Quantitative Analysis for Management TWELFTH EDITION

1 Quantitative Analysis for Management TWELFTH EDITION GLOBAL EDITION Charles Harwood Professor of Management Science Crummer Graduate School of Business, Rollins College Professor of Information and Management Sciences, Florida State University Professor of Decision Sciences, University of Houston-Clear Lake Associate Professor of Management Sciences, University of Houston-Downtown PEARSON Boston Columbus Indianapolis New York San Francisco Upper Saddle River Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto Delhi Mexico City S o Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo CONTENTS { PREFACE 13 CHARTER 1 Introduction to Quantitative Analysis 19 Introduction 20 What Is Quantitative Analysis ? 20 Business Analytics 21 The Quantitative Analysis Approach 22 Defining the Problem 22 Developing a Model 22 Acquiring Input Data 23 Developing a Solution 23 Testing the Solution 24 Analyzing the Results and Sensitivity Analysis 24 Implementing the Results 24 The Quantitative Analysis Approach and Modeling in the Real World 26 How to Develop a Quantitative Analysis Model 26 The Advantages of Mathematical Modeling 27 Mathematical Models Categorized by Risk 27 The Role of Computers and Spreadsheet Models in the Quantitative Analysis Approach 28 Possible Problems in the Quantitative Analysis Approach 31 Defining the Problem 31 Developing a Model 32 Acquiring Input Data 33 Developing a Solution 33 Testing the Solution 34 Analyzing the Results 34}

2 Implementation Not Just the Final Step 35 Lack of Commitment and Resistance to Change 35 Lack of Commitment by Quantitative Analysts 35 Summary 35 Glossary 36 KeyEquations 36 Seif-Test 36 Discussion Questions and Problems 37 Case Study: Food and Beverages at Southwestern University Football Games 39 Bibliography 39 CHARTER 2 23 24 25 26 28 Appendix : Probability Concepts and Applications 41 Introduction 42 Fundamental Concepts 42 Two Basic Rules of Probability 42 Types of Probability 43 Mutually Exclusive and Collectively Exhaustive Events 44 Unions and Intersections of Events 45 Probability Rules for Unions, Intersections, and Conditional Probabilities 46 Revising Probabilities with Bayes' Theorem 47 General Form of Bayes' Theorem 49 Further Probability Revisions 49 Random Variables 50 Probability Distributions 52 Probability Distribution of a Discrete Random Variable 52 Expected Value of a Discrete Probability Distribution 52 Variance of a Discrete Probability Distribution 53 Probability Distribution of a Continuous Random Variable 54 The Binomial Distribution 55 Solving Problems with the Binomial Formula 56 Solving Problems with Binomial Tables 57 The Normal Distribution 58 Area Under the Normal Curve 60 Using the Standard Normal Table 60 Haynes Construction Company Example 61 The Empirical Rule 64 The F Distribution 64 The Exponential Distribution 66 Arnolds Muffler Example 67

3 The Poisson Distribution 68 Summary 70 Glossary 70 Key Equations 71 Solved Problems 72 Seif Test 74 Discussion Questions and Problems 75 Case Study: WTVX 81 Bibliography 81 Derivation of Bayes' Theorem 81 6 CONTENTS 7 CHARTER 3 Decision Analysis 83 Introduction 84 The Six Steps in Decision Making 84 Types of Decision-Making Environments 85 Decision Making Under Uncertainty 86 Optimistic 86 Pessimistic 87 Criterion of Realism (Hurwicz Criterion) 87 Equally Likely (Laplace) 88 Minimax Regret 88 Decision Making Under Risk 89 Expected Monetary Value 89 Expected Value of Perfect Information 90 Expected Opportunity Loss 92 Sensitivity Analysis 92 A Minimization Example 93 Using Software for Payoff Table Problems 95 QM for Windows 95 Excel QM 96 Decision Trees 97 Efficiency of Sample Information 102 Sensitivity Analysis 102 How Probability Values Are Estimated by Bayesian Analysis 103 Calculating Revised Probabilities 103 Potential Problem in Using Survey Results 105 Utility Theory 106 Measuring Utility and Contracting a Utility Curve 107 Utility as a Decision-Making Criterion 110 Summary 112 Glossary 112 KeyEquations 113 Solved Problems 113 Seif-Test 118 Discussion Questions and Problems 119 Case Study.

