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Library of Congress Cataloging-in-Publication Data:

Handbook of decision analysis / Gregory S. Parnell ... [et al.].

p. cm.

 Includes bibliographical references and index.

 ISBN 978-1-118-17313-8 (cloth : alk. paper) 1. Decision making. I. Parnell, Gregory S.

 HD30.23.H3542 2012

 658.4'03–dc23

2012032884

List of Figures

FIGURE 1.1  | Decision analysis process
FIGURE 2.1  | Dimensions of decision complexity
FIGURE 3.1  | The scope of decision analysis
FIGURE 3.2  | A taxonomy of decision analysis practice
FIGURE 3.3  | Single objective decision analysis
FIGURE 3.4  | Two approaches to multiple objective decision analysis
FIGURE 3.5  | Example of indifference curves
FIGURE 3.6  | Benefits of value-focused thinking
FIGURE 4.1  | Divergent and convergent thinking
FIGURE 5.1  | Six elements of decision quality
FIGURE 5.2  | Suggested prescription for resolving decisions
FIGURE 5.3  | The dialogue decision process
FIGURE 5.4  | The decision analysis cycle
FIGURE 5.5  | Systems decision process
FIGURE 5.6  | Strictly analytical process
FIGURE 5.7  | Advocacy process
FIGURE 5.8  | The Geneptin dialogue decision process
FIGURE 6.1  | Example vision statement
FIGURE 6.2  | Format of the decision hierarchy
FIGURE 6.3  | RNAS decision hierarchy
FIGURE 6.4  | Geneptin decision hierarchy
FIGURE 6.5  | Data center location decision hierarchy
FIGURE 7.1  | Objectives hierarchy for car purchase
FIGURE 7.2  | Functional value hierarchy for car purchase
FIGURE 7.3  | Comparison of objectives and functional objectives hierarchy
FIGURE 7.4  | RNAS means–ends objectives hierarchy
FIGURE 7.5  | Data center functional value hierarchy
FIGURE 8.1  | Example strategy table
FIGURE 8.2  | Defining two alternatives in a strategy table
FIGURE 8.3  | Nested strategy tables
FIGURE 8.4  | RNAS strategy table
FIGURE 8.5  | Geneptin strategy table
FIGURE 9.1  | Example influence diagram
FIGURE 9.2  | Example of an Input Table
FIGURE 9.3  | RNAS cash flow by business unit
FIGURE 9.4  | Example of a sources of value waterfall chart
FIGURE 9.5  | Waterfall chart of difference in value between alternatives
FIGURE 9.6  | Example value components chart
FIGURE 9.7  | Example of a tornado diagram
FIGURE 9.8  | Example of a difference tornado diagram
FIGURE 9.9  | Four types of value functions for increasing value
FIGURE 9.10  | Geneptin influence diagram
FIGURE 9.11  | Geneptin drill-down ID for market share
FIGURE 9.12  | Geneptin tornado diagram
FIGURE 9.13  | Data center location functional value hierarchy
FIGURE 9.14  | Data center value components chart
FIGURE 9.15  | Data center cost versus value plot
FIGURE 9.16  | Data center waterfall chart
FIGURE 9.17  | Data center sensitivity analysis for latency unnormalized swing weight without change in preferred alternative
FIGURE 9.18  | Data center latency swing weight sensitivity with change in the preferred alternative
FIGURE 10.1  | Roughneck ID fragment: decisions
FIGURE 10.2  | Roughneck ID fragment with decisions and objectives
FIGURE 10.3  | RNAS ID fragment with decisions, win-scenario variables, and value node
FIGURE 10.4  | RNAS ID fragment with predecessors of NPV Cash Flow
FIGURE 10.5  | RNAS ID fragment with predecessors of Investment/Divestment proceeds
FIGURE 10.6  | Restaurant ID fragments
FIGURE 10.7  | Complete RNAS influence diagram
FIGURE 10.8  | Probability wheel
FIGURE 10.9  | Expert assessment template
FIGURE 10.10  | RNAS documentation of oil price assessment
FIGURE 11.1  | Influence diagram for capacity planning example
FIGURE 11.2  | Schematic decision tree for capacity planning example
FIGURE 11.3  | Partial display of evaluated decision tree for capacity planning example
FIGURE 11.4  | Extended Swanson–Megill and Brown–Johnson distributions
FIGURE 11.5  | RNAS E&P profit and loss statement—growth strategy
FIGURE 11.6  | RNAS value components chart
FIGURE 11.7  | RNAS value components, as compared with the Growth strategy
FIGURE 11.8  | RNAS EV cash flows
FIGURE 11.9  | RNAS direct tornado diagrams
FIGURE 11.10    | Direct and delta tornado diagrams for team hybrid and divest hybrid
FIGURE 11.11  | One-way sensitivity analysis of RNAS hybrid strategies to Gas Price
FIGURE 11.12  | Calculating value of clairvoyance on royalty in capacity planning example
FIGURE 11.13  | Tar sands tornado diagram
FIGURE 11.14  | Tar sands construction threshold exploits optionality
FIGURE 11.15  | RNAS S-curves
FIGURE 11.16  | Tar sands value-risk profiles
FIGURE 11.17  | Flying bars chart for RNAS strategies
FIGURE 11.18  | Assessing risk tolerance
FIGURE 11.19  | EV and CE versus size of deal
FIGURE 11.20  | Geneptin flying bar chart
FIGURE 11.21  | Geneptin waterfall chart
FIGURE 12.1  | RNAS investment efficiency curve
FIGURE 12.2  | RNAS E&P production
FIGURE 12.3  | Funding areas and levels
FIGURE 12.4  | One possible portfolio
FIGURE 12.5  | Benefit vs. cost plot of one possible portfolio
FIGURE 12.6  | Trade space of portfolios
FIGURE 12.7  | Selected portfolio for Cost C, better portfolio for same Cost (X), cheaper portfolio with same benefit (Y)
FIGURE 12.8  | Curve with decreasing slope
FIGURE 12.9  | Curve with varying slope
FIGURE 12.10  | Combination of levels for curve with varying slope
FIGURE 12.11  | Trade space of data center projects
FIGURE 12.12  | Data for applications projects
FIGURE 12.13  | B/C for applications projects
FIGURE 12.14  | Efficient frontier for the data center
FIGURE 13.1  | Communication
FIGURE 13.2  | Decision analysis participants and communications paths
FIGURE 13.3  | Communicating with senior leaders
FIGURE 13.4  | Chart used to tell the story of the best technology
FIGURE 13.5  | Tar sands decision tree
FIGURE 13.6  | Data center cost versus value plot
FIGURE 14.1  | LNG plant completion date tornado diagram
FIGURE 14.2  | Plot of IA value versus life cycle phase
FIGURE 14.3  | Base practices causing the IMS schedule delay
FIGURE A.1  | Possibility tree
FIGURE A.2  | Possibility tree with two distinctions
FIGURE A.3  | Probability tree with two distinctions
FIGURE A.4  | Reversing the order of a tree
FIGURE A.5  | Probability distribution as a histogram
FIGURE A.6  | Probability distribution in cumulative form
FIGURE A.7  | Cumulative probability distribution of a discrete measure
FIGURE B.1  | Elements of an influence diagram
FIGURE B.2  | Types of influences
FIGURE B.3  | Probabilities conditional on a decision and Howard canonical form
FIGURE C.1  | The decision conferencing process

