Modern Risk Quantification in Complex Projects

2020 ◽  
Author(s):  
Yuri G. Raydugin
Keyword(s):  
Monte Carlo ◽  
System Dynamics ◽  
Project Risk ◽  
Risk Quantification ◽  
Complex Projects ◽  
Non Linear ◽  
Business Cases ◽  
Project Structure ◽  
Dynamics Models ◽  
Cost Risk

There are multiple complaints that existing project risk quantification methods—both parametric and Monte Carlo—fail to produce accurate project duration and cost-risk contingencies in a majority of cases. It is shown that major components of project risk exposure—non-linear risk interactions—pertaining to complex projects are not taken into account. It is argued that a project system consists of two interacting subsystems: a project structure subsystem (PSS) and a project delivery subsystem (PDS). Any misalignments or imbalances between these two subsystems (PSS–PDS mismatches) are associated with the non-linear risk interactions. Principles of risk quantification are developed to take into account three types of non-linear risk interactions in complex projects: internal risk amplifications due to existing ‘chronic’ project system issues, knock-on interactions, and risk compounding. Modified bowtie diagrams for the three types of risk interactions are developed to identify and address interacting risks. A framework to visualize dynamic risk patterns in affinities of interacting risks is proposed. Required mathematical expressions and templates to factor relevant risk interactions to Monte Carlo models are developed. Business cases are discussed to demonstrate the power of the newly-developed non-linear Monte Carlo methodology (non-linear integrated schedule and cost risk analysis (N-SCRA)). A project system dynamics methodology based on rework cycles is adopted as a supporting risk quantification tool. Comparison of results yielded by the non-linear Monte Carlo and system dynamics models demonstrates a good alignment of the two methodologies. All developed Monte Carlo and system dynamics models are available on the book’s companion website.

Project Zemblanity Business Case (II)

2020 ◽  
pp. 235-254
Author(s):  
Yuri G. Raydugin
Keyword(s):  
Monte Carlo ◽  
Risk Analysis ◽  
Confidence Level ◽  
Business Case ◽  
Project Risk ◽  
Project Duration ◽  
Risk Quantification ◽  
Complex Projects ◽  
Non Linear ◽  
Before And After

This chapter is devoted to the second business case of project Zemblanity. Based on the developed risk quantification principles for complex projects, two non-linear Monte Carlo schedule and cost risk analysis (N-SCRA) models are developed. These models factor in all relevant risk interactions before and after addressing. Modified ‘non-linear’ project risk registers that take into account the risk interactions are developed as inputs to the Monte Carlo models. It is shown that before risk interaction addressing the forecast project duration and cost are unacceptably high due to unaddressed risk interactions. Agreed risk interaction addressing measures factored to the models result in the acceptable project duration and cost. A joint confidence level (JCL) concept is used to amend the N-SCRA results at the P70 confidence level to distinguish stretched targets and management reserves using JCL70. The two workable N-SCRA models are available on the book’s companion website.

Project System Dynamics

2020 ◽  
pp. 255-270
Author(s):  
Yuri G. Raydugin
Keyword(s):  
Monte Carlo ◽  
Risk Analysis ◽  
System Dynamics ◽  
System Dynamic ◽  
Risk Exposure ◽  
Project Risk ◽  
Project Duration ◽  
Non Linear ◽  
Before And After ◽  
Sd Model

The purpose of this chapter is to develop project system dynamic (SD) models that mirror non-linear Monte Carlo N-SCRA models of project Zemblanity. Only schedule part of risk exposure is considered. Required recalculations of parameters is undertaken. As these are no one-to-one relations between the parameters of the SD and Monte Carlo models, required assumptions are applied. These can be used for mutual calibrations of the two types of models. Two SD models are built that reflect on the project risk exposure before and after risk interaction addressing. Limitations of the project SD modelling are revealed. The SD modelling results demonstrate a good alignment of corresponding non-linear schedule and cost risk analysis (N-SCRA) and SD models. One additional SD model is built to explicitly demonstrate a contribution of risk compounding to overall project duration. The three workable SD models are available on the book’s companion website.

Project System Dynamics (SD)

2020 ◽  
pp. 131-142
Author(s):  
Yuri G. Raydugin
Keyword(s):  
Monte Carlo ◽  
System Dynamics ◽  
The Third ◽  
Free Model ◽  
Non Linear ◽  
Linear Behaviour

This chapter demonstrates project system dynamics (SD) models that mirror the Monte Carlo L-SCRA models developed in Chapter 5 (project Zemblanity). Historic overview of project system dynamics is undertaken: from claims and dispute resolutions to modelling of project rework cycles and project changes. Approaches to model extra risk-related works, out-of-sequence works, impacts on productivity of works, etc. are introduced. Three SD models are built that combine engineering and construction rework cycles (the two-rework-cycle models). The first is a risk-free model to become aligned with the project Zemblanity schedule. The second is a model that takes into account only quality-related risks. The third factors in all risks of project Zemblanity. Results of the SD modelling demonstrate good alignment with L-SCRA Monte Carlo outcomes and explain reasons for a non-linear behaviour of the project Zemblanity system. All three workable SD models are available on the book’s companion website.