4 Starting Right Corporation 127 Case Study: Blake Electronics 128 Bibliography 130 CHARTER 4 Regression Models 131 Introduction 132 Scatter Diagrams 132 Simple Linear Regression 133 Measuring the Fit of the Regression Model 135 Coefficient of Determination 136 Correlation Coefficient 136 Assumptions of the Regression Model 138 Estimating the Variance 139 Testing the Model for Significance 139 Triple A Construction Example 141 The Analysis of Variance (ANOVA) Table 141 Triple A Construction ANOVA Example 142 Using Computer Software for Regression 142 Excel 2013 142 Excel QM 143 QM for Windows 145 Multiple Regression Analysis 146 Evaluating the Multiple Regression Model 147 Jenny Wilson Realty Example 148 Binary or Dummy Variables 149 Model Building 150 Stepwise Regression 151 Multicollinearity 151 Nonlinear Regression 151 Cautions and Pitfalls in Regression Analysis 154 Summary 155 Glossary 155 KeyEquations 156 Solved Problems 157 Seif Test 159 Discussion Questions and Problems 159 Case Study: North-South Airline 164 Bibliography 165 Appendix.

5 Formulasfor Regression Calculations 165 CHARTER 5 Forecasting 167 Introduction 168 Types of Forecasting Models 168 Qualitative Models 168 Causal Models 169 Time-Series Models 169 Components of a Time-Series 169 Measures of Forecast Accuracy 171 Forecasting Models Random Variations Only 174 Moving Averages 174 Weighted Moving Averages 174 Exponential Smoothing 176 Using Software for Forecasting Time Series 178 Forecasting Models Trend and Random Variations 181 Exponential Smoothing with Trend 181 Trend Projections 183 Adjustingfor Seasonal Variations 185 Seasonal Indices 186 Calculating Seasonal Indices with No Trend 186 Calculating Seasonal Indices with Trend 187 Forecasting Models Trend, Seasonal, and Random Variations 188 The Decomposition Method 188 Software for Decomposition 191 Using Regression with Trend and Seasonal Components 192 Monitoring and Controlling Forecasts 193 Adaptive Smoothing 195 Summary 195 Glossary 196 KeyEquations 196 Solved Problems 197 Self-Test 198 Discussion Questions and Problems 199 Case Study: Forecasting Attendance at SWU Football Games 202 Case Study: Forecasting Monthly Sales 203 Bibliography 204 8 CONTENTS CHARTER 6 Appendix : Inventory Control Models 205 Introduction 206 Importance of Inventory Control 207 Decoupling Function 207 Storing Resources 207 Irregul r Supply and Demand 207 Quantity Discounts 207 Avoiding Stockouts and Shortages 207 Inventory Decisions 208 Economic Order Quantity.

6 Determining How Much to Order 209 Inventory Costs in the EOQ Situation 210 Finding the EOQ 212 Sumco Pump Company Example 212 Purchase Cost of Inventory Items 213 Sensitivity Analysis with the EOQ Model 214 Reorder Point: Determining When to Order 215 EOQ Without the Instantaneous Receipt Assumption 216 Annual Carrying Cost for Production Run Model 217 Annual Setup Cost or Annual Ordering Cost 217 Determining the Optimal Production Quantity 218 Brown Manufacturing Example 218 Quantity Discount Models 220 Brass Department Store Example 222 Use of Safety Stock 224 Single-Period Inventory Models 229 Marginal Analysis with Discrete Distributions 230 Cafe du Donut Example 231 Marginal Analysis with the Normal Distribution 232 Newspaper Example 232 ABC Analysis 234 Dependent Demand.

