Mindset and Global Recession 300 Using the Model to Generate Scenarios 301 Archive Scenario 1: 10-Year Supply Squeeze Followed by SupplyGlut 301 Archive Scenario 2: Quota Busting in a Green World 306 Scenario from the Mid-1990s to 2020: Asian Boom with Quota Busting, Cautious Upstream Investment and Russian Oil 309 A High Price Scenario from the Mid-1990s to 2020: How to PushOil Price Over $60 per Barrel 314 A 2010–2034 Scenario: Subdued Global Oil Economy with Shale Gale and OPEC Supply Boost 317 Modified 2010–2034 Scenario: Subdued Global Oil Economy with Shale Gale and Punitive Saudi Supply Control 322 2010–2034 Thought Experiment: Subdued Global Oil Economy with a Shale Gale and Mooted US Supply Control – The ‘Saudi America’ Hypothesis 324 Devising New Scenarios 327 Effect of Global Economy and Environment on Demand 327 Cartel Quota Bias 327 Opportunists’ Capacity Bias 328 Oil Price Bias 328 Capex Optimism 328 Time to Build Trust in Russia (in Oil World 1995 and 2010) 329 Endnote: A Brief History of the Oil Producers’ Project 329 References 331 Chapter 9 Public Sector Applications of Strategic Modelling 333 Urban Dynamics – Growth and Stagnation in Cities 334 Urban Model Conceptualisation 335 Medical Workforce Dynamics and Patient Care 340 Background 341 Medical Workforce Planning Model 342 Quality of Patient Care 346 Base Run – Changing Composition of the Medical Workforce 348 Base Run – Quality of Patient Care 350 Intangible Effects of the European Working Time Directive 351 Modelling Junior Doctor Morale 351 Overview of the Complete Model 353 The Formulation of Work–Life Balance and Flexibility 354 Simulations of the Complete Model 355 Conclusions from the Medical Workforce Study 359 Fishery Dynamics and Regulatory Policy 361 Fisheries Management 361 A Simple Harvested Fishery – Balancing Catch and Fish Regeneration 363 A Harvested Fishery with Endogenous Investment – Coping with a Tipping Point 366 Simulated Dynamics of a Harvested Fishery with Endogenous Investment 369 Control and Regulation – Policy Design for Sustainable Fisheries 371 Formulation of Deployment Policy 373 Stock and Flow Equations for Ships at Sea, Ships in Harbour and Scrap Rate 375 Simulated Dynamics of a Regulated Fishery – The Base Case 375 Policy Design – A Higher Benchmark for Fish Density 379 Dynamics of a Weakly Regulated Fishery 381 Policy Design – Lower Exit Barriers Through Quicker Scrapping of Idle Ships 383 Sustainability, Regulation and Self-Restraint 387 Conclusion 387 Appendix – Alternative Simulation Approaches 388 From Urban Dynamics to SimCity 389 Discrete-event Simulation and System Dynamics 390 Conclusions on Alternative Approaches to Simulation Modelling 396 References 398 Chapter 10 Model Validity, Mental Models and Learning 403 Mental Models, Transitional Objects and Formal Models 404 Models of Business and Social Systems 406 Tests for Building Confidence in Models 407 Model Confidence Building Tests in Action: A Case Study in Fast-moving Consumer Goods 410 Soap Market Overview 410 The Modelling Project 411 Model Structure Tests and the Soap Industry Model 412 Boundary Adequacy and Structure Verification Tests Applied to a Simple Soap Model 413 A Refined View of the Market 416 Boundary Adequacy and Sector Map of the Complete Soap Industry Model 417 Managerial Decision-making Processes in the Old English Bar Soap Company 419 Managerial Decision-making Processes in Global Personal Care 420 Managerial Decision-making Processes in Supermarkets 421 Equation Formulation Tests and the Soap Industry Model 422 Substitution of Bar Soap by Shower Gel 423 Brand Switching Between Competing Bar Soap Products 424 Model Behaviour Tests and Fit to Data 428 Tests of Fit on Simulations of the Soap Industry Model – The Base Case 432 Tests of Learning from Simulation 436 Comparing Simulations with Expectations and Interpreting Surprise Behaviour 436 Partial Model Simulations to Examine Pet Theories and Misconceptions 437 Family Member Tests 438 Policy Implication Tests 439 Understanding Competitive Dynamics in Fast-moving Consumer Goods 439 Summary of Confidence Building Tests 441 Conclusion – Model Fidelity and Usefulness 444 Endnote: The Loops of Feedback 447 References 449 About the Website Resources 451 Index 452

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Book SynopsisPractical guide that can be used by executives to make well-informed decisions on cybersecurity issues to better protect their business Emphasizes, in a direct and uncomplicated way, how executives can identify, understand, assess, and mitigate risks associated with cybersecurity issues Covers ''What to Do When You Get Hacked?'' including Business Continuity and Disaster Recovery planning, Public Relations, Legal and Regulatory issues, and Notifications and Disclosures Provides steps for integrating cybersecurity into Strategy; Policy and Guidelines; Change Management and Personnel Management Identifies cybersecurity best practices that executives can and should use both in the office and at home to protect their vital information Table of ContentsForeword xiii Preface xvii Acknowledgments xxiii 1.0 Introduction 1 1.1 Defining Cybersecurity 1 1.2 Cybersecurity is a Business Imperative 2 1.3 Cybersecurity is an Executive-Level Concern 4 1.4 Questions to Ask 4 1.5 Views of Others 7 1.6 Cybersecurity is a Full-Time Activity 7 2.0 Why Be Concerned? 9 2.1 A Classic Hack 9 2.2 Who Wants Your Fortune? 12 2.3 Nation-State Threats 13 2.3.1 China 13 2.3.2 Don’t Think that China is the Only One 17 2.4 Cybercrime is Big Business 20 2.4.1 Mercenary Hackers 20 2.4.2 Hacktivists 25 2.4.3 The Insider Threat 26 2.4.4 Substandard Products and Services 29 2.5 Summary 36 3.0 Managing Risk 37 3.1 Who Owns Risk in Your Business? 37 3.2 What are Your Risks? 38 3.2.1 Threats to Your Intellectual Property and Trade Secrets 38 3.2.2 Technical Risks 42 3.2.3 Human Risks 47 3.3 Calculating Your Risk 54 3.3.1 Quantitative Risk Assessment 55 3.3.2 Qualitative Risk Assessment 63 3.3.3 Risk Decisions 71 3.4 Communicating Risk 77 3.4.1 Communicating Risk Internally 78 3.4.2 Regulatory Communications 79 3.4.3 Communicating with Shareholders 86 3.5 Organizing for Success 89 3.5.1 Risk Management Committee 89 3.5.2 Chief Risk Officers 90 3.6 Summary 91 4.0 Build Your Strategy 95 4.1 How Much “Cybersecurity” Do I Need? 95 4.2 The Mechanics of Building Your Strategy 97 4.2.1 Where are We Now? 99 4.2.2 What do We have to Work with? 103 4.2.3 Where do We Want to be? 104 4.2.4 How do We Get There? 107 4.2.5 Goals and Objectives 108 4.3 Avoiding Strategy Failure 111 4.3.1 Poor Plans, Poor Execution 111 4.3.2 Lack of Communication 113 4.3.3 Resistance to Change 114 4.3.4 Lack of Leadership and Oversight 117 4.4 Ways to Incorporate Cybersecurity into Your Strategy 118 4.4.1 Identify the Information Critical to Your Business 119 4.4.2 Make Cybersecurity Part of Your Culture 119 4.4.3 Consider Cybersecurity Impacts in Your Decisions 119 4.4.4 Measure Your Progress 120 4.5 Plan For Success 121 4.6 Summary 123 5.0 Plan For Success 125 5.1 Turning Vision into Reality 125 5.1.1 Planning for Excellence 127 5.1.2 A Plan of Action 128 5.1.3 Doing Things 131 5.2 Policies Complement Plans 140 5.2.1 Great Cybersecurity Policies for Everyone 140 5.2.2 Be Clear about Your Policies and Who Owns Them 188 5.3 Procedures Implement Plans 190 5.4 Exercise Your Plans 191 5.5 Legal Compliance Concerns 193 5.6 Auditing 195 5.7 Summary 196 6.0 Change Management 199 6.1 Why Managing Change is Important 199 6.2 When to Change? 201 6.3 What is Impacted by Change? 205 6.4 Change Management and Internal Controls 209 6.5 Change Management as a Process 214 6.5.1 The Touhill Change Management Process 215 6.5.2 Following the Process 216 6.5.3 Have a Plan B, Plan C, and maybe a Plan D 220 6.6 Best Practices in Change Management 220 6.7 Summary 224 7.0 Personnel Management 227 7.1 Finding the Right Fit 227 7.2 Creating the Team 229 7.2.1 Picking the Right Leaders 230 7.2.2 Your Cybersecurity Leaders 233 7.3 Establishing Performance Standards 237 7.4 Organizational Considerations 240 7.5 Training for Success 242 7.5.1 Information Every Employee Ought to Know 242 7.5.2 Special Training for Executives 246 7.6 Special Considerations for Critical Infrastructure Protection 249 7.7 Summary 258 8.0 Performance Measures 261 8.1 Why Measure? 261 8.2 What to Measure? 267 8.2.1 Business Drivers 267 8.2.2 Types of Metrics 271 8.3 Metrics and the C-Suite 272 8.3.1 Considerations for the C-Suite 273 8.3.2 Questions about Cybersecurity Executives Should Ask 275 8.4 The Executive Cybersecurity Dashboard 277 8.4.1 How Vulnerable Are We? 277 8.4.2 How Effective Are Our Systems and Processes? 282 8.4.3 Do We Have the Right People, Are They Properly Trained, and Are They Following Proper Procedures? 286 8.4.4 Am I Spending the Right Amount on Security? 287 8.4.5 How Do We Compare to Others? 288 8.4.6 Creating Your Executive Cybersecurity Dashboard 289 8.5 Summary 291 9.0 What To Do When You Get Hacked 293 9.1 Hackers Already Have You Under Surveillance 293 9.2 Things to do Before it’s Too Late: Preparing for the Hack 295 9.2.1 Back Up Your Information 296 9.2.2 Baseline and Define What is Normal 296 9.2.3 Protect Yourself with Insurance 297 9.2.4 Create Your Disaster Recovery and Business Continuity Plan 298 9.3 What to do When Bad Things Happen: Implementing Your Plan 299 9.3.1 Item 1: Don’t Panic 300 9.3.2 Item 2: Make Sure You’ve Been Hacked 301 9.3.3 Item 3: Gain Control 302 9.3.4 Item 4: Reset All Passwords 303 9.3.5 Item 5: Verify and Lock Down All Your External Links 304 9.3.6 Item 6: Update and Scan 305 9.3.7 Item 7: Assess the Damage 305 9.3.8 Item 8: Make Appropriate Notifications 307 9.3.9 Item 9: Find Out Why It Happened and Who Did It 309 9.3.10 Item 10: Adjust Your Defenses 310 9.4 Foot Stompers 310 9.4.1 The Importance of Public Relations 310 9.4.2 Working with Law Enforcement 315 9.4.3 Addressing Liability 317 9.4.4 Legal Issues to Keep an Eye On 318 9.5 Fool Me Once… 319 9.6 Summary 320 10.0 Boardroom Interactions 323 Appendix A: Policies 347 Appendix B: General Rules for Email Etiquette: Sample Training Handout 357 Glossary 361 Select Bibliography 371 Index 373

