Chapter 1 Strategy and Competition 1
Chapter Overview 1
Snapshot Application：Apple Adopts a New Business Strategy and Shifts Its Core Competency from Computers to Portable Music 3
1.1 Manufacturing Matters 5
Manufacturing Jobs Outlook 6
1.2 A Framework for Operations Strategy 7
Strategic Dimensions 8
1.3 The Classical View of Operations Strategy 9
Time Horizon 9
Focus 11
Evaluation 12
Consistency 12
1.4 Competing in the Global Marketplace 14
Problems for Sections 1.1-1.4 16
Snapshot Application：Global Manufacturing Strategies in the Automobile Industry 17
1.5 Strategic Initiatives：Reengineering the Business Process 18
1.6 Strategic Initiatives：Just-in-Time 21
1.7 Strategic Initiatives：Time-Based Competition 23
1.8 Strategic Initiatives：Competing on Quality 24
Problems for Sections 1.5-1.8 26
1.9 Matching Process and Product Life Cycles 27
The Product Life Cycle 27
The Process Life Cycle 28
The Product-Process Matrix 29
Problems for Section 1.9 31
1.10 Learning and Experience Curves 31
Learning Curves 32
Experience Curves 34
Learning and Experience Curves and Manufacturing Strategy 36
Problems for Section 1.10 36
1.11 Capacity Growth Planning：A Long-Term Strategic Problem 38
Economies of Scale and Economies of Scope 38
Make or Buy：A Prototype Capacity Expansion Problem 39
Dynamic Capacity Expansion Policy 40
Issues in Plant Location 44
Problems for Section 1.11 46
1.12 Summary 47
Additional Problems for Chapter 1 48
Appendix 1-A Present Worth Calculations 50
Bibliography 51
Chapter 2 Forecasting 52
Chapter Overview 52
2.1 The Time Horizon in Forecasting 55
2.2 Characteristics of Forecasts 56
2.3 Subjective Forecasting Methods 56
2.4 Objective Forecasting Methods 57
Causal Models 57
Time Series Methods 58
Snapshot Application：Advanced Forecasting，Inc.，Serves the Semiconductor Industry 59
Problems for Sections 2.1-2.4 59
2.5 Notation Conventions 61
2.6 Evaluating Forecasts 61
Problems for Section 2.6 63
2.7 Methods for Forecasting Stationary Series 64
MovingAverages 64
Problems on Moving Averages 67
Exponential Smoothing 67
Multiple-Step-Ahead Forecasts 71
Comparison of Exponential Smoothing and MovingAverages 72
Problems for Section 2.7 73
Snapshot Application：Sport Obermeyer Slashes Costs with Improved Forecasting 74
2.8 Trend-Based Methods 75
Regression Analysis 75
Problems for Section 2.8 76
Double Exponential Smoothing Using Holt’s Method 77
More Problems for Section 2.8 78
2.9 Methods for Seasonal Series 79
Seasonal Factors for Stationary Series 79
Seasonal Decomposition Using Moving Averages 81
Problems for Section 2.9 83
Winters ’s Method for Seasonal Problems 84
More Problems for Section 2.9 89
2.10 Box-Jenkins Models 89
Estimating the Autocorrelation Function 90
The Autoregressive Process 93
The Moving-Average Process 94
Mixtures：ARMA Models 96
ARIMA Models 96
Using ARIMA Models for Forecasting 98
Summary of the Steps Required for Building ARIMA Models 99
Case Study：Using Box-Jenkins Methodology to Predict Monthly International Airline Passenger Totals 100
SnapshotApplication：A Simple ARIMA Model Predicts the Performance of the U.S.Economy 104
Box-Jenkins Modeling—A Critique 104
Problems for Section 2.10 104
2.11 Practical Considerations 105
Model Identification and Monitoring 105
Simple versus Complex Time Series Methods 106
2.12 Overview of Advanced Topics in Forecasting 107
Simulation as a Forecasting Tool 107
Forecasting Demand in the Presence of Lost Sales 108
2.13 Linking Forecasting and Inventory Management 110
Snapshot Application：Predicting Economic Recessions 111
2.14 Historical Notes and Additional Topics 112
2.15 Summary 113
Additional Problems on Forecasting 113
