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.

Project practitioners and decision makers complain that both parametric and Monte Carlo methods fail to produce accurate project duration and cost contingencies in the majority of cases. Apparently, these methods have unacceptably high systematic errors as they miss out critically important components of project risk exposure. In the case of complex projects, the components associated with structural and delivery complexity are often overlooked. Modern Risk Quantification in Complex Projects: Non-linear Monte Carlo and System Dynamics Methodologies zeroes in on the 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 new 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 of forecasting project outcomes with high accuracy even in the case of most complex and difficult projects, including notorious projects-outliers, and it has a much lower rate of systematic error. In this book, the power of project system dynamics is uncovered. It can be adopted as an accurate risk quantification methodology in complex projects, and the 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.

This book integrates for readers three areas of knowledge, pertaining to risk-based project decision making: project risk management (PRM), complexity theory, and decision-making under deep uncertainty (DMDU). Readers will appreciate that in practice, too often relevant complexity and uncertainty factors are either ignored or overlooked resulting in epic project failures. The author discusses a variety of methodologies and a decision-tree-type framework to determine why, when and how particular methodologies should be applied to ensure project success. These include nonlinear Monte Carlo techniques, a dynamic adaptive methodology to adapt to external environment changes, game theory for devising robust decision-making criteria, systems dynamics and cost escalation modelling, as well as risk-based & economic-based alternatives selection methodologies. This book will be an eye-opener for many PRM practitioners, helping to increase their chances of project success by properly handling inescapable project-complexity and deep-uncertainty implications in specific contexts.

Risks can be identified, evaluated, and mitigated, but the underlying uncertainty remains elusive. Risk is present across all industries and sectors. As a result, organizations and governments worldwide are currently experiencing higher levels of risk and have had to make risky decisions during times of crisis and instability, including the COVID-19 pandemic, economic and climate perils, and global tensions surrounding terrorism. It is essential that new studies are undertaken to understand strategies taken during these times to better equip business leaders to navigate risk management in the future. Global Risk and Contingency Management Research in Times of Crisis examines the impact of crises including the COVID-19 pandemic, which has tested organizational risk and contingency management plans. It provides significant insights that should benefit business leaders on risk and contingency management in times of crisis. It emphasizes strategies that leaders can undertake to identify potential future risks and examines decisions made in past crises that can act as examples of what to do and what not to do during future crisis events. Covering topics such as auditing theories, risk assessment, and educational inequality, this premier reference source is a crucial resource for business leaders, executives, managers, decision makers, policymakers, students, government officials, entrepreneurs, librarians, researchers, and academicians.

The brief will describe how to develop a risk analysis applied to a project , through a sequence of steps: risk management planning, risk identification, risk classification, risk assessment, risk quantification, risk response planning, risk monitoring and control, process close out and lessons learning. The project risk analysis and management process will be applied to large engineering projects, in particular related to the oil and gas industry. The brief will address the overall range of possible events affecting the project moving from certainty (project issues) through uncertainty (project risks) to unpredictability (unforeseeable events), considering both negative and positive events. Some quantitative techniques (simulation, event tree, Bayesian inference, etc.) will be used to develop risk quantification. The brief addresses a typical subject in the area of project management, with reference to large engineering projects concerning the realization of large plants and infrastructures. These projects are characterized by a high level of change, uncertainty, complexity and ambiguity. The brief represents an extension of the material developed for the course Project Risk Analysis and Management of the Master in Strategic Project Management (Erasmus Mundus) developed jointly by Politecnico di Milano, Heriot Watt University (Edimburgh) and Umea (Sweden). The brief may be used both in courses addressing project management subjects and by practitioners as a guide for developing an effective project risk management plan.