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Book SynopsisThe guide to targeting and leveraging business opportunities using big data & analytics By leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior. Analytics in a Big Data World reveals how to tap into the powerful tool of data analytics to create a strategic advantage and identify new business opportunities. Designed to be an accessible resource, this essential book does not include exhaustive coverage of all analytical techniques, instead focusing on analytics techniques that really provide added value in business environments. The book draws on author Bart Baesens'' expertise on the topics of big data, analytics and its applications in e.g. credit risk, marketing, and fraud to provide a clear roadmap for organizations that want to use data analytics to their advantage, but need a good starting point. Baesens has conducted extensive research on big daTable of ContentsPreface xiii Acknowledgments xv Chapter 1 Big Data and Analytics 1 Example Applications 2 Basic Nomenclature 4 Analytics Process Model 4 Job Profiles Involved 6 Analytics 7 Analytical Model Requirements 9 Notes 10 Chapter 2 Data Collection, Sampling, and Preprocessing 13 Types of Data Sources 13 Sampling 15 Types of Data Elements 17 Visual Data Exploration and Exploratory Statistical Analysis 17 Missing Values 19 Outlier Detection and Treatment 20 Standardizing Data 24 Categorization 24 Weights of Evidence Coding 28 Variable Selection 29 Segmentation 32 Notes 33 Chapter 3 Predictive Analytics 35 Target Definition 35 Linear Regression 38 Logistic Regression 39 Decision Trees 42 Neural Networks 48 Support Vector Machines 58 Ensemble Methods 64 Multiclass Classification Techniques 67 Evaluating Predictive Models 71 Notes 84 Chapter 4 Descriptive Analytics 87 Association Rules 87 Sequence Rules 94 Segmentation 95 Notes 104 Chapter 5 Survival Analysis 105 Survival Analysis Measurements 106 Kaplan Meier Analysis 109 Parametric Survival Analysis 111 Proportional Hazards Regression 114 Extensions of Survival Analysis Models 116 Evaluating Survival Analysis Models 117 Notes 117 Chapter 6 Social Network Analytics 119 Social Network Definitions 119 Social Network Metrics 121 Social Network Learning 123 Relational Neighbor Classifier 124 Probabilistic Relational Neighbor Classifier 125 Relational Logistic Regression 126 Collective Inferencing 128 Egonets 129 Bigraphs 130 Notes 132 Chapter 7 Analytics: Putting It All to Work 133 Backtesting Analytical Models 134 Benchmarking 146 Data Quality 149 Software 153 Privacy 155 Model Design and Documentation 158 Corporate Governance 159 Notes 159 Chapter 8 Example Applications 161 Credit Risk Modeling 161 Fraud Detection 165 Net Lift Response Modeling 168 Churn Prediction 172 Recommender Systems 176 Web Analytics 185 Social Media Analytics 195 Business Process Analytics 204 Notes 220 About the Author 223 Index 225