Appendix 2-A Forecast Errors for Moving Averages and Exponential Smoothing 118
Appendix 2-B Derivation of the Equations for the Slope and Intercept for Regression Analysis 120
Appendix 2-C Glossary of Notation for Chapter 2 122
Bibliography 122
Chapter 3 Aggregate Planning 124
Chapter Overview 124
3.1 Aggregate Units of Production 127
3.2 Overview of the Aggregate Planning Problem 128
3.3 Costs in Aggregate Planning 130
Problems for Sections 3.1-3.3 132
3.4 A Prototype Problem 133
Evaluation of a Chase Strategy（Zero Inventory Plan） 135
Evaluation of the Constant Workforce Plan 136
Mixed Strategies and Additional Constraints 138
Problems for Section 3.4 139
3.5 Solution of Aggregate Planning Problems by Linear Programming 141
Cost Parameters and Given Information 141
Problem Variables 142
Problem Constraints 142
Rounding the Variables 143
Extensions 144
Other Solution Methods 146
3.6 Solving Aggregate Planning Problems by Linear Programming：An Example 147
Problems for Sections 3.5 and 3.6 149
3.7 The Linear Decision Rule 152
3.8 Modeling Management Behavior 153
Problems for Sections 3.7 and 3.8 155
3.9 Disaggregating Aggregate Plans 155
Snapshot Application：Welch’s Uses Aggregate Planning for Production Scheduling 157
Problems for Section 3.9 158
3.10 Production Planning on a Global Scale 158
3.11 Practical Considerations 159
3.12 Historical Notes 160
3.13 Summary 161
Additional Problems on Aggregate Planning 162
Appendix 3-A Glossary of Notation for Chapter 3 167
Bibliography 168
Supplement 1 Linear Programming 169
S1.1 Introduction 169
S1.2 A Protorype Linear Programming Problem 169
S1.3 Statement of the General Problem 171
Definitions of Commonly Used Terms 172
Features of Linear Programs 173
S1.4 Solving Linear Programming Problems Graphically 174
Graphing Linear Inequalities 174
Graphing the Feasible Region 176
Finding the Optimal Solution 177
Identifying the Optimal Solution Directly by Graphical Means 179
S1.5 The Simplex Method：An Overview 180
S1.6 Solving Linear Programming Problems with Excel 181
Entering Large Problems Efficiently 185
S1.7 Interpreting the Sensitivity Report 187
Shadow Prices 187
Objective Function Coefficients and Right-Hand Sides 188
Adding a New Variable 188
Using Sensitivity Analysis 189
S1.8 Recognizing Special Problems 191
Unbounded Solutions 191
Empty Feasible Region 192
Degeneracy 194
Multiple Optimal Solutions 194
Redundant Constraints 194
S1.9 The Application of Linear Programming to Production and Operations Analysis 195
Bibliography 197
Chapter 4 Inventory Control Subject to Known Demand 198
Chapter Overview 198
4.1 Types of Inventories 201
4.2 Motivation for Holding Inventories 202
4.3 Characteristics of Inventory Systems 203
4.4 Relevant Costs 204
Holding Cost 204
Order Cost 206
Penalty Cost 207
Problems for Sections 4.1-4.4 208
4.5 The EOQ Model 210
The Basic Model 210
Inclusion of Order Lead Time 213
Sensitivity 214
EOQ and JIT 215
Problems for Section 4.5 216
4.6 Extension to a Finite Production Rate 218
Problems for Section 4.6 219
4.7 Quantity Discount Models 220
OptimalPolicy for All-Units Discount Schedule 221
Summary of the Solution Technique for All-Units Discounts 223
Incremental Quantity Discounts 223
Summary of the Solution Technique for Incremental Discounts 225
Other Discount Schedules 225
Problems for Section 4.7 226
4.8 Resource-Constrained Multiple Product Systems 227
Problems for Section 4.8 230
4.9 EOQ Models for Production Planning 230
Problems for Section 4.9 234
4.10 Power-of-Two Policies 235
4.11 Historical Notes and Additional Topics 237
Snapshot Application：Mervyn’s Recognized for State-of-the-Art Inventory Control System 238
4.12 Summary 239
