Contents
Preface = xv
1 Introduction to Quantitiative Analysis = 1
1.1 Introduction = 2
1.2 What Is Quantitiative Analysis? = 2
Applications of QA : The Indispensable Role of Management Science at Reynolds Metals Company = 5
1.3 The Quantitative Analysis Approach = 3
Applications of QA : Using QA to Clean Up the Big apple = 8
1.4 possible Problem in the QA Approach = 9
1.5 Implementation-Not Just the Final Step = 12
1.6 Quantitative Analysis and Computer-Based Information Systems = 14
1.7 Use of AB : QM, STORM, and Spreadsheets = 18
1.8 Summary = 22
Glossary = 22
Discussion Questions = 23
Bibliography = 24
2 Probability Concepts = 25
2.1 Introduction = 26
2.2 Fundamental Concepts = 26
2.3 Mutually Exclusive Collectively Exhaustive Events = 29
2.4 Statistically Independent Events = 31
2.5 Statistically Dependent Events = 33
Applications of QA : Qantas Airways = 35
2.6 Revising Probabilities with Bayes's Theorem = 36
2.7 Further Probability Revisions = 38
2.8 Summary = 39
Glossary = 39
Key Equations = 40
Discussion Questions and Problems = 41
Bibliography = 44
Appendix : Derivation of Bayes's Theorem = 45
3 Probability Distributions = 47
3.1 Introduction = 48
3.2 Random Variables = 48
3.3 Probability Distributions = 50
3.4 The Binomail Distribution = 56
3.5 The Poisson Distribution = 59
3.6 The Normal Distribution = 61
Applications of QA : Using Probability Distributions to Search for Sunken Gold = 61
3.7 The Exponential Distribution = 69
3.8 Summary = 70
Glossary = 71
Key Equations = 72
Discussion Questions and Problems = 73
Case Studies : WTVX = 78
Century Chemical Company = 78
Bibliography = 79
4 Forecasting = 81
4.1 Introduction = 82
4.2 Types of Forecasts = 83
4.3 Scatter Diagrams = 84
4.4 Time Series Forecasting Models = 85
4.5 Causal Forecasting Methods = 100
4.6 Monitoring and Controlling Forecasts = 106
Applications of QA : Flood Forecasting at NOAA = 108
4.7 Using the Computer to Forecast = 109
4.8 Summary = 116
Glossary = 118
Key Equations = 118
Solved Problems = 120
Discussion Questions and Problems = 122
Case Studies : The North-South Airline = 130
Kwik Lube = 131
Bibliography = 132
Appendix : Forecasting with Spreadsheets = 133
5 Fundamentals of Decision theory = 137
5.1 Introduction = 138
5.2 The Six Steps in Decision Theory = 138
5.3 Types of Decision-Making Environments = 140
5.4 Decision Making under Risk = 141
5.5 Decision Making under Uncertainty = 146
Applications of QA : Using Decision Theory in Forest Management = 149
5.6 Marginal Analysis with a Large Number of Alternatives and States of Nature = 150
5.7 Using the Computer to Solve Decision Theory Problems = 155
5.8 Summary = 157
Glossary = 158
Key Equations = 159
Solved Problems = 159
Discussion Questons and Problems = 162
Case Study : Starting Right Corporation = 170
Bibliography = 171
Appendix : Solving Decision Theory Problems with Spreadsheets = 171
6 Decision Trees and Utility Theory = 177
6.1 Introduction = 178
6.2 Decision Trees = 178
Applications of QA : Testing Student Athletes for Drug Use : A Decison-Making Model = 184
6.3 How Probabilty Values Are Estimated by Bayesian Analysis = 185
Applications of QA : Decision Trees in Selecting Drilling Sites = 188
6.4 Utility Theory = 188
6.5 Use of AB : QM in Decision Theory = 195
6.6 Summary = 200
Glossary = 200
Key Equations = 201
Solved Problems = 201
Discussion Questions and Problems = 206
Case Study : Blake Electronecs = 216
Sixty-Six Year-Old Patient with a Hernia = 218
Bibliography = 218
Appendix : Using Spreadsheet Analysis to Solve Decision Tree and Bayesian Analysis Problems = 219
7 Statistical Quality Control = 223
7.1 Introduction = 224
7.2 Defining and Measuring Quality = 224
7.3 Total Quality Management (TQM) = 226
7.4 Statistical Process Control (SPC) = 227
Applications of QA : How Velcro Got Hooked on Quality = 227
7.5 Control Charts for Variables = 229
Applications of QA : Stalking Six Sigma at Motorola = 230
7.6 Control Charts for Attributes = 235
7.7 Summary = 237
Glossary = 238
Key Equations = 238
