Quantitative business modeling

Rather than giving instruction in models and solving problems, this textbook focuses on the process of modeling and the use of models in analyzing various managerial situations. The process of modeling is highly relevant to all business disciplines and is a critical skill for all professionals. The emphasis of this text will be on the integration and development of modeling skills including problem recognition, data collection, model formulation, analysis, and communicating and implementing the results.

Preface	p. xvii
About the Authors	p. xxiii
Chapter 1 Decision Making and Quantitative Modeling	p. 1
1.1 Quantitative Business Modeling	p. 7
Definition of a Model	p. 9
Benefits and Drawbacks of Modeling	p. 10
Types of Models	p. 11
Effective Modelers	p. 14
1.2 The Modeling Process	p. 14
A Five-Step Modeling Process	p. 16
Step 1 Opportunity/Problem Recognition	p. 17
Step 2 Model Formulation	p. 17
Step 3 Data Collection	p. 21
Step 4 Analysis of the Model	p. 23
Step 5 Implementation and Project Management	p. 25
1.3 Detailed Modeling Example	p. 28
Step 1 Opportunity/Problem Recognition	p. 28
Step 2 Model Formulation	p. 29
Step 3 Data Collection	p. 30
Step 4 Analysis of the Model	p. 30
Step 5 Implementation and Project Management	p. 30
1.4 Software for Modeling	p. 33
Questions	p. 33
Experiential Exercises	p. 34
Modeling Exercises	p. 35
Case: Henry Ford Hospital	p. 36
Endnotes	p. 37
Bibliography	p. 37
Chapter 2 Data Collection and Analysis	p. 38
2.1 Data Collection	p. 39
2.2 Summarizing Data	p. 42
Descriptive Statistics	p. 42
Statistical Displays	p. 44
2.3 Probability and Random Variables	p. 47
Subjective Probablility	p. 48
Logical Probability	p. 48
Experimental Probability	p. 48
Event Relationships and Probability Laws	p. 48
Probability Distributions	p. 51
2.4 Common Probability Distributions	p. 52
The Binomial Distribution	p. 53
The Poisson Distribution	p. 54
The Exponential Distribution	p. 55
The Normal Distribution	p. 56
The t Distribution	p. 58
2.5 Distributions of Sample Statistics	p. 58
2.6 Chi-Square Goodness of Fit Test	p. 60
2.7 Point and Interval Estimation	p. 64
Interval Estimation of a Mean	p. 65
Determining the Size of the Sample for a Normal Distribution	p. 68
Interval Estimation and Determination of Sample Size for a Proportion	p. 69
2.8 Hypothesis Testing	p. 71
Hypothesis Tests for Means	p. 73
Comparing Multiple Means--Analysis of Variance (ANOVA)	p. 77
2.9 Detailed Modeling Example	p. 81
Step 1 Opportunity/Problem Recognition	p. 81
Step 2 Model Formulation	p. 82
Step 3 Data Collection	p. 82
Step 4 Analysis of the Model	p. 85
Step 5 Implementation	p. 86
Questions	p. 89
Experiential Exercise	p. 89
Modeling Exercises	p. 90
Case: Fiberease Inc.	p. 93
Case: InterAccess Inc.	p. 95
Case: eApp Inc.	p. 95
Endnote	p. 96
Bibliography	p. 96
Chapter 3 Statistical Models: Regression and Forecasting	p. 97
3.1 The Modeling Process for Statistical Studies	p. 99
3.2 The Simple Linear Regression Model	p. 100
Calculating the Regression Model Parameters	p. 103
The Coefficient of Determination and the Correlation Coefficient	p. 105
Regression Analysis Assumptions	p. 109
Using the Regression Model	p. 110
3.3 The Multiple Regression Model	p. 112
3.4 Developing Regression Models	p. 115
Step 1 Identify Candidate Independent Variables to Include in the Model	p. 115
Step 2 Transform the Data	p. 117
Step 3 Select the Variables to Include in the Model	p. 118
Step 4 Analyze the Residuals	p. 118
3.5 Regression Hypothesis Tests	p. 119
3.6 Time Series Analysis	p. 121
Components of a Time Series	p. 121
Time Series Models	p. 123
3.7 Detailed Modeling Example	p. 130
Step 1 Opportunity/Problem Recognition	p. 130
Step 2 Model Formulation	p. 131
Step 3 Data Collection	p. 131
Step 4 Analysis of the Model	p. 131
Step 5 Implementation	p. 135
Questions	p. 140
Experiential Exercise	p. 140
Modeling Exercises	p. 141
Case: Resale Value of Long's Automobile	p. 144
Case: Lewisville Crate Company	p. 144
Bibliography	p. 147
Chapter 4 Optimization and Mathematical Programming	p. 148
4.1 The Modeling Process for Optimization Studies	p. 153
Optimization	p. 153