Additional Problems on Deterministic Inventory Models 240
Appendix 4-A Mathematical Derivations for Multiproduct Constrained EOQ Systems 244
Appendix 4-B Glossary of Notation for Chapter 4 246
Bibliography 246
Chapter 5 Inventory Control Subject to Uncertain Demand 248
Chapter Overview 248
Overview of Models Treated in This Chapter 252
5.1 The Nature of Randomness 253
5.2 Optimization Criterion 255
Problems for Sections 5.1 and 5.2 256
5.3 The Newsboy Model 257
Notation 257
Development of the Cost Function 258
Determining the Optimal Policy 259
Optimal Policy for Discrete Demand 261
Extension to Include Starting Inventory 261
Snapshot Application：Using Inventory Models to Manage the Seed-Corn Supply Chain at Syngenta 262
Extension to Multiple Planning Periods 263
Problems for Section 5.3 264
5.4 Lot Size-Reorder Point Systems 266
Describing Demand 267
Decision Variables 267
Derivation of the Expected Cost Function 267
The Cost Function 269
Inventory Level versus Inventory Position 271
5.5 Service Levels in（Q，R）Systems 272
Type 1 Service 272
Type 2 Service 273
Optimal（Q，R）Policies Subject to Type 2 Constraint 274
Imputed Shortage Cost 275
Scaling of Lead Time Demand 276
Estimating Sigma When Inventory Control and Forecasting Are Linked 276
Lead Time Variability 277
Calculations in Excel 278
Negative Safety Stock 278
Problems for Sections 5.4 and 5.5 279
5.6 Additional Discussion of Periodic-Review Systems 281
（s，S）Policies 281
Service Levels in Periodic-Review Systems 281
Problems for Section 5.6 282
Snapshot Application：Tropicana Uses Sophisticated Modeling for Inventory Management 283
5.7 Multiproduct Systems 283
ABCAnalysis 283
Exchange Curves 285
Problems for Section 5.7 288
5.8 Overview of Advanced Topics 289
Multi-echelon Systems 289
Perishable Inventory Problems 290
Snapshot Application：Triad’s Inventory Systems Meet Markets’Needs 291
5.9 Historical Notes and Additional Readings 292
5.10 Summary 293
Additional Problems on Stochastic Inventory Models 294
Appendix 5-A Notational Conventions and Probability Review 300
Appendix 5-B Additional Results and Extensions for the Newsboy Model 301
Appendix 5-C Derivation of the Optimal （Q，R）Policy 304
Appendix 5-D Probability Distributions for Inventory Management 304
Appendix 5-E Glossary of Notation for Chapter 5 308
Bibliography 309
Chapter 6 Supply Chain Management 311
Chapter Overview 311
The Supply Chain as a Strategic Weapon 315
Snapshot Application：Wal-Mart Wins with Solid Supply Chain Management 316
6.1 The Transportation Problem 316
The Greedy Heuristic 319
6.2 Solving Transportation Problems with Linear Programming 320
6.3 Generalizations of the Transportation Problem 322
Infeasible Routes 323
Unbalanced Problems 323
6.4 More General Network Formulations 324
Problems for Sections 6.1-6.4 327
Snapshot Application：IBM Streamlines Its Supply Chain for Spare Parts Using Sophisticated Mathematical Models 328
6.5 Distribution Resource Planning 330
Problems for Section 6.5 332
6.6 Determining Delivery Routes in Supply Chains 332
Practical Issues in Vehicle Scheduling 336
Snapshot Application：Air Products Saves Big with Routing and Scheduling Optimizer 337
Problems for Section 6.6 337
6.7 Designing Products for Supply Chain Efficiency 338
Postponement in Supply Chains 339
Additional Issues in Supply Chain Design 340
Snapshot Application：Dell Computer Designs the Ultimate Supply Chain 342
Problems for Section 6.7 342
6.8 The Role of Information in the Supply Chain 343
The Bullwhip Effect 344
Snapshot Application：Saturn Emerges as an Industry Leader with Scientific Supply Chain Management 347
Electronic Commerce 347
Electronic Data Interchange 348