Solved Problems = 239
Discussion Questions and Problems = 240
Case Studies : Bayfield Mud Company = 244
Morristown Daily Tribune = 246
Bibliography = 247
8 Inventory Control Models : 1 = 249
8.1 Introduction = 250
8.2 Importance of Inventory Control = 251
Applications of QA : Using Expert Systems in Inventory Management and Logistics = 253
8.3 The Inventory Decision = 253
8.4 Economic Order Quantity (EQO) : Determining How Much to Order = 254
8.5 Reorder point (ROP) : Determining When to Order = 260
8.6 Fixed Period Inventory Control System = 261
Applications of QA : Inland Steel Uses Systems Contracts to Control Inventory Costs = 261
8.7 Sensitivity Analysis = 264
Applications of QA : Blue Bell Trims Its Inventory = 265
8.8 Summary = 266
Glossary = 267
Key Equations = 267
Solved Problems = 268
Discussion Questions and Problems = 269
Case Studies : Sturdivant Sound Systems = 273
Western Ranchman Outfitters = 274
Bibliography = 275
Appendix : Determining EOQ with Calcules = 275
Appendix : Using spreadsheet Analysis to Solve Basic Inventory Problems = 276
9 Inventory Control Models : Ⅱ = 277
9.1 Introduction = 278
9.2 EOQ without the Instantaneous Receipt Assumption = 278
9.3 Quantity Discount Models = 282
9.4 Planned Shortages = 285
9.5 Use of Safety Stock = 290
9.6 ABC Analysis and Joint Ordering = 298
9.7 Dependent Demand : The Case for Material Requirements Planning (MRP) = 300
9.8 The Kanban System = 307
Applications of QA : Using Just-in-Time (JIT) Principles to Improve Ecuador's Health Care Delivery System = 308
9.9 Using the Computer to Solve Inventory Control Problems = 310
9.10 Summary = 319
Glossary = 319
Key Equations = 320
Solved Problems = 321
Discussion Questions and Problems = 323
Case Study : Professional Video Management = 331
Bibliography = 333
Appendix : Solving the Planned Shortages (Back Order) Model with Calculus = 333
Appendix : Using Spreadshets to Solve Inventory Problems = 334
10 Linear Programming : Graphical and Computer Methods = 337
10.1 Introduction = 338
10.2 Requirements of a Linear Programming Problem = 338
10.3 Formulating Linear Programming Problem = 340
10.4 Graphical Solution to a Linear Programming Problem = 342
Applications of QA : Linear Programming at New England Apple Products = 343
Applications of QA : Selecting Tenants in a Shopping Mall = 348
10.5 Solving Flair Furniture's LP Problem by AB : QM, STORM, LINDO, and What's Best! = 353
10.6 An Introduction to Sensitivity Analysis = 356
10.7 Solving Minimization Problems = 358
Applications of QA : Manpower Planning at United Airlines with LP = 359
10.8 Summary of the Graphical Solution Method = 363
10.9 A Few Special Issues in Linear Programming = 365
Glossary = 370
Solved Problems = 371
Discussion Questions and Problems = 374
Case Study : Golding Landscaping and Plants, Inc. = 382
Bibliography = 383
Appendix : Linear Programming with Spreadsheet Software = 384
11 LInear Programming Applications : With Computer Analyses in AB : QM, STORM, LINDO, and What's Best! = 387
11.1 Introduction = 388
11.2 Marketing Applications = 388
11.3 Manufacturing Applications = 392
11.4 Employee Scheduling Applications = 400
Applications of QA : Crew Pairing Optimization at American Airlines = 406
11.5 Financial Applications = 408
11.6 Transportation Applications = 409
11.7 Ingredient Blending Applications = 415
Problems = 420
Case Study : Chase Manhattan Bank = 431
12 Linear Programming : The Simplex Method = 433
12.1 Introduction = 434
12.2 How to Set Up the Initial Simplex Solution = 434
12.3 Simplex Solution Procedures = 440
12.4 The Second Simplex Tableau = 441
12.5 Developing the Third Tableau = 446
12.6 Review of Procedures for Solving LP Maximization Problems = 449
12.7 Surplus and Artificial Variables = 450
12.8 Solving Minimization Problems = 452
12.9 Review of Procedures for Solving LP Minimization Problems = 461
12.10 Special Cases in Using the Simplex Mehtod = 461
12.11 Karmarkar's Algorithm = 464
Applications of QA : Finding Fast Algorithms Means Better Airline Service = 465