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Book SynopsisA step-by-step guide to data mining applications in CRM. Following a handbook approach, this book bridges the gap between analytics and their use in everyday marketing, providing guidance on solving real business problems using data mining techniques. The book is organized into three parts.Table of ContentsPreface xiii Acknowledgments xv 1 An overview of data mining: The applications, the methodology, the algorithms, and the data 1 1.1 The applications 1 1.2 The methodology 4 1.3 The algorithms 6 1.3.1 Supervised models 6 1.3.1.1 Classification models 7 1.3.1.2 Estimation (regression) models 9 1.3.1.3 Feature selection (field screening) 10 1.3.2 Unsupervised models 10 1.3.2.1 Cluster models 11 1.3.2.2 Association (affinity) and sequence models 12 1.3.2.3 Dimensionality reduction models 14 1.3.2.4 Record screening models 14 1.4 The data 15 1.4.1 The mining datamart 16 1.4.2 The required data per industry 16 1.4.3 The customer “signature”: from the mining datamart to the enriched, marketing reference table 16 1.5 Summary 20 Part I The Methodology 21 2 Classification modeling methodology 23 2.1 An overview of the methodology for classification modeling 23 2.2 Business understanding and design of the process 24 2.2.1 Definition of the business objective 24 2.2.2 Definition of the mining approach and of the data model 26 2.2.3 Design of the modeling process 27 2.2.3.1 Defining the modeling population 27 2.2.3.2 Determining the modeling (analysis) level 28 2.2.3.3 Definition of the target event and population 28 2.2.3.4 Deciding on time frames 29 2.3 Data understanding, preparation, and enrichment 33 2.3.1 Investigation of data sources 34 2.3.2 Selecting the data sources to be used 34 2.3.3 Data integration and aggregation 35 2.3.4 Data exploration, validation, and cleaning 35 2.3.5 Data transformations and enrichment 38 2.3.6 Applying a validation technique 40 2.3.6.1 Split or Holdout validation 40 2.3.6.2 Cross or n‐fold validation 45 2.3.6.3 Bootstrap validation 47 2.3.7 Dealing with imbalanced and rare outcomes 48 2.3.7.1 Balancing 48 2.3.7.2 Applying class weights 53 2.4 Classification modeling 57 2.4.1 Trying different models and parameter settings 57 2.4.2 Combining models 60 2.4.2.1 Bagging 61 2.4.2.2 Boosting 62 2.4.2.3 Random Forests 63 2.5 Model evaluation 64 2.5.1 Thorough evaluation of the model accuracy 65 2.5.1.1 Accuracy measures and confusion matrices 66 2.5.1.2 Gains, Response, and Lift charts 70 2.5.1.3 ROC curve 78 2.5.1.4 Profit/ROI charts 81 2.5.2 Evaluating a deployed model with test–control groups 85 2.6 Model deployment 88 2.6.1 Scoring customers to roll the marketing campaign 88 2.6.1.1 Building propensity segments 93 2.6.2 Designing a deployment procedure and disseminating the results 94 2.7 Using classification models in direct marketing campaigns 94 2.8 Acquisition modeling 95 2.8.1.1 Pilot campaign 95 2.8.1.2 Profiling of high‐value customers 96 2.9 Cross‐selling modeling 97 2.9.1.1 Pilot campaign 98 2.9.1.2 Product uptake 98 2.9.1.3 Profiling of owners 99 2.10 Offer optimization with next best product campaigns 100 2.11 Deep‐selling modeling 102 2.11.1.1 Pilot campaign 102 2.11.1.2 Usage increase 103 2.11.1.3 Profiling of customers with heavy product usage 104 2.12 Up‐selling modeling 105 2.12.1.1 Pilot campaign 105 2.12.1.2 Product upgrade 107 2.12.1.3 Profiling of “premium” product owners 107 2.13 Voluntary churn modeling 108 2.14 Summary of what we’ve learned so far: it’s not about the tool or the modeling algorithm. It’s about the methodology and the design of the process 111 3 Behavioral segmentation methodology 112 3.1 An introduction to customer segmentation 112 3.2 An overview of the behavioral segmentation methodology 113 3.3 Business understanding and design of the segmentation process 115 3.3.1 Definition of the business objective 115 3.3.2 Design of the modeling process 115 3.3.2.1 Selecting the segmentation population 115 3.3.2.2 Selection of the appropriate segmentation criteria 116 3.3.2.3 Determining the segmentation level 116 3.3.2.4 Selecting the observation window 116 3.4 Data understanding, preparation, and enrichment 117 3.4.1 Investigation of data sources 117 3.4.2 Selecting the data to be used 117 3.4.3 Data integration and aggregation 118 3.4.4 Data exploration, validation, and cleaning 118 3.4.5 Data transformations and enrichment 122 3.4.6 Input set reduction 124 3.5 Identification of the segments with cluster modeling 126 3.6 Evaluation and profiling of the revealed segments 128 3.6.1 “Technical” evaluation of the clustering solution 128 3.6.2 Profiling of the revealed segments 132 3.6.3 Using marketing research information to evaluate the clusters and enrich their profiles 138 3.6.4 Selecting the optimal cluster solution and labeling the segments 139 3.7 Deployment of the segmentation solution, design and delivery of differentiated strategies 139 3.7.1 Building the customer scoring model for updating the segments 140 3.7.1.1 Building a Decision Tree for scoring: fine‐tuning the segments 141 3.7.2 Distribution of the segmentation information 141 3.7.3 Design and delivery of differentiated strategies 142 3.8 Summary 142 Part II The Algorithms 143 4 Classification algorithms 145 4.1 Data mining algorithms for classification 145 4.2 An overview of Decision Trees 146 4.3 The main steps of Decision Tree algorithms 146 4.3.1 Handling of predictors by Decision Tree models 148 4.3.2 Using terminating criteria to prevent trivial tree growing 149 4.3.3 Tree pruning 150 4.4 CART, C5.0/C4.5, and CHAID and their attribute selection measures 150 4.4.1 The Gini index used by CART 151 4.4.2 The Information Gain Ratio index used by C5.0/C4.5 155 4.4.3 The chi‐square test used by CHAID 158 4.5 Bayesian networks 170 4.6 Naive Bayesian networks 172 4.7 Bayesian belief networks 176 4.8 Support vector machines 184 4.8.1 Linearly separable data 184 4.8.2 Linearly inseparable data 187 4.9 Summary 191 5 Segmentation algorithms 192 5.1 Segmenting customers with data mining algorithms 192 5.2 Principal components analysis 192 5.2.1 How many components to extract? 194 5.2.1.1 The eigenvalue (or latent root) criterion 196 5.2.1.2 The percentage of variance criterion 197 5.2.1.3 The scree test criterion 198 5.2.1.4 The interpretability and business meaning of the components 198 5.2.2 What is the meaning of each component? 199 5.2.3 Moving along with the component scores 201 5.3 Clustering algorithms 203 5.3.1 Clustering with K‐means 204 5.3.2 Clustering with TwoStep 211 5.4 Summary 213 Part III The Case Studies 215 6 A voluntary churn propensity model for credit card holders 217 6.1 The business objective 217 6.2 The mining approach 218 6.2.1 Designing the churn propensity model process 218 6.2.1.1 Selecting the data sources and the predictors 218 6.2.1.2 Modeling population and level of data 218 6.2.1.3 Target population and churn definition 218 6.2.1.4 Time periods and historical information required 219 6.3 The data dictionary 219 6.4 The data preparation procedure 221 6.4.1 From cards to customers: aggregating card‐level data 221 6.4.2 Enriching customer data 225 6.4.3 Defining the modeling population and the target field 228 6.5 Derived fields: the final data dictionary 232 6.6 The modeling procedure 232 6.6.1 Applying a Split (Holdout) validation: splitting the modelling dataset for evaluation purposes 232 6.6.2 Balancing the distribution of the target field 232 6.6.3 Setting the role of the fields in the model 239 6.6.4 Training the churn model 239 6.7 Understanding and evaluating the models 241 6.8 Model deployment: using churn propensities to target the retention campaign 248 6.9 The voluntary churn model revisited using RapidMiner 251 6.9.1 Loading the data and setting the roles of the attributes 251 6.9.2 Applying a Split (Holdout) validation and adjusting the imbalance of the target field’s distribution 252 6.9.3 Developing a Naïve Bayes model for identifying potential churners 252 6.9.4 Evaluating the performance of the model and deploying it to calculate churn propensities 253 6.10 Developing the churn model with Data Mining for Excel 254 6.10.1 Building the model using the Classify Wizard 256 6.10.2 Selecting the classification algorithm and its parameters 257 6.10.3 Applying a Split (Holdout) validation 257 6.10.4 Browsing the Decision Tree model 259 6.10.5 Validation of the model performance 259 6.10.6 Model deployment 263 6.11 Summary 266 7 Value segmentation and cross‐selling in retail 267 7.1 The business background and objective 267 7.2 An outline of the data preparation procedure 268 7.3 The data dictionary 272 7.4 The data preparation procedure 272 7.4.1 Pivoting and aggregating transactional data at a customer level 272 7.4.2 Enriching customer data and building the customer signature 276 7.5 The data dictionary of the modeling file 279 7.6 Value segmentation 285 7.6.1 Grouping customers according to their value 285 7.6.2 Value segments: exploration and marketing usage 287 7.7 The recency, frequency, and monetary (RFM) analysis 290 7.7.1 RFM basics 290 7.8 The RFM cell segmentation procedure 293 7.9 Setting up a cross‐selling model 295 7.10 The mining approach 295 7.10.1 Designing the cross‐selling model process 296 7.10.1.1 The data and the predictors 296 7.10.1.2 Modeling population and level of data 296 7.10.1.3 Target population and definition of target attribute 296 7.10.1.4 Time periods and historical information required 296 7.11 The modeling procedure 296 7.11.1 Preparing the test campaign and loading the campaign responses for modeling 298 7.11.2 Applying a Split (Holdout) validation: splitting the modelling dataset for evaluation purposes 298 7.11.3 Setting the roles of the attributes 299 7.11.4 Training the cross‐sell model 300 7.12 Browsing the model results and assessing the predictive accuracy of the classifiers 301 7.13 Deploying the model and preparing the cross‐selling campaign list 308 7.14 The retail case study using RapidMiner 309 7.14.1 Value segmentation and RFM cells analysis 310 7.14.2 Developing the cross‐selling model 312 7.14.3 Applying a Split (Holdout) validation 313 7.14.4 Developing a Decision Tree model with Bagging 314 7.14.5 Evaluating the performance of the model 317 7.14.6 Deploying the model and scoring customers 317 7.15 Building the cross‐selling model with Data Mining for Excel 319 7.15.1 Using the Classify Wizard to develop the model 319 7.15.2 Selecting a classification algorithm and setting the parameters 320 7.15.3 Applying a Split (Holdout) validation 322 7.15.4 Browsing the Decision Tree model 322 7.15.5 Validation of the model performance 325 7.15.6 Model deployment 329 7.16 Summary 331 8 Segmentation application in telecommunications 332 8.1 Mobile telephony: the business background and objective 332 8.2 The segmentation procedure 333 8.2.1 Selecting the segmentation population: the mobile telephony core segments 333 8.2.2 Deciding the segmentation level 335 8.2.3 Selecting the segmentation dimensions 335 8.2.4 Time frames and historical information analyzed 335 8.3 The data preparation procedure 335 8.4 The data dictionary and the segmentation fields 336 8.5 The modeling procedure 336 8.5.1 Preparing data for clustering: combining fields into data components 340 8.5.2 Identifying the segments with a cluster model 342 8.5.3 Profiling and understanding the clusters 344 8.5.4 Segmentation deployment 354 8.6 Segmentation using RapidMiner and K‐means cluster 354 8.6.1 Clustering with the K‐means algorithm 354 8.7 Summary 359 Bibliography 360 Index 362