Web-Based Transactions Systems 349
RFID Technology Provides Faster Product Flow 350
Problems for Section 6.8 351
6.9 Multilevel Distribution Systems 351
Problems for Section 6.9 354
6.10 Designing the Supply Chain in a Global Environment 355
Snapshot Application：Norwegian Company Implements Decision Support System to Streamline Its Supply Chain 356
Snapshot Application：Timken Battles Imports with Bundling 358
Supply Chain Management in a Global Environment 359
Snapshot Application：Digital Equipment Corporation Uses Mathematical Modeling to Plan Its Global Supply Chain 360
Trends in Offshore Outsourcing 360
Problems for Section 6.10 361
6.11 Summary 362
Bibliography 362
Chapter 7 Push and Pull Production Control Systems：MRP and JIT 364
Chapter Overview 364
MRP Basics 367
JIT Basics 369
7.1 The Explosion Calculus 370
Problems for Section 7.1 374
7.2 Alternative Lot-Sizing Schemes 376
EOQ Lot Sizing 376
The Silver-Meal Heuristic 377
Least Unit Cost 378
Part Period Balancing 379
Problems for Section 7.2 380
7.3 Incorporating Lot-Sizing Algorithms into the Explosion Calculus 382
Problems for Section 7.3 383
7.4 Lot Sizing with Capacity Constraints 384
Problems for Section 7.4 387
7.5 Shortcomings of MRP 388
Uncertainty 388
Capacity Planning 389
Rolling Horizons and System Nervousness 390
Additional Considerations 392
Snapshot Application：Raymond Corporation Builds World-Class Manufacturing with MRP Ⅱ 393
Problems for Section 7.5 394
7.6 JIT Fundamentals 395
The Mechanics of Kanban 395
Single Minute Exchange of Dies 397
Advantages and Disadvantages of the Just-in-Time Philosophy 398
Implementation of JIT in the United States 401
Problems for Section 7.6 402
7.7 A Comparison of MRP and JIT 403
7.8 JIT or Lean Production？ 404
7.9 Historical Notes 405
7.10 Summary 406
Additional Problems for Chapter 7 407
Appendix 7-A Optimal Lot Sizing for Time-Varying Demand 411
Appendix 7-B Glossary of Notation for Chapter 7 415
Bibliography 416
Chapter 8 Operations Scheduling 417
Chapter Overview 417
8.1 Production Scheduling and the Hierarchy of Production Decisions 420
8.2 Important Characteristics of Job Shop Scheduling Problems 422
Objectives of Job Shop Management 422
8.3 Job Shop Scheduling Terminology 423
8.4 A Comparison of Specific Sequencing Rules 425
First-Come，First-Served 425
Shortest Processing Time 426
Earliest Due Date 426
Critical Ratio Scheduling 427
8.5 Objectives in Job Shop Management：An Example 428
Problems for Sections 8.1-8.5 429
8.6 An Introduction to Sequencing Theory for a Single Machine 430
Shortest-Processing-Time Scheduling 431
Earliest-Due-Date Scheduling 432
Minimizing the Number of Tardy Jobs 432
Precedence Constraints：Lawler’s Algorithm 433
Snapshot Application：Millions Saved with Scheduling System for Fractional Aircraft Operators 435
Problems for Section 8.6 435
8.7 Sequencing Algorithms for Multiple Machines 437
Scheduling n Jobs on Two Machines 438
Extension to Three Machines 439
The Two-Job Flow Shop Problem 441
Problems for Section 8.7 444
8.8 Stochastic Scheduling：Static Analysis 445
Single Machine 445
Multiple Machines 446
The Two-Machine Flow Shop Case 447
Problems for Section 8.8 448
8.9 Stochastic Scheduling：Dynamic Analysis 449
Selection Disciplines Independent of Job Processing Times 451
Selection Disciplines Dependent onJob Processing Times 452
The cμ Rule 454
Problems for Section 8.9 454
8.10 Assembly Line Balancing 455
Problems for Section 8.10 459
Snapshot Application：Manufacturing Divisions Realize Savings with Scheduling Software 461
8.11 Simulation：A Valuable Scheduling Tool 462
8.12 Post-MRP Production Scheduling Software 463
8.13 Historical Notes 463
