Decision Making in Systems Engineering and Management

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.

• List of Figures xiiiList of Tables xxiii1 Working with Systems 11.1 Introduction 11.2 The Systems Engineering Perspective 71.2.1 Systems Trends That Challenge System Engineers 81.2.2 Fundamental Tasks of Systems Engineers 121.2.3 Relationship of Systems Engineers to Other Engineering Disciplines 141.2.4 Education, Training, and Knowledge of Systems Engineers 151.3 Systems thinking 171.4 System life cycles 201.4.1 System life cycle model 231.5 Other major system life cycle models 291.6 Systems Decision Process (SDP) 341.7 Stakeholders and Vested Interests 39References 472 Applied Systems Thinking 512.1 Holism FramingSystems 512.1.1 Systems versus Analytic Thinking 542.1.2 Check on Learning 562.2 Element Dependencies 572.2.1 Check on Learning 582.3 Expansive and Contractive Thinking 592.3.1 Check on Learning 602.4 Structure 612.5 Classifying Systems 682.6 Boundaries 692.7 Visibility and Spatial Arrangement 722.7.1 Visibility 722.7.2 Spatial Arrangement 742.7.3 Check on Learning 762.8 Evolution and Dynamics 77References 813 System Representations 833.1 Introduction 833.2 System Model Concepts 843.2.1 What Models Are 853.2.2 Role of Models in Solution Design 863.2.3 Qualities of useful models 873.2.4 Building System Models 893.2.5 Characteristics of models 953.2.6 Exercise the Model 963.2.7 Revise the model 973.3 Systemigrams 983.3.1 Systemigram Rules 993.4 Directional Dependency (D2) Diagrams 1023.4.1 D2 diagrams into math representations 1033.5 DSM and DMM Models 1073.5.1 Dependency Structure Matrix (DSM) 1083.5.2 System Adjacency Matrices 1143.5.3 Check on Learning 1203.5.4 Domain Mapping Matrix (DMM) 1203.6 System Dynamics 1223.7 IDEF0 Models 1293.8 Simulation Modeling 1383.8.1 Analytical Methods versus Simulation 1383.8.2 Check on Learning 1433.9 Determining Simulation Sample Size 143References 1474 The Systems Decision Process 1514.1 Introduction 1514.2 Value versus Alternative Focused Thinking 1514.3 The SDP in Detail 1544.3.1 The System Environment 1564.3.2 When to Use the Systems Decision Process 1594.3.3 Check on Learning 1614.4 The Role of Stakeholders 164References 1695 Problem Definition 1715.1 Purpose of the Problem Definition Phase 1715.1.1 Comparison with Other Systems Engineering Processes 1735.2 Research and “What is?” 1745.2.1 Check on Learning 1785.3 Stakeholder Analysis 1795.3.1 Techniques for Stakeholder Analysis 1815.3.2 At Completion FCRMatrix 1955.4 Requirements Analysis 1975.4.1 Margins 2015.5 Functional Analysis 2045.6 Assessing System Readiness 2135.7 Initial Risk Assessment 2185.7.1 Risk identification 2195.7.2 Risk Mitigation 229References 2316 Value Modeling 2356.1 Introduction 2356.2 Qualitative Value Modeling 2396.2.1 Measures 2426.3 Quantitative Value Model 2496.3.1 Value Functions 2516.3.2 Value Increment Method 2566.3.3 Weighting Options 259References 2757 Solution Design 2777.1 Introduction 2777.2 Ideation Techniques 2797.2.1 Brainstorming 2797.2.2 Brainwriting 2827.2.3 Design Thinking 2827.2.4 Affinity Diagramming 2847.2.5 Delphi 2857.2.6 Groupware 2877.2.7 Lateral and Parallel Thinking and Six Thinking Hats 2877.2.8 Morphology 2877.2.9 EndsMeansChains 2897.2.10 Other Ideation Techniques 2897.3 Screening and Feasibility 2917.4 Improving Candidate Alternatives 2967.4.1 Design of Experiments 2997.4.2 Fractional factorial design 3047.4.3 Pareto analysis 312References 3158 Costing Systems 3178.1 Introduction 3178.2 Types of