Spreadsheet modeling and decision analysis : a practical introduction to business analytics

Description

Cover Preface Brief Contents Contents Chapter 1: Introduction to Modeling and Decision Analysis 1-0 Introduction 1-1 The Modeling Approach to Decision Making 1-2 Characteristics and Benefits of Modeling 1-3 Mathematical Models 1-4 Categories of Mathematical Models 1-5 Business Analytics and the Problem-Solving Process 1-6 Anchoring and Framing Effects 1-7 Good Decisions vs. Good Outcomes 1-8 Summary 1-9 References Questions and Problems Case 1-1 Patrick's Paradox Chapter 2: Introduction to Optimization and Linear Programming 2-0 Introduction 2-1 Applications of Mathematical Optimization 2-2 Characteristics of Optimization Problems 2-3 Expressing Optimization Problems Mathematically 2-4 Mathematical Programming Techniques 2-5 An Example LP Problem 2-6 Formulating LP Models 2-7 Summary of the LP Model for the Example Problem 2-8 The General Form of an LP Model 2-9 Solving LP Problems: An Intuitive Approach 2-10 Solving LP Problems: A Graphical Approach 2-11 Special Conditions in LP Models 2-12 Summary 2-13 References Questions and Problems Case 2-1 For the Lines They Are A-Changin' (with Apologies to Bob Dylan) Chapter 3: Modeling and Solving LP Problems in a Spreadsheet 3-0 Introduction 3-1 Spreadsheet Solvers 3-2 Solving LP Problems in a Spreadsheet 3-3 The Steps in Implementing an LP Model in a Spreadsheet 3-4 A Spreadsheet Model for the Blue Ridge Hot Tubs Problem 3-5 How Solver Views the Model 3-6 Using Analytic Solver 3-7 Using Excel's Built-in Solver 3-8 Goals and Guidelines for Spreadsheet Design 3-9 Make vs. Buy Decisions 3-10 An Investment Problem 3-11 A Transportation Problem 3-12 A Blending Problem 3-13 A Production and Inventory Planning Problem 3-14 A Multiperiod Cash Flow Problem 3-15 Data Envelopment Analysis 3-16 Summary 3-17 References Questions and Problems Case 3-1 Putting the Link in the Supply Chain Case 3-2 Foreign Exchange Trading at Baldwin Enterprises Case 3-3 The Wolverine Retirement Fund Case 3-4 Saving the Manatees Chapter 4: Sensitivity Analysis and the Simplex Method 4-0 Introduction 4-1 The Purpose of Sensitivity Analysis 4-2 Approaches to Sensitivity Analysis 4-3 An Example Problem 4-4 The Answer Report 4-5 The Sensitivity Report 4-6 Ad Hoc Sensitivity Analysis 4-7 Robust Optimization 4-8 The Simplex Method 4-9 Summary 4-10 References Questions and Problems Case 4-1 A Nut Case Case 4-2 Parket Sisters Case 4-3 Kamm Industries Chapter 5: Network Modeling 5-0 Introduction 5-1 The Transshipment Problem 5-2 The Shortest Path Problem 5-3 The Equipment Replacement Problem 5-4 Transportation/Assignment Problems 5-5 Generalized Network Flow Problems 5-6 Maximal Flow Problems 5-7 Special Modeling Considerations 5-8 Minimal Spanning Tree Problems 5-9 Summary 5-10 References Questions and Problems Case 5-1 Hamilton & Jovanovich Case 5-2 Old Dominion Energy Case 5-3 US Express Case 5-4 The Major Electric Corporation Chapter 6: Integer Linear Programming 6-0 Introduction 6-1 Integrality Conditions 6-2 Relaxation 6-3 Solving the Relaxed Problem 6-4 Bounds 6-5 Rounding 6-6 Stopping Rules 6-7 Solving ILP Problems Using Solver 6-8 Other ILP Problems 6-9 An Employee Scheduling Problem 6-10 Binary Variables 6-11 A Capital Budgeting Problem 6-12 Binary Variables and Logical Conditions 6-13 The Line Balancing