Trade-off Analytics E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Wiley Series in Systems Engineering and Management
- Sprache: Englisch
Presents information to create a trade-off analysis framework for use in government and commercial acquisition environments
This book presents a decision management process based on decision theory and cost analysis best practices aligned with the ISO/IEC 15288, the Systems Engineering Handbook, and the Systems Engineering Body of Knowledge. It provides a sound trade-off analysis framework to generate the tradespace and evaluate value and risk to support system decision-making throughout the life cycle. Trade-off analysis and risk analysis techniques are examined. The authors present an integrated value trade-off and risk analysis framework based on decision theory. These trade-off analysis concepts are illustrated in the different life cycle stages using multiple examples from defense and commercial domains.
- Provides techniques to identify and structure stakeholder objectives and creative, doable alternatives
- Presents the advantages and disadvantages of tradespace creation and exploration techniques for trade-off analysis of concepts, architectures, design, operations, and retirement
- Covers the sources of uncertainty in the system life cycle and examines how to identify, assess, and model uncertainty using probability
- Illustrates how to perform a trade-off analysis using the INCOSE Decision Management Process using both deterministic and probabilistic techniques
Trade-off Analytics: Creating and Exploring the System Tradespace is written for upper undergraduate students and graduate students studying systems design, systems engineering, industrial engineering and engineering management. This book also serves as a resource for practicing systems designers, systems engineers, project managers, and engineering managers.
Gregory S. Parnell, PhD, is a Research Professor in the Department of Industrial Engineering at the University of Arkansas. He is also a senior principal with Innovative Decisions, Inc., a decision and risk analysis firm and has served as Chairman of the Board. Dr. Parnell has published more than 100 papers and book chapters and was lead editor of Decision Making for Systems Engineering and Management, Wiley Series in Systems Engineering (2nd Ed, Wiley 2011) and lead author of the Handbook of Decision Analysis (Wiley 2013). He is a fellow of INFORMS, the INCOSE, MORS, and the Society for Decision Professionals.
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Table of Contents
Title Page
Copyright
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
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Guide
Table of Contents
Foreword
Preface
Begin Reading
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
Trade-Off Analytics
Creating and Exploring the System Tradespace
Edited by
Gregory S. Parnell
Copyright © 2017 by John Wiley & Sons, Inc. All rights reserved
Published by John Wiley & Sons, Inc., Hoboken, New Jersey
Published simultaneously in Canada
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Library of Congress Cataloging-in-Publication Data:
Names: Parnell, Gregory S., editor.
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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List of Contributors
Paul Beery
, Systems Engineering Department, Naval Postgraduate School, Monterey, CA, USA
Robert F. Bordley
, Systems Engineering and Design, University of Michigan, Ann Arbor, MI, USA; Booz Allen Hamilton, Troy, MI, USA
Matthew Cilli
, U.S. Army Armament Research Development and Engineering Center (ARDEC), Systems Analysis Division, Picatinny, NJ, USA
Simon R. Goerger
, Institute for Systems Engineering Research, Information Technology Laboratory (ITL), U.S. Army Engineer Research and Development Center (ERDC), Vicksburg, MS, USA
Gina Guillaume-Joseph
, MITRE Corporation, McLean, VA, USA
Alexander D. MacCalman
, Department of Systems Engineering, United States Military Academy, West Point, NY, USA
John E. MacCarthy
, Systems Engineering Education Program, Institute for Systems Research, University of Maryland, College Park, MD, USA
Azad M. Madni
, Department of Astronautical Engineering, Systems Architecting and Engineering and Astronautical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
James N. Martin
, The Aerospace Corporation, El Segundo, CA, USA
Kirk Michealson
, Tackle Solutions, LLC, Chesapeake, VA, USA
William D. Miller
, Innovative Decisions, Inc., Vienna, VA, USA
Gregory S. Parnell
, Department of Industrial Engineering, University of Arkansas, Fayetteville, AR, USA
Edward A. Pohl
, Department of Industrial Engineering, University of Arkansas, Fayetteville, AR, USA
Donna H. Rhodes
