Megaproject Risk Analysis and Simulation

A Dynamic Systems Approach

Prince Boateng, Zhen Chen, Stephen O. Ogunlana

• Publisher: Emerald Publishing Limited
• Year: 2017
• Print ISBN: 9781786358318
• eBook ISBN: 9781787143012
• Topic: Business
• Subtopic: Project Management
• Index: Business

Providing new knowledge on risk analysis and simulation for megaprojects, this book is essential reading for both academics and practitioners. Its focus is on technical descriptions of a newly developed dynamic systems approach to megaproject risk analysis and simulation. This is backed up by a discussion of the methodology as applied in a comprehensive case study on the Edinburgh Tram Network (ETN) project. The book informs both academic researchers and megaproject stakeholders with the latest information on risk as applied to megaprojects. As well as the complete case study, the book includes a general risk analysis framework for megaprojects, an analytic network process (ANP) method for risk quantification, a system dynamics (SD) method for risk simulation, and practical guides for the application of the dynamic systems approach in megaproject research and practice.

Chapter 1

Introduction

1.1. Introduction

Major stakeholders on megaprojects have been facing risks associated with social, technical, economic, environmental and political (STEEP) issues that may lead to significant cost and time overruns compared with initial budget and schedule estimates. Although much attention has been devoted to managing risks in megaproject delivery, results have not always been satisfactory in most cases across the world in the past. There have been increasing needs for advanced tools to support better risk assessment so as to inform decision-making in megaproject management. Regarding the complexity of megaprojects, and the great scope for risks and their interaction in mega construction and development projects, it has been widely accepted that quantitative approaches are necessary supplements in risk analysis process. However, there has been little attempt to apply sophisticated methods recommended by industry standards for risk analysis in megaproject practice. Through developing and using a dynamic systems approach over a four-year period, the authors of the book have developed a new tool that can significantly identify major project risk factors and provide predications on time and cost overruns with over 80% accuracy compared to real figures in one megaproject, that is the Edinburgh Tram Network (ETN) project. This book summarizes their research into megaproject risk analysis and simulation to inform both academic researchers and megaproject stakeholders who have interest in qualitative and quantitative risk analysis and simulation for megaprojects across the world.

This book covers all aspects of a real case study oriented research into megaproject risk analysis and simulation through a dynamic systems approach. A case study on the ETN project is used as an example of megaproject to develop a general technical framework called SDANP for STEEP risks analysis and simulation on megaprojects. An analytic network process (ANP) is adopted for risk quantification modelling, while a system dynamics (SD) (Brookes, 2015; Dimitriou, 2014; Flyvbjerg, 2014; Flyvbjerg, Bruzelius, & Rothengatter, 2003; Mentis, 2015; Priemus, 2014; Renuka, Umarani, & Kamal, 2014; Spirkova, 2014; Van de Graaf & Sovacool, 2014) for risk simulation over time. Both the ANP and SD provide practical guides for the application of the dynamic systems approach in megaproject research and practice.

By providing crucial background information for those who want to understand the dynamics of risks over time and their assessment during the decision-making processes on large transport infrastructure projects (Hickman et al., 2015), this book can prove an important source of information for academics, researchers and students in the fields of transport, infrastructure, project management, management science, economic analysis (cost–benefit analysis), public policy, environmental policy and ethics. Practitioners, politicians and policy-makers involved in large transport infrastructure projects can also find this book to be a useful reference on risk analysis and simulation for megaproject management.

