Decision Making in Systems Engineering and Management

DECISION MAKING IN SYSTEMS ENGINEERING AND MANAGEMENT A thoroughly updated overview of systems engineering management and decision making In the newly revised third edition of Decision Making in Systems Engineering and Management, the authors deliver a comprehensive and authoritative overview of the systems decision process, systems thinking, and qualitative and quantitative multi-criteria value modeling directly supporting decision making throughout the system lifecycle. This book offers readers major new updates that cover recently developed system modeling and analysis techniques and quantitative and qualitative approaches in the field, including effective techniques for addressing uncertainty. In addition to Excel, six new open-source software applications have been added to illustrate key topics, including SIPmath Modeler Tools, Cambridge Advanced Modeller, SystemiTool2.0, and Gephi 0.9.2. The authors have reshaped the book’s organization and presentation to better support educators engaged in remote learning. New appendices have been added to present extensions for a new realization analysis technique and getting started steps for each of the major software applications. Updated illustrative examples support modern system decision making skills and highlight applications in hardware, organizations, policy, logistic supply chains, and architecture. Readers will also find: Thorough introductions to working with systems, the systems engineering perspective, and systems thinking In-depth presentations of applied systems thinking, including holism, element dependencies, expansive and contractive thinking, and concepts of structure, classification, and boundaries Comprehensive explorations of system representations leading to analysis In-depth discussions of supporting system decisions, including the system decision process (SDP), tradespace methods, multi-criteria value modeling, working with stakeholders, and the system environment Perfect for undergraduate and graduate students studying systems engineering and systems engineering management, Decision Making in Systems Engineering and Management will also earn a place in the libraries of practicing system engineers and researchers with an interest in the topic.

