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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Serie: Wiley Corporate F&A
- Sprache: Englisch
An easy to implement, practical, and proven risk management methodology for project managers and decision makers Drawing from the author's work with several major and mega capital projects for Royal Dutch Shell, TransCanada Pipelines, TransAlta, Access Pipeline, MEG Energy, and SNC-Lavalin, Project Risk Management: Essential Methods for Project Teams and Decision Makers reveals how to implement a consistent application of risk methods, including probabilistic methods. It is based on proven training materials, models, and tools developed by the author to make risk management plans accessible and easily implemented. * Written by an experienced risk management professional * Reveals essential risk management methods for project teams and decision makers * Packed with training materials, models, and tools for project management professionals Risk Management has been identified as one of the nine content areas for Project Management Professional (PMP®) certification. Yet, it remains an area that can get bogged down in the real world of project management. Practical and clearly written, Project Risk Management: Essential Methods for Project Teams and Decision Makers equips project managers and decision makers with a practical understanding of the basics of risk management as they apply to project management. (PMP and Project Management Professional are registered marks of the Project Management Institute, Inc.)
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Veröffentlichungsjahr: 2013
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Contents
Cover
Contents
Epigraph
Series Page
Title Page
Copyright
Dedication
Foreword
Preface
Acknowledgments
Part One: Fundamental Uncertainty of a Project Outcome
Chapter One: Nature of Project Uncertainties
PHASES OF PROJECT DEVELOPMENT AND PROJECT OBJECTIVES
QUEST FOR PREDICTABILITY OF PROJECT OUTCOME
SOURCES AND TYPES OF DEVIATIONS FROM PROJECT OBJECTIVES
KEY OBJECTS OF RISK (OR UNCERTAINTY) MANAGEMENT: DO WE REALLY
KNOW
WHAT WE TRY TO MANAGE?
UNCERTAINTY EXPOSURE CHANGERS
CONCLUSION
NOTES
Chapter Two: Main Components of a Risk Management System
RISK MANAGEMENT PLAN
ORGANIZATIONAL FRAMEWORK
RISK MANAGEMENT PROCESS
RISK MANAGEMENT TOOLS
CONCLUSION
NOTES
Chapter Three: Adequacy of Methods to Assess Project Uncertainties
REVIEW OF DETERMINISTIC QUALITATIVE (SCORING) METHODS
REVIEW OF DETERMINISTIC QUANTITATIVE METHODS
REVIEW OF PROBABILISTIC QUALITATIVE METHODS
REVIEW OF PROBABILISTIC QUANTITATIVE METHODS
CONCLUSION
NOTES
Part Two: Deterministic Methods
Chapter Four: Uncertainty Identification
WHEN RISK MANAGEMENT BECOMES BORING
THREE DIMENSIONS OF RISK MANAGEMENT AND UNCERTAINTY IDENTIFICATION
RISK IDENTIFICATION WORKSHOPS
SOURCES OF UNCERTAINTIES AND RISK BREAKDOWN STRUCTURE
BOWTIE DIAGRAMS FOR UNCERTAINTY IDENTIFICATION
THREE-PART UNCERTAINTY NAMING
ROLE OF BIAS IN UNCERTAINTY IDENTIFICATION
ROOM FOR UNKNOWN UNKNOWNS
CONCLUSION
NOTES
Chapter Five: Risk Assessment and Addressing
DEVELOPING A RISK ASSESSMENT MATRIX
USING A RISK ASSESSMENT MATRIX FOR ASSESSMENT AS-IS
FIVE ADDRESSING STRATEGIES
ASSESSMENT AFTER ADDRESSING
PROJECT EXECUTION THROUGH RISK ADDRESSING (PETRA)
ROLE OF BIAS IN UNCERTAINTY ASSESSMENT
CONCLUSION
NOTES
Chapter Six: Response Implementation and Monitoring
MERGING RISK MANAGEMENT WITH TEAM WORK PLANS
MONITOR AND APPRAISE
WHEN UNCERTAINTIES SHOULD BE CLOSED
WHEN SHOULD RESIDUAL UNCERTAINTIES BE ACCEPTED?
CONCLUSION
NOTE
Chapter Seven: Risk Management Governance and Organizational Context
RISK MANAGEMENT DELIVERABLES FOR DECISION GATES
OWNERSHIP OF UNCERTAINTIES AND ADDRESSING ACTIONS
MANAGEMENT OF SUPERCRITICAL RISKS
RISK REVIEWS AND REPORTING
BIAS AND ORGANIZATIONAL CONTEXT
CONCLUSION
NOTES
Chapter Eight: Risk Management Tools
THREE DIMENSIONS OF RISK MANAGEMENT AND STRUCTURE OF THE UNCERTAINTY REPOSITORY
RISK DATABASE SOFTWARE PACKAGES
DETAILED DESIGN OF A RISK REGISTER TEMPLATE IN MS EXCEL
COMMERCIAL TOOLS FOR PROBABILISTIC RISK ANALYSES
CONCLUSION
NOTES
Chapter Nine: Risk-Based Selection of Engineering Design Options
CRITERIA FOR ENGINEERING DESIGN OPTION SELECTION
SCORING RISK METHOD FOR ENGINEERING DESIGN OPTION SELECTION
DECISION TREE FOR ENGINEERING DESIGN OPTION SELECTION (CONTROLLED OPTIONS)
CONCLUSION
NOTE
Chapter Ten: Addressing Uncertainties through Procurement
SOURCES OF PROCUREMENT RISKS
QUANTITATIVE BID EVALUATION
PACKAGE RISK MANAGEMENT POST-AWARD
CONCLUSION
NOTES
Chapter Eleven: Cost Escalation Modeling
OVERVIEW OF THE COST ESCALATION APPROACH
EXAMPLE OF COST ESCALATION MODELING
SELECTING THE RIGHT TIME TO PURCHASE
CONCLUSION
NOTES
Part Three: Probabilistic Monte Carlo Methods
Chapter Twelve: Applications of Monte Carlo Methods in Project Risk Management
FEATURES, VALUE, AND POWER OF MONTE CARLO METHODS
INTEGRATION OF DETERMINISTIC AND PROBABILISTIC ASSESSMENT METHODS
UNCERTAINTY OBJECTS INFLUENCING OUTCOME OF PROBABILISTIC ANALYSES
ORIGIN AND NATURE OF UNCERTAINTIES
ROLE OF CORRELATIONS IN COST AND SCHEDULE RISK ANALYSES
PROJECT COST RESERVE
PROJECT SCHEDULE RESERVE
ANATOMY OF INPUT DISTRIBUTIONS
PROBABILISTIC BRANCHING
