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Risk, Opportunity, Uncertainty and Other Random Models

Book Description

Risk, Opportunity, Uncertainty and Other Random Models (Volume V in the Working Guides to Estimating and Forecasting series) goes part way to debunking the myth that research and development cost are somewhat random, as under certain conditions they can be observed to follow a pattern of behaviour referred to as a Norden-Rayleigh Curve, which unfortunately has to be truncated to stop the myth from becoming a reality! However, there is a practical alternative in relation to a particular form of PERT-Beta Curve.

However, the major emphasis of this volume is the use of Monte Carlo Simulation as a general technique for narrowing down potential outcomes of multiple interacting variables or cost drivers. Perhaps the most common of these in the evaluation of Risk, Opportunity and Uncertainty. The trouble is that many Monte Carlo Simulation tools are ‘black boxes’ and too few estimators and forecasters really appreciate what is happening inside the ‘black box’. This volume aims to resolve that and offers tips into things that might need to be considered to remove some of the uninformed random input that often creates a misinformed misconception of ‘it must be right!’

Monte Carlo Simulation can be used to model variable determine Critical Paths in a schedule, and is key to modelling Waiting Times and cues with random arisings. Supported by a wealth of figures and tables, this is a valuable resource for estimators, engineers, accountants, project risk specialists as well as students of cost engineering.