Table of Contents:
List of Figures xiii List of Tables xxiii 1 Working with Systems 1 1.1 Introduction 1 1.2 The Systems Engineering Perspective 7 1.2.1 Systems Trends That Challenge System Engineers 8 1.2.2 Fundamental Tasks of Systems Engineers 12 1.2.3 Relationship of Systems Engineers to Other Engineering Disciplines 14 1.2.4 Education, Training, and Knowledge of Systems Engineers 15 1.3 Systems thinking 17 1.4 System life cycles 20 1.4.1 System life cycle model 23 1.5 Other major system life cycle models 29 1.6 Systems Decision Process (SDP) 34 1.7 Stakeholders and Vested Interests 39 References 47 2 Applied Systems Thinking 51 2.1 Holism Framing Systems 51 2.1.1 Systems versus Analytic Thinking 54 2.1.2 Check on Learning 56 2.2 Element Dependencies 57 2.2.1 Check on Learning 58 2.3 Expansive and Contractive Thinking 59 2.3.1 Check on Learning 60 2.4 Structure 61 2.5 Classifying Systems 68 2.6 Boundaries 69 2.7 Visibility and Spatial Arrangement 72 2.7.1 Visibility 72 2.7.2 Spatial Arrangement 74 2.7.3 Check on Learning 76 2.8 Evolution and Dynamics 77 References 81 3 System Representations 83 3.1 Introduction 83 3.2 System Model Concepts 84 3.2.1 What Models Are 85 3.2.2 Role of Models in Solution Design 86 3.2.3 Qualities of useful models 87 3.2.4 Building System Models 89 3.2.5 Characteristics of models 95 3.2.6 Exercise the Model 96 3.2.7 Revise the model 97 3.3 Systemigrams 98 3.3.1 Systemigram Rules 99 3.4 Directional Dependency (D2) Diagrams 102 3.4.1 D2 diagrams into math representations 103 3.5 DSM and DMM Models 107 3.5.1 Dependency Structure Matrix (DSM) 108 3.5.2 System Adjacency Matrices 114 3.5.3 Check on Learning 120 3.5.4 Domain Mapping Matrix (DMM) 120 3.6 System Dynamics 122 3.7 IDEF0 Models 129 3.8 Simulation Modeling 138 3.8.1 Analytical Methods versus Simulation 138 3.8.2 Check on Learning 143 3.9 Determining Simulation Sample Size 143 References 147 4 The Systems Decision Process 151 4.1 Introduction 151 4.2 Value versus Alternative Focused Thinking 151 4.3 The SDP in Detail 154 4.3.1 The System Environment 156 4.3.2 When to Use the Systems Decision Process 159 4.3.3 Check on Learning 161 4.4 The Role of Stakeholders 164 References 169 5 Problem Definition 171 5.1 Purpose of the Problem Definition Phase 171 5.1.1 Comparison with Other Systems Engineering Processes 173 5.2 Research and “What is?” 174 5.2.1 Check on Learning 178 5.3 Stakeholder Analysis 179 5.3.1 Techniques for Stakeholder Analysis 181 5.3.2 At Completion FCR Matrix 195 5.4 Requirements Analysis 197 5.4.1 Margins 201 5.5 Functional Analysis 204 5.6 Assessing System Readiness 213 5.7 Initial Risk Assessment 218 5.7.1 Risk identification 219 5.7.2 Risk Mitigation 229 References 231 6 Value Modeling 235 6.1 Introduction 235 6.2 Qualitative Value Modeling 239 6.2.1 Measures 242 6.3 Quantitative Value Model 249 6.3.1 Value Functions 251 6.3.2 Value Increment Method 256 6.3.3 Weighting Options 259 References 275 7 Solution Design 277 7.1 Introduction 277 7.2 Ideation Techniques 279 7.2.1 Brainstorming 279 7.2.2 Brainwriting 282 7.2.3 Design Thinking 282 7.2.4 Affinity Diagramming 284 7.2.5 Delphi 285 7.2.6 Groupware 287 7.2.7 Lateral and Parallel Thinking and Six Thinking Hats 287 7.2.8 Morphology 287 7.2.9 EndsMeans Chains 289 7.2.10 Other Ideation Techniques 289 7.3 Screening and Feasibility 291 7.4 Improving Candidate Alternatives 296 7.4.1 Design of Experiments 299 7.4.2 Fractional factorial design 304 7.4.3 Pareto analysis 312 References 315 8 Costing Systems 317 8.1 Introduction 317 8.2 Types of Costs 323 8.3 Cost Estimating Techniques 324 8.3.1 Estimating by Analogy 325 8.3.2 Parametric Estimation Using Cost Estimating Relationships 326 8.3.3 Learning Curves 339 8.4 Time Effects on Cost 345 8.4.1 Time Value of Money 345 8.4.2 Inflation 346 8.4.3 Net Present Value 348 8.4.4 Breakeven Analysis and Replacement Analysis 350 References 353 9 Decision Making via Tradespace Analysis 355 9.1 Introduction 355 9.2 Tradespace Properties 358 9.3 Scoring Solution Alternatives 360 9.4 Scoring Options 363 9.4.1 Candidate Systems Scoring 364 9.4.2 Candidate Components Scoring 367 9.5 Tradespace Analysis on Scoring Results 372 9.5.1 Analyzing Sensitivity on Weights 377 9.5.2 Sensitivity Analysis on Weights Using Excel 379 9.6 Applying Valuefocused Thinking 380 9.6.1 Improving nonDominated Alternatives 384 9.6.2 Improving Dominated Alternatives 385 9.7 Supporting the Tradespace Decision 386 9.8 Use valuefocused thinking to improve solutions 388 9.8.1 Decision Analysis of Dependent Risks 389 9.9 Reporting and Decision Handoff 392 9.9.1 Developing the Report 392 9.9.2 Developing the Presentation 393 9.9.3 Presenting Analysis Results 394 9.9.4 Concluding the Presentation 395 9.9.5 Using a Storyline Approach 396 References 399 10 Stochastic Tradespace Analysis 401 10.1 Introduction 401 10.2 Uncertainty Concepts 403 10.3 Flaw of Averages Considerations 406 10.4 Uncertainty Distributions 409 10.5 Monte Carlo Uncertainty Simulation 410 10.6 Cost Uncertainty Modeling 413 10.7 Realization Analysis 417 10.7.1 Level 1 Analysis Choice Set Reduction 419 References 429 11 System Reliability 433 11.1 Modeling System Reliability 433 11.2 Math Models in Reliability 434 11.2.1 Common Continuous Reliability Distributions 438 11.2.2 Common Discrete Distributions 444 11.2.3 Check on Learning 446 11.3 Reliability Block Diagrams 446 11.3.1 Series System 449 11.3.2 Parallel System 454 11.3.3 Combined Series and Parallel RBD 455 11.3.4 Koutof N Systems 456 11.3.5 Complex Systems 456 11.4 Component Reliability Importance Measures 458 11.4.1 Importance Measure for Series System 459 11.4.2 Importance Measure for Parallel System 461 11.4.3 Check on Learning 463 11.5 Allocating and Improving Reliability 463 11.5.1 Check on Learning 465 11.6 Markov models of repairable systems 465 11.6.1 Kolmogorov Differential Equations 466 11.6.2 Transient Analysis 466 11.6.3 Steady State Analysis 468 11.6.4 CTMC Models of Repairable Systems 469 11.6.5 Modeling Multiple Machine Problems 471 References 477 12 Solution Implementation 479 12.1 Introduction 479 12.2 Solution Implementation Phase 481 12.3 The Initiating Process 483 12.4 Planning 485 12.5 Executing 488 12.6 Monitoring and Controlling 489 12.7 Closing 492 12.8 Implementation During Life Cycle Stages 492 12.8.1 Implementation in “Produce the System” 492 12.8.2 Implementation in “Deploy the System” 494 12.8.3 Implementation in “Operate the System” 496 12.8.4 Check on Learning 499 References 503 13 EpilogueProfessional Practice 505 13.1 Systems Engineering Activities 507 13.2 Working with the systems development team 510 13.3 Building an Interdisciplinary Team 513 13.4 Systems engineering responsibilities 517 13.5 Roles of the Systems Engineer 524 13.6 Characteristics of the Ideal Systems Engineer 525 13.7 Summary 526 References 527 Appendix A: Realization Analysis Levels 0 and 2 529 A.1 Level 0 Analysis Refined Choice Set Identification 530 A.2 Level 2 Analysis Postselection Insights 533 References 537 Appendix B: Software Fundamentals 539 B.1 SystemiTool 539 B.2 Cambridge Advanced Modeller (CAM) 540 B.3 Mathematica 542 B.4 Gephi 543 B.5 Vensim PLE 544 B.6 SIPmath 545  

About the Author :
Patrick J. Driscoll, PhD, is a Professor Emeritus of Operations Research and in the Department of Systems Engineering at the United States Military Academy. He was lead author and editor for the 3rd edition of Decision Making in Systems Engineering and Management. He has over 30 years’ experience teaching systems engineering, mathematics, and operational topics and is the former USMA Transformation Chair, Deputy Department Head, and Program Director for Systems Engineering. Gregory S. Parnell, PhD, is a Professor of Practice in the Department of Industrial Engineering at the University of Arkansas and Director, System Design and Analytics Laboratory (SyDL), and Director of the M.S. in Operations Management and M.S. In Engineering Management programs. He previously taught at the United States Military Academy, the U.S. Air Force Academy, the Virginia Commonwealth University, and the Air Force Institute of Technology. Dale L. Henderson, PhD, is a Principal Research Scientist at Amazon and former Assistant Professor in the Department of Systems Engineering at the United States Military Academy.