7 The Case for Material Requirements Planning 234 Material Structure Tree 235 Gross and Net Material Requirements Plan 236 Two or More End Products 237 Just-In-Time Inventory Control 239 Enterprise Resource Planning 240 Summary 241 Glossary 241 KeyEquations 242 Solved Problems 243 Self-Test 245 Discussion Questions and Problems 246 Case Study: Martin-Pullin Bicycle Corporation 253 Bibliography 254 Inventory Control with QMfor Windows 255 CHARTER 7 CHARTER 8 &2 Linear Programming Models: Graphical and Computer Methods 257 Introduction 258 Requirements of a Linear Programming Problem 258 Formulating LP Problems 259 Flair Furniture Company 259 Graphical Solution to an LP Problem 261 Graphical Representation of Constraints 261 Isoprofit Line Solution Method 265 Corner Point Solution Method 268 Slack and Surplus 270 Solving Flair Furniture's LP Problem Using QMfor Windows, Excel 2013.

8 And Excel QM 271 Using QM for Windows 271 Using Excel's Solver Command to Solve LP Problems 272 Using Excel QM 275 Solving Minimization Problems 277 Holiday Meal Turkey Ranch 277 Four Special Cases in LP 281 No Feasible Solution 281 Unboundedness 281 Redundancy 282 Alternate Optimal Solutions 283 Sensitivity Analysis 284 High Note Sound Company 285 Changes in the Objective Function Coefficient 286 QM for Windows and Changes in Objective Function Coefficients 286 Excel Solver and Changes in Objective Function Coefficients 287 Changes in the Technological Coefficients 288 Changes in the Resources or Right-Hand-Side Values 289 QM for Windows and Changes in Right-Hand-Side Values 290 Excel Solver and Changes in Right-Hand-Side Values 290 Summary 292 Glossary 292 Solved Problems 293 Self-Test 297 Discussion Questions and Problems 298 Case Study: Mexicana Wire Works 306 Bibliography 308 Linear Programming Applications 309 Introduction 310 Marketing Applications 310 Media Selection 310 Marketing Research 311 Manufacturing Applications 314 Production Mix 314 Production Scheduling 315 CONTENTS 9 CHARTER 9 Appendix : CHARTER 10 Employee Scheduling Applications 319 Labor Flanning 319 Financial Applications 321 Portfolio Selection 321 Track Loading Problem 324 Ingredient Blending Applications 326 Diet Problems 326 Ingredient Mix and Blending Problems 327 Other Linear Programming Applications 329 Summary 331 Self-Test 331 Problems 332 Case Study.

9 Cable & Moore 339 Bibliography 340 Transportation, Assignment, and Network Models 341 Introduction 342 The Transportation Problem 343 Linear Program for the Transportation Example 343 Solving Transportation Problems Using Computer Software 343 A General LP Model for Transportation Problems 344 Facility Location Analysis 345 The Assignment Problem 348 Linear Program for Assignment Example 348 The Transshipment Problem 350 Linear Program for Transshipment Example 350 Maximal-Flow Problem 353 Example 353 Shortest-Route Problem 355 Minimal-Spanning Tree Problem 356 Summary 360 Glossary 361 Solved Problems 361 Self-Test 363 Discussion Questions and Problems 364 Case Study: Andrew-Carter, Inc. 375 Case Study: Northeastern Airlines 376 Case Study: Southwestern University Traffic Problems 377 Bibliography 378 Using QMfor Windows 378 Integer Programming, Goal Programming, and Nonlinear Programming 381 Introduction 382 Integer Programming 382 Harrison Electric Company Example of Integer Programming 382 Using Software to Solve the Harrison Integer Programming Problem 384 Mixed-Integer Programming Problem Example 386 Modeling with 0-1 (Binary) Variables 388 Capital Budgeting Example 388 Limiting the Number of Alternatives Selected 390 Dependent Selections 390 Fixed-Charge Problem Example 390 Financial Investment Example 392 Goal Programming 392 Example of Goal Programming.

10 Harrison Electric Company Revisited 394 Extension to Equally Important Multiple Goals 395 Ranking Goals with Priority Levels 395 Goal Programming with Weighted Goals 396 Nonlinear Programming 397 Nonlinear Objective Function and Linear Constraints 398 Both Nonlinear Objective Function and Nonlinear Constraints 398 Linear Objectiv