List of Tables

TABLE 1.1  | List of Technical Products and Soft Skills
TABLE 1.2  | Comparison of Three Decision Analysis Application Areas
TABLE 1.3  | Section Location of Illustrative Examples in Each Chapter
TABLE 2.1  | Techniques for Stakeholder Analysis
TABLE 4.1  | Advantages and Disadvantages of Surveys
TABLE 5.1  | Fitting the Process to the Decision
TABLE 6.1  | Concern List by Stakeholder
TABLE 6.2  | Stakeholder Issue Identification Matrix
TABLE 7.1  | Preference for Types of Value Measure
TABLE 8.1  | Strategy Table in Matrix Format
TABLE 8.2  | Data Center Strategy Generation Table
TABLE 9.1  | Deterministic Results for Manufacturing Technology Example
TABLE 9.2  | Overall Value Metric for Manufacturing Technology Example
TABLE 9.3  | The Elements of the Swing Weight Matrix
TABLE 9.4  | Data Center Single-Dimensional Value Functions
TABLE 9.5  | Data Center Swing Weight Matrix
TABLE 9.6  | Data Center Scores on Each Value Measure
TABLE 9.7  | Data Center Single-Dimensional Value Calculations for Each Value Measure
TABLE 9.8  | Data Center Normalized Swing Weights
TABLE 9.9  | Data Center Weighted Value and Total Value Calculations
TABLE 9.10    | Data Center Life Cycle Cost and Value for Each Alternative
TABLE 11.1  | Optimal Downstream Decisions in Capacity Planning Example
TABLE 11.2  | RNAS Value Components
TABLE 11.3  | Two-Way Sensitivity Analysis of RNAS Hybrid Strategies to Gas Price and E&P Value
TABLE 12.1  | RNAS Portfolio Metrics
TABLE 12.2  | Projects Requesting Funding
TABLE 12.3  | MODA Value Scales
TABLE 12.4  | Values for Projects
TABLE 12.5  | Project Order-of-Buy
TABLE 12.6  | Best Portfolio for $450M
TABLE 13.1  | Decision Team Communication Objectives and Stakeholder Objectives
TABLE 14.1  | Decision Implementation Roles and Questions
TABLE A.1  | Equalities When Combining Uncertain Quantities
TABLE C.1  | Advantages and Disadvantages