The Modeling Process	p. 154
Structure of the Chapter	p. 156
4.2 Linear Programming	p. 156
The Output-Mix Problem	p. 157
The Blending Problem	p. 157
Formulating the Linear Programming Model	p. 157
Output-Mix and Blending Problems: Two Examples	p. 158
Example: The Blending (Minimization) Problem	p. 160
The General LP Model	p. 161
Advantages, Assumptions, and Solution Methods	p. 162
Distribution Problems; Transportation, Transshipment, Assignment	p. 164
4.3 Analysis of the Model by the Graphical Method	p. 165
Example 1 A Maximization Problem	p. 165
Example 2 A Minimization Problem	p. 172
Utilization of the Resources--Slack and Surplus Variables	p. 174
Special Situations	p. 175
4.4 Solving Linear Programming Models with Excel	p. 177
Using Excel's Solver	p. 177
Solving Large Problems	p. 181
Back to Startron's Dilemma	p. 185
4.5 Sensitivity ("What-If") Analysis	p. 189
Why a Sensitivity Analysis?	p. 189
Sensitivity Analysis: Objective Function	p. 190
Sensitivity Analysis: Right-Hand Sides	p. 192
Sensitivity Analysis with Excel	p. 192
4.6 Integer Programming	p. 196
Overview of Integer Programming	p. 196
Example: Southern General Hospital	p. 197
The Zero--One Model	p. 200
Example: The Fixed-Charge Situation	p. 201
4.7 Detailed Modeling Example	p. 203
Step 1 Opportunity/Problem Recognition	p. 203
Step 2 Model Formulation	p. 203
Step 3 Data Collection	p. 203
Step 4 Analysis of the Model	p. 205
Step 5 Implementation	p. 208
Questions	p. 210
Experiential Exercise	p. 211
Modeling Exercises	p. 211
Case: The Daphne Jewelry Company	p. 217
Case: Hensley Valve Corp. (A)	p. 219
Case: Hensley Valve Corp. (B)	p. 219
Bibliography	p. 220
Chapter 5 Decision Analysis	p. 221
5.1 The Modeling Process for Decision Analysis Studies	p. 222
The Modeling Process	p. 223
Structure of the Chapter	p. 224
5.2 The Decision Analysis Situation	p. 224
Mary's Dilemma	p. 224
The Structure of Decision Tables	p. 225
Classification of Decision Situations	p. 228
5.3 Decisions Under Certainty	p. 228
Complete Enumeration	p. 229
Example: Assignment of Employees to Machines	p. 229
Computation with Analytical Models	p. 230
5.4 Decisions Under Uncertainty	p. 230
Equal Probabilities (Laplace) Criterion	p. 231
Pessimism (Maximin or Minimax) Criterion	p. 231
Optimism (Maximax or Minimin) Criterion	p. 232
Coefficient of Optimism (Hurwicz) Criterion	p. 233
Regret (Savage) Criterion	p. 237
5.5 Decisions Under Risk	p. 237
Objective and Subjective Probabilities	p. 238
Solution Procedures to Decision Making Under Risk	p. 238
Notes on Implementation	p. 242
Sensitivity Analysis	p. 242
5.6 Decision Trees for Risk Analysis	p. 243
Structure of a Decision Tree	p. 243
Evaluating a Decision Tree	p. 245
The Multiperiod, Sequential Decision Case	p. 246
5.7 The Value of Additional Information	p. 250
Information Quality: Perfect Versus Imperfect Information	p. 250
The Value of Perfect Information	p. 251
5.8 Imperfect Information and Bayes' Theorem	p. 253
Bayes' Theorem	p. 253
Using Revised Probabilities with Imperfect Information	p. 254
Calculating Revised Probabilities	p. 259
Computing the Revised Probabilities	p. 260
5.9 Detailed Modeling Example	p. 262
Step 1 Opportunity/Problem Recognition	p. 262
Step 2 Model Formulation	p. 262
Step 3 Data Collection	p. 263
Step 4 Analysis of the Model	p. 263
Step 5 Implementation	p. 265
Questions	p. 270
Experiential Exercises	p. 270
Modeling Exercises	p. 271
Case: Maintaining the Water Valves	p. 276
Case: The Air Force Contract	p. 277
Endnotes	p. 278
Bibliography	p. 278
Chapter 6 Queuing Theory	p. 279
6.1 The Modeling Process for Queuing Studies	p. 282
Step 1 Opportunity/Problem Recognition	p. 282
Step 2 Model Formulation	p. 282
Step 3 Data Collection	p. 283
Step 4 Analysis of the Model	p. 283
Step 5 Implementation	p. 283
6.2 The Queuing Situation	p. 284
Characteristics of Waiting Line Situations	p. 284
The Structure of a Queuing System	p. 285
The Managerial Problem	p. 286
The Costs Involved in a Queuing Situation	p. 287
6.3 Modeling Queues	p. 288
Queuing Model Notation	p. 288
Deterministic Queuing Systems	p. 289
The Arrival Process	p. 290
The Service Process	p. 292