, Sociotechnical Systems Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA
C. Robert Kenley
, School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
Garry Roedler
, Corporate Engineering LM Fellow, Engineering Outreach Program Manager, Lockheed Martin Corporation King of Prussia, PA
Adam M. Ross
, Systems Engineering Advancement Research Initiative (SEAri), Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
Sam Savage
, School of Engineering, Stanford University, Stanford, CA, USA
Andres Vargas
, Department of Industrial Engineering, University of Arkansas, Fayetteville, AR, USA
Clifford Whitcomb
, Systems Engineering Department, Naval Postgraduate School in Monterey, Monterey, CA, USA
About the Authors
Gregory S. Parnell is Director, M.S. in Operations Management and Research Professor in the Department of Industrial Engineering at the University of Arkansas. He teaches systems engineering, decision analysis, operations management, and IE design courses. He coedited Decision Making for Systems Engineering and Management, Wiley Series in Systems Engineering, 2nd Ed, Wiley & Sons Inc., 2011, and cowrote the Wiley & Sons Handbook of Decision Analysis, 2013. Dr Parnell has taught at West Point, the United Stated Air Force Academy, Virginia Commonwealth University, and the Air Force Institute of Technology. He is a fellow of the International Committee on Systems Engineering (INCOSE), the Institute for Operations Research & Management Science, Military Operations Research Society, the Society for Decision Professionals, and the Lean Systems Society. During his Air Force career, he served in a variety of R&D positions and operations research positions including at the Pentagon where he led two analysis divisions supporting senior Air Force leadership. He is a retired Colonel in the US Air Force. Dr Parnell received a B.S. in Aerospace Engineering from the University of Buffalo, an M.E. in Industrial & Systems Engineering from the University of Florida, an M.S. in Systems Management from the University of Southern California, and a Ph.D. in Engineering-Economic Systems from Stanford University.
Robert F. Bordley is an adjunct professor of decision analysis and systems engineering at the University of Michigan and a full-time consultant for Booz Allen Hamilton. Bob was formerly technical Fellow at General Motors and a Program Director at the National Science Foundation. His Ph.D., M.S., and MBA in Operations Research are from the University of California, Berkeley with an M.S. in Systems Science, B.S. in Physics, and B.A. in Public Policy from Michigan State University. He is an INCOSE-certified expert systems engineering professional (ESEP), an INFORMS-certified analytic professional (CAP), a professional statistician (PStat), and a certified Project Management Professional (PMP). Bob is a Fellow of the Institute for Operations Research and Management Sciences, a Fellow of the American Statistical Association, and a Fellow of the Society of Decision Professionals. Bob also received the 2004 Best Decision Analysis Publication Award. At the National Science Foundation, he served as Program Director for Decision, Risk and Management Sciences. As Technical Fellow at General Motors, he received GM's Chairman Award, President's Council Award, Research Award of Excellence, GM's Engineering Award of Excellence, and UAW-GM Quality Award. Bob led the mission analysis group in Project Trilby, which helped launch GM's vehicle systems engineering effort as well as its R&D portfolio management group. Bob was also a Technical Director on GM's corporate strategy staff and served as internal consultant to GM's marketing, product planning, and quality engineering staffs. At Booz Allen Hamilton, Bob supports requirements management and concept selection for the Army.
Matthew Cilli received his Ph.D. in Systems Engineering from Stevens Institute of Technology in Hoboken, NJ, and leads an analytics group at the US Army's Armament Research Development and Engineering Center (ARDEC) in Picatinny, NJ. His research interests are focused on improving strategic decision-making through the integrated application of holistic thinking and analytics. Prior to his current position, Dr Cilli accumulated over 20 years of experience developing proposals, securing resources, and leading effective technology development programs for the US Army. Dr Cilli graduated from Villanova University, Villanova, PA, with a Bachelor of Electrical Engineering and a Minor in Mathematics in May 1989. He is also a graduate of the Polytechnic University, Brooklyn, NY, with a Master of Science – Electrical Engineering received in January 1992 and in May 1998, graduated from the University of Pennsylvania, Wharton Business School, Philadelphia, PA, with a Masters of Technology Management.
