Table of Contents

Title Page

Wiley Series in Systems Engineering and Management

List of Contributors

About the Authors

Foreword

Preface

Need for More Effective Trade Studies
Audience
Themes
Book Organization
Course Outlines Using the Textbook
Notional Course Objectives
Illustrative Academic Course Outlines
Illustrative Professional Short Course Outline
Reference

Acknowledgments

International Council on Systems Engineering (Incose) Corporate Advisory Board (CAB)
Incose Technical Directors
Incose Decision Analysis Working Group
Chapter Authors
Chapter Reviewers
Department of Industrial Engineering at the University of Arkansas
Research Assistant
Final Note

About the Companion Website

Chapter 1: Introduction to Trade-Off Analysis

1.1 Introduction
1.2 Trade-off Analyses Throughout the Life Cycle
1.3 Trade-off Analysis to Identify System Value
1.4 Trade-off Analysis to Identify System Uncertainties and Risks
1.5 Trade-off Analyses can Integrate Value and Risk Analysis
1.6 Trade-off Analysis in the Systems Engineering Decision Management Process
1.7 Trade-off Analysis Mistakes of Omission and Commission
1.8 Overview of the Book
1.9 Key Terms
1.10 Exercises
References

Chapter 2: A Conceptual Framework and Mathematical Foundation for Trade-Off Analysis

2.1 Introduction
2.2 Trade-Off Analysis Terms
2.3 Influence Diagram of the Tradespace
2.4 Tradespace Exploration
2.5 Summary
2.6 Key Words
2.7 Exercises
References

Chapter 3: Quantifying Uncertainty

3.1 Sources of Uncertainty in Systems Engineering
3.2 The Rules of Probability and Human Intuition
3.3 Probability Distributions
3.4 Estimating Probabilities
3.5 Modeling Using Probability
3.6 Summary
3.7 Key Terms
3.8 Exercises
References

Chapter 4: Analyzing Resources

4.1 Introduction
4.2 Resources
4.3 Cost Analysis
4.4 Affordability Analysis
4.5 Key Terms
4.6 Excercises
References

Chapter 5: Understanding Decision Management

5.1 Introduction1
5.2 Decision Process Context
5.3 Decision Process Activities
5.4 Summary
5.5 Key Terms
5.6 Exercises
References

Chapter 6: Identifying Opportunities

6.1 Introduction
6.2 Knowledge
6.3 Decision Traps
6.4 Techniques
6.5 Tools
6.6 Illustrative Examples
6.7 Key Terms
6.8 Exercises
References

Chapter 7: Identifying Objectives and Value Measures

7.1 Introduction
7.2 Value-Focused Thinking
7.3 Shareholder and Stakeholder Value
7.4 Challenges in Identifying Objectives
7.5 Identifying the Decision Objectives
7.6 The Financial or Cost Objective
7.7 Developing Value Measures
7.8 Structuring Multiple Objectives
7.9 Illustrative Examples
7.10 Summary
7.11 Key Terms
7.12 Exercises
References

Chapter 8: Developing and Evaluating Alternatives

8.1 Introduction
8.2 Overview of Decision-making, Creativity, and Teams
8.3 Alternative Development Techniques
8.4 Assessment of Alternative Development Techniques
8.5 Alternative Evaluation Techniques
8.6 Assessment of Alternative Evaluation Techniques
8.7 Key Terms
8.8 Exercises
References

Chapter 9: An Integrated Model for Trade-Off Analysis

9.1 Introduction
9.2 Conceptual Design Example
9.3 Integrated Approach Influence Diagram
9.4 Other Types of Trade-Off Analysis
9.5 Simulation Tools
9.6 Summary
9.7 Key Terms
9.8 Exercises
References

Chapter 10: Exploring Concept Trade-Offs

10.1 Introduction
10.2 Defining the Concept Space and System Concept of Operations
10.3 Exploring the Concept Space
10.4 Trade-off Analysis Frameworks
10.5 Tradespace and System Design Life Cycle
10.6 From Point Trade-offs to Tradespace Exploration
10.7 Value-based Multiattribute TRADESPACE ANALYSIS
10.8 Illustrative Example
10.9 Conclusions
10.10 Key Terms
10.11 Exercises
References