Measures for the Service	p. 293
The Waiting Line	p. 294
6.4 Analysis of the Basic Queue (M/M/1 FCFS/[infinity]/[infinity])	p. 295
Poisson-Exponential Model Characteristics	p. 295
Measure of Performance (Operating Characteristics)	p. 296
Managerial Use of the Measures of Performance	p. 298
Using Excel's Goal Seek Function	p. 298
6.5 More Complex Queuing Situations	p. 298
Multifacility Queuing Systems (M/M/K FCFS/[infinity]/[infinity])	p. 299
Example: Multichannel Queue	p. 301
Example: Multichannel Queue at Macro-Market	p. 301
Serial (Multiphase) Queues	p. 304
Example: Serial Queue--Three-Station Process	p. 304
6.6 Detailed Modeling Example	p. 306
Step 1 Opportunity/Problem Recognition	p. 306
Step 2 Model Formulation	p. 306
Step 3 Data Collection	p. 306
Step 4 Analysis of the Model	p. 307
Step 5 Implementation	p. 308
Questions	p. 309
Experiential Exercise	p. 310
Modeling Exercises	p. 310
Case: City of Help	p. 315
Case: Newtown Maintenance Division	p. 315
Bibliography	p. 316
Chapter 7 Simulation	p. 317
7.1 General Overview of Simulation	p. 319
Types of Simulation	p. 320
Uses of Simulation	p. 322
Advantages and Disadvantages of Simulation	p. 322
7.2 The Modeling Process for Monte Carlo Simulation	p. 323
Step 1 Opportunity/Problem Recognition	p. 323
Step 2 Model Formulation	p. 323
Step 3 Data Collection	p. 324
Step 4 Analysis of the Model	p. 324
Step 5 Implementation	p. 327
7.3 The Monte Carlo Methodology	p. 327
The Tourist Information Center	p. 327
Simulation Terminology	p. 328
Generating Random Variates in the Monte Carlo Process	p. 330
7.4 Time Independent, Discrete Simulation	p. 332
Example: Marvin's Service Station	p. 333
Solution by Simulation	p. 333
7.5 Time Dependent Simulation	p. 339
Simulation Analysis with Discrete Distributions	p. 240
Simulation with Continuous Probability Distributions	p. 342
7.6 Risk Analysis	p. 342
7.7 Detailed Modeling Example	p. 344
Step 1 Opportunity/Problem Recognition	p. 344
Steps 2 and 3 Model Formulation and Data Collection	p. 344
Step 4 Analysis of the Model	p. 347
Step 5 Implementation	p. 348
Appendix Crystal Ball 2000	p. 350
Questions	p. 350
Experiential Exercise	p. 359
Modeling Exercises	p. 360
Case: Medford Delivery Service	p. 366
Case: Warren Lynch's Retirement	p. 366
Case: Cartron, Inc.	p. 369
Endnotes	p. 371
Bibliography	p. 371
Chapter 8 Implementation and Project Management	p. 372
8.1 Implementation and Project Modeling	p. 373
The Project Modeling Process	p. 373
Structure of the Chapter	p. 374
8.2 Implementing the Modeling Study	p. 375
Soft Aspects	p. 375
Rational Issues and Reconsideration	p. 377
The Role of Project Management	p. 378
Example: Moose Lake	p. 378
8.3 Planning the Project	p. 381
Step 1 Analysis of the Project	p. 382
Step 2 Sequence the Activities	p. 382
Step 3 Estimate Activity Times and Costs	p. 382
8.4 Scheduling the Project	p. 383
Step 4 Construct the Network	p. 383
Step 5 Event Analysis	p. 385
PERT/CPM Network Characteristics	p. 391
Estimating Activity Times in PERT	p. 393
Finding the Probabilities of Completion in PERT	p. 394
Example: Finding the Probability of Completion within a Desired Time, D	p. 397
Example: Finding the Duration Associated with a Desired Probability	p. 399
Determining the Distribution of Project Completion Times with Simulation	p. 399
8.5 Step 6: Monitoring and Controlling the Project	p. 403
Monitoring the Project	p. 403
Controlling the Project	p. 403
Example: Resource Allocation Schedule	p. 405
Critical Path Method (CPM): Cost-Time Trade-Offs	p. 406
Example: Finding the Least-Cost Plan	p. 409
Example: Least-Cost Plan for 22 Days	p. 441
Analyzing Cost-Time Trade-Offs with Excel's Solver	p. 314
8.6 Detailed Modeling Example	p. 418
Step 1 Opportunity/Problem Recognition	p. 418
Step 2 Model Formulation	p. 418
Step 3 Data Collection	p. 421
Step 4 Analysis of the Model	p. 423
Step 5 Implementation	p. 424
Questions	p. 426
Experiential Exercise	p. 426
Modeling Exercises	p. 426
Case: NutriTech	p. 431
Case: Dart Investments	p. 432
Bibliography	p. 433
Appendix A Mathematics	p. 435
Appendix B Tables	p. 441
Index	p. 451

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