- Cover
- Title
- Copyright
- Dedication
- Contents
- List of Figures
- List of Tables
- Foreword
- 1 Introduction and objectives
- 1.1 Why write this book? Who might find it useful? Why five volumes?
- 1.2 Features you'll find in this book and others in this series
- 1.2.1 Chapter context
- 1.2.2 The lighter side (humour)
- 1.2.3 Quotations
- 1.2.4 Definitions
- 1.2.5 Discussions and explanations with a mathematical slant for Formula-philes
- 1.2.6 Discussions and explanations without a mathematical slant for Formula-phobes
- 1.2.7 Caveat augur
- 1.2.8 Worked examples
- 1.2.9 Useful Microsoft Excel functions and facilities
- 1.2.10 References to authoritative sources
- 1.2.11 Chapter reviews
- 1.3 Overview of chapters in this volume
- 1.4 Elsewhere in the ‘Working Guide to Estimating & Forecasting’ series
- 1.4.1 Volume I: Principles, Process and Practice of Professional Number Juggling
- 1.4.2 Volume II: Probability, Statistics and Other Frightening Stuff
- 1.4.3 Volume III: Best Fit Lines and Curves, and Some Mathe-Magical Transformations
- 1.4.4 Volume IV: Learning, Unlearning and Re-Learning Curves
- 1.4.5 Volume V: Risk, Opportunity, Uncertainty and Other Random Models
- 1.5 Final thoughts and musings on this volume and series
- References
- 2 Norden-Rayleigh Curves for solution development
- 2.1 Norden-Rayleigh Curves: Who, what, where, when and why?
- 2.2 Breaking the Norden-Rayleigh ‘Rules’
- 2.3 Beta Distribution: A practical alternative to Norden-Rayleigh
- 2.4 Triangular Distribution: Another alternative to Norden-Rayleigh
- 2.5 Truncated Weibull Distributions and their Beta equivalents
- 2.6 Estimates to Completion with Norden-Rayleigh Curves
- 2.7 Chapter review
- References
- 3. Monte Carlo Simulation and other random thoughts
- 3.1 Monte Carlo Simulation:Who, what, why, where, when and how
- 3.1.1 Origins of Monte Carlo Simulation: Myth and mirth
- 3.1.2 Relevance to estimators and planners
- 3.1.3 Key principle: Input variables with an uncertain future
- 3.1.4 Common pitfalls to avoid
- 3.1.5 Is our Monte Carlo output normal?
- 3.1.6 Monte Carlo Simulation: A model of accurate imprecision
- 3.1.7 What if we don't know what the true Input Distribution Functions are?
- 3.2 Monte Carlo Simulation and correlation
- 3.2.1 Independent random uncertain events – How real is that?
- 3.2.2 Modelling semi-independent uncertain events (bees and hedgehogs)
- 3.2.3 Chain-Linked Correlation models
- 3.2.4 Hub-Linked Correlation models
- 3.2.5 Using a Hub-Linked model to drive a background isometric correlation
- 3.2.6 Which way should we go?
- 3.2.7 A word of warning about negative correlation in Monte Carlo Simulation
- 3.3 Modelling and analysis of Risk, Opportunity and Uncertainty
- 3.3.1 Sorting the wheat from the chaff
- 3.3.2 Modelling Risk Opportunity and Uncertainty in a single model
- 3.3.3 Mitigating Risks, realising Opportunities and contingency planning
- 3.3.4 Getting our Risks, Opportunities and Uncertainties in a tangle
- 3.3.5 Dealing with High Probability Risks
- 3.3.6 Beware of False Prophets: Dealing with Low Probability High Impact Risks
- 3.3.7 Using Risk or Opportunity to model extreme values of Uncertainty
- 3.3.8 Modelling Probabilities of Occurrence
- 3.3.9 Other random techniques for evaluating Risk, Opportunity and Uncertainty
- 3.4 ROU Analysis: Choosing appropriate values with confidence
- 3.4.1 Monte Carlo Risk and Opportunity Analysis is fundamentally flawed!
- 3.5 Chapter review
- References
- 3.1 Monte Carlo Simulation:Who, what, why, where, when and how
- 4 Risk, Opportunity and Uncertainty: A holistic perspective
- 5 Factored Value Technique for Risks and Opportunities
- 6 Introduction to Critical Path and Schedule Risk Analysis
- 6.1 What is Critical Path Analysis?
- 6.2 Finding a Critical Path using Binary Activity Paths in Microsoft Excel
- 6.3 Using Binary Paths to find the latest start and finish times, and float
- 6.4 Using a Critical Path to Manage Cost and Schedule
- 6.5 Modelling variable Critical Paths using Monte Carlo Simulation
- 6.6 Chapter review
- References
- 7 Finally, after a long wait ... Queueing Theory
- 7.1 Types of queues and service discipline
- 7.2 Memoryless queues
- 7.3 Simple single channel queues (M/M/1 and M/G/l)
- 7.4 Multiple channel queues (M/M/c)
- 7.5 How do we spot a Poisson Process?
- 7.6 When is Weibull viable?
- 7.7 Can we have a Poisson Process with an increasing/decreasing trend?
- 7.8 Chapter review
- References
- Epilogue
- Glossary of estimating and forecasting terms
- Legend for Microsoft Excel Worked Example Tables in Greyscale
- Index
-
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