Chapter 11: Architecture Evaluation Framework

11.1 Introduction
11.2 Key Considerations in Evaluating Architectures
11.3 Architecture Evaluation Elements
11.4 Steps in an Architecture Evaluation Process
11.5 Example Evaluation Taxonomy
11.6 Summary
11.7 Key Terms
11.8 Exercises
References

Chapter 12: Exploring the Design Space

12.1 Introduction
12.2 Example 1: Liftboat
12.3 Example 2: Cruise Ship Design
12.4 Example 3: NATO Naval Surface Combatant Ship
12.5 Key Terms
12.6 Exercises
References

Chapter 13: Sustainment Related Models and Trade Studies

13.1 Introduction
13.2 Availability Modeling and Trade Studies
13.3 Sustainment Life Cycle Cost Modeling and Trade Studies14
13.4 Optimization in Availability Trade Studies
13.5 Monte Carlo Modeling
13.6 Chapter Summary
13.7 Key Terms
13.8 Exercises
References

Chapter 14: Performing Programmatic Trade-Off Analyses

14.1 Introduction
14.2 System Acceptance Decisions and Trade Studies
14.3 Product Cancelation Decision Trade Study
14.4 Product Retirement Decision Trade Study
14.5 Key Terms
14.6 Exercises
References

Chapter 15: Summary and Future Trends

15.1 Introduction
15.2 Major Trade-Off Analysis Themes
15.3 Future of Trade-Off Analysis
15.4 Summary
References

Index

End User License Agreement

List of Illustrations

Chapter 1: Introduction to Trade-Off Analysis

Figure 1.1 Eagle's beak chart
Figure 1.2 INCOSE decision management process
Figure 1.3 Relationships among trade-off study mistakes and impacts
Figure 1.4 Outline of the book

Chapter 2: A Conceptual Framework and Mathematical Foundation for Trade-Off Analysis

Figure 2.1 Overview of integrated trade-off value, cost, and risk analysis
Figure 2.2 Single-dimensional value functions

Chapter 3: Quantifying Uncertainty

Figure 3.1 Uncertainty in gambling
Figure 3.2 Venn diagram
Figure 3.3 Normal distribution
Figure 3.4 Weibull distribution
Figure 3.5 Beta distribution
Figure 3.6 Triangular distribution
Figure 3.7 Default decision
Figure 3.8 Bayes net example
Figure 3.9 Monte Carlo simulation
Figure 3.10 Tornado diagram for sensitivity analysis

Chapter 4: Analyzing Resources

Figure 4.1 Resource space
Figure 4.2 The three components of the resource space
Figure 4.3 Example of skills for people resources
Figure 4.4 Example of types of facility resources
Figure 4.5 Cost resource by class
Figure 4.6 Example resource framework
Figure 4.7 Life cycle costing concept map (Source: Parnell et al. 2011. Reproduced with permission of John Wiley & Sons)
Figure 4.8 Power CERs
Figure 4.9 Exponential CER
Figure 4.10 Logarithm CER
Figure 4.11 Regression plot
Figure 4.12 Normal probability plot
Figure 4.13 Residual plot
Figure 4.14 Plot of learning curves (Source: Parnell et al. 2011. Reproduced with permission of John Wiley & Sons)
Figure 4.15 Ninety percent learning curve for cumulative average cost and unit cost for 32 units
Figure 4.16 Fitted line plot
Figure 4.17 Example net present calue (NPV) tornado chart for a 5-year program
Figure 4.18 Example cumulative net present value hurricane chart
Figure 4.19 Triangular distribution for embedded flight software
Figure 4.20 PDF for total person-months for embedded flight software development
Figure 4.21 PDF for software development cost
Figure 4.22 CDF for total software development costs
Figure 4.23 Sensitivity chart
Figure 4.24 “Big A” and “little a” (Source: MORS Affordability Analysis Community of Practice 2015)
Figure 4.25 Affordability analysis framework (Source: Courtesy of the Military Operations Research Society Affordability Analysis Community of Practice (or MORS AA CoP))
Figure 4.26 Lean six sigma high-level overview product example (Source: Courtesy of the Military Operations Research Society Affordability Analysis Community of Practice (or MORS AA CoP))