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Book SynopsisA well-balanced and accessible introduction to the elementary quantitative methods and Microsoft Office Excel applications used to guide business decision making Featuring quantitative techniques essential for modeling modern business situations, Introduction to Quantitative Methods in Business: With Applications Using Microsoft Office Excel provides guidance to assessing real-world data sets using Excel. The book presents a balanced approach to the mathematical tools and techniques with applications used in the areas of business, finance, economics, marketing, and operations. The authors begin by establishing a solid foundation of basic mathematics and statistics before moving on to more advanced concepts. The first part of the book starts by developing basic quantitative techniques such as arithmetic operations, functions and graphs, and elementary differentiations (rates of change), and integration. After a review of these techniques, theTable of ContentsPreface xiii 1. The Mathematical Toolbox 1 1.1 Introduction 1 1.2 Linear Functions 2 1.3 Solving a Simple Linear Equation for one Unknown Variable 3 1.3.1 Solving Two Simultaneous Linear Equations for Two Unknown Variables 4 1.4 Summation Notation 6 1.5 Sets 12 1.5.1 Subset, Empty Set, Universal Set, and Complement of A Set 13 1.5.2 Intersection and Union 14 1.6 Functions and Graphs 15 1.6.1 Vertical Line Test 16 1.7 Working with Functions 17 1.7.1 Evaluating Functions 17 1.7.2 Graphing Functions 18 1.8 Differentiation and Integration 21 1.8.1 Derivative 22 1.8.2 Derivatives of Logarithmic and Exponential Functions 26 1.8.3 Higher Order Derivatives 26 1.8.4 Integration 28 1.8.5 The Definite Integral 29 1.8.6 Some Rules of Integration 31 1.9 Excel Applications 34 Chapter 1 Review 40 Eercises 41 Appendix 1.A: A Review of Basic Mathematics 45 Eercises 63 2. Applications of Linear and Nonlinear Functions 66 2.1 Introduction 66 2.2 Linear Demand and Supply Functions 66 2.3 Linear Total Cost and Total Revenue Functions 69 2.4 Market Equilibrium 71 2.5 Graphical Presentation of Equilibrium 72 2.6 Applications of Nonlinear Functions 73 2.7 Present Value of an Income Stream 78 2.8 Average Values 79 2.9 Marginal Values 80 2.10 Elasticity 81 2.11 Some Additional Business Applications 84 2.12 Excel Applications 84 Chapter 2 Review 86 Eercises 87 Excel Applications 90 3. Optimization 91 3.1 Introduction 91 3.2 Unconstrained Optimization 91 3.2.1 Models of Profit and Revenue Maximization 91 3.2.2 Solution by Trial and Error (Approximate) Method 92 3.2.3 Solution Using the Calculus Approach 93 3.2.4 Solution by Trial and Error (Approximate) Method 96 3.2.5 Solution Using the Calculus Approach 97 3.3 Models of Cost Minimization: Inventory Cost Functions and Eoq 99 3.3.1 Solution by Trial and Error Method 101 3.3.2 Solution Using the Calculus Approach 103 3.4 Constrained Optimization: Linear Programming 105 3.4.1 Linear Programming: Maximization 106 3.4.1.1 Solution by Graphical Method: First Approach 106 3.4.1.2 Solution by Graphical Method: Second Approach 109 3.4.2 Linear Programming: Minimization 114 3.5 Excel Applications 121 Chapter 3 Review 125 Chapter 3 Eercises 126 Excel Applications 130 4. What Is Business Statistics? 131 4.1 Introduction 131 4.2 Data Description 132 4.2.1 Some Important Concepts in Statistics 132 4.2.2 Scales of Data Measurement 132 4.3 Descriptive Statistics: Tabular and Graphical Techniques 134 4.4 Descriptive Statistics: Numerical Measures of Central Tendency or Location of Data 144 4.4.1 Population Mean 144 4.4.2 Sample Mean 145 4.4.3 Weighted Mean 147 4.4.4 Mean of a Frequency Distribution: Grouped Data 148 4.4.5 Geometric Mean 149 4.4.6 Median 151 4.4.7 Quantiles, Quartiles, 4.5 Descriptive Statistics: Measures of Dispersion—Variability or Spread 155 4.5.1 Range 155 4.5.2 Variance 155 4.5.3 Standard Deviation 158 4.5.4 Coefficient of Variation 160 4.5.5 Some Important Uses of the Standard Deviation 163 4.5.6 Empirical Rule 165 4.6 Measuring Skewness 166 4.7 Excel Applications 169 Chapter 4 Review 186 Eercises 188 Excel Applications 191 5. Probability and Applications 194 5.1 Introduction 194 5.2 Some Useful Definitions 195 5.3 Probability Sources 196 5.3.1 Objective Probability 196 5.3.2 Subjective Probability 196 5.4 Some Useful Definitions Involving Sets of Events in the Sample Space 197 Complement of a Given Set A 199 Mutually Exclusive Events 200 5.5 Probability Laws 200 5.5.1 General Rule of Addition 200 5.5.2 Rule of Complements 202 5.5.3 Conditional Probability 202 5.5.4 General Rule of Multiplication (Product Rule) 203 5.5.5 Independent Events 204 5.5.6 Probability Tree Approach 204 5.6 Contingency Table 208 5.7 Excel Applications 213 Chapter 5 Review 214 Eercises 215 Excel Applications 218 6. Random Variables and Probability Distributions 219 6.1 Introduction 219 6.2 Probability Distribution of a Discrete Random Variable X 220 6.3 Expected Value, Variance, and Standard Deviation of a Discrete Random Variable X 222 6.3.1 Some Basic Rules of Expectation 224 6.3.2 Some Useful Properties of Variance of X 225 6.3.3 Applications of Expected Values 225 6.4 Continuous Random Variables and Their Probability Distributions 230 6.5 A Specific Discrete Probabilty Distribution: the Binomial Case 232 6.5.1 Binomial Probability Distribution 232 6.5.2 Mean and Standard Deviation of the Binomial Random Variable 237 6.5.3 Cumulative Binomial Probability Distribution 238 6.6 Excel Applications 241 Chapter 6 Review 245 Eercises 245 Appendix 6.A 252 About the Companion Website 263 Index 265

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Book SynopsisThis authoritative Wiley Blackwell Handbook in Organizational Psychology focuses on individual and organizational applications of Internet-enabled technologies within the workplace. The editors have drawn on their collective experience in collating thematically structured material from leading writers based in the US, Europe, and Asia Pacific. Coinciding with the growing international interest in the application of psychology to organizations, the work offers a unique depth of analysis from an explicitly psychological perspective. Each chapter includes a detailed literature review that offers academics, researchers, scientist-practitioners, and students an invaluable frame of reference. Coverage is built around competencies set forth by regulatory agencies including the APA and BPS, and includes E-Recruiting, E-Leadership, and E-Learning; virtual teams; cyberloafing; ergonomics of human-computer interaction at work; permanent accessibility and work-life balance; and trust in online Table of ContentsAbout the Editors vii About the Contributors ix Foreword xv 1 The Psychology of the Internet @ Work 1Guido Hertel, Dianna L. Stone, Richard D. Johnson, and Jonathan Passmore Part I Individual Perspectives 19 2 Digitized Communication at Work 21Nicole C. Krämer and Stephan Winter 3 Ergonomics of Information Technologies at Work 39Ben V. Hanrahan and John M. Carroll 4 Competencies for Web-Based Work and Virtual Collaboration 61Stefan Krumm and Julian Schulze 5 User Experience, Gamification, and Performance 79Meinald T. Thielsch and Jörg Niesenhaus 6 Trust in Virtual Online Environments 103Sirkka L. Jarvenpaa, Celeste Cantu, and Shi Ying Lim 7 Workplace Cyberdeviance 131Steven D. Charlier, Gary W. Giumetti, Cody J. Reeves, and Lindsey S. Greco 8 Blended Working 157Nico W. van Yperen and Burkhard Wörtler 9 Flexwork, Work–Family Boundaries, and Information and Communication Technologies 175Ronald E. Rice 10 Mobile Computing and Hand-Held Devices at Work 195Humayun Zafar Part II Organizational Perspectives 211 11 E-Recruiting: Using Technology to Attract Job Applicants 213Derek Chapman and Anna F. Gödöllei 12 Social Networking Systems, Search Engines, and the Employment Process 231Kimberly M. Lukaszewski and Andrew F. Johnson 13 The Evolution of E-Selection 257David N. Dickter, Victor Jockin, and Tanya Delany 14 E-Leadership 285Surinder Kahai, Bruce J. Avolio, and John Sosik 15 Virtual Teams 315M. Travis Maynard, Lucy L. Gilson, Nicole C. Jones Young, and Matti Vartiainen 16 Online Employee Surveys and Online Feedback 347Bernad Batinic and Carrie Kovacs 17 E-Learning 369Richard D. Johnson and Kenneth G. Brown Part III Societal and Cross-Sectorial Perspectives 401 18 Robots in the Digitalized Workplace 403Jochen J. Steil and Günter W. Maier 19 Social Issues Associated with the Internet at Work 423Dianna L. Stone, Dianna Krueger, and Stephen Takach 20 Employee Age Differences in Using Internet-Based Tools at Work 449Gabriela Burlacu, Donald M. Truxillo, and Talya N. Bauer 21 The Future of Work 481Stela Lupushor and Alex Fradera Index 509