Foreword

This handbook represents a significant advance for decision professionals. Written for practitioners by practitioners who respect the theoretical foundations of decision analysis, it provides a useful map of the tools and capabilities of effective practitioners. I anticipate that this and future editions will become the primary repository of the body of knowledge for practicing decision professionals.

This Handbook Is Timely

The practice of decision analysis (DA) is at a major inflection point. That high-quality decisions can generate immense value is being demonstrated again and again. Leaders of organizations are increasingly aware of how opportunities are lost by making “satisficing” decisions—that is, decisions that are “good enough.” The benefit-to-cost ratio of investing in better decisions is frequently a thousand to one. I know of no better opportunity for value creation anywhere. As Frank Koch,¹ president of the Society of Decision Professionals (SDP), has said, “Benefit to cost ratios … are immense simply because the added cost of doing DA is negligible. We would still be paying the analysts and decision makers without DA; they would simply be talking about different things. The incremental cost of having a better, more relevant conversation is zero, so regardless of the benefit, the ratio is infinite! Even if I throw in the cost of training and learning some software, that’s measured in thousands and the benefits are clearly measured in millions.”

Why is this huge opportunity still a secret from most decision makers? It is because we humans are wired to believe that we are making good decisions even when we leave value on the table. We are wired to be satisfied with good enough. We shape our memories with hindsight and rationalization. The burgeoning set of literature from the behavioral decision sciences documents many of our biases and draws attention to the gap between true decision quality (DQ) (see Chapter 5) and our natural decision-making tendencies.

Our individual cognitive biases are amplified by social behavior, like groupthink. We assume that advocacy decision processes in use by most organizations produce good decisions, yet they are designed to suppress good alternatives. We assume that agreement is the same as DQ, yet we see a lot of agreement around nonsense. It is not uncommon to hear statements like, “I can’t believe it—what were we thinking?”

If DQ can create immense additional value in specific decisions, can we develop DQ as an organizational competence? The answer is yes, and Chevron has shown the way. Over the period in which it has implemented a deep and broad adoption of DQ, Chevron has outperformed its peer group of major oil companies in creating shareholder value. While many organizations have pockets of organizational decision quality (ODQ), to my knowledge, Chevron has the broadest and deepest adoption to date. And by “adoption,” I don’t just mean better analytics. All the major oil companies have the analytics to deal address uncertainties and risk. The difference is that the whole Chevron organization seems to be in passionate and collaborative pursuit of value creation based on quality decisions linked with effective execution. I believe that Chevron’s success is the beginning of a big wave of broad adoption of organizational DQ.²

The immense value left behind by our satisficing behaviors represents the biggest opportunity for our business and societal institutions in the coming decades. If we begin to think of these opportunity losses as an available resource, we will want to mine this immense value potential. The courts—led by the Delaware Supreme Court—are raising the bar in their interpretation of a board director’s duty of “good faith.” In the coming years, board and top management’s best defense is their documented practice of DQ.

Decision Professionals: The Practitioner Perspective

The Society of Decision Professionals³ states that the mission of decision professionals is to:

Bring DQ to important and complex decisions.

Provide quality insights and direction through use of practical tools and robust methodologies.

Promote high professional standards and integrity in all work done by decision professionals.

Advance the profession to the benefit of mankind through helping decision makers.

The role of a decision professional as a practitioner of DA and facilitator of organizational alignment is gaining acceptance. Dozens of organizations have established internal groups of professionals, designed career ladders, and developed specific competency requirements. The recently formed SDP has created a certification process and career ladder that specifies increasing competency levels for practitioners.