Chapter 5: Understanding Decision Management

Figure 5.3 Trade-off studies throughout the system's development life cycle
Figure 5.1 Decision analysis process (Courtesy of Matthew Cilli)
Figure 5.2 Integrated Systems Engineering Decision Management (ISEDM) Process Map
Figure 5.4 Key properties of a high-quality set of fundamental objectives
Figure 5.5 Example of an objectives hierarchy
Figure 5.6 Value function examples
Figure 5.7 Swing weight matrix
Figure 5.8 Objectives hierarchy for sUAV example
Figure 5.9 Graphical representations of value function graphs for sUAV example
Figure 5.10 Weights for sUAV example
Figure 5.11 Assessment flow diagram (AFD) for a hypothetical gun design choice activity (lead author's original graphic)
Figure 5.12 Radar value graph structure
Figure 5.13 Tornado graph structure
Figure 5.14 Value component graph structure
Figure 5.15 Stakeholder value scatterplot structure
Figure 5.16 Value component chart for sUAV
Figure 5.17 Value scatterplot for the sUAV example
Figure 5.18 Weightings as generated by focus group 1 and focus group 2
Figure 5.19 Weight sensitivity line graph structure
Figure 5.20 Stakeholder value scatterplot with uncertainty
Figure 5.21 sUAV performance value sensitivity to changes in priority weight of “avoid impeding soldier sprint” objectives
Figure 5.22 sUAV stakeholder value scatterplot with uncertainty
Figure 5.23 Decision support model construct

Chapter 6: Identifying Opportunities

Figure 6.1 Opportunity space role in tradespace development
Figure 6.2 Potential impact of Ill-framed opportunity space roll in tradespace development
Figure 6.3 Format of a decision hierarchy (Source: Parnell et al. 2013. Reproduced with permission of John Wiley & Sons)
Figure 6.4 Example of a vision statement
Figure 6.5 Influence diagram – same as Figure 2.1
Figure 6.6 Commercial drone decision hierarchy example
Figure 6.7 Example influence diagram for desired capability

Chapter 7: Identifying Objectives and Value Measures

Figure 7.1 Comparison of objectives and functional value hierarchy
Figure 7.2 Squad functional value hierarchy showing decomposition from function to objective to measure

Chapter 8: Developing and Evaluating Alternatives

Figure 8.1 Two phases of alternative development (Source: Parnell et al. 2013. Reproduced with permission of John Wiley & Sons)
Figure 8.2 Morphological box for bicycle suspension system (Source: Ullman 2010. Reproduced with permission of McGraw-Hill Education)
Figure 8.3 Allocation of functions to physical components for interceptor system architecture alternatives (Adapted with permission of Salvatore F. (2008). The Value of Architecture. NDIA 11th Annual Systems Engineering Conference. San Diego, US-CA)
Figure 8.4 Generic physical architecture of a hammer
Figure 8.5 Morphological box used to instantiate architectures (Source: Buede 2009. Reproduced with permission of John Wiley & Sons)
Figure 8.6 Strategy table for fifth-generation Corvette (Adapted from Barrager 2001)
Figure 8.7 Zigzagging between domains from (Szatkowski, 2000)
Figure 8.8 Typical QFD house of quality matrix
Figure 8.9 Typical QFD house of quality matrix mapping by transposing previous HOQ header row to the next HOQ column

Chapter 9: An Integrated Model for Trade-Off Analysis

Figure 9.1 Concept diagram for the integrated trade-off analysis
Figure 9.2 Integrated approach influence diagram
Figure 9.3 Functional allocation to subsystems
Figure 9.4 Value component chart for the assault scenario
Figure 9.5 Deterministic Pareto chart for the assault scenario
Figure 9.6 The integrated approach
Figure 9.7 Stochastic Pareto chart for the assault scenario
Figure 9.8 Value cumulative distribution chart for the assault scenario
Figure 9.9 Value and cost stochastic tornado diagrams for the performance alternative from the assault scenario
Figure 9.10 Value measure and cost component linkage to system features
Figure 9.11 Example of value measure and cost component linkages to system features
Figure 9.12 Value model named range data entry setup and SIPmath ribbon