1.2. The Problem With Megaprojects

1.2.1. Megaproject Risks

Flyvbjerg, Skamris Holm, and Buhl (2003) found that 258 highway and rail projects ($90 billion worth) in 20 countries did not perform well on budgets as estimated, and about 90% of these projects suffered cost overruns, with the average rail project costing 45% more than what were projected, while it was over 20% in average for highway projects. Based on a continuous research, Flyvbjerg et al. (2003) underscored that cost overrun has not decreased over 70 years in the 20th century and seems to be a global phenomenon, which can also be attested on many megaprojects. For example, the Pusan and Muckho harbour project suffered significant cost overruns in the mid-1970s and relied on an extra $75 million loan for it to complete (2003), and the Big Dig project was estimated at a cost of $2.6 billion but was completed at a cost of $14.6 billion, additionally completion was delayed from 2002 to 2005 (2003). These projects have made the learned society and the public acutely aware of the problems of project delay and cost overruns in megaprojects. In addition, these technical problems also indicate clearly that construction cost estimating on major infrastructure projects has not improved in accuracy in the past more than half century, and the magnitude of underestimated project costs has been almost in the same order according to Flyvbjerg, Holm, and Buhl (2002) and Salling and Leleur (2015). It has been identified by Flyvbjerg et al. (2002) that the main possible reason for cost and time overruns in many megaprojects across the world was to simplify the marginalization of risks during feasibility studies by undependably assuming what the World Bank calls the ‘Everything Goes According to Plan’ (EGAP) principle. There have been increasing needs for new ideas and techniques (Davies, MacAulay, DeBarro, & Thurston, 2014; Flyvbjerg et al., 2002; Kwak, Walewski, Sleeper, & Sadatsafavi, 2014) in order to tackle all risks associated with those significant problems for making the right decisions (Mentis, 2015) on both business and project towards successful megaprojects.

Evidence gleaned through research worldwide suggests that large and complex infrastructure projects such as airport, bridge and highway are usually money pits where funds are simply ‘swallowed up’ without delivering sufficient returns. These problems are as a result of unbalanced subjective beliefs and information in assessing risks and uncertainties, and taking corrective actions to effectively control and manage the identified risks at the right time (Brookes, 2015; Dimitriou, 2014; Flyvbjerg, 2014; Flyvbjerg et al., 2003; Mentis, 2015; Priemus, 2014; Renuka et al., 2014; Spirkova, 2014; Van de Graaf & Sovacool, 2014). Flyvbjerg, (2014) further asserts that the track record of megaprojects under his study was terrible during developmental phases and reflected many credibility problems especially on transportation megaprojects. Proost et al. (2014) and Salling and Leleur (2015) emphasised that costs for transportation megaprojects were often grossly underestimated while traffic is often overestimated, and the perceived failure of the project was subject to a public enquiry, which concluded that the planned budget and schedule were hardly realistic although some of the cost increases were justified spending indeed. In reality, significant wastes were caused by design delays, over-optimistic programming and uncertain authority at the construction and development stages of megaprojects.

The construction industry, like many other industries is a free-enterprise system, and has sizeable risks built into its structure and project based processes (Ball, 2014; Fulford & Standing, 2014; Guo, Chang-Richards, Wilkinson, & Li, 2014). From the initiation to the closing stages, construction process especially that for megaprojects is complex and characterized by a number of uncertainties and interactions (Brookes, 2015) that can negatively influence the project delivery in many ways (Brookes, 2015; Dimitriou, 2014; Flyvbjerg, 2014; Flyvbjerg et al., 2003; Mentis, 2015; Priemus, 2014; Renuka et al., 2014; Spirkova, 2014; Van de Graaf & Sovacool, 2014). For example, uncertainties about changes in weather conditions (Mentis, 2015), sub-contractor delays (Diab & Nassar, 2012; Eizakshiri, Chan, & Emsley, 2015), community resistance (Jordhus-Lier, 2015), political interferences (Kennedy, 2015) and unpredictable site conditions (Adam, Josephson, & Lindahl, 2014; Boateng, Chen, & Ogunlana, 2012) can compromise the completion of megaproject development on time and on budget. Although economic and fiscal risks from natural disasters are quantifiable by using modern techniques, they remain difficult to incorporate into the megaproject decision-making process. As a result, many megaprojects fail to achieve their time, cost and quality goals (Brookes, 2015) due to a lack of accurate assessment and timely control of risks associated with STEEP issues.

1.2.2. Megaproject Risk Assessment

Generally speaking, it has become a matured way to use statistical techniques for risk assessment and there have been many commercial software packages such as Palisade’s @RISK to support professional risk analysis. Researchers such as KarimiAzari, Mousavi, Mousavi, and Hosseini (2011) and Nieto-Morote and Ruz-Vila (2011) proposed the use of risk analysis techniques that are based on estimating probabilities and probability distributions for time and cost-related risk assessment in projects. However, these techniques have limitations in terms of encouraging project participants to not only develop in-depth understanding of underlying risk elements and risk dynamics within interactive structures which constitute megaproject risk systems but also render explicit latent concepts and assumptions which are implicit to current risk assessment. Through learning from problems that led to significant cost and time overruns in megaproject delivery across the world, the authors of the book have a further review into the use of statistical techniques for risk assessment, and found some weaknesses. For example, these techniques do not allow for risks and uncertainties remedial measures in a complex project environment, and do not permit lessons and knowledge, which can be learned from previous projects with similar working environments, to be effectively captured and re-used for developing new projects, and as a result do not facilitate continuous learning and improvement at both enterprise and society level. All these weaknesses indicate a need for advanced risk assessment techniques to effectively tackle complex STEEP risks in mega construction and development projects.