12.12 Summary = 466
Glossary = 466
Key Equation = 467
Solved Problems = 468
Discussion Questions and Problems = 470
Case Study : Coastal States Chemicals and Fertilizers = 476
Bibliography = 477
13 Linear Programming : Sensitivity Analysis and Duality = 479
13.1 Introduction = 480
13.2 Sensitivity Analysis = 480
13.3 The Dual in Linear Programming = 490
Applications of QA : Linear Programming at Dairyman's Cooperative = 491
Applications of QA : Optimizing Wood Procurement in Cabinet Manufacturing = 494
13.4 The Role of Computer Software in Sensitivity Analysis : Looking at High Note Sound Company with AB : QM, STORM, LINDO, and What's Best! = 495
13.5 Summary = 495
Glossary = 500
Solved Problem = 501
Discussion Question and Problems = 503
Case Study : Red Brand Canners = 512
14 Transportation and Assignment Problems = 515
14.1 Introduction = 516
14.2 Setting Up a Transportation Problem = 518
14.3 Developing an Initial Solution : Northwest Corner Rule = 520
14.4 Stepping-Stone Method : Finding a Least-Cost Solution = 522
Applications of QA : Transportation Problem for Irish Pharmaceutical Distributor = 529
14.5 MODI Method = 529
14.6 Vogel's approximation Method : Another Way to Find an Initial Solution = 542
14.7 Unbalanced Transportation Problems = 536
Applications of QA : Moving Sand with the Transportation Approach = 538
14.8 Degeneracy in Transportation Problems = 539
14.9 More than One Optimal Solution = 542
14.10 Computer Solution to the Transportation Problem = 542
14.11 Facility Location Analysis = 546
14.12 Approach of the Assignment Model = 547
14.13 Dummy Rows and Dummy Columns = 555
Applications of QA : Scheduling American League Umpires with the Assignment Model = 556
14.14 Maximization Assignment Problems = 557
14.15 Using the Computer to Solve Assignment Problems = 558
Glossary = 561
Key Equations = 562
Solved Problems = 562
Discussion Questions and Problems = 569
Case Studies : Custom Vans, Inc. = 581
Old Oregon Wood Store = 583
Bibliography = 584
15 Integer Programming, Goal Programming, and the Branch and Bound Method = 585
15.1 Introduction = 586
15.2 Integer Programming = 586
Applications of QA : Selling Seats at American Airlines Using Integer Programming = 591
15.3 The Branch and Bound Method = 596
Applications of QA : An Integer Programming System for Assigning Classes to Rooms = 597
15.4 Goal Programming = 605
Applications of QA : branch and Bound Technique for Establishing Insurance Sales Territories = 606
15.5 Nonlinear Programming = 616
Applications of QA : A Goal Programming Model for Prison Expenditures in Virginia = 620
15.6 Summary = 621
Golssary = 621
Solved Problems = 622
Discussion Questions and Problems = 624
Case Studies : Schank Marketing Research = 630
The Oakton River Bridge = 630
The Puyallup Mall Bibliography = 632
16 Waiting Lines : Queuing Theory = 635
16.1 Introduction = 636
16.2 waiting Line Costs = 636
16.3 Characteristics of a Queuing System = 639
Applications of QA : Queuing Theory at Eastman Kodak = 642
16.4 Single-Channel Queuing Model with Poisson Arrivals and Exponential Service Times = 644
Applications of QA : Queuing up Customers on L. L. Bean's Phone Network = 645
16.5 Multipe-Channel Queuing Model with Poisson Arrivals and Exponential Service Times = 650
16.6 Constant Service Time Model = 655
16.7 Finite Population Model = 656
16.8 More Complex Queuing Models and the Use of Simulation = 659
16.9 Summary = 660
Glossary = 660
Key Equations = 661
Solved Probems = 663
Discussion Questions and Problems = 666
Case Studies : The Shader Lane Hotel = 672
New England Castings = 673
Bibliography = 674
Appendix : Queuing Analysis with Spreadsheets = 675
17 Simulation = 677
17.1 Introdution = 678
17.2 Advantages and Disadantages of Simulation = 679
17.3 Monte Carlo Simulation = 681
17.4 Simulation and Inventory Analysis = 687
17.5 Simulation of a Queuing Problem = 692
17.6 Simulation Model for a Maintenance Policy = 696
Applications of QA : Simulating Canadian National Railways Line Capacity = 697