Chapter 10: Exploring Concept Trade-Offs

Figure 10.1 Concept, architecture, design, and system abstraction layer examples
Figure 10.2 Example tradespace reflecting designer-controlled parameterized concepts in terms of stakeholder value metrics (Source: Ross, Massachusetts Institute of Technology, 2006. Reproduced with permission of Ross)
Figure 10.3 Concept of flexibility in tradespaces (Source: Ross 2005. Reproduced with permission of John Wiley & Sons)
Figure 10.4 A trade-off hyperspace for an unmanned vehicle
Figure 10.5 System CONOPS ontology (Adapted from Madni 2015c,d)
Figure 10.6 Anatomy of a real option (Source: Mikaelian et al. 2008. Reproduced with permission of Donna H. Rhodes)
Figure 10.7 Four types of trade-offs: 1) local points, 2) frontier points, 3) frontier sets, and 4) full tradespace exploration (Source: Ross 2005. Reproduced with permission of John Wiley & Sons)
Figure 10.8 Multiconcept tradespace with sensor swarms, aircraft, satellites, and systems of systems (SoS) composed of pairs of assets (Source: Chattopadhyay, Massachusetts Institute of Technology, 2009. Reproduced with permission of Ross)
Figure 10.9 Preference change by stakeholder shifts tradespace (Source: Ross 2005. Reproduced with permission of John Wiley & Sons)
Figure 10.10 Example tradespace: uncertainty mitigation through system portfolios (Source: Walton 2002. Reproduced with permission of the Massachusetts Institute of Technology)
Figure 10.11 Tradespace generation and exploration
Figure 10.12 Decision-maker to attribute mapping for a maritime security system
Figure 10.13 Single-attribute utility (SAU) curves for the security mission
Figure 10.14 GUI screenshot for the maritime security agent-based discrete event simulator
Figure 10.15 Example point design for a maritime security system (N = 1)
Figure 10.16 Example Pareto frontier points for a maritime security system (N = 6)
Figure 10.17 Example full tradespace and Pareto frontier set for maritime security system (N = 8586)
Figure 10.18 Tradespaces colored by number of Hermes and Shadows
Figure 10.19 Tradespaces colored by number of manned patrol boats and type of command authority
Figure 10.20 Tradespaces colored by operators per UAV and number of geographic zones

Chapter 11: Architecture Evaluation Framework

Figure 11.1 Role of architecture in the decision space
Figure 11.2 Architecture evaluation in context of other architecture processes
Figure 11.3 Addressing stakeholder concerns through the use of views and models
Figure 11.4 Architecture evaluation framework
Figure 11.5 Objectives-driven architecture evaluation
Figure 11.6 Architecture evaluation approaches: choosing one or more “lines of attack”
Figure 11.7 Value assessment methods: addressing stakeholder concerns
Figure 11.8 Architecture analysis methods: measuring architecture attributes
Figure 11.9 Architecture measurement scales and protocols
Figure 11.10 Key elements in the measurement process of ISO/IEC 15939
Figure 11.11 Business impact methods example
Figure 11.12 Mission impact methods example
Figure 11.13 Architecture attributes example

Chapter 12: Exploring the Design Space

Figure 12.1 Liftboat docked at the Bollinger Shipyard in Louisiana (Courtesy of Cliff Whitcomb)
Figure 12.2 Liftboat leg internals using a plate construction (Courtesy of Cliff Whitcomb)
Figure 12.3 Liftboat leg internals using a lattice construction (Courtesy of Cliff Whitcomb)
Figure 12.4 Liftboat tradespace for the two responses with respect to the three factors
Figure 12.5 Liftboat design tradespace of displacement and lift weight versus leg length and leg diameter, at a leg thickness of 1.875 in
Figure 12.6 Distribution of liftboat model displacement outputs for N = 5000
Figure 12.7 Trade-off space for cruise ships with variables set to the point with the highest revenue
Figure 12.8 Cruise ship design tradespace of revenue, acquisition cost, operating cost, and beam versus passenger capacity and brand quality
Figure 12.9 Cruise ship design tradespace of revenue, acquisition cost, operating cost, and beam versus passenger capacity and brand quality
Figure 12.10 Notional FSSF ship design (Source: NATO. Reproduced with permission of NATO)
Figure 12.11 Stakeholder value functions for the various high-level needs
Figure 12.12 Cost versus OMOE for FSSF surface combatant variants
Figure 12.13 Magnified plot region showing only Pareto frontier of nondominated variants for cost versus OMOE for FSSF surface combatants
Figure 12.14 Effects plot for NATO FSSF surface combatant ship
Figure 12.15 OMOE effects Pareto
Figure 12.16 Cost effects Pareto
Figure 12.17 Trade-off space of speed versus payload (Ship A11)
Figure 12.18 Trade-off space of speed versus payload where payload is restricted and the system becomes infeasible (Ship A11a)
Figure 12.19 Trade-off space of speed versus range (Ship A11a)
Figure 12.20 Trade-off space of speed versus range (Ship A11b)
Figure 12.21 Trade-off space of speed versus payload (Ship A11b)
Figure 12.22 Trade-off space of speed versus payload (Ship A11c)
Figure 12.23 Trade-off space of margin and payload (Ship A11d)