8.14 Summary 464
Additional Problems on Scheduling 465
Bibliography 471
Supplement 2 Queuing Theory 473
S2.1 Introduction 473
S2.2 Structural Aspects of Queuing Models 474
S2.3 Notation 475
S2.4 Little’s Formula 476
S2.5 The Exponential and Poisson Distributions in Queuing 476
Aside 477
S2.6 Birth and Death Analysis for the M/M/1 Queue 478
S2.7 Calculation of the Expected System Measures for the M/M/1 Queue 481
S2.8 The Waiting Time Distribution 482
S2.9 Solution of the General Case 484
S2.10 Multiple Servers in Parallel：The M/M/c Queue 485
S2.11 The M/M/1 Queue with a Finite Capacity 489
S2.12 Results for Nonexponential Service Distributions 492
S2.13 The M/G/∞ Queue 493
S2.14 Optimization of Queuing Systems 495
Typical Service System Design Problems 495
Modeling Framework 495
S2.15 Simulation of Queuing Systems 498
Bibliography 499
Chapter 9 Project Scheduling 500
Chapter Overview 500
9.1 Representing a Project as a Network 503
9.2 Critical Path Analysis 505
Finding the Critical Path 508
Problems for Sections 9.1 and 9.2 511
9.3 Time Costing Methods 513
Problems for Section 9.3 517
9.4 Solving Critical Path Problems with Linear Programming 518
Linear Programming Formulation of the Cost-Time Problem 521
Problems for Section 9.4 523
9.5 PERT：Project Evaluation and Review Technique 523
Path Independence 528
Problems for Section 9.5 531
SnapshotApplication：Warner Robins StreamlinesAircraft Maintenance with CCPM Project Management 533
9.6 Resource Considerations 533
Resource Constraints for Single-Project Scheduling 533
Resource Constraints for Multiproject Scheduling 535
Resource Loading Profiles 536
Problems for Section 9.6 538
9.7 Organizational Issues in Project Management 540
9.8 Historical Notes 541
9.9 Project Management Software for the PC 542
Snapshot Application：Project Management Helps United Stay on Schedule 543
Snapshot Application：Thomas Brothers Plans Staffing with Project Management Software 543
Snapshot Application：Florida Power and Light Takes Project Management Seriously 543
9.10 Summary 544
Additional Problems on Project Scheduling 545
Appendix 9-A Glossary of Notation for Chapter 9 548
Bibliography 549
Chapter 10 Facilities Layout and Location 550
Chapter Overview 550
Snapshot Application：Sun Microsystems Pioneers New Flex Office System 553
10.1 The Facilities Layout Problem 554
10.2 Patterns of Flow 555
Activity Relationship Chart 555
From-To Chart 557
10.3 Types of Layouts 559
Fixed Position Layouts 559
Product Layouts 559
Process Layouts 560
Layouts Based on Group Technology 560
Problems for Sections 10.1-10.3 562
10.4 A Prototype Layout Problem and the Assignment Model 564
The Assignment Algorithm 565
Problems for Section 10.4 567
10.5 More Advanced Mathematical Programming Formulations 568
Problem for Section 10.5 569
10.6 Computerized Layout Techniques 569
CRAFT 570
COFAD 574
ALDEP 575
CORELAP 576
PLANET 577
Computerized Methods versus Human Planners 577
Dynamic Plant Layouts 578
Other Computer Methods 578
Problems for Section 10.6 579
10.7 Flexible Manufacturing Systems 582
Advantages of Flexible Manufacturing Systems 584
Disadvantages of Flexible Manufacturing Systems 584
Decision Making and Modeling of the FMS 585
The Future of FMS 588
Problems for Section 10.7 590
10.8 Locating New Facilities 590
Snapshot Application：Kraft Foods Uses Optimization and Simulation to Determine Best Layout 591
Measures of Distance 592
Problems for Section 10.8 593
10.9 The Single-Facility Rectilinear Distance Location Problem 593
Contour Lines 596
Minimax Problems 597
Problems for Section 10.9 600
10.10 Euclidean Distance Problems 601
The Gravity Problem 601
The Straight-Line Distance Problem 602