MERGE BIAS AS AN ADDITIONAL REASON WHY PROJECTS ARE OFTEN LATE
INTEGRATED COST AND SCHEDULE RISK ANALYSIS
INCLUDING UNKNOWN-UNKNOWN ALLOWANCE IN PROBABILISTIC MODELS
CONCLUSION
NOTES
Chapter Thirteen: Preparations for Probabilistic Analysis
TYPICAL WORKFLOWS OF PROBABILISTIC COST AND SCHEDULE ANALYSES
PLANNING MONTE CARLO ANALYSIS
BASELINES AND DEVELOPMENT OF PROXIES
WHY USING PROXIES IS THE RIGHT METHOD
MAPPING OF UNCERTAIN EVENTS
BUILDING AND RUNNING MONTE CARLO MODELS
CONCLUSION
NOTES
Chapter Fourteen: Using Outputs of Monte Carlo Analyses in Decision Making
ANATOMY OF OUTPUT DISTRIBUTIONS
OVERALL PROJECT UNCERTAINTY AND CONFIDENCE LEVELS OF BASELINES
PROJECT RESERVE CRITERIA
UNCERTAINTY OF COST OUTCOME AND CLASSES OF BASE ESTIMATES
COST RESERVE DRAWDOWN
SENSITIVITY ANALYSIS
USING WHAT-IF SCENARIOS FOR ADVANCED SENSITIVITY ANALYSIS
ARE WE READY FOR CONSTRUCTION, LOGISTICS, OR TURNAROUND WINDOWS?
VALIDATING RESULTS AND CLOSING PROBABILISTIC ANALYSIS
CONCLUSION
NOTES
Part Four: Risk Management Case Study: Project Curiosity
Chapter Fifteen: Putting Together the Project Curiosity Case Study
SCOPE OF THE CASE STUDY
PROJECT CURIOSITY BASELINES
PROJECT RISK MANAGEMENT SYSTEM ADOPTED BY PROJECT CURIOSITY
OVERVIEW OF PROJECT UNCERTAINTY EXPOSURE OF PROJECT CURIOSITY
TEMPLATES FOR PROBABILISTIC COST AND SCHEDULE ANALYSES
BUILDING AND RUNNING PROJECT PROBABILISTIC COST AND SCHEDULE MODELS
THREE WHAT-IF SCENARIOS
CONCLUSION
NOTES
Chapter Sixteen: Decision Making
KEY POINTS OF THE PROBABILISTIC ANALYSIS REPORT
DECISION GATE REVIEW BOARD FINDINGS AND RECOMMENDATIONS
CONCLUSION
NOTE
About the Author
Index
End User License Agreement
List of Tables
Chapter One: Nature of Project Uncertainties
TABLE 1.1 Project Development Phases
Chapter Two: Main Components of a Risk Management System
TABLE 2.1 Roles and Responsibilities in Risk Management
Chapter Three: Adequacy of Methods to Assess Project Uncertainties
TABLE 3.1 Information for Chance Event Decision Making
Chapter Seven: Risk Management Governance and Organizational Context
TABLE 7.1 Generic Risk Management Requirements for Decision Gates
Chapter Eight: Risk Management Tools
TABLE 8.1 General Specifications of a Risk Register Software Package
Chapter Eleven: Cost Escalation Modeling
TABLE 11.1 Macroeconomic Indexes Selected for Modeling
TABLE 11.3 Assessment of Competitive Situation
Chapter Twelve: Applications of Monte Carlo Methods in Project Risk Management
TABLE 12.1 Probabilistic Origin of Merge Bias
Chapter Thirteen: Preparations for Probabilistic Analysis
TABLE 13.1 Specification of Inputs to Probabilistic Models
Chapter Fifteen: Putting Together the Project Curiosity Case Study
TABLE 15.1 Project Base Estimate and Project Duration at Summary Level
List of Illustrations
Chapter One: Nature of Project Uncertainties
FIGURE 1.1 Project Definition, Execution, Value, and Outcome
FIGURE 1.2 Three Uncertainty Degrees of Freedom
FIGURE 1.3 Uncertainty versus Certainty of Impacts and Likelihoods
FIGURE 1.4 Main Types of Project Uncertainty Changers
Chapter Two: Main Components of a Risk Management System
FIGURE 2.1 Three Dimensions of Risk Management
FIGURE 2.2 Risk Management Process
Chapter Three: Adequacy of Methods to Assess Project Uncertainties
FIGURE 3.1 Deterministic and Probabilistic versus Qualitative and Quantitative Methods of Uncertainty Assessment
FIGURE 3.2 Sample Risk Assessment Matrix
FIGURE 3.3 Representation of Activity Duration as a Triangular Distribution
FIGURE 3.4 Sample Decision Tree with Two Chance Events
FIGURE 3.5 Example of Triangular Distribution for a Foundation Cost
FIGURE 3.6 Overview of Probabilistic Cost Risk Analysis Inputs and Outputs
FIGURE 3.7 Comparison of Uncorrelated and Correlated Probabilistic Models
Chapter Four: Uncertainty Identification
FIGURE 4.1 AND and OR Logic
FIGURE 4.2 Bowtie Diagram
Chapter Five: Risk Assessment and Addressing
FIGURE 5.1 Depicting Downside Uncertainty Assessments
FIGURE 5.2 Depicting Upside Uncertainty Assessments
FIGURE 5.3 Conceptual Template of an Uncertainty Register
FIGURE 5.4 Conceptual Template for PETRA Methodology for Uncertain Events
Chapter Seven: Risk Management Governance and Organizational Context
FIGURE 7.1 Sample of Monthly Risk Reporting
Chapter Eight: Risk Management Tools
FIGURE 8.1 Sample Project Uncertainty Repository
Chapter Nine: Risk-Based Selection of Engineering Design Options
FIGURE 9.1 Sample Template for Engineering Design Option Selection
FIGURE 9.2 Decision-Making Template
FIGURE 9.3 Controlled Options Decision Tree
Chapter Ten: Addressing Uncertainties through Procurement
FIGURE 10.1 Simplified Bidding Process and Risk Management Prior to Contract Award
Chapter Eleven: Cost Escalation Modeling
FIGURE 11.1 Assessment of Regional Capital Expenditure
Chapter Twelve: Applications of Monte Carlo Methods in Project Risk Management
FIGURE 12.1 Integrated Deterministic and Probabilistic Workflow
FIGURE 12.2 General Cost Uncertainty before Addressing
FIGURE 12.3 General Cost Uncertainty after Addressing
FIGURE 12.4 Correlation Coefficients
FIGURE 12.5 Triangular Input Distribution
FIGURE 12.6 Example of Probabilistic Branching
FIGURE 12.7 Probabilistic Branching
FIGURE 12.8 Merge Bias: Converging Two Paths in a Node
FIGURE 12.9 Origin of Schedule-Driven Costs