Chapter 13: Sustainment Related Models and Trade Studies

Figure 13.1 System operational concept
Figure 13.2 System operational concept (cont.)
Figure 13.3 Mission timeline
Figure 13.4 System reliability block diagram
Figure 13.5 Influence diagram for the FMDS availability model
Figure 13.6 Reduction in availability due to each failure mode
Figure 13.7 Cost category contributions to the TSLCC
Figure 13.8 A_o as a function of the maximum flight time (T_mf), number of drones per unit (N_dpu), and number of control elements (N_cpu)
Figure 13.9 A_o as a function of total system life cycle cost (TSLCC) for the designs provide in Figure 13.8 (N_cpu = 3)
Figure 13.10 The “A_o Input Parameters” portion of the Control Panel tab
Figure 13.11 The “Decision Variables, Constraints, and Results” portion of the Control Panel tab
Figure 13.12 The Calculations tab
Figure 13.13 The life cycle cost tab
Figure 13.14 Solver window
Figure 13.15 Optimization results
Figure 13.16 Availability/TSLCC tradespace (T_tf = 4 h)
Figure 13.17 Tornado diagram for N_c = 7 and N_d = 6
Figure 13.18 Postflight preparation time triangular distribution
Figure 13.19 Cumulative density functions for control/drone combinations
Figure 13.20 Tornado diagram for the FMDS system