Problem 6-14 The Fixed-Charge Problem 6-15 Minimum Order/Purchase Size 6-16 Quantity Discounts 6-17 A Contract Award Problem 6-18 The Branch-and-Bound Algorithm (Optional) 6-19 Summary 6-20 References Questions and Problems Case 6-1 Optimizing a Timber Harvest Case 6-2 Power Dispatching at Old Dominion Case 6-3 The MasterDebt Lockbox Problem Case 6-4 Removing Snow in Montreal Chapter 7: Goal Programming and Multiple Objective Optimization 7-0 Introduction 7-1 Goal Programming 7-2 A Goal Programming Example 7-3 Comments about Goal Programming 7-4 Multiple Objective Optimization 7-5 An MOLP Example 7-6 Comments on MOLP 7-7 Summary 7-8 References Questions and Problems Case 7-1 Removing Snow in Montreal Case 7-2 Planning Diets for the Food Stamp Program Case 7-3 Sales Territory Planning at Caro-Life Chapter 8: Nonlinear Programming and Evolutionary Optimization 8-0 Introduction 8-1 The Nature of NLP Problems 8-2 Solution Strategies for NLP Problems 8-3 Local vs. Global Optimal Solutions 8-4 Economic Order Quantity Models 8-5 Location Problems 8-6 Nonlinear Network Flow Problem 8-7 Project Selection Problems 8-8 Optimizing Existing Financial Spreadsheet Models 8-9 The Portfolio Selection Problem 8-10 Sensitivity Analysis 8-11 Solver Options for Solving NLPs 8-12 Evolutionary Algorithms 8-13 Forming Fair Teams 8-14 The Traveling Salesperson Problem 8-15 Summary 8-16 References Questions and Problems Case 8-1 Tour de Europe Case 8-2 Electing the Next President Case 8-3 Making Windows at Wella Case 8-4 Newspaper Advertising Insert Scheduling Chapter 9: Regression Analysis 9-0 Introduction 9-1 An Example 9-2 Regression Models 9-3 Simple Linear Regression Analysis 9-4 Defining "Best Fit" 9-5 Solving the Problem Using Solver 9-6 Solving the Problem Using the Regression Tool 9-7 Evaluating the Fit 9-8 The R2 Statistic 9-9 Making Predictions 9-10 Statistical Tests for Population Parameters 9-11 Introduction to Multiple Regression 9-12 A Multiple Regression Example 9-13 Selecting the Model 9-14 Making Predictions 9-15 Other Model Selection Issues 9-16 Binary Independent Variables 9-17 Statistical Tests for the Population Parameters 9-18 Polynomial Regression 9-19 Summary 9-20 References Questions and Problems Case 9-1 Diamonds Are Forever Case 9-2 Fiasco in Florida Case 9-3 The Georgia Public Service Commission Chapter 10: Data Mining 10-0 Introduction 10-1 Data Mining Overview 10-2 Classification 10-3 Classification Data Partitioning 10-4 Discriminant Analysis 10-5 Logistic Regression 10-6 k-Nearest Neighbor 10-7 Classification Trees 10-8 Neural Networks 10-9 Naive Bayes 10-10 Comments on Classification 10-11 Prediction 10-12 Association Rules (Affinity Analysis) 10-13 Cluster Analysis 10-14 Time Series 10-15 Summary 10-16 References Questions and Problems Case 10-1 Detecting Management Fraud Chapter 11: Time Series Forecasting 11-0 Introduction 11-1 Time Series Methods 11-2 Measuring Accuracy 11-3 Stationary Models 11-4 Moving Averages 11-5 Weighted Moving Averages 11-6 Exponential Smoothing 11-7 Seasonality 11-8 Stationary Data with Additive Seasonal Effects 11-9 Stationary Data with Multiplicative Seasonal Effects 11-10 Trend Models 11-11 Double Moving Average 11-12 Double Exponential Smoothing (Holt's Method) 11-13 Holt-Winter's Method for Additive Seasonal Effects 11-14 Holt-Winter's Method for Multiplicative Seasonal Effects 