Simon R. Goerger is the ERDC Director of the Institute for Systems Engineering Research (ISER) at the Information Technology Laboratory (ITL) of the Engineer Research and Development Center (ERDC) in Vicksburg, MS. He has been an Operations Research Analyst with the US Army Corps of Engineers since 2012. Prior to working for the Corps of Engineers, he was a Colonel in the US Army serving as the Director of the Department of Defense Readiness Reporting System (DRRS) Implementation Office (DIO). Simultaneously, he served as Senior Defense Readiness Analyst in the Office of the Undersecretary of Defense (Personnel and Readiness). Simon has served as an Assistant Professor and the Director of the Operations Research Center of Excellence in the Department of Systems Engineering at the United States Military Academy, West Point, NY, before deploying to serve as the Joint Multinational Networks Division Chief, Coalition Forces Land Combatant Command/US Army Central Command, Kuwait. He received his Bachelor of Science from the United States Military Academy, his Master of Science (M.S.) in National Security Strategy from the National War College, and his M.S. in Computer Science and Doctorate of Philosophy in Modeling and Simulations from the Naval Postgraduate School. He is a board member for the Military Operations Research Society. His research interests include decision analysis, systems modeling, tradespace analysis, and combat modeling and simulations.
Dr Gina Guillaume-Joseph is an Information Systems Engineer at The MITRE Corporation in McLean, Virginia. In her current role, she acts as a trusted advisor to senior leadership in Federal Agencies by partnering with them to design enhancements to their work systems. Dr Guillaume-Joseph's work has led to improvements that allow the systems and processes to operate more efficiently and effectively in fulfillment of specific functions. Her various roles have included project manager, software developer, test engineer, and quality assurance engineer within the private, government consulting, nonprofit, and telecommunications arenas. Dr Guillaume-Joseph is President of the INCOSE Washington Metro Area (WMA) Chapter. Dr Guillaume-Joseph has a strong record of success based on direct personal contributions. She leads and develops teams that are adaptive, flexible, and highly responsive in the exceptionally dynamic environment of Government support. Her accomplishments and successes are based on strong program performance, leadership discipline, a commitment to developing relevant, innovative and adaptive solutions, and a vigilant focus on best value solutions for her clients. Dr Guillaume-Joseph has advanced knowledge of software development lifecycle activities, such as agile, waterfall, iterative, incremental, and associated processes including planning, requirements management, design and development, testing, and deployment. Her strong communication skills make her adept at conveying specialized technical information to nontechnical audiences. Dr Guillaume-Joseph received her B.A. in Computer Science from Boston College and M.S. in Information Technology Systems from the University of Maryland. She obtained her Ph.D. in Systems Engineering from George Washington University with a topic focused on Predicting Software Project Failure Outcomes using Predictive Analytics and Modeling.
C. Robert Kenley is an Associate Professor of Engineering Practice in Purdue's School of Industrial Engineering in West Lafayette, IN. He teaches courses in systems engineering at Purdue and has over 30 years of experience in industry, academia, and government as a practitioner, consultant, and researcher in systems engineering. He has published papers on systems requirements, technology readiness assessment and forecasting, Bayes nets, applied meteorology, the impacts of nuclear power plants on employment, agent-based simulation, and model-based systems engineering. Professor Kenley holds a Bachelor of Science in Management from Massachusetts Institute of Technology (MIT), a Master of Science in Statistics from Purdue University, and a Doctor of Philosophy in Engineering-Economic Systems from Stanford University.
Azad M. Madni is a Professor of Astronautical Engineering and the Technical Director of the multidisciplinary Systems Architecting and Engineering (SAE) Program at the University of Southern California's Viterbi School of Engineering. He is also a Professor of USC's Keck School of Medicine and Rossier School of Education. Dr Madni is the founder and Chairman of Intelligent Systems Technology, Inc., a high-tech company specializing in modeling, simulation, and gaming technologies for education and training. His research has been sponsored by several prominent government agencies including DARPA, DHS S&T, MDA, DTRA, ONR, AFOSR, AFRL, ARI, RDECOM, NIST, DOE, and NASA, as well as major aerospace companies including Boeing, Northrop Grumman, and Raytheon. He is the Co-Editor-in-Chief of Engineered Resilient Systems: Challenges and Opportunities in the 21st Century, Procedia Computer Science, 2014. His recent awards include the 2011 INCOSE Pioneer Award and the 2014 Lifetime Achievement Award from INCOSE-LA. He is a Fellow of AAAS, AIAA, IEEE, INCOSE, SDPS, and IETE. He is the Strategic Advisor of the INCOSE Systems Engineering Journal. He received his B.S., M.S., and Ph.D. degrees from the University of California, Los Angeles. He is also a graduate of AEA/Stanford Executive Institute for senior executives.