With regards to the increasing complexity and dynamics of risks in mega construction projects and with new procurements methods, the tendency today is to use risk quantification and modelling more as vehicles to promote effective risk response planning amongst multi-disciplinary project team members. According to Giezen (2012) and Kardes, Ozturk, Cavusgil, and Cavusgil (2013), a simple but an effective risk management approach can provide a framework for project managers to identify and respond to potential risk factors quickly and to underpin effective and consistent communications throughout the construction supply chain. In addition, such a risk management framework can assist project members to implement early contingency plans to deal with problems resulting from the project environment. Mousavi, Tavakkoli-Moghaddam, Azaron, Mojtahedi, and Hashemi (2011) argued that the proliferation of techniques and software packages purporting to provide project risk management (PRM) facilities unfortunately have failed to achieve anticipated and satisfactory results in practice, and it is therefore in need for using non-parametric jack-knife resampling technique to rank risks in megaprojects such as highway projects to meet the needs of project managers. For research into innovations in megaproject risk assessment, Mahato and Ogunlana (2011) applied the SD method for a case study on conflict dynamics in a dam construction project in Thailand, Chen and Khumpaisal (2009) applied the ANP method for risks assessment in a large urban regeneration project in the United Kingdom, Boateng, Chen, and Ogunlana (2015) applied ANP for risk prioritization in transportation megaprojects in the United Kingdom and Chen, Li, Ren, Xu, and Hong (2011) applied the ANP method for a total environmental risk assessment for three large international hub airports in China. These research initiatives have not only demonstrated the advantages of using ANP and SD in megaproject risk assessment but also indicate the possibility and usefulness to form a new technical framework that integrates these powerful methods for megaproject risk assessment.

1.2.3. A New Risk Assessment Framework

It has been found from research that many of the risk management approaches developed by contractors and consultants are not dynamic enough to analyse and assess risks (Too & Too, 2010). As a result, communicating construction project risks become poor, incomplete, and inconsistent throughout the entire supply chain network of megaprojects. As emphasized by Davies et al. (2014), it is vital for a successful management team to make innovation happen in megaproject delivery, and an effective risk management approach can provide a framework to identify and assess potential risks so that response actions can be taken to mitigate risks. This book therefore presents a new dynamic systems approach to megaproject risk analysis and simulation. It covers the prioritization and assessment of complex STEEP risks in megaprojects at construction stage and tests a novel risk analysis model on the ETN project. The model incorporates not only tangibles, such as work-to-do and project cost, but also intangibles, such as uncertainties, grievances, and inadequate project complexity analysis in the risk assessment process by using ANP to prioritize STEEP risks and using SD to simulate the dynamics of such risks over the project delivery time. The model is the core of the new SDANP framework to increase both analytical and dynamic capabilities over traditional risk assessment methods, which focus on analytical parameters such as cost, duration, quality, probabilities, etc. but show a lack of incorporating heuristics.

Against the backdrop of risks in megaproject development and need for innovations in megaproject risk management, the authors, in the process of developing a new megaproject risk assessment framework first employed a combination of quasi-ethnography, interviews and the literature to identify different STEEP risk factors that impacted on the performance of the ETN project at the construction phase. The identified risk factors were then prioritized by using ANP to establish a set of the most salient STEEP variables on the project. These risk factors include material and energy price increases as a result of the 2008 recession, and inflation and changes as a result of government funding policies. The selected risk factors based on ANP ranking were then modelled within SD computing environment in order to appraise their measured impacts on the cost, time and quality performance of the project. The approach is used to gain a fuller understanding of the interrelationships between the multiple variables in the system, and to demonstrate the potential benefits of the SDANP methodology. The intention of the book therefore is to explore and model, by using the new SDANP framework, problems caused by STEEP risks to construction cost, time and performance and to provide insights and toolkits that can lead to organizational learning and risk control in megaproject development. Since effective knowledge gain and reuse have been adopted in the new risk assessment framework, it is expected that the new methodology can be used to improve the accuracy of risks estimation and prediction, and thereby effectively reduce the problem of cost and time overruns as well as quality deficiencies during the delivery of megaprojects.