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Book SynopsisDiscover what doesand doesn'twork when designing and building a data governance program In A Practitioner's Guide to Operationalizing Data Governance, veteran SAS and data management expert Mary Anne Hopper walks readers through the planning, design, operationalization, and maintenance of an effective data governance program. She explores the most common challenges organizations face during and after program development and offers sound, hands-on advice to meet tackle those problems head-on. Ideal for companies trying to resolve a wide variety of issues around data governance, this book: Offers a straightforward starting point for companies just beginning to think about data governance Provides solutions when company employees and leaders don'tfor whatever reasontrust the data the company has Suggests proven strategies for getting a data governance program that's gone off the rails back on track Complete with visual examplesTable of ContentsAcknowledgments xiii Chapter 1 Introduction 1 Intended Audience 2 Experience 2 Common Challenge Themes 4 Chapter 2 Rethinking Data Governance 17 Results You Can Expect with Common Approaches to Data Governance 18 What Does Work 21 Rethinking Data Governance Summary 23 Chapter 3 Data Governance and Data Management 25 Results You Can Expect Focusing Purely on Data Governance or Data Management 26 SAS Data Management Framework 26 Aligning Data Governance and Data Management Outcomes 38 Misaligning Data Governance and Data Management 43 Data Governance and Data Management Summary 45 Chapter 4 Priorities 47 Results You Can Expect Using the Most Common Approaches to Prioritization 48 A Disciplined Approach to Priorities 50 Utilizing the Model 55 Priorities Summary 64 Chapter 5 Common Starting Points 65 Results You Can Expect with Too Many Entry Points 66 Building a Data Portfolio 66 Metadata 67 Data Quality 70 Data Profiling 75 Common Starting Points Summary 76 Chapter 6 Data Governance Planning 77 Results You Can Expect Without Planning 78 Defining Objectives 78 Defining Guiding Principles 85 Data Governance Planning Summary 88 Chapter 7 Organizational Framework 91 Results You Can Expect When There Is No Defined Organizational Structure 92 Organizational Framework Roles 92 Defining a Framework 94 Aligning the Model to Existing Structures 97 Aligning the Framework to the Culture 100 Simplifying the Model 103 Defining the Right Data Stewardship Model 104 Organizational Framework Summary 109 Chapter 8 Roles and Responsibilities 111 Results You Can Expect When Roles and Responsibilities Are Not Clearly Defined 112 Aligning Actions and Decisions to Program Objectives 112 Using a RACI Model 119 Defining Roles and Responsibilities 126 Data Governance Steering Committee 126 Data Management 131 Naming Names 131 Roles and Responsibilities Summary 135 Chapter 9 Operating Procedures 137 Results You Can Expect Without Operating Procedures 138 Operating Procedures 138 Workflows 146 Operating Procedures Summary 152 Chapter 10 Communication 153 Results You Can Expect Without Communication 154 Communication Plan Components 154 Sample Communication Plan 156 Communication Summary 160 Chapter 11 Measurement 161 Results You Can Expect Without Measurement 162 What Measurements to Define 162 Program Scorecard – A Starting Point 166 Program Scorecard Sample 172 Measurement Summary 173 Chapter 12 Roadmap 175 Results You Can Expect Without a Roadmap 176 First Step in Defining a Roadmap: Implementing Your Framework 176 Defining a Roadmap 178 Formality First or Save It for Later? 184 Critical Success Factors 185 Roadmap Summary 188 Chapter 13 Policies 189 Results You Can Expect Without Policies 190 Breaking Down a Policy 190 Contents of a Policy 192 Policy Example – Metadata 193 Policy Example – Data Quality 200 Policy Summary 204 Chapter 14 Data Governance Maturity 207 Results You Can Expect With Maturity 208 Data Governance Maturity Cycle 209 Maturing Your Program 215 Summary 216 About the Author 217 Glossary of Terms 219 Index 221

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Book Synopsis1. Optimization in Energy Markets 1.1 Classification of optimization problems1.1.1 Linear versus Nonlinear Problems 1.1.2 Deterministic versus Stochastic Problems 1.1.3 Static versus Dynamic Problems1.2 Optimal portfolio selection among different investment alternatives1.3 Energy Asset Optimization 1.3.1 Generation Asset Investment Valuation with Real Option Methodology 1.3.2 Generation, Transportation and Storage Asset Operational Optimization and Valuation 1.4 Energy Trading and Optimization 1.4.1 Asset allocation with Capital Constraints 1.4.2 Intraday trading 2. Optimization Methods2.1 Linear Optimization2.1.1 LP problems2.2 Nonlinear Optimization2.2.1 Unconstrained problem2.2.2 Constrained Problems with Equality Constraints2.2.3 Constrained Problems with Inequalities Constraints2.3 Pricing financial assets2.3.1 Pricing in energy markets2.3.2 Pricing in incomplete markets2.3.3 A motivating exampTrade ReviewEnergy markets are extremely competitive markets. Optimization of business decisions is fundamental for performance maximization. This book represents an excellent synthesis of optimization theory and practice applied to a wide and significant range of cutting-edge business problems characterizing power and natural gas markets.'- Domenico De Luca, CEO, Axpo Trading and Member of Executive Board Axpo Group'Optimization methods play an important role when making decisions and managing risk in today's liberalized energy markets. When planning a power plant or entering a structured gas contract, stochastic control is the key mathematical tool to assess the inherent risk. The authors of this book present an excellent account of the problems and methods for optimization in energy and power markets. The scope ranges from a rigorous theoretical analysis of the control problems, through numerical methods and to in-depth discussions of relevant practical case studies. This book is unique in providing a solid mathematical analysis of various optimization problems, yet never losing the market practice out of sight. It will be an invaluable reference for both academics and practitioners in power and gas markets.' - Fred Espen Benth, Professor of Mathematical Finance at the University of Oslo, Department of Mathematics and Deputy ManagerTable of Contents1. Optimization in Energy Markets 1.1 Classification of optimization problems1.1.1 Linear versus Nonlinear Problems 1.1.2 Deterministic versus Stochastic Problems 1.1.3 Static versus Dynamic Problems1.2 Optimal portfolio selection among different investment alternatives1.3 Energy Asset Optimization 1.3.1 Generation Asset Investment Valuation with Real Option Methodology 1.3.2 Generation, Transportation and Storage Asset Operational Optimization and Valuation 1.4 Energy Trading and Optimization 1.4.1 Asset allocation with Capital Constraints 1.4.2 Intraday trading 2. Optimization Methods2.1 Linear Optimization2.1.1 LP problems2.2 Nonlinear Optimization2.2.1 Unconstrained problem2.2.2 Constrained Problems with Equality Constraints2.2.3 Constrained Problems with Inequalities Constraints2.3 Pricing financial assets2.3.1 Pricing in energy markets2.3.2 Pricing in incomplete markets2.3.3 A motivating example: utility indifference pricing2.4 Deterministic Dynamic Programming2.5 Stochastic Dynamic Programming, discrete time2.5.1 A motivating example2.5.2 The general case2.5.3 Tree methods2.5.4 Least Square Monte Carlo methods2.5.5 Naïve Monte Carlo with Linear Programming2.6 Stochastic Dynamic Programming, continuous time2.6.1 The Hamilton-Jacobi-Bellman equation2.7 Deterministic numerical methods2.7.1 Finite Difference Method for HJB equation2.7.2 Boundary conditions2.8 Probabilistic numerical methods2.8.1 Tree methods, continuous time2.8.2 Computationally simple trees in dimension 12.8.3 Lattice of trees2.8.4 Monte Carlo methods3. Cases on Static Optimization3.1 Case A: investment alternatives3.2 Case B: Optimal generation mix for an electricity producer: a mean-variance approach3.3 Conclusions 4. Valuing project's exibilities using the diagrammatic approach4.1 Introduction4.2 Description of the Investment Problem4.3 Traditional evaluation Methods4.4 Modelling Electricity Price Dynamics4.5 Valuing Investment Flexibilities By Means Of The Lattice Approach4.5.1 Investment alternative A4.5.2 Investment alternative B4.5.3 Investment alternative C4.6 Conclusions5. Virtual Power Plant Contracts5.1 Introduction5.2 Valuation Problem5.2.1 Example6. Algorithms comparisonThe Swing Case6.1 Introduction6.2 Swing contracts6.2.1 Indexed strike price modelling for gas swing contracts6.2.2 The stochastic control problem6.2.3 Dynamic Programming6.3 Finite difference algorithm6.3.1 Boundary conditions6.3.2 The algorithm6.4 Least Square Monte Carlo algorithm6.4.1 The algorithm, and a reduction to one dimension6.5 Naïve Monte Carlo with Linear Programming6.6 Numerical Experiments6.6.1 Finite differences6.6.2 Least Square Monte Carlo6.6.3 One year contract6.7 Conclusions7. Storage contracts7.1 The contract7.2 The evaluation problem7.3 The optimal strategy (in the case of a physical gas storage)7.4 The implementation7.4.1 The gas cave7.4.2 The gas spot price7.4.3 The boundary conditions7.4.4 Numerical experiment, no-penalty case7.4.5 Numerical experiment, penalty case8. Optimal Trading Strategies in Intraday Power Markets8.1 Intraday power markets8.1.1 Intraday power price features8.1.2 Conclusions8.2 Optimal Algorithmic Trading in Auction-Based Intraday Power Markets8.2.1 The optimization problem8.2.2 Example: Italian intra-day market8.3 Optimal Algorithmic Trading in Continuous Time Power Markets8.3.1 The optimization problem8.3.2 Example: EPEX Spot market

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Book SynopsisThe Blackwell Encyclopedia of Management Information Systems has been updated with recent developments in the use of information systems in organizations and the information systems function that plans, implements, and operates the systems.