Alexander D. MacCalman is an Army Special Forces Officer in the Operations Research System Analyst Functional Area and has a Masters in Operations Research and a Ph.D. in Modeling and Simulations from the Naval Postgraduate School. He served in various assignments within the Special Operations and Army Analytical communities. He is currently an Assistant Professor in the Department of Systems Engineering at the United States Military Academy and works as the Systems Engineering Program Director. His research interests are in simulation experiments and how they can inform decision analysis and trade decisions.
John MacCarthy is currently serving as the Director of the Systems Engineering Education Program at the University of Maryland's Institute for Systems Research (College Park). Prior to taking this position, he completed a 28-year career as a systems engineer that included serving as a research staff member at the Institute for Defense Analyses, a senior technology and policy advisor for an senior government executive, as well as a variety of systems engineering leadership positions within Northrop Grumman and TRW (e.g., Senior Systems Engineer/Manager, Lead Systems Engineer, Manager of Proposal Operations, Deputy Director of the Center for Advanced Technology, and others). He has extensive experience in applying the full range of systems engineering processes to diverse domains that included very large defense systems and system of systems, a national nuclear waste disposal system, and a number of smaller state and local government systems. During his last 8 years in the industry, Dr MacCarthy taught a variety of graduate-level systems engineering courses as an Adjunct Professor at the University of Maryland, Baltimore County. He began his career as an Assistant Professor of physics at Muhlenberg College. He holds a Ph.D. in Physics from the University of Notre Dame, an M.S. in Systems Engineering from George Mason University, and a B.A. in Physics from Carleton College. His professional experience and interests include systems engineering; systems analysis, modeling and simulation; communications and sensor networks; sustainment engineering; life cycle cost analysis; the acquisition process; and science and engineering education.
James N. Martin is a Principal Engineer with The Aerospace Corporation. He teaches courses for The Aerospace Institute on architecting and systems engineering. Dr Martin is an enterprise architect and systems engineer developing solutions for information systems and space systems. He previously worked for Raytheon Systems Company as a lead systems engineer and architect on airborne and satellite communications networks. He has also worked at AT&T Bell Labs on wireless telecommunications products and underwater fiber optic transmission products. His book, Systems Engineering Guidebook, was published by CRC Press in 1996. He is an INCOSE Fellow and was leader of the Standards Technical Committee. Dr Martin is the founder and current leader of INCOSE's Systems Science Working Group. He received from INCOSE the Founders Award for his long and distinguished achievements in the field. Dr Martin was a key author on the BKCASE project in development of the SE Body of Knowledge (SEBOK). His main SEBOK contribution was the articles on Enterprise Systems Engineering. Dr Martin led the working group responsible for developing ANSI/EIA 632, a US national standard that defines the processes for engineering a system. He is the INCOSE representative to ISO for international standards on architecture, one of which is dealing with architecture evaluation, the topic of the chapter he wrote for this book. Dr Martin received his Ph.D. from George Mason University in Enterprise Architecture as well as a BS from Texas A&M University and an M.S. from Stanford University.
Kirk Michealson is the President of Tackle Solutions, LLC, a consulting firm for operations research analysis, project management, and training. He is an Operations Research Analyst, Fellow of the Military Operations Research Society (MORS), Lean Six Sigma Black Belt, retired Naval Officer, and an Adjunct Professor for the University of Arkansas' M.S. in Operations Management program teaching Decision Support Tools, Analytics, and Decision Models. He has degrees in Operations Research, graduating with a B.S. from the United States Naval Academy and an M.S from the Naval Postgraduate School. As a MORS Fellow, he leads the Affordability Analysis Community of Practice developing an affordability analysis process for government and industry and received the Clayton J. Thomas Award for lifetime achievement as an Operations Research Practitioner. Kirk was formerly a technical Fellow for Operations Research Analysis at Lockheed Martin where he was responsible for designing an Operations Analysis (OA) Practitioner's Success Profile and Competency Model determining the necessary skills and expertise to be a successful OA at Lockheed Martin and developing the corporate-wide experimentation process as the corporation's experimentation lead. Kirk is a retired Commander in the US Navy, and during his naval career, he was a surface warfare officer serving on ships and in various operations research positions supporting senior Navy and Department of Defense leadership.
William D. Miller