Chapter 14: Performing Programmatic Trade-Off Analyses

Figure 14.1 Generic decision tree for acceptance decision
Figure 14.2 Excel model used to solve the example problem
Figure 14.3 Graph of risk associated with each decision option
Figure 14.4 Receiver operating characteristic curve (for T_d = 4000 h, N_fat = 34)
Figure 14.5 Receiver operating characteristic curves (for T_d = 4000 h, N_fat1 = 34, N_fat2 = 38)
Figure 14.6 Comparison of receiver operating characteristic curves for different test designs
Figure 14.7 Acceptance decision model
Figure 14.8 Expected cost versus decision option
Figure 14.9 R coding for the predictive model
Figure 14.10 p-Value significance and AIC scores for Model A
Figure 14.11 Actual project outcome versus prediction for Model A test data
Figure 14.12 ROC curve for training set and test set for Model A
Figure 14.13 Confusion matrix for train and test of Model A
Figure 14.15 Accuracy measures for test data set Model A
Figure 14.14 Accuracy measures for training data set Model A
Figure 14.16 Significance p-values and AIC for Model B
Figure 14.19 Accuracy measures for Model B
Figure 14.20 Actual project outcomes versus predictions for Predictive Model B
Figure 14.21 Generic acquisition phases, decision points, and incorporation of Predictive Model B use in DoDI directive 5000.02 operation of the Defense Acquisition System in determining if the program is viable and whether contractors are fully capable of delivering a successful system within scope, cost, quality, and schedule
Figure 14.22 Incrementally deployed software intensive program milestone decisions, decision points, and incorporation of Predictive Model B in DoDI directive 5000.02 operation of the Defense Acquisition System to inform these critical software project decisions in determining project cancelation or continuation
Figure 14.23 Project management, ISO/IEC/IEEE 15288 and 12207 systems and software life cycle processes overlay and predictive model incorporation
Figure 14.24 Reactor compartment packages buried in Trench 94 at DOE Hanford Nuclear Reservation in Washington state as of November 2009 (Source: Knot 2012)
Figure 14.25 USS Enterprise is the first nuclear-powered aircraft carrier commissioned in 1961 and decommissioned in 2011 (Source: US Deparment of the Navy 2015)
Figure 14.26 Enterprise reactor compartment package barge loading concept for preferred alternative (Source: Knot 2012)
Figure 14.27 The major components of a generic offshore oil and gas platform (Source: http://www.lumina.com/case-studies/a-win-win-solution-for-californias-offshore-oil-rigs. Reproduced with permission of Max Henrion, Lumina Decision Systems, Inc)
Figure 14.28 Decision tree showing the decommissioning alternatives considered in the study. Options with green boxes were analyzed in greater detail and gray boxes were omitted from quantitative analysis (Source: http://www.lumina.com/case-studies/a-win-win-solution-for-californias-offshore-oil-rigs. Reproduced with permission of Max Henrion, Lumina Decision Systems, Inc) (The reader is referred to the online version of this book for color indication)
Figure 14.29 Graphical user interface (GUI) for PLATFORM with separated components to define decision options, perform quantitative cost analysis of the scenarios, and conduct multiattribute analysis including all attributes (Source: http://www.lumina.com/case-studies/a-win-win-solution-for-californias-offshore-oil-rigs. Reproduced with permission of Max Henrion, Lumina Decision Systems, Inc)
Figure 14.30 Analytica influence diagram showing selected variables and influences involved in calculating the programmatic costs for decommissioning (Source: http://www.lumina.com/case-studies/a-win-win-solution-for-californias-offshore-oil-rigs. Reproduced with permission of Max Henrion, Lumina Decision Systems, Inc)
Figure 14.31 Influence diagram showing how the multiattribute analysis is based on the results of analysis of the eight key attributes used to evaluate the costs and benefits of alternative decommissioning options (Source: Henrion et al. 2015)
Figure 14.32 Definition of levels for impact on marine mammals is a qualitative attribute and contains a description and conditions that would give rise to that level. Scores of 70% and 50% are example scores to illustrate user input (Source: Henrion et al. 2015)
Figure 14.33 User interface screen to assist users in assessing swing weights for each attribute in estimating the value to a stakeholder by changing each attribute from its Worst to its Best outcome, relative to most important attribute. Cost are identified as most important attribute and assigned swing weights of 100 (Source: http://www.lumina.com/case-studies/a-win-win-solution-for-californias-offshore-oil-rigs. Reproduced with permission of Max Henrion, Lumina Decision Systems, Inc)
Figure 14.34 Range sensitivity analysis tornado chart of the difference in value between complete removal and partial removal for platform Harmony, changing the swing weights for each attribute from 0 low to 100 high and cost uncertainty from 10th to 90th percentile while keeping the other variables as their base values (Source: http://www.lumina.com/case-studies/a-win-win-solution-for-californias-offshore-oil-rigs. Reproduced with permission of Max Henrion, Lumina Decision Systems, Inc)
Figure 14.35 Preferred decision, partial removal or complete removal for each platform according to the swing weight set for Strict Compliance. The bottom row shows the number of platforms recommended for complete removal. The platforms are ordered by depth (Source: http://www.lumina.com/case-studies/a-win-win-solution-for-californias-offshore-oil-rigs. Reproduced with permission of Max Henrion, Lumina Decision Systems, Inc)
Figure 14.36 The Vee diagram and the placement of the retirement and decommissioning activity in the project life cycle (Source: U.S. Department of Transportation 2013)

Chapter 15: Summary and Future Trends

Figure 15.1 Example developmental training program for systems engineers

List of Tables

Chapter 1: Introduction to Trade-Off Analysis

Table 1.1 Partial List of Decision Opportunities throughout the Life Cycle
Table 1.2 Sources of Systems Risk
Table 1.3 Decision Management Process
Table 1.4 Trade-Off Mistakes
Table 1.5 Illustrative Examples

Chapter 2: A Conceptual Framework and Mathematical Foundation for Trade-Off Analysis

Table 2.1 Key Terms with Examples

Chapter 3: Quantifying Uncertainty

Table 3.1 Distribution of Injuries From a Major Accident
Table 3.2 Distributions and Their Parameters and Functions
Table 3.3 Vehicle Mass Combinations