Chapter Thirteen: Preparations for Probabilistic Analysis
FIGURE 13.1 Probabilistic Schedule Analysis Workflow
FIGURE 13.2 Probabilistic Cost Analysis Workflow
FIGURE 13.3 Concept of Conversion of Deterministic Data into Inputs to Probabilistic Schedule Model
FIGURE 13.4 Concept of Conversion of Deterministic Data into Inputs to Probabilistic Cost Model
Chapter Fourteen: Using Outputs of Monte Carlo Analyses in Decision Making
FIGURE 14.1 Typical Output of Probabilistic Analysis
FIGURE 14.2 Comparison of Cost Uncertainties of Two Projects
FIGURE 14.3 Three Methods to Increase Baseline Confidence Levels
FIGURE 14.4 Optimal Risk Management Curve
FIGURE 14.5 Project Reserves and Primary Accuracy Range
FIGURE 14.6 Sensitivity Chart of Schedule General Uncertainties
FIGURE 14.7 Sensitivity Chart of Schedule Uncertain Events
FIGURE 14.8 Sensitivity Chart of Schedule General Uncertainties and Uncertain Events (Negative Sensitivities Excluded)
FIGURE 14.9 Criticality Index Chart for Normal Activities with General Uncertainties and Uncertain Events
FIGURE 14.10 Sensitivity Chart of Cost General Uncertainties and Uncertain Events
Chapter Fifteen: Putting Together the Project Curiosity Case Study
FIGURE 15.1 Proxy Schedule of Project Curiosity
Chapter Sixteen: Decision Making
FIGURE 16.1 Project Curiosity Completion Dates As-Is
FIGURE 16.2 Project Curiosity Completion Dates To-Be-FID
FIGURE 16.3 Results of What-if Scenario: No Regulatory Delay (R1)
FIGURE 16.4 FID Dates in Presence of Regulatory Delay (R1)
FIGURE 16.5 FID Dates in Absence of Regulatory Delay (R1)
FIGURE 16.6 Project Completion Dates if Early Procurement (R11) Is Not Approved
FIGURE 16.7 Project Curiosity Cost Distribution (Unknown Uncertainties Included)
FIGURE 16.8 Project Curiosity Cost Distribution (Unknown Uncertainties Excluded)
FIGURE 16.9 Required Project Curiosity Cost Reserves versus Levels of Confidence
Guide
Cover
Table of Contents
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Additional praise for Project Risk Management: Essential Methods for Project Teams and Decision Makers
This book provides an overview of the risk landscape and zeroes in on the top most practical and efficient risk methodologies. This no-nonsense angle stems from Yuri’s hands-on experience with a number of mega-projects. A must-read for all project practitioners who wish to separate the wheat from the chaff!
Manny Samson, President, MRK2 Technical Consulting Limited
Over the years that I received Yuri’s risk management support, I have found his approach to identifying, addressing, and assessing the project risks very efficient, refreshing, and thought provoking, even in areas of work and projects that were new to him!
Martin Bloem, PQS, Principal, Project Cost Services Inc.
Dr. Raydugin’s practical approach to risk management provides the reader with a refreshing and rich experience to an old subject. His thorough examples and attention to details allows the reader to unlock the black box mysteries of risk management to further enhance the project management toolbox. This is a must-read for project management practitioners of all levels in all industries.
Mikhail Hanna, PhD, PMP, Manager of Project Services, SNC-Lavalin Inc., and Project Management Lecturer
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The Wiley Corporate F&A series provides information, tools, and insights to corporate professionals responsible for issues affecting the profitability of their company, from accounting and finance to internal controls and performance management.
Project Risk Management
Essential Methods for Project Teams and Decision Makers
YURI RAYDUGIN
Cover image: Courtesy of the Author. Cover design: Wiley
Copyright © 2013 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:
ISBN 978-1-118-48243-8 (Hardcover)
ISBN 978-1-118-74624-0 (ebk)
ISBN 978-1-118-74613-4 (ebk)
To my uncle Yuri, Lt., 97th Special Brigade, 64th Army, killed in action October 27, 1942, in Stalingrad, age 19.
To my parents, Nina and Grigory, my wife, Irina, and my sons, Eugene and Roman.
To my former, current, and future co-workers.
Foreword
I ASKED THE AUTHOR why he would want me to write a foreword to a book on risk management when this is not an area of my expertise. He replied that I was exactly the type of reader the book was aimed at—“decision makers and project team members.” He wanted the book to be a resource for both decision makers and project team members like myself. The book is written at a high level without encumbering it with much mathematics, but still has enough detail that the importance of a thorough assessment of the uncertainties in a project for the successful management of the project would be clear! This intrigued my curiosity, so I agreed to read the book. To my surprise, I found it an enjoyable read with tips on how to address issues that I might have ignored (e.g., broiler black swans) or not even realized existed (e.g., unknown unknowns). There is even some Russian humor, such as in the sidebar on “flying to the sun.” Such gems like avoiding risk in flying to the sun by only flying at night made me laugh, but I can see how similarly ridiculous risk avoidance strategies could pass muster if the right people aren’t at the table.
