book

Risk, Opportunity, Uncertainty and Other Random Models

by Alan Jones

September 2018

Intermediate to advanced

316 pages

8h 40m

English

Routledge

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1.1 Why write this book? Who might find it useful? Why five volumes?1.1.1 Why write this series? Who might find it useful?1.1.2 Why five volumes?1.2 Features you'll find in this book and others in this series1.2.1 Chapter context1.2.2 The lighter side (humour)1.2.3 Quotations1.2.4 Definitions1.2.5 Discussions and explanations with a mathematical slant for Formula-philes1.2.6 Discussions and explanations without a mathematical slant for Formula-phobes1.2.7 Caveat augur1.2.8 Worked examples1.2.9 Useful Microsoft Excel functions and facilities1.2.10 References to authoritative sources1.2.11 Chapter reviews1.3 Overview of chapters in this volume1.4 Elsewhere in the ‘Working Guide to Estimating & Forecasting’ series1.4.1 Volume I: Principles, Process and Practice of Professional Number Juggling1.4.2 Volume II: Probability, Statistics and Other Frightening Stuff1.4.3 Volume III: Best Fit Lines and Curves, and Some Mathe-Magical Transformations1.4.4 Volume IV: Learning, Unlearning and Re-Learning Curves1.4.5 Volume V: Risk, Opportunity, Uncertainty and Other Random Models1.5 Final thoughts and musings on this volume and seriesReferences
2.1 Norden-Rayleigh Curves: Who, what, where, when and why?2.1.1 Probability Density Function and Cumulative Distribution Function2.1.2 Truncation options2.1.3 How does a Norden-Rayleigh Curve differ from the Rayleigh Distribution?2.1.4 Some practical limitations of the Norden-Rayleigh Curve2.2 Breaking the Norden-Rayleigh ‘Rules’2.2.1 Additional objectives: Phased development (or the ‘camelling’)2.2.2 Correcting an overly optimistic view of the problem complexity:The Square Rule2.2.3 Schedule slippage due to resource ramp-up delays: The Pro Rata Product Rule2.2.4 Schedule slippage due to premature resource reduction2.3 Beta Distribution: A practical alternative to Norden-Rayleigh2.3.1 PERT-Beta Distribution: A viable alternative to Norden-Rayleigh?2.3.2 Resource profiles with Norden-Rayleigh Curves and Beta Distribution PDFs2.4 Triangular Distribution: Another alternative to Norden-Rayleigh2.5 Truncated Weibull Distributions and their Beta equivalents2.5.1 Truncated Weibull Distributions for solution development2.5.2 General Beta Distributions for solution development2.6 Estimates to Completion with Norden-Rayleigh Curves2.6.1 Guess and Iterate Technique2.6.2 Norden-Rayleigh Curve fitting with Microsoft Excel Solver2.6.3 Linear transformation and regression2.6.4 Exploiting Weibull Distribution's double log linearisation constraint2.6.5 Estimates to Completion – Review and conclusion2.7 Chapter reviewReferences

3.1 Monte Carlo Simulation:Who, what, why, where, when and how3.1.1 Origins of Monte Carlo Simulation: Myth and mirth3.1.2 Relevance to estimators and planners3.1.3 Key principle: Input variables with an uncertain future3.1.4 Common pitfalls to avoid3.1.5 Is our Monte Carlo output normal?3.1.6 Monte Carlo Simulation: A model of accurate imprecision3.1.7 What if we don't know what the true Input Distribution Functions are?3.2 Monte Carlo Simulation and correlation3.2.1 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 models3.2.4 Hub-Linked Correlation models3.2.5 Using a Hub-Linked model to drive a background isometric correlation3.2.6 Which way should we go?3.2.7 A word of warning about negative correlation in Monte Carlo Simulation3.3 Modelling and analysis of Risk, Opportunity and Uncertainty3.3.1 Sorting the wheat from the chaff3.3.2 Modelling Risk Opportunity and Uncertainty in a single model3.3.3 Mitigating Risks, realising Opportunities and contingency planning3.3.4 Getting our Risks, Opportunities and Uncertainties in a tangle3.3.5 Dealing with High Probability Risks3.3.6 Beware of False Prophets: Dealing with Low Probability High Impact Risks3.3.7 Using Risk or Opportunity to model extreme values of Uncertainty3.3.8 Modelling Probabilities of Occurrence3.3.9 Other random techniques for evaluating Risk, Opportunity and Uncertainty3.4 ROU Analysis: Choosing appropriate values with confidence3.4.1 Monte Carlo Risk and Opportunity Analysis is fundamentally flawed!3.5 Chapter reviewReferences
4.1 Top-down Approach to Risk, Opportunity and Uncertainty4.1.1 Top-down metrics4.1.2 Marching Army Technique: Cost-schedule related variability4.1.3 Assumption Uplift Factors: Cost variability independent of schedule variability4.1.4 Lateral Shift Factors: Schedule variability independent of cost variability4.1.5 An integrated Top-down Approach4.2 Bridging into the unknown: Slipping and Sliding Technique4.3 Using an Estimate Maturity Assessment as a guide to ROU maturity4.4 Chapter reviewReferences
5.1 The wrong way5.2 A slightly better way5.3 The best way5.4 Chapter reviewReference
6.1 What is Critical Path Analysis?6.2 Finding a Critical Path using Binary Activity Paths in Microsoft Excel6.3 Using Binary Paths to find the latest start and finish times, and float6.4 Using a Critical Path to Manage Cost and Schedule6.5 Modelling variable Critical Paths using Monte Carlo Simulation6.6 Chapter reviewReferences
7.1 Types of queues and service discipline7.2 Memoryless queues7.3 Simple single channel queues (M/M/1 and M/G/l)7.3.1 Example of Queueing Theory in action M/M/1 or M/G/l7.4 Multiple channel queues (M/M/c)7.4.1 Example of Queueing Theory in action M/M/c or M/G/c7.5 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 reviewReferences

Overview

This volume considers risk and uncertainty and how to model them, including the ubiquitous Monte Carlo Simulation. This book forms the backdrop for the guidance on Monte Carlo Simulation, and provides advice on the do’s and don’ts. It can also be used to test other assumptions in a more general modelling sense.

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Publisher Resources

ISBN: 9781351661287