Costs 3238.3 Cost Estimating Techniques 3248.3.1 Estimating by Analogy 3258.3.2 Parametric Estimation Using Cost Estimating Relationships 3268.3.3 Learning Curves 3398.4 Time Effects on Cost 3458.4.1 Time Value of Money 3458.4.2 Inflation 3468.4.3 Net Present Value 3488.4.4 Breakeven Analysis and Replacement Analysis 350References 3539 Decision Making via Tradespace Analysis 3559.1 Introduction 3559.2 Tradespace Properties 3589.3 Scoring Solution Alternatives 3609.4 Scoring Options 3639.4.1 Candidate Systems Scoring 3649.4.2 Candidate Components Scoring 3679.5 Tradespace Analysis on Scoring Results 3729.5.1 Analyzing Sensitivity on Weights 3779.5.2 Sensitivity Analysis on Weights Using Excel 3799.6 Applying ValuefocusedThinking 3809.6.1 Improving nonDominatedAlternatives 3849.6.2 Improving Dominated Alternatives 3859.7 Supporting the Tradespace Decision 3869.8 Use valuefocusedthinking to improve solutions 3889.8.1 Decision Analysis of Dependent Risks 3899.9 Reporting and Decision Handoff 3929.9.1 Developing the Report 3929.9.2 Developing the Presentation 3939.9.3 Presenting Analysis Results 3949.9.4 Concluding the Presentation 3959.9.5 Using a Storyline Approach 396References 39910 Stochastic Tradespace Analysis 40110.1 Introduction 40110.2 Uncertainty Concepts 40310.3 Flaw of Averages Considerations 40610.4 Uncertainty Distributions 40910.5 Monte Carlo Uncertainty Simulation 41010.6 Cost Uncertainty Modeling 41310.7 Realization Analysis 41710.7.1 Level 1 Analysis ChoiceSet Reduction 419References 42911 System Reliability 43311.1 Modeling System Reliability 43311.2 Math Models in Reliability 43411.2.1 Common Continuous Reliability Distributions 43811.2.2 Common Discrete Distributions 44411.2.3 Check on Learning 44611.3 Reliability Block Diagrams 44611.3.1 Series System 44911.3.2 Parallel System 45411.3.3 Combined Series and Parallel RBD 45511.3.4 KoutofN Systems 45611.3.5 Complex Systems 45611.4 Component Reliability Importance Measures 45811.4.1 Importance Measure for Series System 45911.4.2 Importance Measure for Parallel System 46111.4.3 Check on Learning 46311.5 Allocating and Improving Reliability 46311.5.1 Check on Learning 46511.6 Markov models of repairable systems 46511.6.1 Kolmogorov Differential Equations 46611.6.2 Transient Analysis 46611.6.3 Steady State Analysis 46811.6.4 CTMC Models of Repairable Systems 46911.6.5 Modeling Multiple Machine Problems 471References 47712 Solution Implementation 47912.1 Introduction 47912.2 Solution Implementation Phase 48112.3 The Initiating Process 48312.4 Planning 48512.5 Executing 48812.6 Monitoring and Controlling 48912.7 Closing 49212.8 Implementation During Life Cycle Stages 49212.8.1 Implementation in “Produce the System” 49212.8.2 Implementation in “Deploy the System” 49412.8.3 Implementation in “Operate the System” 49612.8.4 Check on Learning 499References 50313 EpilogueProfessionalPractice 50513.1 Systems Engineering Activities 50713.2 Working with the systems development team 51013.3 Building an Interdisciplinary Team 51313.4 Systems engineering responsibilities 51713.5 Roles of the Systems Engineer 52413.6 Characteristics of the Ideal Systems Engineer 52513.7 Summary 526References 527Appendix A: Realization Analysis Levels 0 and 2 529A.1 Level 0 Analysis RefinedChoice Set Identification 530A.2 Level 2 Analysis PostselectionInsights 533References 537Appendix B: Software Fundamentals 539B.1 SystemiTool 539B.2 Cambridge Advanced Modeller (CAM) 540B.3 Mathematica 542B.4 Gephi 543B.5 Vensim PLE 544B.6 SIPmath 545