Chapter 4: Analyzing Resources

Table 4.1 Example Soft Skills
Table 4.2 Example Hard Skills
Table 4.3 Example Set of Hard and Soft Skills for Management
Table 4.4 Example Set of Roles and Functions for People Resources
Table 4.5 Facility Examples
Table 4.6 LCC Techniques by Life Cycle Stage
Table 4.7 AACE International Cost Estimate Classification Matrix
Table 4.8 Unmanned Aerial Vehicle (UVA) Work Breakdown Structure (WBS)
Table 4.9 Linear Transformations for CERs
Table 4.10 Square Footage and Facility Costs for Manufacturing Facility Construction
Table 4.11 Factors for Various Learning Rates
Table 4.12 Unit Cost and Cumulative Average Cost
Table 4.13 Accounting System Data
Table 4.14 Natural Logarithm of Cumulative Units Completed and Cumulative Average Hours
Table 4.15 Example Net Present Value Calculations
Table 4.16 Example Net Present Value Inflation and Interest Rates

Chapter 5: Understanding Decision Management

Table 5.1 Crosswalk Between SE Terms in Figure 5.2 and INCOSE Systems Engineering Handbook V4 and ISO/IEC/IEEE 15288:2015
Table 5.2 Crosswalk Between Fundamental Objectives and Stakeholder Need Statements
Table 5.3 Illustrating the One-to-One Mapping of Objective and Measure
Table 5.4 Properties of a High-Quality Measure
Table 5.5 Measures for sUAV Example
Table 5.6 End and Inflection Points of sUAV Value Functions
Table 5.8 Descriptions for Buzzard I, Buzzard II, Cardinal I, and Cardinal II
Table 5.10 Descriptions for Robin I, Robin II, Dove I, and Dove II
Table 5.7 sUAV Physical Architecture Description
Table 5.11 Physical Architecture to Fundamental Objective Mapping
Table 5.12 Structured Scoring Sheet for a Given Measure
Table 5.13 Consequence Scorecard Structure
Table 5.14 Consequence Scorecard Example for sUAV Case Study
Table 5.15 Value Scorecard Structure
Table 5.16 Value Scorecard for sUAV example

Chapter 6: Identifying Opportunities

Table 6.1 Stakeholder Analysis Techniques
Table 6.2 Advantages and Disadvantages of Popular Survey Methods

Chapter 7: Identifying Objectives and Value Measures

Table 7.1 Preference for Types of Value Measure
Table 7.2 Value Model Structure

Chapter 8: Developing and Evaluating Alternatives

Table 8.1 The Modes for Making Decisions
Table 8.2 Belbin's Nine Roles for Team Members
Table 8.3 Ullman's Suggestions for Increasing Team Performance
Table 8.4 Ten-Step Process for Controlled Convergence
Table 8.5 Example of Generic Solutions and Specific Alternatives Using the First TRIZ Inventive Principle
Table 8.6 First TRIZ Inventive Principle Interpreted for Marketing, Sales, and Advertising
Table 8.7 Assessment of Alternative Development Techniques
Table 8.8 Initial Quantitative Measures of Value for Alternatives
Table 8.9 Initial Scoring of Pugh Matrix
Table 8.10 Updated Scoring of Pugh Matrix with Different Datum
Table 8.11 Best Practices in Forming a Basis for Good Decisions
Table 8.12 Assessment of Alternative Evaluation Techniques

Chapter 9: An Integrated Model for Trade-Off Analysis

Table 9.1 Squad Enhancement Alternatives and System Features
Table 9.2 Swing Weight Matrix for the Assault Scenario
Table 9.3 Swing Weight Matrix for the Defense Scenario
Table 9.4 Value Measures for the Squad Enhancement Design Example
Table 9.5 Squad Scores on Each Value Measure
Table 9.6 Squad Enhancement Design Example Value Functions

Chapter 10: Exploring Concept Trade-Offs

Table 10.1 Example Trade-Off Analysis Frameworks
Table 10.2 Example Value Functions
Table 10.3 Comparison of Tradespace Exploration with Optimization and Decision-Theoretic Approaches
Table 10.4 List of Attributes for Maritime Security Case
Table 10.5 System Concepts
Table 10.6 Design Variables Parametrizing System's Form and CONOPs
Table 10.7 Attributes, Cost, and MAU for Selected Pareto Points

Chapter 11: Architecture Evaluation Framework

Table 11.1 Distinctions between Value Assessment and Architecture Analysis
Table 11.2 Architecture Analysis Objectives and Criteria Examples

Chapter 12: Exploring the Design Space

Table 12.1 Fractional Factorial Design for Liftboat
Table 12.2 Cruise Ship Taguchi L18 Experimental Design
Table 12.3 Stakeholder Needs for an FSSF
Table 12.4 Prioritized Stakeholder Needs
Table 12.5 DOE Data Table for FSSF Surface Combatant
Table 12.6 Cost and OMOE for FSSF Surface Combatant Variants
Table 12.7 Design Variant