While there are important similarities between becoming a successful practitioner and becoming a tenured academic, there are also major differences. The decision professional is motivated by bringing clarity to complex decision situations and creating value potential in support of decision makers. He or she is less interested in the specialization required for peer-reviewed publication. Instead, the practitioner wants to acquire practical tools and relevant skills that include both analytical and facilitation skills (project management, conflict resolution, and other so-called “soft skills”).

The ability to address both organizational and analytical complexity (see Figure F.1) are of great importance to the practitioner. As I like to say, “If you can only deal with the analytical complexity, you can get the right answer—but nobody cares. If you can only facilitate in the face of organizational complexity, you can resolve conflicts and gain agreement—but it can be agreement around nonsense.” To bring full value, we need to deliver the combination—agreement around the best answer, the answer that generates the greatest value potential.

Figure F.1 Two dimensions of competence.

Individual decision professionals can deliver this if we have the competency in both areas. However, many practitioners are significantly better in one or the other—either strong analytical capabilities or strong social/emotional intelligence and facilitation skills. Therefore, many practitioners find it best to team up with others to deliver the full value of DQ. To make such teaming effective, there must be mutual respect for the other competency and a recognition that value creation from the combination is the goal. It bears repeating: We need to gain alignment around the best answer—the answer that creates the greatest value potential.

As practitioners we are always approximating and simplifying. We are practical decision engineers and decision facilitators who want robust solutions that are effective in creating a lot of potential value. We are organizational facilitators who are not satisfied unless the best decision is owned by the decision makers and implementers. Incisiveness with tools that produce insight and processes that foster effective engagement are more important to us than another refinement to the axioms of normative decision theory. In my experience, the academic debates at the edges of decision science over the last two decades have contributed surprisingly little to the practice. Seldom is the primary challenge in solving real decision problems a matter of advanced theory.

Our goal should be to make our concepts and methods as simple and accessible as possible. As I am writing this, I am participating in a 2-week program to teach incoming high school freshmen the basics of decision quality and help them apply the concepts to significant school decision projects. I recommend that all decision professionals become engaged with spreading decision skills to youth⁴ for the simple reason that it will make one a better decision professional. Senior executives and ninth graders have about the same attention span (albeit for different reasons) and want to get to the essence simply and clearly. Even when we employ advanced tools, our results should always be made transparent.

Our Profession

What does it mean to be in a profession? A profession differs from a trade. In providing a professional service, we recognize that the customer cannot fully judge our service and must trust the integrity of the professional to act in the customer’s best interest—even when the customer does not wish to hear it. Our customers are the decision makers—the leaders of organizations. We have the responsibility to speak “truth to power.”

We also have the obligation to not “fake it.” Decision professionals must be able to recognize which tools are normative (that is consistent with the norms of decision theory) and which are not but may be useful in practice. We also have to recognize destructive or limited practices. A true decision professional avoids making claims that can be proven to violate the basic norms of decision theory.

As with the medical field, we have to protect our profession from quackery. The profession is beginning to step up to this challenge, taking measures to assure quality and certify competence. This is, of course, a sensitive area in a field that incorporates science, art, and engineering. While I recognize the risks of trying to come to agreement on a definition of decision competence, I support this trend fully and applaud the start that the Society of Decision Professionals has made.

The Biggest Challenge

In this nascent profession, our biggest challenge is to gain greater mindshare among decision makers. The fraction of important and complex decisions being made with the support of decision professionals is still very small. We can make faster progress if we unify our brand and naming conventions. I urge all practitioners to use a common language to make more headway with our audiences.

Here are my suggestions:

Let’s call ourselves “decision professionals” instead of decision analysts, decision consultants, decision advisors, decision facilitators, and so on.

Let’s use the term “decision quality” as the overall name that combines getting to the right answer via DA and gaining organizational alignment via process leadership, decision facilitation, and other soft skills.

Let’s refer to DA as the field that provides decision professionals with the analytical power to find the best alternative in situations of uncertainty, dynamics, complex preferences, and complex systems. The use of the term “DA” also means we will be consistent with the norms of decision theory—usually with a single decision-making body—whose preferences are aligned.

Multiparty decisions—negotiation, collaboration, competition (game theory)—need to become a part of the decision professional’s domain of expertise, whether or not these areas are considered a subset of or adjacent to DA.

Decision professionals frequently act as mediators and facilitators with “soft skills” to lead decision processes to reach sound conclusions and to gain alignment and commitment for action. While these skills are not usually considered a part of DA, they are as crucial as model building to the decision professional.