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Book SynopsisThis text offers the ideal approach for economics and business students seeking to understand the mathematics relevant to them. Each chapter demonstrates basic mathematical techniques, while also explaining the economic analysis and business context where each is used. By following the worked examples and tackling the practice problems, students will discover how to use and apply each of these techniques. Now in its second edition, the text features expanded summaries of economic analysis, new sections on matrix algebra and linear programming, and additional demonstrations of economics applications. Demonstrates mathematical techniques while explaining their economic and business applications Engages the reader with numerous worked examples and practice problems Features new sections on matrix algebra and linear programming Includes a companion website with the book, containing the award winning MathEcon software, Excel files, PowerpoTable of ContentsPreface. Features of the Book. List of MathEcon Screens. List of Excel Worksheets. 1. Functions in Economics. 2. Equations in Economics. 3. Macroeconomic Models. 4. Changes, Rates, Finance and Series. 5. Differentiation in Economics. 6. Maximum and Minimum Values. 7. Further Rules of Differentiation. 8. Partial Differentiation in Economics. 9. Constrained Maxima and Minima. 10. Integration in Economics. 11. Linear Programming. 12. Matrices in Economics. Index

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Book SynopsisThe topics discussed include system design methodology, data structure theory, semantic con­ siderations, calculus-based database operations, database management functions, and the issues of integrity, security, concurrency, and recoverabil­ ity.Table of Contents1 Data Structures and Databases in Digital Scene Analysis.- 1. Introduction.- 2. Data Structures.- 2.1. Definitions and Basic Concepts.- 2.2. Use of Lists for Scene Representation and Processing.- 2.3. Use of Quad Trees for Image representation and Processing.- 3. Databases.- 3.1. Definitions and Basic Concepts.- 3.2. Hierarchical and Relational Models for Scene Representation and Processing.- 3.3. Use of Relational Tables for Three-Dimensional Object Location.- 4. Examples of Existing Systems.- 4.1. Multisensor Image Database System (MIDAS).- 4.2. Relational Pictorial Database System.- 5. Summary.- References.- 2 An Overview of Database Management Technology.- 1. Introduction.- 2. Motivations.- 2.1. Large Shared Files.- 2.2. Rapid Social Change.- 2.3. Man-Computer Cooperation.- 3. Database as a New Systems Methodology.- 3.1. Output-Oriented Approach.- 3.2. Database-Oriented Approach.- 4. Logical Database Structure.- 4.1. Relational View of the Real World.- 4.2. Geometric Representation of Relations.- 4.3. Semantic Constraints.- 4.4. Tuple Constraints.- 4.5. Dependencies.- 4.6. Interrelation Constraints.- 4.7. Other Static Constraints.- 4.8. Definition of Logical Database Structure.- 5. Database Operations.- 5.1. Functions of Tuples.- 5.2. Alpha Operation.- 5.3. Relational Algebra.- 5.5. Information Algebra.- 5.6. Imaginary Tuples.- 5.7. Navigations.- 5.8. Disadvantages of Set Operations.- 5.9. Tuple-by-Tuple Operations.- 5.10. Data Manipulation Requirements.- 6. Other Requirements.- 6.1. Integrity.- 6.2. Security.- 6.3. Concurrency.- 6.4. Recoverability.- 6.5. Database Distribution.- 6.6. Environmental Requirements.- 7. Physical Representation of the Database.- 7.1. Data Associations in Computer Storage.- 7.2. Basic File Organizations.- 7.3. Representation of Entity Relations.- 7.4. Representation of Relationship Relations.- 7.5. Localization.- 8. Database Management Functions.- 8.1. Data Description.- 8.2. Data Manipulation.- 8.3. Binding.- 8.4. Functions for Database Administrators.- 8.5. Database Management Languages.- 9. Database Management Systems.- 9.1. Selection Criteria for Database Management Systems.- 9.2. Directory-Type Classification of Database Management Systems.- 9.3. Database Operations.- 9.4. Environmental Requirements.- 9.5. Physical Representation.- 9.6. Host Language Interfaces.- 9.7. Other Criteria.- 10. End-User Languages.- 10.1. Application Routines.- 10.2. Language for Real-Time Users.- 10.3. Languages for Casual Users.- 10.4. Languages for Parameter Users.- 10.5. Self-Contained Systems.- 10.6. High-Level Languages on the Host Language Systems.- 10.7. Language Extensibility.- 11. Future Research Directions.- 11.1. Database Machines.- 11.2. Deductive Processes.- 11.3. Neutral-Language Query Processing.- References.- 3 Processing of Pattern-Based Information, Part I: Inductive Inference Methods Suitable for use in Pattern Recognition and Artificial Intelligence.- 1. Introduction.- 1.1. Preamble.- 1.2. The Decision Rule Inference Problem in Pattern Recognition.- 1.3. Inference in Artificial Intelligence.- 1.4. Basic Tenets of the Present Approach to Inductive Inference of Decision Rules.- 2. Representation of Patterns.- 3. Algorithm For Decision Rule Inference.- 3.1. The Nature of the Task.- 3.2. Some Concepts and Definitions Used in the Algorithms.- 3.3. Hypotheses Generation and Test.- 3.4. Problem Reduction in the “CD” Hypothesis Generation.- 3.5. Sentential Form of the Decision Rules.- 3.6. An Illustration of Suggested Algorithm.- 4. Structure of the Controls for Systematic Implementation of the Inference Procedure.- 5. A Brief description of the Implementation.- 6. Examples.- References.- 4 Processing of Pattern-Based Information, Part II: Description of Inductive Inference in Terms of Transition Networks.- 1. Introduction.- 2. Description of the Inductive Inference Transition Network.- 3. An Inference Algorithm Represented by an Inductive Inference Transition Network.- 3.1. A Brief Description of the Inference Algorithm.- 3.2. The ITN Representing the Inference Algorithm.- 4. Implementation and Illustrative examples.- References.- 5 Automated Logic Design of MOS Networks.- 1. Introduction.- 2. Basic Properties.- 3. Algorithms for Designing Networks with a Minimum Number of Negative Gates.- 4. Synthesis of MOS Cells.- 5. Design of Irredundant MOS Networks.- 5.1. Conventional design procedures of MOS Networks.- 5.2. Outline of the design Algorithm of Irredundant MOS Networks.- 5.3. Maximum Permissible Function.- 5.4. Design Algorithm of Irredundant MOS Networks.- 5.5. Examples.- 6. Interactive design.- 6.1. Advantages and Disadvantages of Algorithm DIMN.- 6.2. Interactive Design.- 7. Conclusion.- References.

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Table of Contents1. Getting Started2. The Inventory File3. Installing Wordpress4. Ansible Roles5. Parameterising Playbooks6. Writing Your Own Modules7. Orchestrating AWS8. Testing with Test Kitchen9. Advanced AnsibleAppendix A. Installing AnsibleAppendix B. YAML Files

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Book SynopsisDesign and build innovative, custom, data-driven Alexa skills for home or business. Working through several projects, this book teaches you how to build Alexa skills and integrate them with online APIs. If you have basic Python skills, this book will show you how to build data-driven Alexa skills. You will learn to use data to give your Alexa skills dynamic intelligence, in-depth knowledge, and the ability to remember. Data-Driven Alexa Skills takes a step-by-step approach to skill development. You will begin by configuring simple skills in the Alexa Skill Builder Console. Then you will develop advanced custom skills that use several Alexa Skill Development Kit features to integrate with lambda functions, Amazon Web Services (AWS), and Internet data feeds. These advanced skills enable you to link user accounts, query and store data using a NoSQL database, and access real estate listings and stock prices via web APIs. What You Will LearnSet up and configure your development environmTable of ContentsPart I: Getting Started Chapter 1: Voice User Interfaces Chapter 2: Routines and Blueprints Chapter 3: The Developer Accounts Chapter 4: Creating the VUI for a Custom Data-driven Skill Chapter 5: Writing the Back-end Code Chapter 6: Publishing an Alexa Skill Part II: Custom Skill Development Chapter 7: Custom Alexa Skills Chapter 8: Beyond Hello World Chapter 9: Configuring the VUI Chapter 10: Using APL to Present on Screens Chapter 11: Coding the Lambda Function Chapter 12: Unit Testing an Alexa Skill Chapter 13: Storing the Data Part III: Using APIs in Advanced Skills Chapter 14: A Personal Net Worth Skill Chapter 15: The Real Estate API Chapter 16: The Stock Market API Chapter 17: What’s Next?