Chapter 13: Sustainment Related Models and Trade Studies

Table 13.1 Mission Activities and Nominal Times
Table 13.2 Summary of Failure Modes
Table 13.3 Excel Instantiation of the FMDS Analytic Availability Model
Table 13.4 Summary of Factors Affecting System Availability
Table 13.5 Availability Sensitivity/Trade Study (“No Standby” Drone Failure Mode)
Table 13.6 Effect of Doubling the Maximum Flight Time
Table 13.7 Availability Sensitivity/Trade Study (“No Standby” Control Element Failure Mode)
Table 13.8 Effect of Increasing the Reliability of the Control Element
Table 13.9 Effect of Increasing the Maximum Flying Time of the Drone, the Reliability of the Control Element, and the Probability of Having Drone Spare Parts
Table 13.10 Model Input Parameters and LCC Calculations
Table 13.11 Total O&S Life Cycle Cost Model (Reference Values)
Table 13.12 Integrated Life Cycle Cost Model for N_dpu = 6, N_cpu = 3, and T_mf = 5 h
Table 13.13 Total Life Cycle Cost (C_tlc) as a Function of the Maximum Flight Time (T_mf) and Number of Drones per Unit (N_dpu)

Chapter 14: Performing Programmatic Trade-Off Analyses

Table 14.1 Outcome Future Costs
Table 14.2 Decision Tree Probabilities
Table 14.3 Decision Tree Expected Costs
Table 14.4 Example Decision Tree Inputs
Table 14.5 Table of MI versus C (Time-Terminated Test, T_d = 4000 h and N_fo = 34)
Table 14.6 Parameters Used to Calculate C
Table 14.7 Expressions of Possible Outcomes of Tests and Associated Probabilities
Table 14.8 Matrix of Possible Test Outcomes of Tests and Associated Probabilities
Table 14.9 Test Design Trade Space
Table 14.10 Receiver Operating Characteristic Table (T_d = 4000 h, N_fat = 34)
Table 14.11 Receiver Operating Characteristic Table (T_d = 4000 h, N_fat1 = 34, N_fat2 = 36)
Table 14.12 Finding T_d and N_fat that Yield Desired Confidence and Power (M_d3 = 125 h)
Table 14.13 Simple Mathematical Models for the C_dd Components
Table 14.14 Simple Mathematical Models for the S_dpt Components
Table 14.15 Example Excel Implementation of the Cost Model
Table 14.16 Cost Model Results
Table 14.17 Translation for Acceptance Decision Model
Table 14.18 Snapshot of Failed Software Projects ID for Coding
Table 14.19 Failure Factor ID Coding
Table 14.20 One-Time Cost of Decommissioning the Legacy HR System and Migrating its Functionality to the New Web-Enabled Platform
Table 14.21 Cost Gains from Decommissioning the Legacy HR System and Migrating Its Functionality to Faster Web-Enabled Technology Platform
Table 14.22 The Retirement Decision Matrix When Translating the Resulting Net Present Value of a System
Table 14.23 Calculations for the NPV, ROI, and Break-Even Analysis for Decommissioning the Legacy System and Developing the New Web-Enabled System
Table 14.24 Several Relevant Factors in Determining the Decision to Retire/Decommission the ENTERPRISE
Table 14.25 Summary of Finding and Characteristics of the Eight Attributes Included in the Multiattribute Analysis. The Analysis Focused on Identifying the Difference Between the Complete and Partial Removal Alternatives Across all Eight Attributes

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Title: Trade-off analytics : creating and exploring the system tradespace / [edited by] Gregory S. Parnell.

Description: Hoboken, New Jersey : John Wiley & Sons Inc., [2017] | Includes bibliographical references and index.

Identifiers: LCCN 2016023582| ISBN 9781119237532 (cloth) | ISBN 9781119238300 (epub) | ISBN 9781119237556 (Adobe PDF)

Subjects: LCSH: Systems engineering–Decision making. | Multiple criteria decision making.

Classification: LCC TA168 .T73 2017 | DDC 620.0068/4–dc23 LC record available at https://lccn.loc.gov/2016023582

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