Modern Risk Quantification in Complex Projects

Auteur
Dr Yuri G. Raydugin is Principal Consultant of Risk Services & Solution Inc., a Canadian consulting company. Yuri worked for Royal Dutch Shell, TransCanada Pipelines, SNC-Lavalin, and Saudi Aramco, managing risks of several megaprojects. He has been involved in risk management of projects with combined budget of about $150B. Yuri has an engineering degree in nuclear physics from Urals Polytechnics Institute, Russia, a PhD in physics and mathematics from Russia's Academy of Sciences, and an MBA in business strategy from Henley Management College in England. He is a member of the Association of Professional Engineers and Geoscientists of Alberta (APEGA) and Saudi Council of Engineers (SCE).

Texte du rabat

A modified non-linear Monte Carlo methodology is developed to dramatically increase the accuracy of contingency development in complex project. It is achieved through counting of non-linear risk interactions in complex projects consistently that have been completely missed out by the traditional methods.

Résumé
Project practitioners and decision makers complain that both parametric and Monte Carlo methods fail to produce accurate project duration and cost contingencies in majority of cases. Apparently, the referred methods have unacceptably high systematic errors as they miss out critically important components of project risk exposure. In the case of complex projects overlooked are the components associated with structural and delivery complexity. Modern Risk Quantification in Complex Projects: Non-linear Monte Carlo and System Dynamics Methodologies zeroes in on most crucial but systematically overlooked characteristics of complex projects. Any mismatches between two fundamental interacting subsystems - a project structure subsystem and a project delivery subsystem - result in non-linear interactions of project risks. Three kinds of the interactions are distinguished - internal risk amplifications stemming from long-term ('chronic') project system issues, knock-on interactions, and risk compounding. Affinities of interacting risks compose dynamic risk patterns supported by a project system. A methodology to factor the patterns into Monte Carlo modelling referred to as non-linear Monte Carlo schedule and cost risk analysis (N-SCRA) is developed and demonstrated. It is capable to forecast project outcomes with high accuracy even in the case of most complex and difficult projects including notorious projects-outliers: it has a much lower systematic error. The power of project system dynamics is uncovered. It can be adopted as an accurate risk quantification methodology in complex projects. Results produced by the system dynamics and the non-linear Monte Carlo methodologies are well-aligned. All built Monte Carlo and system dynamics models are available on the book's companion website.

Contenu

• 1: High-level overview of project risk management

• 2: Conventional PRM Methodologies

• 3: Overview of conventional risk quantification methods

• 4: Overview of unconventional risk quantification methods

• 5: Project Zemblanity business case (I): linear Monte Carlo schedule and cost risk analysis (L-SCRA)

• 6: A brief introduction to system dynamics

• 7: Project system dynamics: a linear case

• 8: Introduction to project complexity

• 9: Interactions in a project system

• 10: A project structure subsystem (PSS)

• 11: A project delivery subsystem (PDS)

• 12: Project system maturity evaluation

• 13: Non-linear multipliers for risk interactions

• 14: Non-linear Monte Carlo modeling requirements

• 15: Project Zemblanity business case (II): non-linear Monte Carlo schedule and cost risk analysis (N-SCRA)

• 16: Project system dynamics: a non-linear case

• 17: Spin-off discussions on project complexity

• 18: Conclusion