Although not specifically called expert opinion, the problem of anchoring or subconscious bias on the part of experts as well as hidden agendas or consciousbias is pointed out as a failure of the process to properly quantify risks (uncertainties). The hierarchy or vertical integration of risk management is particularly sensitive to this. If the error occurs at the corporate level and is passed down to the business unit charged with delivering the project, an impossible situation arises. However, this is not the focus of this book; rather it is concerned with uncertainty issues at the business-unit and project levels, but still there has to be communication across corporate levels.
Although I earlier mentioned two terms that might be considered jargon, the book attempts to minimize their use and even suggests alternative terminology. I had a special reason for mentioning these two because they are particularly applicable to the oil and gas industry. The best recent example of unknown unknowns is the explosion in the supply of natural gas. The United States is going from a country that was building terminals to import LNG (liquefied natural gas) to now building export terminals for surplus gas. This has developed over the past 10 years due to technology breakthroughs with regard to horizontal drilling and multistage fracturing, which allows natural gas to be developed commercially from tight petroleum source rocks. This has far-reaching implications as natural gas is a cleaner-burning fuel than coal and therefore internally will be the fuel of choice for utilities in the future, replacing coal. The integration of unknown unknowns seems to be an impossible task at first glance. Some light at the end of the tunnel appears in this book through correlation of past experience as to their severity by industry.
A black swan is an event that is a game changer when it occurs in a project’s external environment but that has a very low probability of happening. A broiler black swan is politically or commercially motivated and may hide a hidden agenda. An example of this is the development of the oil sands of Alberta, which has been labeled the filthiest oil on the planet and has been subject to condemnation globally by environmentally motivated groups. The author does not offer solutions to these issues; rather he is concerned with their identification and impact on the project. Once they are identified, methods to reduce their impact would be part of the risk management plan.
Being a scientist and reading a practical book on risk management authored by an engineer who has also dabbled in science and business is rather a novel exercise. Certainty is paramount in the engineer’s discipline while uncertainty is the scientist’s mantra. My discipline, geology, where decisions are made based on relatively few data, is probably one of the most important disciplines in which to apply a quantitative approach to risk management. Being from the old school, where risk was addressed in a strictly deterministic manner, it was nice to see that the author supports the premise that the uncertainties are first identified, assessed, and addressed in a deterministic manner in the initial project phases, followed by integrated probabilistic cost and schedule risk analysis (based on expert opinion and Monte Carlo statistical methodology) to define project reserves and sensitivities (including reputation, environment, and safety). Deterministic analysis is concerned with approval and implementation of addressing actions. Probabilistic analysis is concerned with optimization of baselines and allocation of adequate reserves to ensure a high probability of project success. Issues occur when moving from deterministic to probabilistic analysis. One should be aware from the start of the need to convert deterministic data to probabilistic inputs. Double counting is an issue. There is a need to get correlations properly introduced in the probability model when the uncertainties aren’t truly independent. This book addresses these issues.
Not being a statistician, I appreciate the author’s explanation of the difference between deterministic modeling and the probabilistic approach using the Monte Carlo method: “The Monte Carlo method… mimics… thousands of fully relevant albeit hypothetical projects.” An analogy to this would be government. The ideal ruler of a country is a monarch who makes all the decisions (akin to the deterministic model) while rule by the people (akin to the probabilistic model) is focused on compromise. The best system depends on the situation and the individual monarch. Neither is perfect, and checks and balances are needed, which have evolved into a system where there are elected representatives of the people (with biases) with the lead representative being a “monarch with diminished powers” due to the checks and balances in place. This is exactly what the author proposes, the “integration of deterministic and probabilistic assessment.”
It is interesting that the author is a nuclear engineer, and that Monte Carlo techniques were developed to help build the atomic bomb and now have come full circle where they are the most widely applied method in risk management. One of the strengths (and there are many) of this book is that the author is able to explain many of the procedures developed for risk assessment by analogy to physics principles while keeping the mathematics to a minimum. This is consciously done based on experience, as the author says: “My practical experience of introducing mathematical definitions related to utility functions… was a real disaster.”
I fully agree with the author when he quotes Leonardo da Vinci: “Simplicity is the ultimate sophistication.” Following this theme, Plato advised that the highest stage of learning is when experience allows one to understand the forest, and not just the trees (not Plato’s phraseology—he used the analogy of a ladder); perhaps a slight connection can be made with Nike, which popularized the expression “Just Do It!” This would suggest that the risk assessment is only as good as the quality of the expert opinion given. Risks are identified and their importance assessed so that the focus is on only the most important risks. The less important risks are adequately addressed so that they are within the companies’ risk tolerance. The more important risks are also addressed and tracked to be able to respond quickly if they approach being critical (nuclear terminology again). The author states, “Unfortunately, it is not unusual that project risks are identified and addressing actions are proposed just for show. This show is called a decision gate.” I think this is only a warning. Decision gates play an important role in both risk management and work plans. While deterministic scoring methods have their place, probabilistic assessment methods can be a utopia for practitioners who do not fully understand uncertainty assessment. To avoid falling into these traps, it is important to have someone well versed in risk management (a risk manager) to lead the team.
The book is nicely laid out in four parts. The first part looks at the risk management process at a high level, followed by more detailed descriptions of deterministic methods, followed by the probabilistic Monte Carlo method. Finally, a detailed example is presented on carbon capture and storage, illustrating the methodology of risk management step by step to the final outcome. The book accomplishes its purpose of being a practical recipe book for the nonexpert who is a decision maker or a project team member and who wishes to understand how to conduct a robust risk management process while avoiding the many pitfalls.
Dr. Bill Gunter, P. Geol., Honorary Nobel Laureate, Alberta Innovates—Technology Futures
Preface
THAT WAS THE DAY—when a grandiose multibillion-dollar mega-project that I had recently joined was pronounced dead. It had drilling pads, processing facilities, an upgrader, pipelines, roads, camps, and an airfield in its scope. It turned out that several critical risks were not properly addressed, which made uncertainty of the project outcome unacceptably high. As a result of a decision gate review, a few-million-dollar de-risking project was announced instead to prove core in situ technologies standing behind the project and address some other critical risks stemming from the key project assumptions.