Problems for Section 10.10 603
10.11 Other Location Models 604
Locating Multiple Facilities 605
Further Extensions 606
Problems for Section 10.11 608
10.12 Historical Notes 609
10.13 Summary 610
Additional Problems on Layout and Location 611
Spreadsheet Problems for Chapter 10 616
Appendix 10-A Finding Centroids 617
Appendix 10-B Computing Contour Lines 619
Bibliography 622
Chapter 11 Quality and Assurance 624
Chapter Overview 624
Overview of This Chapter 628
11.1 Statistical Basis of Control Charts 629
Problems for Section 11.1 631
11.2 Control Charts for Variables：The-X and R Charts 633
-X Charts 636
Relationship to Classical Statistics 636
R Charts 638
Problems for Section 11.2 639
11.3 Control Charts for Attributes：The p Chart 641
p Charts for Varying Subgroup Sizes 643
Problems for Section 11.3 644
11.4 The c Chart 646
Problems for Section 11.4 648
11.5 Classical Statistical Methods and Control Charts 649
Problem for Section 11.5 649
11.6 Economic Design ofXCharts 650
Problems for Section 11.6 656
11.7 Overview of Acceptance Sampling 657
Snapshot Application：Navistar Scores with Six-Sigma Quality Program 659
11.8 Notation 660
11.9 Single Sampling for Attributes 660
Derivation of the OC Curve 662
Problems for Section 11.9 664
11.10 Double Sampling Plans for Attributes 665
Problems for Section11. 10 666
11.11 Sequential Sampling Plans 667
Problems for Section 11.11 671
11.12 Average Outgoing Quality 672
Snapshot Application：Motorola Leads the Way with Six-Sigma Quality Programs 674
Problems for Section 11.12 674
11.13 Total Quality Management 675
Definitions 675
Listening to the Customer 675
Competition Based on Quality 677
Organizing for Quality 678
Benchmarking Quality 679
The Deming Prize and the Baldrige Award 680
ISO 9000 682
Quality：The Bottom Line 683
11.14 Designing Quality into the Product 684
Design，Manufacturing，and Quality 686
11.15 Historical Notes 688
11.16 Summary 689
Additional Problems on Quality and Assurance 691
Appendix 11-A Approximating Distributions 695
Appendix 11-B Glossary of Notation for Chapter 11 on Quality and Assurance 697
Bibliography 698
Chapter 12 Reliability and Maintainability 700
Chapter Overview 700
12.1 Reliability of a Single Component 704
Introduction to Reliability Concepts 704
Preliminary Notation and Definitions 705
The Exponential Failure Law 707
Problems for Section 12.1 710
12.2 Increasing and Decreasing Failure Rates 712
Problems for Section 12.2 714
12.3 The Poisson Process in Reliability Modeling 715
Series Systems Subject to Purely Random Failures 718
Problems for Section 12.3 719
12.4 Failures of Complex Equipment 720
Components in Series 720
Components in Parallel 721
Expected Value Calculations 721
K Out of N Systems 722
Problems for Section 12.4 724
12.5 Introduction to Maintenance Models 724
12.6 Deterministic Age Replacement Strategies 726
The Optimal Policy in the Basic Case 726
A General Age Replacement Model 728
Problems for Section 12.6 732
12.7 Planned Replacement under Uncertainty 732
Planned Replacement for a Single Item 732
Block Replacement for a Group of Items 736
Problems for Section 12.7 738
12.8 Analysis of Warranty Policies 740
The Free Replacement Warranty 740
The Pro Rata Warranty 742
Extensions and Criticisms 744
Problems for Section 12.8 744
12.9 Software Reliability 745
Snapshot Application：Reliability-Centered Maintenance Improves Operations at Three Mile Island Nuclear Plant 746
12.10 Historical Notes 747
12.11 Summary 748
Additional Problems on Reliability and Maintainability 749
Appendix 12-A Glossary of Notation on Reliability and Maintainability 751
Bibliography 753
Appendix：Tables 754
Index 772