Risk, Opportunity, Uncertainty and Other Random Models - Alan Jones

Risk, Opportunity, Uncertainty and Other Random Models

(Autor)

Buch | Softcover
316 Seiten
2024
Routledge (Verlag)
978-1-032-83879-3 (ISBN)
39,85 inkl. MwSt
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.
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.

Alan R. Jones is Principal Consultant at Estimata Limited, aconsultancy service specialising in Estimating Skills Training. He is a Certified Cost Estimator/Analyst (US) and Certified Cost Engineer (CCE) (UK). Prior to setting up his own business, he enjoyed a 40-year career in the UK aerospace and defence industry as an estimatorAlan is a Fellow of the Association of Cost Engineers and a member of the International Cost Estimating and Analysis Association. Historically (some four decades ago), Alan was a graduate in Mathematics from Imperial College of Science and Technology in London, and was an MBA Prize-winner at the Henley Management College.

List of Figures

List of Tables

Foreword






Introduction and objectives





Why write this book? Who might find it useful? Why five volumes?





Why write this series? Who might find it useful?



Why five volumes?




Features you’ll find in this book and others in this series





Chapter context



The lighter side (humour)



Quotations



Definitions



Discussions and explanations with a mathematical
slant for Formula-philes




Discussions and explanations without a mathematical
slant for Formula-phobes




Caveat augur



Worked examples



Useful Microsoft Excel functions and facilities



References to authoritative sources



Chapter reviews




Overview of chapters in this volume



Elsewhere in the ‘Working Guide to Estimating & Forecasting’ series





Volume I: Principles, Process and Practice of Professional
Number Juggling




Volume II: Probability, Statistics and Other Frightening Stuff



Volume III: Best Fit Lines and Curves, and
Some Mathe-Magical Transformations




Volume IV: Learning, Unlearning and Re-Learning Curves



Volume V: Risk, Opportunity, Uncertainty and Other

Random Models




Final thoughts and musings on this volume and series

References




Norden-Rayleigh Curves for solution development





Norden-Rayleigh Curves:Who, what, where, when and why?





Probability Density Function and Cumulative Distribution Function



Truncation options



How does a Norden-Rayleigh Curve differ from the
Rayleigh Distribution?




Some practical limitations of the Norden-Rayleigh Curve




Breaking the Norden-Rayleigh ‘Rules’





Additional objectives: Phased development (or the ‘camelling’)



Correcting an overly optimistic view of the problem
complexity:The Square Rule




Schedule slippage due to resource ramp-up delays:
The Pro Rata Product Rule




Schedule slippage due to premature resource reduction




Beta Distribution: A practical alternative to Norden-Rayleigh





PERT-Beta Distribution: A viable alternative to Norden-Rayleigh?



Resource profiles with Norden-Rayleigh Curves

and Beta Distribution PDFs




Triangular Distribution: Another alternative to Norden-Rayleigh



Truncated Weibull Distributions and their Beta equivalents





Truncated Weibull Distributions for solution development



General Beta Distributions for solution development




Estimates to Completion with Norden-Rayleigh Curves





Guess and Iterate Technique



Norden-Rayleigh Curve fitting with Microsoft Excel Solver



Linear transformation and regression



Exploiting Weibull Distribution’s double log linearisation constraint



Estimates to Completion – Review and conclusion




Chapter review



References






Monte Carlo Simulation and other random thoughts





Monte Carlo Simulation:Who, what, why, where,
when and how






Origins of Monte Carlo Simulation: Myth and mirth



Relevance to estimators and planners



Key principle: Input variables with an uncertain future



Common pitfalls to avoid



Is our Monte Carlo output normal?



Monte Carlo Simulation: A model of accurate imprecision



What if we don’t know what the true Input Distribution

Functions are?




Monte Carlo Simulation and correlation





Independent random uncertain events – How real is that?



Modelling semi-independent uncertain events
(bees and hedgehogs)




Chain-Linked Correlation models



Hub-Linked Correlation models



Using a Hub-Linked model to drive a background
isometric correlation




Which way should we go?



A word of warning about negative correlation in Monte Carlo Simulation




Modelling and analysis of Risk, Opportunity and Uncertainty





Sorting the wheat from the chaff



Modelling Risk Opportunity and Uncertainty in a single model



Mitigating Risks, realising Opportunities and contingency planning



Getting our Risks, Opportunities and Uncertainties in a tangle



Dealing with High Probability Risks



Beware of False Prophets: Dealing with Low Probability
High Impact Risks




Using Risk or Opportunity to model extreme values
of Uncertainty




Modelling Probabilities of Occurrence



Other random techniques for evaluating Risk, Opportunity and Uncertainty




ROU Analysis: Choosing appropriate values with confidence





Monte Carlo Risk and Opportunity Analysis is

fundamentally flawed!




Chapter review



References






Risk, Opportunity and Uncertainty: A holistic perspective





Top-down Approach to Risk, Opportunity and Uncertainty





Top-down metrics



Marching Army Technique: Cost-schedule related variability



Assumption Uplift Factors: Cost variability independent
of schedule variability




Lateral Shift Factors: Schedule variability independent
of cost variability




An integrated Top-down Approach




Bridging into the unknown: Slipping and
Sliding Technique




Using an Estimate Maturity Assessment as a guide to ROU maturity



Chapter review

References




Factored Value Technique for Risks and Opportunities





The wrong way



A slightly better way



The best way



Chapter review

Reference




Introduction to Critical Path and Schedule Risk Analysis





What is Critical Path Analysis?



Finding a Critical Path using Binary Activity Paths in Microsoft Excel



Using Binary Paths to find the latest start and finish times, and float



Using a Critical Path to Manage Cost and Schedule



Modelling variable Critical Paths using Monte Carlo Simulation



Chapter review

References




Finally, after a long wait … Queueing Theory





Types of queues and service discipline



Memoryless queues



Simple single channel queues (M/M/1 and M/G/1)





Example of Queueing Theory in action M/M/1 or M/G/1




Multiple channel queues (M/M/c)





Example of Queueing Theory in action M/M/c or M/G/c




How do we spot a Poisson Process?



When is Weibull viable?



Can we have a Poisson Process with an increasing/decreasing trend?



Chapter review



References

Epilogue

Glossary of estimating and forecasting terms

Legend for Microsoft Excel Worked Example Tables in Greyscale

Index

Erscheint lt. Verlag 24.6.2024
Reihe/Serie Working Guides to Estimating & Forecasting
Verlagsort London
Sprache englisch
Maße 156 x 234 mm
Themenwelt Kunst / Musik / Theater Design / Innenarchitektur / Mode
Wirtschaft Volkswirtschaftslehre
ISBN-10 1-032-83879-5 / 1032838795
ISBN-13 978-1-032-83879-3 / 9781032838793
Zustand Neuware
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