“The King is dead, long live risk management!”
This was a pivotal point in my ongoing interest in project risk management, one that defined my career path for many years ahead.
There were two valuable lessons learned related to that project. First, despite the fact that the majority of the project team members were high-level specialists in project management, engineering, procurement, construction, project services, stakeholder relations, safety, environment, and so on, they were not comfortable enough in selection and application of project risk management methods. Second, even though decision makers at the divisional and corporate levels had tons of project development experience, they did not pay due attention to some particular categories of uncertainties. In both cases, the situation was exacerbated by manifestations of bias based on a degree of overconfidence and desire to push the project through decision gates to sanctioning. Another bias factor that blindfolded decision makers was the impressively growing price of oil at that time.
These two lessons led to a quest for a few simple but really effective, adequate, and practical methods of project risk management. Those methods should be understandable enough by both project teams and decision makers. To simplify, input–black box–output engagement relations between project teams, risk management, and decision makers were contemplated.
In this simplified picture of the risk management world, project team members should provide high-quality unbiased information (input) related to their disciplines to feed just a few risk methods. To do this they should know the logic behind required input information as well as its specs. It would not hurt if project team members were aware of outputs expected after processing the information they provided.
Similarly, decision makers should not merely be familiar with information required as inputs to the risk management black box. They should be utterly comfortable with interpretation of results (output) coming from the black box as outputs and be able to use them in informed, risk-based and, again, unbiased decision making.
To assure quality inputs and outputs project team members and decision makers (project practitioners) should know the methods of project risk management well enough. In a way, the black box should be seen by them as a practical risk management toolbox (not a mysterious black box) that contains a small number of slick and handy instruments. The practitioners would not be able to use all of those tools on their own, but they certainly should be familiar with their purpose, value, and applicability.
Obviously, this approach requires a project risk manager who maintains the toolbox and applies the tools in the right ways. This book defines his or her role as a custodian of the toolbox and should help to ensure that the correct inputs and outputs are provided and used.
This book is based on my project risk management involvement in almost two dozen mega-projects in the owners’ and the engineering, procurement and construction (EPC) organization environment, but I won’t name them since I’m bound by multiple confidentiality agreements (unless my participation was recognized in project reports available in the public domain). The majority of those projects belong to oil and gas, petrochemicals, and the energy industry. Pipeline, conventional oil, heavy oil and oil sands production, conventional and unconventional gas extraction, refinery, upgrader, chemical plant, CO2 sequestration, power generation, transmission line, gasification, and liquefied natural gas (LNG) projects are among them. Only the people I worked with on those projects could have guessed that some of them (both projects and people) would implicitly shine through this book. My former and current co-workers may also recognize and recall our multiple discussions as well as training sessions I provided.
The methods and insights described in this book are applicable to more than just mega-projects. The same methods and insights could be relevant to a few-hundred-thousand-dollar tie-in project, a major project to construct a several-hundred-kilometer pipeline in Alberta, and a pipeline mega-project connecting Alberta with refineries in the southern United States or eastern Canada or LNG export terminals in British Columbia. (According to common industry practice, a project is conditionally defined as mega if it has a budget of at least $1 billion. Capital projects of more than $10 million to $100 million or so could be considered major depending on their complexity and organization.)
Are the methods and insights of this book applicable to other industries? Yes and no. They are certainly applicable to any infrastructure, civil engineering, mining, metallurgy, chemicals, wind or nuclear power generation projects, and so on, regardless of their sizes. However, this book probably gets too much of capital projects’ flavor to be directly applicable to IT, pharmaceutical, consumer goods, defense, air/space initiatives, and so on, especially if R&D activities are part of those projects. I am not familiar enough with those industries and the project development practices adopted there. Prudently speaking, some efforts would be required to convert the insights of this book and make them fully adaptable to them. At the same time, a general discussion on applicability of the project risk management methods of this book to R&D projects is provided. (This does not mean that I discourage representatives of those industries to buy this book. On the contrary, I strongly believe that the main ideas and principles of risk management are universal.)
This book is not an academic study. It is rather an attempt to share my practical experience and learned lessons. This should possibly exempt me from a requirement of the extensive literature review that is common in academic books. There are three reasons for this.
First, project practitioners usually do not have regular access to academic journals. Or they simply do not have enough time for regular literature reviews. Second, it will not be an exaggeration to say that most project practitioners, including risk managers, do not read a lot of academic books. Even if they do, they do not often apply the risk management methods found in academic books to practice. Quite often risk management methodologies are forced by vendors of corresponding risk management software packages. Along with their IT tools they impose their understanding of risk management, which is not always adequate. Third, to engage project practitioners and not scare them away, I use references to a very few, really good academic books as well as to some relevant academic articles that I did read (or wrote), but only where absolutely necessary.
Some authors of academic articles might discover that this book reflects on ideas that are similar to theirs. This shows my practical contribution to support and confirmation of those ideas, which is done even without my knowledge of their origin. Where there are contradictions with some brilliant ideas, please write such misalignments off as my being biased, under-informed, or too practical.
This book is not a quest for full completeness of all known project risk management methods. On the contrary, this is a quest for selective incompleteness. Only the few practically important risk methods that make a real difference in the real project environment are part of this book. The rest of them are either briefly discussed just to outline the “edge of practical importance” or not cited at all.
On the other hand, there is a certain completeness of risk methods as they represent a sufficiently minimal set that covers all main aspects of modern risk management of capital projects and all types of uncertainties, called objects in this book. No more and no less than that! The attempt by authors to include all known risk methods and produce a sort of risk management War and Peace is one of the key reasons that practitioners are reluctant to read academic books: it is not always clear what is really important for practice when reading those types of books. A limited number of methods are required when managing the risks of real capital projects. The rest of them are not very relevant to practice. This explains the words “Essential Methods” in the book’s subtitle. Several preliminary subtitles discussed with my publisher actually contained words such as “Pareto Principle, ” “20/80 Rule, ” “Lean Approach, ” and so on. Even though these were not used, in essence I preach (but do not pontificate upon) these concepts here.
The style of the book is close to a field manual or travel notes on a journey in the field of risk management. I tried not to use a “high horse” for that journey, which is not always true of the risk management book genre. Needless to say, pontificating on a risk topic is an absolute taboo.