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Book SynopsisTake advantage of Python to automate complex systems with readable code. This new edition will help you move from operations/system administration into easy-to-learn coding.You''ll start by writing command-line scripts and automating simple DevOps-style tasks followed by creating reliable and fast unit tests designed to avoid incidents caused by buggy automation. You''ll then move on to more advanced cases, like using Jupyter as an auditable remote-control panel and writing Ansible and Salt extensions.The updated information in this book covers best practices for deploying and updating Python applications. This includes Docker, modern Python packaging, and internal Python package repositories. You''ll also see how to use the AWS API, and the Kubernetes API, and how to automate Docker container image building and running. Finally, you''ll work with Terraform from Python to allow more flexible templating and customTable of ContentsChapter 1 (Installing Python) Different ways to install Python: • Compiling from source • OS packages • pyenv Chapter 2 (Packaging) (31 pages – 11 new pages) How pip works and how to build packages. The following sections need to change Section about pip (adds 4 pages) • Add explanation about how the resolver works • Explain pip-compile Poetry and pipenv (changes 2 pages, adds 2 pages) • Needs to be separated into two sections• Poetry section updated to reflect changes in Poetry • Pipenv section updated to reflect changes in Pipenv 4setup.py and wheel (rewritten, changes 1 page, adds 2 pages) • python -m build and setup.cfg • Add details about binary wheels and manylinux • Show a complete example Chapter 3: Interactive usage How to use the interactive interpreter, other text-mode interactive consoles, and Jupyter. Chapter 4: OS Automation (16 pages – 4 new pages) Automating OS-related things like files and processes. Section about files (2 pages added)• Cover using struct to parse binary data • Cover pathlib New section: low-level networking (2 pages) Cover socket, socket options, and how it relates to TCP networking. 5 Chapter 5: Testing (30 pages – 10 new pages) Writing unit tests for DevOps code. Section about testing files (4 pages added) • Improve performance of file testing using tmpfs and preloading libraries • Add information about temporary directory context manager Section about testing networking (4 pages added) • Show how to test httpx with the WSGI support • Show how to test low-level socket networking with DI Section about testing processes (2 pages changed) • Mention run and Popen • Show how to write tests with DI on run and Popen 6 Chapter 6: Text manipulation How to work with text: searching, modifiying, formatting, etc. Chapter 7: Requests -> httpx (rewritten – 10 new pages) • Focus on httpx instead • Cover async usage Chapter 8: Cryptography Symmetric and asymmetric encryption and digital signatures, and how to use them in DevOps code. Chapter 9: Paramiko Using paramiko to automate SSH use. Chapter 10: Salt Stack Using salt stack and writing new modules. Chapter 11: Ansible Using ansible and writing new modules. Chapter 12: Docker (5 new pages) • Clean up examples – they are hard to read • Show complete example of layering, not just talk in theory • Show complete example of running, not just talk in theory • Add section about how to build containers for Python applications Chapter 13: AWS Automating AWS using the boto3 library. New: Chapter 14: Kubernetes (10 pages) Chapter goal: Learn how to automate k8s with Python and how to run Python applications on k8s • Packaging Python applications for kubernetes – Using secrets – Thinking in Pods • Automating k8s from Python using the REST API • Writing k8s operators with Python New: Chapter 15: Terraform (5 pages) • Using the Terraform Python CDK • Generating Terraform JSON from Python

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Book SynopsisSucceed in your conversion to SAP S/4HANA. This book will help you understand the core aspects and implement a conversion project.You will start with an overview of the SAP S/4HANA conversion tools: Readiness Check, Simplification Item Check report, Maintenance Planner, Custom Code Analysis, SUM (Software Update Manager), and more. You will understand the preparation activities for SAP FI (Finance), SAP CO (Controlling), SAP AA (Asset Accounting), Material Ledger, and COPA (Controlling-Profitability Analysis). And you will find the SAP CVI (Customer/Vendor Integration) steps that can help consultants understand the mandatory activities to be completed as a part of preparation on the SAP ECC (ERP Central Component) system. You will learn the preparation activities for conversion of accounting to SAP S/4HANA, and migration activities: customizing, asset accounting, controlling, and house bank accounts. You will gain knowledge on data migration activities suTable of ContentsChapter 1: SAP S/4HANA Journey.- Chapter 2: Planning for a Conversion to SAP S/4HANA.- Chapter 3:: An overview on SAP S/4HANA & Conversion tools (Technical & Functional).- Chapter 4: Customer Vendor Integration(CVI).- Chapter 5: Financial Accounting Preparation before SUM.- Chapter 6: Asset Accounting Preparation before SUM.- Chapter 7: Other Functional Applications’ Preparation Prior to Conversion.- Chapter 8: Conversion Cockpit - Conversion of Accounting to SAP S/4HANA.- Chapter 9: An overview on SAP S/4HANA Conversion project Task.

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Book SynopsisThis book helps SAP architects and SAP Basis administrators deploy and operate SAP S/4HANA systems on the most common public cloud platforms. Market-leading cloud offerings are covered, including Amazon Web Services, Microsoft Azure, and Google Cloud. You will gain an end-to-end understanding of the initial implementation of SAP S/4HANA systems on those platforms. You will learn how to move away from the big monolithic SAP ERP systems and arrive at an environment with a central SAP S/4HANA system as the digital core surrounded by cloud-native services. The book begins by introducing the core concepts of Hyperscaler cloud platforms that are relevant to SAP. You will learn about the architecture of SAP S/4HANA systems on public cloud platforms, with specific content provided for each of the major platforms. The book simplifies the deployment of SAP S/4HANA systems in public clouds by providing step-by-step instructions and helping you deal with thecomplexity of such a deployment. ConteTable of Contents1. Introduction to Public Cloud and Hyperscalers2. SAP S/4HANA systems on Public Cloud3. SAP S/4HANA Deployment and Migration4. SAP S/4HANA on AWS Elastic Compute Cloud – Concepts and Architecture5. SAP S/4HANA on AWS Elastic Compute Cloud – Deployment 6. SAP S/4HANA on Microsoft Azure – Concepts and Architecture7. SAP S/4HANA on Microsoft Azure – Deployment 8. SAP S/4HANA on Google Cloud – Concepts and Architecture9. SAP S/4HANA on Google Cloud – Deployment and Setup10. Summary and Outlook

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Book SynopsisDoes digital transformation ever stop? The answer is a resounding no and this book guides you in developing an SAP enterprise architecture that prepares you for constant technology changes. The book introduces enterprise architecture, the role it plays in executing successful business strategy, and its application in SAP. A detailed step-by-step guide teaches you how to utilize SAP Enterprise Architecture Designer to model the four key areas: business, data, landscape, and requirements. Executives will gain insight into the considerations that will aid them in building their digital transformation road map while remaining agile to adapt to unforeseen circumstances. and adapting to the new normal. SAP partners and consultants will find their place in SAP's future. By the end of this book, you will learn what SAP enterprise architecture is and how to develop it along with its best practices. You Will UnderstandThefundamentals of enterprise architectureSAP enterprise architectureHowTable of ContentsChapter 1: Introducing Enterprise ArchitectureYou will cover an introduction to Enterprise Architecture. This will set the tone of the book as it will answer the “why” of Enterprise Architecture which will form the foundation of the “how” once it starts detailing the technical aspects of SAP Enterprise Architecture. GoalsIntroducing the readers to Enterprise ArchitectureSub topic What is Enterprise Architecture The purpose of Enterprise Architecture The principles of Enterprise Architecture Chapter 2: Strategic Enterprise Architecture This chapter details how Enterprise Architecture is key to executing your business strategy through building an IT infrastructure that automates your business’s core capabilities. Goals Understanding the role of Enterprise Architecture in Business Strategy Understanding the role of Enterprise Architecture in driving Digital Transformation Sub topic The role of Enterprise Architecture in Business Strategy Enterprise Architecture as a driver for Digital Transformation Chapter 3: Developing an Enterprise ArchitectureAn overview of how to develop and implement an Enterprise Architecture through and understanding of its foundational elements. Goals Define the foundational elements of an Enterprise Architecture Understand the steps involved with developing an Enterprise Architecture Understand what an Enterprise Architecture framework is Sub topic Enterprise Architecture Implementation Methodology Enterprise Architecture Framework Managing Enterprise Architecture Chapter 4: Enterprise Architecture & SAP This chapter will articulate how Enterprise Architecture can be leveraged to optimize your SAP software to create structures with which to develop a plan of action for change to drive growth. Goals Understanding Enterprise Architecture in the context of SAP Understanding the SAP Enterprise Architecture Framework Sub topic Enterprise Architecture in the context of SAP Overview of SAP Enterprise Architecture Framework Chapter 5: Architecture Designer This chapter introduces the SAP Enterprise Architecture Designer and its common functions. Goals Understanding the SAP Enterprise Architecture Designer and its use. Sub topic An overview of SAP Enterprise Architecture Designer Chapter 6: Business Architecture An overview of business architecture and how it is used to define a future state of the business and a roadmap for the Digital Transformation required to get there. Goals Understand Business Architecture Understand the importance of managing business transition. How to manage the transition to a desired future state Sub topic Defining Business Architecture Business Architecture and Enterprise Architecture Managing the transition to a desired future state Business Modelling Chapter 7: Information Architecture This chapter will guide you on structuring information and how to utilize the tools to structure information. Goals Understand how to structure information Understand data movement Sub topic Information Modelling Conceptual data models Databases Chapter 8: Landscape and System Architecture We explore the components that make up the system landscape and how to arrange them for optimal performance. Goals Understanding the system landscape Sub topic System Modelling Location Modelling Chapter 9: Best Practices We explore Enterprise Architecture best practices to ensure the overall alignment of IT and business needs to achieve business strategy. Goals Learn and implement Enterprise Architecture best practices Sub topic Why implement best practices Key enterprise architect best practices Chapter 10: Future of Enterprise Architecture In conclusion we explore the latest insights and predictions about the future of enterprise architecture. Goals Having an informed view about the future of Enterprise Architecture Sub topic Current Trends in Enterprise Architecture Enterprise architecture future predictions