Applications of QA : Simulating Automation at the U. S. Postal Service = 700
17.7 Two Other Types of Simulation Models = 701
17.8 Role of Computers in Simulation = 703
Applications of QA : The Integration of Simulation and Other Models for Netherlands Water Planning = 704
17.9 Summary = 705
Glossary = 705
Solved Problems = 706
Discussion Questions and Problems = 709
Case Studies : Biales Waste Disposal, GmbH = 718
Abjar Transport Company = 719
Bibliography = 719
Appendix : Conducting a Simulation with Spreadsheets = 720
18 Network Models = 723
18.1 Introduction = 724
18.2 PERT = 726
18.3 PERT / Cost = 740
18.4 Critical Path Method = 745
18.5 Minimal-Spanning Tree Technique = 750
18.6 Maximal-Flow Technique = 754
18.7 Shortest-Route Technique = 758
18.8 Using the Computer to Solve Nerwork Problems = 761
18.9 Summary = 772
Glossary = 772
Key Equatons = 774
Solved Problems = 774
Discussion Questions and Problems = 776
Case Studies : Haygood Brothers Construction Company = 786
Bay Community Hospital = 787
The Ranch Development Project = 788
Bibliography = 789
Appendix : Using Spreadsheets to Solve Network Problems = 790
19 Markov Analysis = 793
19.1 Introduction = 794
19.2 States and State Probabilities : A Grocery Stone Example = 794
19.3 Matrix of Transition Probabilities = 796
19.4 Predicitng Future Market Shares = 798
19.5 Markov Analysis of Machine Operations = 798
19.6 Equilibium Conditions = 800
19.7 Absorbing States and the Fundamental matrix : An Accounts Receivable Application = 803
Applications of QA : Using Markov Analysis to Forecast Long-Term Care = 807
19.8 Solving Markov Analysis Problems by Computer = 808
19.9 Summary = 810
Glossary = 810
Key Equations = 810
Solved Problems = 811
Discussion Questions and Problems = 815
Case Study : Rentall Trucks = 821
Bibliography = 822
Appendix : Using Spreadsheets to Solve Markov Analysis Problems = 823
Modules A Mathematical Tools : Determinants and Matrices = 825
A.1 Introduction = 826
A.2 Determinants = 826
A.3 Matrices = 827
A.4 Summary = 831
Glossary = 832
Problems = 832
Bibliography = 833
B Game Theory = 835
B.1 Introduction = 836
B.2 Language of Games = 836
B.3 Pure Strategy Games = 837
B.4 Minimax Criterion = 837
B.5 Mixed Strategy Games = 838
B.6 Dominance = 840
B.7 Games Larger Than 2 x 2 = 841
B.8 Using the Computer to Solve Game Theory Problems = 843
B.9 Summary = 843
Glossary = 843
Discussion Questions and Problems = 843
Bibliography = 846
C Dynamic Programming = 847
C.1 Introduction = 848
C.2 A Shortest-Route Problem Solved by Dynamic Programming = 848
Applications of QA : Dynamic Programming at Weyerhaeuser = 849
C.3 Dynamic Programming Terminology = 852
Applications of QA : Using Dynamic Programming to Assist in the Scheduling of Operation Desert Storm Airlift Operations = 853
C.4 Using the Computer to Solve Dynamic Programming Problems = 854
Glossary = 856
Discussion Questions and Problems = 856
Bibliography = 857
D Decision Theory and the Normal Distribution = 859
D.1 Introduction = 860
D.2 Break-Even Analysis and the Normal Distribution = 860
D.3 EVPI and the Normal Distribution = 863
D.4 Summary = 864
Glossary = 865
Key Equations = 865
Discussion Questions and Problems = 865
Appendix : Derivation of Break-Even Point = 867
E Multifactor Decision Making = 869
E.1 Introduction = 870
E.2 The Multifactor Evaluation Process = 870
E.3 The Analytic Hierarchy Process = 871
E.4 A Comparison of MFEP and AHP = 876
E.5 Summary = 876
Glossary = 877
Key Equations = 877
Discussion Questions and Problems = 877
Bibliography = 879
F Using AB : QM = 881
F.1 Introduction = 882
F.2 Basic Requirements for AB : QM = 883
F.3 Starting AB : QM = 883
F.4 Using AB : QM = 884
F.5 A Forecasting Example of AB : QM = 886
F.6 Limitations of AB : QM = 889
APPENDIXES
A. Areas Under the Standard Normal Table = A－2
B. Unit Normal Loss Intergral = A－3
C. Cumulative Binomial Distribution = A－4
D. Values for $$e^{-\lambda }$$ for use in the Poisson Distribution = A－15
Solutions to Selected Problems = S－1