Although the title of this book is Project Risk Management, I understand this as “project uncertainty management.” This contradiction is explained by the fact that the purpose of risk management is to reduce overall uncertainty of project outcome. Risks, in the narrow understanding of the term, are just one of several uncertainty factors contributing to overall outcome uncertainty. The term risks in a wider understanding could mean almost everything and nothing. As the term uncertainty is less often used in project management and currently less searchable online, it was decided to keep the word Risk in the book title. Part I of the book begins with a discussion on all main categories of uncertainties (or objects of uncertainty management) that together give rise to overall project outcome uncertainty.
Selection of adequate methods for managing a particular category of uncertainty depends on the nature of the challenge. Physicists like to speculate about a method’s or model’s distance to reality. Many fundamental discoveries and their explanations in physics were done using simple but sufficient analytical techniques way before the age of computers. Even powerful computers that facilitate modeling sometimes make errors and mistakes. A selected risk method should be simple enough to be understandable by practitioners but adequate enough to produce meaningful results. We do try to find that golden mean in this book. The level of practicality and simplicity depends on particular risk management topics. If the reader finds some topics too simple and some too complicated, it is due to my searching for a robust trade-off between simplicity and adequacy.
For example, we will discuss features of robust deterministic methods for initial identification, assessment, and addressing project risks in Part II of the book. These are also good for selection of engineering design and procurement options, managing procurement risks, and evaluating cost escalation, being straightforward but pretty informative for those tasks. At the same time they are utterly useless for identifying project sensitivities, developing and allocating project reserves, and evaluating overall cost and schedule uncertainty associated with a project. They have too big a distance to reality for those challenges. In Part III, probabilistic (Monte Carlo) methods are discussed, including information on required inputs and using results in decision making. I tried to refrain from making their distance to reality too short in order to avoid excessive complexity.
However, it is pointless to promote more sophisticated probabilistic Monte Carlo techniques as a replacement for deterministic scoring methods in all situations. In the same way, quantum mechanics is not required for good-old mechanical engineering!
Probabilistic Monte Carlo techniques stemmed from statistical quantum mechanics and came of age in the 1940s in the study of the behavior of the neutron population, which had a certain relevance to the Manhattan project and its Russian counterpart. Branching out from deterministic to probabilistic risk methods in risk management resembles the transition from classic Newtonian physics to the quantum physics of Schrödinger et al.
Deterministic risk methods usually view uncertainties individually in their isolation from the other uncertainties. Probabilistic methods treat them as a population when uncertainties losing their individuality could be correlated with each other and mapped to baselines to collectively represent overall project risk exposure quantitatively. (This slightly resembles statistical behavior of the neutron population in a nuclear reactor, for example.) My challenge here is to describe some pretty sophisticated probabilistic methods used in project risk management, including their inputs and outputs, in very simple terms.
Hence, my overall goal is for a project practitioner to consider my book valuable despite the fact (or rather because) it is not a comprehensive academic volume. I would not even resent if an experienced risk manager, seasoned consultant, or sophisticated academician called it rather simple. Let’s keep in mind what Leonardo da Vinci said: “Simplicity is the ultimate sophistication.”
To explain the last statement I need to reflect on my background a bit. My teenaged skydiving experience aside, my first formal learning of risk management took place as part of the engineering and scientific curriculum at an engineering nuclear physics department. That was a fascinating experience! The level of complexity and sophistication was enormous, to say the least. But what we were taught was constructive simplicity based on the following “two commandments”:
Do not come up with a solution that is more sophisticated than is required!
Do not come up with a solution that is overly simple and inadequate!
I know that this was an attempt to address certain types of technical risks as well as psychological and organizational bias. It was also an attempt to teach young kids to use what is now called “out-of-the-box” thinking.
Whenever the topic of out-of-box thinking comes up in conversations with my friends and co-workers I usually reply that employment as a scientist implied full absence of in-box thinking, which was just part of the job description. There was no box or out-of-box thinking at all; there was just independent thinking. My readers should find traces of this in the book.
A while ago I asked myself if I should get rid of those lessons learned living and working in North America. Probably not! Comparing education in Europe and in North America I cannot help but share my observation: the purpose of education in Europe is knowledge, whereas the purpose of education in North America is immediate action. (My son’s university curriculum provides additional confirmation of this.) Neither system of education is better than the other. In some cases the North American system is much better, but not always. I feel that a broader knowledge base helps one find optimal solutions that are as simple as possible and as adequate as required. At least it should prevent psychological impulses to buy more sophisticated and expensive hardware and software tools or hire more expensive consultants in fancier ties when a challenge with any degree of novelty is looming.
Upon getting my Ph.D. in physics and mathematics in the late 1980s, I felt that I had a big void in my education related to various aspects of business and management. So, my next degree was from Henley Management College. Henley-on-Thames is a lovely and jolly old English town with unbelievably high property prices, which eventually encouraged me to choose Canada as my next destination. One key discovery on my journey to the Henley MBA was the substantial cross-pollination between mathematics and business. Mathematical methods were broadly used in business for decision making, although often in a relatively naive form. But that naïveté seemed to be justified by a quest for practicality and simplicity of applications. This made me a proponent of informed risk-based decision making whenever a decision should be made. Building simple but fully adequate decision-making models still makes my day.
Simplicity of applications is what I kept in mind when writing this book. However, the quest for adequacy of the discussed methods defined the exact level of acceptable simplicity. It’s like a simple supply-and-demand curve in economics that defines how much is required and at what price. “Everything should be made as simple as possible, but not simpler, ” as Albert Einstein used to say.
Besides anonymous examples from mega-projects I took part in, I refer to my pre-Canadian experience for additional allusions, explanations, stories, and anecdotes. For instance, I use several analogies from physics that seem to be related to risk management. Please do not regard those insights as terribly deep philosophically. However, some of them may shed additional light on the topics of this book for readers with a technical background.
Several risk-related topics are not included in this book for the sake of staying focused. For instance, features and detail comparison of risk management in owner and EPC environments, risk management in business development, integration of project risk management with corporate risk management, probabilistic project economics, process hazard analysis (PHA)/ hazard and operability (HAZOP) studies, advanced schedule risk analyses of resource loaded schedules, and so on, are not discussed in this book but could become subjects for my next book, which depends on readers’ and editors’ support.