On behalf of the profession, I would like to express my gratitude to Greg Parnell, Steve Tani, Eric Johnson, and Terry Bresnick for creating this handbook. This handbook represents a valuable contribution to the practitioner community. I expect that it will be the first edition of many to come.

CARL SPETZLER

Notes

¹Frank Koch in a written response to the question: What is the ROI of investing in DA based on your experience at Chevron? Frank Koch retired in 2010 after the Chevron team had been awarded the best practice award for 20 Years of DA at Chevron.

²See the SDG white paper, Chevron Overcomes the Biggest Bias of All (Carl Spetzler, 2011). Available from SDG website, http://www.sdg.com.

³See: http://www.decisionprofessionals.com

⁴Check out The Decision Education Foundation at http://www.decisioneducation.org.

Preface

Our Handbook of Decision Analysis is written for the decision professional. The target audience is the decision analysis practitioner who wants to increase the breadth and depth of his or her technical skills (concepts and mathematics) and soft skills (personal and interpersonal) required for success in our field. We assume the reader has a technical (engineering, science, mathematics, or operations research) or business degree; a course in probability and statistics (Appendix A provides a probability review); and, perhaps, some introduction to single or multiple objective decision analysis in a college course or a professional short course. The book is not designed to introduce new decision analysis mathematics, but rather to make the most common mathematics and best practices available to the practitioner.

The handbook is designed to be supplemental reading for professional decision analysis training courses, a reference for beginning and experienced practitioners, and a supplemental text for an undergraduate or graduate course in decision analysis. Decision analysts work in many industries and government agencies; many work in oil and gas firms, pharmaceutical firms, and military/intelligence agencies. The book should be useful to both domestic and international practitioners.

Our handbook describes the philosophy, technical concepts, mathematics, and art of decision analysis for the decision professional. The handbook includes chapters on the following topics: decision-making challenges; mathematical foundations of decision analysis; decision analysis soft skills; selecting the decision making process for interacting with decision makers and stakeholders; framing the decision; crafting decision objectives; designing creative alternatives to create value; performing deterministic modeling and analysis of alternatives; assessing uncertainty; performing probabilistic modeling and analysis; portfolio decision analysis; communicating with senior decision makers; and implementing decisions.

Figure P.1 provides the organizational structure of the book. Chapter 1 provides an introduction to decision analysis. Chapters 2–4 provide the foundational knowledge required for decision analysis success. Chapters 5–14 provide the decision analysis best practices to create value as sequential, iterative steps. However, the order of the steps should be tailored to the application, and some steps may not apply. For example, if the decision is a choice of the best alternative, the portfolio decision analysis chapter would not apply. Also, some steps can be combined. For example, the decision framing and crafting of the decision objectives may be done at the same time. Chapter 15 provides a summary of the major themes of the book. The chapters that provide the mathematics of decision analysis are outlined with dotted lines.

FIGURE P.1. Chapter organization of the Handbook Decision of Analysis.

The book also includes key insights from decision analysis applications and behavioral decision analysis research. The handbook references decision analysis textbooks, technical books, and research papers for detailed mathematical proofs, advanced topics, and further professional reading.

The handbook has five unique features:

1. The book provides a balanced presentation of technical skills (decision analysis concepts, mathematics, and modeling) and soft skills (strategic thinking, leading teams, managing teams, researching, interviewing individuals, facilitating groups, and communicating).

2. The book integrates the techniques of single and multiple objective decision analysis instead of presenting them in separate sections of the book. Chapter 3 provides our framework.

3. The book uses three substantive illustrative examples (Roughneck North American Strategy, Geneptin Personalized Medicine, and Data Center) to illustrate the key decision analysis concepts and techniques, show the diversity of applications, and demonstrate how the techniques are tailored to different decision problems.

4. The book presents multiple qualitative and quantitative techniques for each key decision analysis task as opposed to presenting one technique for all problems. After describing the techniques, we discuss their advantages and disadvantages.

5. Supplementary material for this book can be found by entering ISBN 9781118173138 at booksupport.wiley.com. This website will contain information on the book and the Microsoft® Office Excel® files used for the three illustrative examples.

We, the coauthors, became decision analysts and strive to be decision professionals because we believe in the power of decision analysis to create value for organizations and enterprises. The art and science of decision analysis has changed our professional and personal decision making.

Writing the handbook has been a great opportunity for us to reflect on what we have learned and to describe the best practices that we use. In addition to our mentors and colleagues, we have also learned a lot from each other in the process of writing (and rewriting) this book! We look forward to hearing your comments on the handbook, and we hope that the material helps your development as a decision professional.

