Alan Jones

Risk, Opportunity, Uncertainty and Other Random Models (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 316
• Erscheinungstermin: 13. September 2018
• Englisch
• ISBN-13: 9781351661287
• Artikelnr.: 67384494

Autorenporträt

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.

Inhaltsangabe

List of Figures
List of Tables
Foreword
1. Introduction and objectives
2. Why write this book? Who might find it useful? Why five volumes?
1. Why write this series? Who might find it useful?
2. Why five volumes?
3. Features you'll find in this book and others in this series
1. Chapter context
2. The lighter side (humour)
3. Quotations
4. Definitions
5. Discussions and explanations with a mathematical
slant for Formula-philes
6. Discussions and explanations without a mathematical
slant for Formula-phobes
7. Caveat augur
8. Worked examples
9. Useful Microsoft Excel functions and facilities
10. References to authoritative sources
11. Chapter reviews
4. Overview of chapters in this volume
5. Elsewhere in the 'Working Guide to Estimating & Forecasting' series
1. Volume I: Principles, Process and Practice of Professional
Number Juggling
2. Volume II: Probability, Statistics and Other Frightening Stuff
3. Volume III: Best Fit Lines and Curves, and
Some Mathe-Magical Transformations
4. Volume IV: Learning, Unlearning and Re-Learning Curves
5. Volume V: Risk, Opportunity, Uncertainty and Other
Random Models
6. Final thoughts and musings on this volume and series
References
7. Norden-Rayleigh Curves for solution development
8. Norden-Rayleigh Curves:Who, what, where, when and why?
1. Probability Density Function and Cumulative Distribution Function
2. Truncation options
3. How does a Norden-Rayleigh Curve differ from the
Rayleigh Distribution?
4. Some practical limitations of the Norden-Rayleigh Curve
9. Breaking the Norden-Rayleigh 'Rules'
1. Additional objectives: Phased development (or the 'camelling')
2. Correcting an overly optimistic view of the problem
complexity:The Square Rule
3. Schedule slippage due to resource ramp-up delays:
The Pro Rata Product Rule
4. Schedule slippage due to premature resource reduction
10. Beta Distribution: A practical alternative to Norden-Rayleigh
1. PERT-Beta Distribution: A viable alternative to Norden-Rayleigh?
2. Resource profiles with Norden-Rayleigh Curves
and Beta Distribution PDFs
11. Triangular Distribution: Another alternative to Norden-Rayleigh
12. Truncated Weibull Distributions and their Beta equivalents
1. Truncated Weibull Distributions for solution development
2. General Beta Distributions for solution development
13. Estimates to Completion with Norden-Rayleigh Curves
1. Guess and Iterate Technique
2. Norden-Rayleigh Curve fitting with Microsoft Excel Solver
3. Linear transformation and regression
4. Exploiting Weibull Distribution's double log linearisation constraint
5. Estimates to Completion - Review and conclusion
14. Chapter review
References
15. Monte Carlo Simulation and other random thoughts
16. Monte Carlo Simulation:Who, what, why, where,
when and how
1. Origins of Monte Carlo Simulation: Myth and mirth
2. Relevance to estimators and planners
3. Key principle: Input variables with an uncertain future
4. Common pitfalls to avoid
5. Is our Monte Carlo output normal?
6. Monte Carlo Simulation: A model of accurate imprecision
7. What if we don't know what the true Input Distribution
Functions are?
17. Monte Carlo Simulation and correlation
1. Independent random uncertain events - How real is that?
2. Modelling semi-independent uncertain events
(bees and hedgehogs)
3. Chain-Linked Correlation models
4. Hub-Linked Correlation models
5. Using a Hub-Linked model to drive a background
isometric correlation
6. Which way should we go?
7. A word of warning about negative correlation in Monte Carlo
Simulation
18. Modelling and analysis of Risk, Opportunity and Uncertainty
1. Sorting the wheat from the chaff
2. Modelling Risk Opportunity and Uncertainty in a single model
3. Mitigating Risks, realising Opportunities and contingency planning
4. Getting our Risks, Opportunities and Uncertainties in a tangle
5. Dealing with High Probability Risks
6. Beware of False Prophets: Dealing with Low Probability
High Impact Risks
7. Using Risk or Opportunity to model extreme values
of Uncertainty
8. Modelling Probabilities of Occurrence
9. Other random techniques for evaluating Risk, Opportunity and
Uncertainty
19. ROU Analysis: Choosing appropriate values with confidence
1. Monte Carlo Risk and Opportunity Analysis is
fundamentally flawed!
20. Chapter review
References
21. Risk, Opportunity and Uncertainty: A holistic perspective
22. Top-down Approach to Risk, Opportunity and Uncertainty
1. Top-down metrics
2. Marching Army Technique: Cost-schedule related variability
3. Assumption Uplift Factors: Cost variability independent
of schedule variability
4. Lateral Shift Factors: Schedule variability independent
of cost variability
5. An integrated Top-down Approach
23. Bridging into the unknown: Slipping and
Sliding Technique
24. Using an Estimate Maturity Assessment as a guide to ROU maturity
25. Chapter review
References
26. Factored Value Technique for Risks and Opportunities
1. The wrong way
2. A slightly better way
3. The best way
4. Chapter review
Reference
27. Introduction to Critical Path and Schedule Risk Analysis
1. What is Critical Path Analysis?
2. Finding a Critical Path using Binary Activity Paths in Microsoft
Excel
3. Using Binary Paths to find the latest start and finish times, and
float
4. Using a Critical Path to Manage Cost and Schedule
5. Modelling variable Critical Paths using Monte Carlo Simulation
6. Chapter review
References
28. Finally, after a long wait ... Queueing Theory
29. Types of queues and service discipline
30. Memoryless queues
31. Simple single channel queues (M/M/1 and M/G/1)
1. Example of Queueing Theory in action M/M/1 or M/G/1
32. Multiple channel queues (M/M/c)
1. Example of Queueing Theory in action M/M/c or M/G/c
33. How do we spot a Poisson Process?
34. When is Weibull viable?
35. Can we have a Poisson Process with an increasing/decreasing trend?
36. Chapter review
References
Epilogue
Glossary of estimating and forecasting terms
Legend for Microsoft Excel Worked Example Tables in Greyscale
Index