1.3. Purpose and Scope

The purpose of this book is to provide the learned society with a technical summary of a dedicated four-year research into megaproject risk analysis and simulation and to make a contribution to the body of knowledge in risk management on megaprojects. The research has been conducted under the Megaproject Management research theme at Heriot-Watt University from 2010 to 2014, and through collaborative research extensively with partners among 24 European countries at COST Action TU1003 (The effective design and delivery of megaprojects in the European Union) from 2011 to 2015. This book focuses on technical descriptions about a newly developed dynamic systems approach to megaproject risk analysis and simulation with regard to its methodology and application in a real case study on the ETN project. This book therefore provides useful information and toolkits for both academic researchers and megaproject stakeholders with the latest information on quantitative and qualitative risks study for further research and development.

This book provides a dynamic risk assessment framework that incorporates ANP into SD models to form a new SDANP methodology which is a comprehensive and analytical approach to prioritize and simulate risks in megaprojects over the time of project delivery. The SDANP methodology described in this book has been developed through the four-year research by achieving the following research objectives for risks analysis and simulation at the construction and development stages of megaproject:

• describe a set of significant risks across all STEEP issues related to megaprojects
• develop a technical framework that utilizes ANP for STEEP risks prioritization
• simulate all identified STEEP risk factors based on their interactions over project period within a SD environment
• integrate ANP and SD models to form a new SDANP methodology as a dynamic systems approach to risk analysis and simulation.

Just as solving any engineering and/or managerial problem requires the definition of system boundaries, writing a book like this requires the definition of the problem scope, as well as the boundaries of the systems and factors to be included in the tentative problem solution. Therefore, the risks considered in this research include an entire set of risks associated with STEEP issues which result in cost and schedule overruns in megaproject delivery. Following data obtained from literature, field study, and questionnaire survey, an incorporated case study on the ETN project was used to develop and validate the proposed risk assessment models for illustrative decision-making process on risk management on megaprojects.

Most of the techniques upon which this research is based were derived from the theories of ANP and SD. For the development of the new SDANP methodology, STEEP risks were decoupled from programmatic risks that include budget, schedule and performance risks, and so these concerns were a critical part of modelling risk in the development of the new methodology. As part of the scope, individual STEEP risk system model fulfilled the following two main conditions when they were developed:

• have a large number of risk components that cannot be influenced by the internal environment of the project
• exhibit social, technical, economic, environmental and political complexity.

STEEP complexity in megaprojects is not a discrete characteristic but can be defined along a continuum, which ranges from very simple elements within a risk cluster to extremely complex interactions across risk clusters. As complexity is relative and a function of current intellectual manageability, which is evolving as new tools and techniques are developed (Remington & Pollack, 2007; Smyth, 2014), it is a big challenge at present to quantitatively measure the degree of complexities and their interactions within individual STEEP sub-systems as well the entire STEEP system which all megaprojects face. The goal of the SDANP risk assessment approach is not to eliminate all risks from the project but to identify significant risk challenges and their complex interactions to the project over its construction and development phases, and to initiate appropriate management responses for risk control by recognizing the consequences of complex risk interactions. From this point of view, this book provides examples illustrating the set of complex STEEP sub-systems and their integration on megaprojects.

The strategic use of the new approach being provided in this book is to inform the learned society by enhancing the understanding of strengths, weaknesses, opportunities and threats among existing PRM tools given by the series of international standards on risk management, including

• ISO 31000:2009 Risk management — Principles and guidelines
• ISO/IEC 31010:2009 Risk management — Risk assessment techniques.

In addition to their implications for risk assessment for megaprojects at the construction and development stages. This book also highlights the importance of the new approach to remove a number of constraints and communicate a sense of dependability into the decision-making process in megaprojects over the time of project delivery. This implies that it provides a complete technical framework to facilitate understanding the criteria used for evaluating and assigning ratings to system elements and the dynamic interrelationship among those elements. The simulation results derived from using the new approach can serve as reliable o...

Table of contents

• Cover
• Title Page
• Chapter 1 Introduction
• Chapter 2 The Edinburgh Tram Network (ETN) Project
• Chapter 3 Megaproject Risks Assessment Framework
• Chapter 4 Megaproject Risk Quantification
• Chapter 5 Risk Simulation
• Chapter 6 Conclusions
• References
• Appendices
• Index