GREGORY S. PARNELL
TERRY A. BRESNICK
STEVEN N. TANI
ERIC R. JOHNSON

Acknowledgments

We have benefited greatly from our decision analysis colleagues and mentors. We would like to acknowledge these contributions in four categories: special, individual, chapter, and handbook reviewers.

Special Acknowledgments

The authors of this book wish to express our deep appreciation to Professor Ronald A. Howard for being our teacher, mentor, and colleague. All four of us studied under Ron at Stanford, and he inspired each of us to pursue a career as a decision professional. His formulation of decision analysis is the basis of our understanding of how to address tough decisions, and his ongoing contributions to the field have strengthened our professional practice.

In addition, the authors appreciate the support of the John Wiley & Sons OR/MS series editor Susanne Steitz-Filler, our colleagues on the board of the Society for Decision Professionals, and the anonymous Wiley OR/MS reviewers who all encouraged us to write a decision analysis handbook for decision professionals.

Individual Acknowledgments

Gregory S. Parnell acknowledges colleagues Dennis Buede, Pat Driscoll, Ralph Keeney, Robin Keller, Craig Kirkwood, and Larry Phillips for their valuable professional advice and friendship. In addition, I acknowledge Terry Bresnick for recruiting me to Innovative Decisions, Inc., which has involved me in decision analysis practice as well as education.

Terry A. Bresnick acknowledges Ed Sondik and Rex Brown for providing DA theoretical background; Cameron Peterson, Roy Gulick, and Larry Phillips for making me understand the importance of the “socio” side of decision analysis and molding my perspective on facilitation; Al Grum for being my mentor as I started my career as a decision analyst; and Dennis Buede for being my collaborator and partner for many years in our DA practice.

Eric R. Johnson acknowledges Leonard Bertrand and Jim Matheson for conversations that contributed to my perspective on decision analysis and to the handbook.

Steven N. Tani acknowledges Jim Matheson for providing vital leadership during the early years of the decision analysis profession at Stanford Research Institute and Carl Spetzler for keeping Strategic Decisions Group true to its values through many transitions in its 31-year history. Both of these colleagues have had a large and positive role in shaping my professional career.

Sean Xinghua Hu acknowledges colleague Stanhope Hopkins for refinement of some of the Geneptin case illustrations and key contribution to the development of the case Excel model.

Chapter Acknowledgments

CHAPTER 1 AND 4: SOFT SKILLS

The authors would like to acknowledge the instructors of the Soft Skills Workshops that has been taught at Institute for Operations Research and Management Science (INFORMS) and Military Operations Research Society (MORS). These individuals include Bill Klimack, Freeman Marvin, Jack Kloeber, Don Buckshaw, Dave Leonardi, and Paul Wicker. We have also benefited from discussions with colleagues at DAAG 2012, including Carl Spetzler. They have motivated us to provide explicit identification and description of soft skills that are essential for decision analysts. The identification methodology that includes personal and intrapersonal skills is our contribution to the field.

CHAPTER 2

The authors would like to acknowledge Dennis Buede and Freeman Marvin, our colleagues at Innovative Decisions, Inc. Dennis and Freeman have developed and co-taught courses on decision analysis, systems engineering, and facilitation. Their collaboration and insights have made this chapter more complete and more focused.

Handbook Chapter Reviewers

After drafting the entire book, we sent each chapter to one or two colleagues for review. We acknowledge the following individuals for their timely reviews and excellent suggestions: Ali Abbas, Ritesh Banerjee, Kevin Carpenter, Jim Chinnis, Ellen Coopersmith, Robin Dillon, Jim Felli, Dave Frye, Roy Guilick, Onder Guven, Ralph Keeney, Rob Kleinbaum, Jeff Keisler, Craig Kirkwood, Jack Kloeber, Ken Kuskey, Bill Klimack, Frank Koch, William Leaf-Herrmann, Pat Leach, Freeman Marvin, Dan Maxwell, Jason Merrick, Cam Peterson, Jan Schulze, Carl Spetzler, and Joe Tatman. Of course, any remaining errors or omission are the responsibility of the authors.

About the Authors

The handbook was written by the first four contributors. The primary authors of each chapter are on its title page; however, all four authors contributed to each chapter. The handbook has four illustrative examples; the Roughneck North American Strategy was written by Eric R. Johnson; the Geneptin was written by Sean Xinghua Hu; the Data Center Location written by Gregory S. Parnell; and the Data Center portfolio was written by Terry A. Bresnick.

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