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Book SynopsisThis book provides all you need to find data from external sources and load and transform that data into Power BI where you can mine it for business insights and a competitive edge. This ranges from connecting to corporate databases such as Azure SQL and SQL Server to file-based data sources, and cloud- and web-based data sources. The book also explains the use of Direct Query and Live Connect to establish instant connections to databases and data warehouses and avoid loading data.The book provides detailed guidance on techniques for transforming inbound data into normalized data sets that are easy to query and analyze. This covers data cleansing, data modification, and standardization as well as merging source data into robust data structures that can feed into your data model. You will learn how to pivot and transpose data and extrapolate missing values as well as harness external programs such as R and Python into a Power Query data flow. You also will see how to handle errors in soTable of Contents1. Discovering and Loading Data with Power BI Desktop2. Discovering and Loading File-Based Data with Power BI Desktop3. Loading Data From Databases and Data Warehouses4. DirectQuery and Live Connect5. Loading Data from the Web and Cloud6. Loading Data from Other Data Sources7. Power Query8. Structuring Data9. Shaping Data10. Data Cleansing11. Data Transformation12. Complex Data Structures13. Organizing, Managing, and Parameterizing Queries14. The M LanguageAppendix A: Sample Data

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Book SynopsisImplement the Snowflake Data Cloud using best practices and reap the benefits of scalability and low-cost from the industry-leading, cloud-based, data warehousing platform. This book provides a detailed how-to explanation, and assumes familiarity with Snowflake core concepts and principles. It is a project-oriented book with a hands-on approach to designing, developing, and implementing your Data Cloud with security at the center. As you work through the examples, you will develop the skill, knowledge, and expertise to expand your capability by incorporating additional Snowflake features, tools, and techniques. Your Snowflake Data Cloud will be fit for purpose, extensible, and at the forefront of both Direct Share, Data Exchange, and Snowflake Marketplace. Building the Snowflake Data Cloud helps you transform your organization into monetizing the value locked up within your data. As the digital economy takes hold, with data volume, velociTable of ContentsPart I. Context 1. The Snowflake Data Cloud 2. Breaking Data Siloes Part II. Concepts 3. Architecture 4. Account Security5. Role Based Access Control (RBAC)6. Account Usage StorePart III. Tools7. Ingesting Data8. Data Pipelines9. Data Presentation10. Semi Structured and Unstructured DataPart IV. Management11. Query Optimizer Basics12. Data Management13. Data Modelling14. Snowflake Data Cloud By Example

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Book SynopsisThere is a call to action reverberating in company boardrooms, earnings calls, technology conferences, and IT departments: every company should be a software company. The call makes intuitive sense. Software, when done right, creates infinite business leverage. It is not a coincidence that 7 out of 10 largest companies in the world are software companies. But how does a company become a software company? This book will? help enterprises transform into a software company.The software-driven future that Marc Andreessen predicted in his now-famous 2011 essay is here but unevenly distributed. While enterprises, and teams within, grasp the software technologies, they lack the context to leverage them - much less understand the fundamental principles that drive the business value from software: What is the real essence of the software-based transformation? If every enterprise is implementing the same technologies, what is the sourceTable of ContentsIntroduction The book is a series of essays exploring 1 or 2 fundamental principles of good software. The first set of essays will cover understanding software technology as a means of producing value. The second set of essays will describe the state-of-the-art process of building software. The third and final set of essays will focus on how people are critical to building great software. Part I: Means Software is a soft technology with leverage similar to money and written language . But enterprises are still figuring out how to harness the potential of this new soft technology. Understanding and mastering software as a means of production is critical to succeeding in the age of software. Chapter 1: Transformation Digital Transformation doesn't come from large and expensive programs to implement newer and better technologies. Instead, the transformation comes from continuously improving at the art of envisioning, building, and shipping software. Chapter 2: Essence The prevailing hypothesis is that enterprises' inability to build good software is due to a lack of capital. It couldn't be further from the truth. The reason enterprises struggle is because they mismanage the complexity of the software development process. Chapter 3: Value "Meeting Stakeholder Requirements", a typical approach for enterprise software programs, is a value-destroying mistake. Good software evolves from adaptive designs which resolve ever-changing user problems. Because users don't want more features, users want their "jobs to be done" . Part II: Process Enterprise software industry is obsessed with implementing best practice processes to build good software. But these processes become an end unto themselves. Understanding the core principles behind these processes can help enterprises to right-size these processes to what they need. Chapter 4: Iteration The currency for creating new value is "working software". But it is easier said than done in an enterprise environment. Building and delivering on progressively shorter planning horizons can get them on a path of iterative learning with “working software”. Chapter 5: Flow A lot of effort in enterprise software goes into managing plans - in estimating effort and deadlines and keeping projects on track. But when value comes from differentiation, the teams must focus on managing the demonstrable flow of value to the users. Chapter 6: Supply Chain Enterprise processes to procure software are extremely slow. By the time the process completes and technology sees action, it is already obsolete. Instead of one-off procurements, a different approach is a consistent supply chain to rent, buy or reuse undifferentiated software components. Chapter 7: Agility Enterprises misunderstand Agility. It isn't about building software fast. Instead, it is about leveraging software's fundamental agility and building solutions that can help an enterprise respond to the accelerating pace of change. Part III: People Following best practices on technologies and processes can result in good software. But the transition from good to great happens when teams are assigned problems and are empowered to create the best solutions within an organizational culture that rewards creativity. Chapter 8: Team Teams build good software, not a collection of resources. Teams develop through long-term shared motivation; from experiencing failures and successes together. When raw material is ideas, like with software, the quality of the team determines the quality of the output. Chapter 9: Culture Culture eats strategy. Therefore, teams need a medium of good culture to thrive. Because innovation always happens within a creative culture, never within a compliance culture. Rigid processes create compliance but dampen creativity. Chapter 10: Management Creative culture requires Creative management. You have to equip and empower teams to solve user and customer problems. And an enterprise must model every important solution attribute within the organization structure first. Epilogue

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Book SynopsisEquip yourself with tools to approach digital transformation within your organization successfully. Today, over 80% of digital transformation engagements fail to reach their objectives (as reported through a survey by Couchbase). The challenge to overcome is that the pace of change in digital has left business users falling behind. Geared towards non-technical professionals, this book seeks to get executives on track to lead this innovation wave. Data as the prime lever in this innovation wave has accelerated the pace of change from earlier innovation waves by 25 years. Companies are finding it hard to adapt as their internal processes do not allow such rapid changes. Companies are stuck with outdated tools to manage DX projects. This leads to outsourcing the responsibility for DX to IT teams and outside system developers and causing great problems.Toolsets from data visualization, simulated prototyping, video editing and Whiteboarding will be introduced and simplified to show you caTable of Contents1. Introduction 2. Digital Transformation and Big Data3. Visual Style and Prototyping Tools 4. The Problem with DX Engagements Today 5. Introduction to data Mining and Algorithms6. Managing the Technology Deluge7. Introduction to No Code AI Toolsets8. Digital Ethics 9. Digital Transformation Future and Next Steps AppendicesReferences

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