Introduction to Project Complexity

2020 ◽  
pp. 145-160
Author(s):  
Yuri G. Raydugin
Keyword(s):  
Literature Review ◽  
Project Delivery ◽  
Ideal Case ◽  
Risk Quantification ◽  
Complex Projects ◽  
Project Complexity ◽  
Project Risks ◽  
Project Structure ◽  
The Ideal

This chapter provides a literature review of the complexity concept and selects practical ideas that can be used for risk quantification in complex projects. This topic has two angles—complexity of project dealing with practical methods and complexity in project that reflects on general complexity ideas coming mostly from physics. A classification of projects—simple, complicated, complex, and chaotic—is adopted. Three types of interactions in project systems are distinguished—pooled, sequential, and reciprocal. As these interactions exist even in ideally developed project systems, only deviations from the ideal case are of interest that are interpreted as project risks and their interactions. Three analogues types of risk interactions—internal risk amplifications, knock-on interactions, and risk compounding are distinguished. Two fundamental interacting subsystems of a project system are introduced—a project structure subsystem (PSS) and a project delivery subsystem (PDS). Project risks and their interactions are associated with PSS–PDS mismatches.

Introduction

2020 ◽  
pp. 1-8
Author(s):  
Yuri G. Raydugin
Keyword(s):  
Decision Making ◽  
Monte Carlo ◽  
Dynamic Modelling ◽  
System Dynamic ◽  
Project Delivery ◽  
Risk Modelling ◽  
Technical Aspects ◽  
Non Linear ◽  
Project Risks ◽  
Project Structure

A roadmap of the book is developed to expedite a fundamental upgrade to the traditional Monte Carlo methodology. Traditionally risks are seen as standalone noninteracting events. As noninteracting risks are attributes of simple project systems, the traditional Monte Carlo methodology can be adequate in simple projects only. As the vast majority of the project is complex, a more sophisticated methodology that takes interacting risks into account is required. Two types of interactions—intra-risk (amplification of standalone risks due to ‘chronic’ project system issues) and inter-risk (knock-on and compounding interactions among risks)—are taken into account. These stem from various imbalances inside and between two fundamental project subsystems—project structure and project delivery subsystems. The methodology developed in this book is labelled ‘non-linear Monte Carlo methodology’. In parallel, system dynamic modelling of project risks exposure is undertaken. Besides the technical aspects, political aspects of risk modelling and project decision-making are reviewed including various realizations of bias. The notorious Hiding Hand concept is challenged by a newly introduced Revealing Hand approach based on the non-linear Monte Carlo methodology. This book has four target auditoria—all project practitioners who are not involved in Monte Carlo modelling, Monte Carlo modellers, students and instructors of various project management programmes, and specialists involved in project claims and dispute resolutions. The content of this book is organized in a way that serves specific needs of the target auditoria members.

Non-linear Multipliers for Risk Interactions

2020 ◽  
pp. 207-220
Author(s):  
Yuri G. Raydugin
Keyword(s):  
Monte Carlo ◽  
Linear Case ◽  
Project Schedule ◽  
Linear Quadratic ◽  
Project Risk ◽  
Dynamic Risk ◽  
Non Linear ◽  
Risk Pattern

The purpose of this chapter is to mathematically describe three types of risk interactions (internal risk amplifications, knock-on, and compounding) associated with the static and dynamic PSS–PDS mismatches. This is required to factor all relevant instances of risk interactions into Monte Carlo models. It is shown that corresponding three types of non-linearity parameters should be introduced to form non-linear (quadratic) multipliers for interacting risks. In the linear case (non-interacting risks) all non-linearity parameters are equal to zero and all non-linear multipliers are equal to one. As a risk may take part in several interactions it has several non-linear multipliers. Required non-linearity parameters and non-linear multipliers for opportunities are also developed. When all relevant instances of risk interactions are factored to a project risk register, they describe an aggregated impact of the affinity of interacting risks (dynamic risk pattern) on project schedule and cost objectives.

Complexity Risk and Modeling Disorder

2017 ◽  
pp. 139-151
Author(s):  
John K. Hollmann
Keyword(s):  
Tipping Point ◽  
System Behavior ◽  
Process Industries ◽  
Cost Overruns ◽  
Risk Quantification ◽  
Black Swans ◽  
Complex Projects ◽  
Non Linear ◽  
The Cost

Despite 50 years of cost accuracy research, companies are generally unable to quantify the worst outcomes. In the process industries about 10 percent of large projects overrun their budgets by 70 percent or more. The system behavior of these blowouts often reflects disorder. For complex projects, the blowout proportion is 15 to 30 percent of projects. Many risk analysts ignore the worst outcomes as “unknown-unknowns” or “black swans”; but they are neither—we know the causes and their impact is somewhat predictable. Cost disasters start with a mix of systemic weakness and risk events. The cost of mundane projects may overrun by 20 to 40% which is bad but no disaster (financiers assume they will overrun by 25%). Add complexity and stress and the projects can cross a “tipping point” into disorder and chaos with cost overruns of 50, 100 or 200 percent-true disasters. This chapter describes complexity risk and the disorder it can lead to, practical measures of complexity and stress and how to incorporate those measures in non-linear risk quantification models.

A Brief Introduction to System Dynamics (SD)

2020 ◽  
pp. 121-130
Author(s):  
Yuri G. Raydugin
Keyword(s):  
Monte Carlo ◽  
Differential Equations ◽  
System Dynamics ◽  
Analytical Solutions ◽  
Feedback Loops ◽  
System Dynamic ◽  
Dynamics Models

This chapter introduces all main concepts of system dynamics (levels/stocks, flows/rates, variables, feedback loops, etc.). It represents a ‘crash course’ on system dynamics. It is used for development of project system dynamics models in Chapter 7 that mirror project Zemblanity Monte Carlo modelling undertaken in Chapter 5. System dynamic concepts are introduced using a so-called bathtub (BT) model. Five versions of the BT model are introduced through corresponding differential equations. All considered differential equations have analytical solutions. All five workable BT system dynamics models are available on the book’s companion website.

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