As is often done in physics, this book is based on a few first principles.
First, the three-dimensional (3D) nature of risk management is introduced. The importance of a fourth dimension (time) is also pointed out. These include vertical (work package–project–business unit–corporation), horizontal (all project disciplines), and in-depth (partners, vendors, contractors, investors) dimensions.
Second, it is shown that to be adequate in risk management we need to talk about uncertainty management, not risk management. Degrees of freedom of uncertainties are introduced, including time. Based on those a comprehensive list of uncertainty “objects” is formulated to ensure that we do not miss or overlook anything major.
Third, main external and internal “uncertainty changers” are introduced that should influence and transform project uncertainty exposure in the course of project development and execution. Uncertainty addressing actions are positioned as one of the key internal uncertainty changers and risk management controls.
Fourth, each of the identified uncertainty object types need adequate but constructively simple methods to get managed. A minimal but comprehensive set of the most efficient and adequate methods (both deterministic and probabilistic) is selected. Those are discussed one by one in Parts II and III of the book.
Some topics are repeated several times in the book with increasing levels of detail and complexity. So, readers interested in getting to the bottom of things through the layers of information should read the whole book. Corresponding chapters could be used for reference purposes independently.
Part I may be seen as a “helicopter view” of risk management. Parts II and III are devoted to specific deterministic and probabilistic methods. Finally, Part IV provides a simplified “straw man” case study of a hypothetical project, Curiosity, where key concepts and methods introduced in Parts I, II, and III are demonstrated again, practically showing their power and value. A simplified sample project base estimate, project schedule, risk register, and integrated cost and schedule risk model are introduced to link the deterministic and probabilistic methods overviewed in this book.
It was possible to devote the case study of Part IV to various types of capital projects, from off-shore oil production, to power generation, to LNG, and so on. I decided to develop a simplified case study of a carbon capture and storage (CCS) project for several reasons. This type of project promotes a “green” approach, has a higher level of complexity, deals with new technologies, includes integration of three subprojects (CO2 capture, pipeline, and geological sequestration), and is characterized by close involvement of external stakeholders, including branches of government. It also has severe commercial risks related to immaturity of the CO2 market and the lack of infrastructure to deliver CO2 for enhanced oil recovery (CO2-EOR). At the same time, putting aside some obvious features of CCS projects, the uncertainty exposure of a CCS project is comparable to that of any capital project. Similar methods would be used to manage the uncertainties of any type of capital project as those methods are universal.
However, the key reason for selecting a CCS project for the case study was that Dr. Gunter, my former co-worker and a top world expert in CCS, kindly agreed to write a foreword to my book. As I had done risk management for more than one CCS project, his interest facilitated my choice immensely. I highly appreciate the valuable comments and insights that Dr. Gunter has contributed to the shaping of this book.
This book can be used not only by project practitioners but also by instructors who teach courses related to project risk management including PMP certification. To facilitate teaching, additional instructor’s ancillaries can be found on www.wiley.com in the form of PowerPoint presentations. These presentations are developed on a chapter-by-chapter basis for all four parts of the book.
The information provided in this book is fully sufficient for the development and implementation of a lean, effective, and comprehensive risk management system for a capital project.
Acknowledgments
I THANK MY FAMILY for support in writing this book, which turned out to be a second full-time job for several months. It deprived us of Christmas holidays, many weekends, and most evenings together.
I highly appreciate the valuable contributions that Dr. Bill Gunter has made. It would be a very different book without his support.
Although I cannot mention them all, there are dozens of former and current co-workers whom I would like to thank. They all contributed directly or indirectly to this book and made it possible, sometimes without knowing it. I am grateful to Manny Samson and Martin Bloem, two of the top cost-estimating specialists in the industry, who shaped my practical understanding of risk management. They set limits on my attempts to be too sophisticated and theoretical.
I often recall working together with prominent scientists Professor Valentine Naish and Corresponding Member of Russian Academy of Sciences Professor Eugene Turov. I will always remember them and their professional and moral authority.
Special thanks go to Doug Hubbard for his practical support in publishing this book. The influence of his excellent books on my writing style cannot be overestimated.
PART ONEFundamental Uncertainty of a Project Outcome
IN WORDS ATTRIBUTED TO Abraham Lincoln, Peter Drucker, Steve Jobs, and several other prominent individuals, the best way to predict the future is to create it. Project development could be understood as an activity to predict future project outcome through creating it. The role of risk management is to ensure a certain level of confidence in what is supposed to get created as a result.
CHAPTER ONENature of Project Uncertainties
Questions Addressed in Chapter 1
What could be expected as a project outcome?
What factors are behind deviations from the expected project outcome?
Do we really know what we try to manage?
What degrees of freedom do uncertainties have?
What are major uncertainty objects and their changers?
When is a decision really a decision and when it is an opportunity?
Is it really risk management? Or is it actually uncertainty management?
MULTIPLE FACTORS INFLUENCE OVERALL project outcome. Their nature and influence depend on how a project is developed and executed, what are project objectives and expectations of stakeholders, and so on. It is not possible to manage factors influencing project outcome without properly understanding their definition. Only when all relevant uncertainty elements are pinned down and all factors leading to uncertainty changes are clearly understood can a minimal set of adequate methods be selected to manage all of them effectively.
Those multiple uncertainty elements are called uncertainty objects in this book. Systematic definitions are proposed for all of them from first principles based on the intrinsic nature of project uncertainties along with main factors that change the objects (uncertainty changers). The purpose of these definitions is not to come up with linguistically flawless descriptions of the objects, but to reflect on their intrinsic nature. The degrees of freedom are used to classify various realizations of uncertainties. This formalized systematic consideration, which resembles symmetry analysis of physical systems, is converted to specific and recognizable types of uncertainties and changers that pertain to any capital project.
PHASES OF PROJECT DEVELOPMENT AND PROJECT OBJECTIVES
Phases of project development used in industries vary as do their definitions. They are also different in the same industry, for instance, in the case of project owners and contractors. We will use a simplified set of project phases that is common in the oil and gas industry in the owner environment (see Table 1.1).