11-15 Modeling Time Series Trends Using Regression 11-16 Linear Trend Model 11-17 Quadratic Trend Model 11-18 Modeling Seasonality with Regression Models 11-19 Adjusting Trend Predictions with Seasonal Indices 11-20 Seasonal Regression Models 11-21 Combining Forecasts 11-22 Summary 11-23 References Questions and Problems Case 11-1 PB Chemical Corporation Case 11-2 Forecasting COLAs Case 11-3 Strategic Planning at Fysco Foods Chapter 12: Introduction to Simulation Using Analytic Solver 12-0 Introduction 12-1 Random Variables and Risk 12-2 Why Analyze Risk? 12-3 Methods of Risk Analysis 12-4 A Corporate Health Insurance Example 12-5 Spreadsheet Simulation Using Analytic Solver 12-6 Random Number Generators 12-7 Preparing the Model for Simulation 12-8 Running the Simulation 12-9 Data Analysis 12-10 The Uncertainty of Sampling 12-11 Interactive Simulation 12-12 The Benefits of Simulation 12-13 Additional Uses of Simulation 12-14 A Reservation Management Example 12-15 An Inventory Control Example 12-16 A Project Selection Example 12-17 A Portfolio Optimization Example 12-18 Summary 12-19 References Questions and Problems Case 12-1 Live Well, Die Broke Case 12-2 Death and Taxes Case 12-3 The Sound's Alive Company Phase One: Projecting Profits Phase Two: Bringing Competition into the Model Phase Three: Bringing Uncertainty into the Model Case 12-4 The Foxridge Investment Group Chapter 13: Queuing Theory 13-0 Introduction 13-1 The Purpose of Queuing Models 13-2 Queuing System Configurations 13-3 Characteristics of Queuing Systems 13-4 Kendall Notation 13-5 Queuing Models 13-6 The M/M/s Model 13-7 The M/M/s Model with Finite Queue Length 13-8 The M/M/s Model with Finite Population 13-9 The M/G/1 Model 13-10 The M/D/1 Model 13-11 Simulating Queues and the Steady-State Assumption 13-12 Summary 13-13 References Questions and Problems Case 13-1 May the (Police) Force Be with You Case 13-2 Call Center Staffing at Vacations Inc. Case 13-3 Bullseye Department Store Chapter 14: Decision Analysis 14-0 Introduction 14-1 Good Decisions vs. Good Outcomes 14-2 Characteristics of Decision Problems 14-3 An Example 14-4 The Payoff Matrix 14-5 Decision Rules 14-6 Nonprobabilistic Methods 14-7 Probabilistic Methods 14-8 The Expected Value of Perfect Information 14-9 Decision Trees 14-10 Creating Decision Trees with Analytic Solver 14-11 Multistage Decision Problems 14-12 Sensitivity Analysis 14-13 Using Sample Information in Decision Making 14-14 Computing Conditional Probabilities 14-15 Utility Theory 14-16 Multicriteria Decision Making 14-17 The Multicriteria Scoring Model 14-18 The Analytic Hierarchy Process 14-19 Summary 14-20 References Questions and Problems Case 14-1 Prezcott Pharma Case 14-2 Hang on or Give Up? Case 14-3 Should Larry Junior Go to Court or Settle? Case 14-4 The Spreadsheet Wars Chapter 15: Project Management 15-0 Introduction 15-1 An Example 15-2 Creating the Project Network 15-3 CPM: An Overview 15-4 The Forward Pass 15-5 The Backward Pass 15-6 Determining the Critical Path 15-7 Project Management Using Spreadsheets 15-8 Gantt Charts 15-9 Project Crashing 15-10 Pert: An Overview 15-11 Simulating Project Networks 15-12 Microsoft Project 15-13 Summary 15-14 References Questions and Problems Case 15-1 Project Management at a Crossroad Case 15-2 The World Trade Center Clean-Up Case 15-3 The Imagination Toy Corporation Index