Table of Contents

  1. Cover
  2. Title
  3. Copyright
  4. Dedication
  5. Contents
  6. List of Figures
  7. List of Tables
  8. Foreword
  9. 1 Introduction and objectives
    1. 1.1 Why write this book? Who might find it useful? Why five volumes?
      1. 1.1.1 Why write this series? Who might find it useful?
      2. 1.1.2 Why five volumes?
    2. 1.2 Features you'll find in this book and others in this series
      1. 1.2.1 Chapter context
      2. 1.2.2 The lighter side (humour)
      3. 1.2.3 Quotations
      4. 1.2.4 Definitions
      5. 1.2.5 Discussions and explanations with a mathematical slant for Formula-philes
      6. 1.2.6 Discussions and explanations without a mathematical slant for Formula-phobes
      7. 1.2.7 Caveat augur
      8. 1.2.8 Worked examples
      9. 1.2.9 Useful Microsoft Excel functions and facilities
      10. 1.2.10 References to authoritative sources
      11. 1.2.11 Chapter reviews
    3. 1.3 Overview of chapters in this volume
    4. 1.4 Elsewhere in the ‘Working Guide to Estimating & Forecasting’ series
      1. 1.4.1 Volume I: Principles, Process and Practice of Professional Number Juggling
      2. 1.4.2 Volume II: Probability, Statistics and Other Frightening Stuff
      3. 1.4.3 Volume III: Best Fit Lines and Curves, and Some Mathe-Magical Transformations
      4. 1.4.4 Volume IV: Learning, Unlearning and Re-Learning Curves
      5. 1.4.5 Volume V: Risk, Opportunity, Uncertainty and Other Random Models
    5. 1.5 Final thoughts and musings on this volume and series
    6. References
  10. 2 Norden-Rayleigh Curves for solution development
    1. 2.1 Norden-Rayleigh Curves: Who, what, where, when and why?
      1. 2.1.1 Probability Density Function and Cumulative Distribution Function
      2. 2.1.2 Truncation options
      3. 2.1.3 How does a Norden-Rayleigh Curve differ from the Rayleigh Distribution?
      4. 2.1.4 Some practical limitations of the Norden-Rayleigh Curve
    2. 2.2 Breaking the Norden-Rayleigh ‘Rules’
      1. 2.2.1 Additional objectives: Phased development (or the ‘camelling’)
      2. 2.2.2 Correcting an overly optimistic view of the problem complexity:The Square Rule
      3. 2.2.3 Schedule slippage due to resource ramp-up delays: The Pro Rata Product Rule
      4. 2.2.4 Schedule slippage due to premature resource reduction
    3. 2.3 Beta Distribution: A practical alternative to Norden-Rayleigh
      1. 2.3.1 PERT-Beta Distribution: A viable alternative to Norden-Rayleigh?
      2. 2.3.2 Resource profiles with Norden-Rayleigh Curves and Beta Distribution PDFs
    4. 2.4 Triangular Distribution: Another alternative to Norden-Rayleigh
    5. 2.5 Truncated Weibull Distributions and their Beta equivalents
      1. 2.5.1 Truncated Weibull Distributions for solution development
      2. 2.5.2 General Beta Distributions for solution development
    6. 2.6 Estimates to Completion with Norden-Rayleigh Curves
      1. 2.6.1 Guess and Iterate Technique
      2. 2.6.2 Norden-Rayleigh Curve fitting with Microsoft Excel Solver
      3. 2.6.3 Linear transformation and regression
      4. 2.6.4 Exploiting Weibull Distribution's double log linearisation constraint
      5. 2.6.5 Estimates to Completion – Review and conclusion
    7. 2.7 Chapter review
    8. References
  11. 3. Monte Carlo Simulation and other random thoughts
    1. 3.1 Monte Carlo Simulation:Who, what, why, where, when and how
      1. 3.1.1 Origins of Monte Carlo Simulation: Myth and mirth
      2. 3.1.2 Relevance to estimators and planners
      3. 3.1.3 Key principle: Input variables with an uncertain future
      4. 3.1.4 Common pitfalls to avoid
      5. 3.1.5 Is our Monte Carlo output normal?
      6. 3.1.6 Monte Carlo Simulation: A model of accurate imprecision
      7. 3.1.7 What if we don't know what the true Input Distribution Functions are?
    2. 3.2 Monte Carlo Simulation and correlation
      1. 3.2.1 Independent random uncertain events – How real is that?
      2. 3.2.2 Modelling semi-independent uncertain events (bees and hedgehogs)
      3. 3.2.3 Chain-Linked Correlation models
      4. 3.2.4 Hub-Linked Correlation models
      5. 3.2.5 Using a Hub-Linked model to drive a background isometric correlation
      6. 3.2.6 Which way should we go?
      7. 3.2.7 A word of warning about negative correlation in Monte Carlo Simulation
    3. 3.3 Modelling and analysis of Risk, Opportunity and Uncertainty
      1. 3.3.1 Sorting the wheat from the chaff
      2. 3.3.2 Modelling Risk Opportunity and Uncertainty in a single model
      3. 3.3.3 Mitigating Risks, realising Opportunities and contingency planning
      4. 3.3.4 Getting our Risks, Opportunities and Uncertainties in a tangle
      5. 3.3.5 Dealing with High Probability Risks
      6. 3.3.6 Beware of False Prophets: Dealing with Low Probability High Impact Risks
      7. 3.3.7 Using Risk or Opportunity to model extreme values of Uncertainty
      8. 3.3.8 Modelling Probabilities of Occurrence
      9. 3.3.9 Other random techniques for evaluating Risk, Opportunity and Uncertainty
      10. 3.4 ROU Analysis: Choosing appropriate values with confidence
      11. 3.4.1 Monte Carlo Risk and Opportunity Analysis is fundamentally flawed!
    4. 3.5 Chapter review
    5. References
  12. 4 Risk, Opportunity and Uncertainty: A holistic perspective
    1. 4.1 Top-down Approach to Risk, Opportunity and Uncertainty
      1. 4.1.1 Top-down metrics
      2. 4.1.2 Marching Army Technique: Cost-schedule related variability
      3. 4.1.3 Assumption Uplift Factors: Cost variability independent of schedule variability
      4. 4.1.4 Lateral Shift Factors: Schedule variability independent of cost variability
      5. 4.1.5 An integrated Top-down Approach
    2. 4.2 Bridging into the unknown: Slipping and Sliding Technique
    3. 4.3 Using an Estimate Maturity Assessment as a guide to ROU maturity
    4. 4.4 Chapter review
    5. References
  13. 5 Factored Value Technique for Risks and Opportunities
    1. 5.1 The wrong way
    2. 5.2 A slightly better way
    3. 5.3 The best way
    4. 5.4 Chapter review
    5. Reference
  14. 6 Introduction to Critical Path and Schedule Risk Analysis
    1. 6.1 What is Critical Path Analysis?
    2. 6.2 Finding a Critical Path using Binary Activity Paths in Microsoft Excel
    3. 6.3 Using Binary Paths to find the latest start and finish times, and float
    4. 6.4 Using a Critical Path to Manage Cost and Schedule
    5. 6.5 Modelling variable Critical Paths using Monte Carlo Simulation
    6. 6.6 Chapter review
    7. References
  15. 7 Finally, after a long wait ... Queueing Theory
    1. 7.1 Types of queues and service discipline
    2. 7.2 Memoryless queues
    3. 7.3 Simple single channel queues (M/M/1 and M/G/l)
      1. 7.3.1 Example of Queueing Theory in action M/M/1 or M/G/l
    4. 7.4 Multiple channel queues (M/M/c)
      1. 7.4.1 Example of Queueing Theory in action M/M/c or M/G/c
    5. 7.5 How do we spot a Poisson Process?
    6. 7.6 When is Weibull viable?
    7. 7.7 Can we have a Poisson Process with an increasing/decreasing trend?
    8. 7.8 Chapter review
    9. References
  16. Epilogue
  17. Glossary of estimating and forecasting terms
  18. Legend for Microsoft Excel Worked Example Tables in Greyscale
  19. Index