The first three phases are often combined to front-end loading (FEL). They precede final investment decision (FID), which is supposed to be made by the end of Define, which is a crucial point for any project (no FID, no project’s future). All project objectives and baselines are supposed to be well developed prior to FID to be reviewed and (hopefully) sanctioned.
TABLE 1.1 Project Development Phases
Project Phase
Description
Identify
Commercial and technical concept is pinned down; its feasibility is considered proven by the end of Identify.
Select
Several conceptual options are outlined; one is selected for further development by the end of Select.
Define
Selected option is developed, including all baselines; it is sanctioned by the end of Define [final investment decision (FID)].
Execute
Approved project is being implemented and completed by the end of Execute.
Operate
After commissioning and startup, project is in operations during its lifetime and decommissioned by the end of Operate.
The main focus of this book is on phases preceding FID (i.e., on FEL). For this reason two main project lifecycle periods could be introduced conditionally and told apart: “Now” (FEL) and “Future.” Operate certainly belongs to Future, which could include dozens of years of project lifetime before decommissioning. Execute seems to hide in a gray area since it’s the beginning of Future. It starts at FID and doesn’t end until the project is complete. One could imagine the high spirits of a project team, decision makers, and project stakeholders when a project FID is made and announced. The boost in energy, enthusiasm, and excitement following the positive FID is certainly an attribute of a “Now-to-Future quantum leap.”
After positive FID a project is likely to have future. So, decision makers, team members, and stakeholders are interested in knowing what sort of actual future characteristics it might get upon completion. If we regard project objectives and baselines as a sketchbook put together for FID, how close would the original (i.e., project completed in Future) resemble sketches done Now?
The answer to this question becomes clear in the course of project execution. By the end of Execute there will be a pretty clear picture. The original could appear even more beautiful and attractive than the sketches of the past. This is a sign of the project’s success. But the original could also get ugly, with sketches being quite irrelevant to reality.
To continue the artistic analogy, the sketches may be done using various styles and techniques. The variety of styles could resemble anything from cubism, expressionism, and pop art, to impressionism, to realism. (Guess what these styles could mean in project management!) A “project development style” adopted by a project in FEL depends on many factors: from maturity of the company project development and governance processes and biases of team members and decision makers, to previous project development experience, to stakeholders’ expectations and activism. But what is even more important is the “project execution style.” Its abrupt change right after FID could make pre-FID sketches completely irrelevant (see Figure 1.1).
QUEST FOR PREDICTABILITY OF PROJECT OUTCOME
Figure 1.1 represents a concept of a value associated with project definition and execution. The term definition means here all activities related to FEL, and not only to the Define phase, whereas the term value could be perceived as an amalgamation of project objectives, baselines, and stakeholders’ expectations compared with the completed project. (In a simplified interpretation it could relate to either project cost or duration.) According to Figure 1.1, a project value may be characterized by a broad spectrum of outcomes, from unconditional success to complete failure. According to benchmarking data and the definition of project failure by the IPA Institute, a staggering 56% of major projects fail due to
FIGURE 1.1 Project Definition, Execution, Value, and Outcome
Budget overspending for more than 25%, and/or
Schedule slipping for more than 25%, and/or
Severe and continuing operational problems holding for at least one year.
1
Imagine what the failure numbers would be if we used 15 or 20% thresholds instead.
Project definition and execution is a battle against multiple factors of uncertainty of the project outcome. Multiple uncertainties and deviations from project objectives should be understood as inputs to project definition and execution that drive overall uncertainty of outcome. Depending on features of project development and execution, this could be either an uphill or downhill battle. Accumulated deviations from multiple project objectives and baselines upon project completion could be both favorable and unfavorable to various degrees. Decision makers, project team members, and stakeholders have a vested interest in the final outcome of a project. Was this delivered within scope and quality, according to the sanctioned budget and by the approved completion date, or was the discrepancy between baselines and reality appalling? Were changes done during project development and execution? Were they properly taken into account? What was the safety record or environmental impact in the course of project delivery? Has the owner’s reputation suffered?
All these questions emphasize multiple dimensions of project goals and uncertainty of project outcome. All project disciplines—engineering, procurement, construction, quality, project services, safety, environment, stakeholder management, and so on—take part in shaping corresponding baselines and managing multiple uncertainties at the work package and project levels. Project risk management has unique positioning, though. It not only evaluates the credibility of all project baselines but must identify and manage deviations from them in all their thinkable realizations due to multiple uncertainties.
SOURCES AND TYPES OF DEVIATIONS FROM PROJECT OBJECTIVES
Multiple uncertainty factors give rise to the overall project outcome and, hence, to deviations from the initially stated project objectives and baselines. A combination of all particular deviations from objectives in the course of project development and execution contributes to the overall uncertainty of the project outcome.
Any project objectives or baselines, such as project base estimates, schedules, or engineering design specifications, are models that try to mimic future project reality. As mentioned in the Preface, each such model may be characterized by its distance to reality.2 It would not be an exaggeration to say that those baselines have quite a large distance to reality by default. All of those baselines are developed in a perfectly utopian uncertainty-free world. For instance, all costs, durations, or performance parameters are one-point numbers, implying that they are fully certain! Such a wonderful level of certainty could be achievable only if all stakeholders of a project welcome it North-Korean style; all subcontractors and suppliers cannot wait to ensure the highest possible quality and just-in-time delivery, demonstrating Gangnam-style excitement; technology license providers and financial institutions greet the project by performing Morris dancing enthusiastically; and regulatory bodies are engaged in encouraging Russian-Cossack-style dancing. It is a nice, utopian picture of a project environment (although those dances more often resemble daily Indo-Pakistani border military dancing of carefully choreographed contempt).
All those multiple uncertainties give rise to multiple deviations from the utopian risk-free baselines shaping the project reality. Let’s introduce a set of standard project objectives in this section as well as reviewing the reasons for deviations from them that are observed in any capital project. Traditionally, three project objectives have been considered (triple constraint, iron triangle, etc.):
Scope/Quality/ Performance
Capital expenditure budget (CapEx)
Schedule
These three objectives imply constraints on each other to exclude apparent dichotomies, as fast delivery of a good project cheaply is not quite possible.
