Multi-Criteria Risk-Based Decision-Making (RBDM)Model for a Multi-Billion-Railway Program (MBRP)

THESIS SUBMITTED TO PARIS SCHOOL OF BUSINESS, PARIS,FOR THE AWARD OF THE DEGREE OF

DOCTOR OF PHILOSOPHY IN BUSINESS ADMINISTRATION(EXECUTIVE DBA)

UNDER THE FACULTY OF BUSINESS ADMINISTRATION

BY

SAAD AHMED AL MUHANNADI

UNDER THE GUIDANCE OF

PROF. JOSSE ROUSSELDEAN OF THE EXECUTIVE DBA PROGRAM

PARIS SCHOOL OF BUSINESS,PARIS, FRANCE

PSB Paris School of Business 59 rue Nationale

75013 Paris, France

December, 2015

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DECLARATION

I hereby declare that the project entitled “Multi-Criteria Risk-Based

Decision-Making (RBDM) Model for a Multi-Billion-Railway Program

(MBRP)” is an original record of the Executive DBA done by me under

the guidance of Prof. Josse Roussel, Dean of the Exec DBA Program in

the Paris School of Business, Paris as part of the Executive DBA

Program. I also declare that it was not previously submitted for the award

of any academic title.

Saad Ahmed Al Muhannadi

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ACKNOWLEDGMENTS

My sincere gratitude extends to beloved teacher Prof. Josse Roussel, Dean of

the Executive DBA Program in the Paris School of Business, Paris, who has

continuously guided me throughout the entire process of the research with all

constructive comments and suggestions to make this research work “Multi-

Criteria Risk-Based Decision-Making (RBDM) Model for a Multi-Billion-

Railway Program (MBRP)” more perfect and complete.

I am grateful to the Qatar Rail and Staffs, in particular the Head of the

Departments for being very supportive throughout my research.

My beloved Parents, thanks for your unlimited kindness and supplications.

"And say, My Lord, Have mercy on both of them as they cared for me when I was

little" Holy Quran*.

My DBA Dissertation is dedicated to the “State of QATAR”

My beloved family, thanks for the patience and love. I always need your love

and care for me today, tomorrow, and then. By God willing, I hope you all will

be the best generation of our family and shine your future by knowledge and

experience.

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Multi-Criteria Risk-Based Decision-Making (RBDM) Modelfor a Multi-Billion-Railway Program (MBRP)

This Research is related to the development of an enhanced model for Risk-Based

Decision-Making (RBDM) applicable for a Multi-Billion Railway Program (MBRP). As the

MBRP is a typical mega-project, consisting of many contractual, physical, and other

components, and stakeholder interests, its design, planning and construction have

proven to be very risky due to high costs and longtime spans. Historically, many of such

projects had enormous cost overruns and schedule slippages, and thus avoidance of

these risks through a well-established process for making decisions is a specific

challenge for my DBA research. The RBDM will employ a Multi-Criteria (MC) approach,

based on probability of occurrence and consequences of each potential risk during

design, planning and construction activities, with both internal and external impacts taken

into account.

The RBDM model applicable for MBRPs will be validated on a current schedule-

driven MBRP in Qatar and compared to other available models. The construction works

for a MBRP are ranked high on the potential risk scale, particularly those carried out for

the underground structures for railway and metro lines, where many uncertainties

(geological and others) are met and need to be defined quantitatively by various

approaches. The proposed model will be used to evaluate and select the most suitable

risk treatment options for meeting the project schedule without jeopardizing the other

criteria.

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TABLE OF CONTENTS

I. AN OVERVIEW OF THE RESEARCH 01

II. POSITIONING OF THE RESEARCH PROGRAM 07

II.1. Research Objectives 07

II.2. Research Questions 08

II.3. Research Hypotheses 09

II.4. Expected Contribution 09

III. LITERATURE REVIEW 11

III.1. Risk-Based Decision-Making Process 11

III.2. Multi-Criteria Decision-Making 17

III.3. Allocation of Risks 22

III.4. Risk Management 26

III.5. Risk Assessment Tools 29

III.6. Decision-Making Models 34

III.7. Applicability of the Existing Models 37

III.8. Requirements for a new RBDM for MBRPs 44

III.8.1. Qualitative risk analysis 44

III.8.2. Quantitative analysis of uncertainty and risk 45

IV. RESEARCH DESIGN AND METHODOLOGY 49

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IV.1. Research Design 49

IV.2. Methodology to be Applied 50

IV.3. Input Data 53

IV.4. RBDM Processes 55

IV.5. Output 65

IV.6. Validation of the RBDM Model 66

IV.7. Data Collection 66

IV.8. Conclusion 67

V. ANALYSIS OF RISK DATA – A MULTIPLE CRITERIA MODEL 69

V.1. Step 1. Qualitative deterministic analysis 69

V.2. Step 2 – Decision-making: Phase I 71

V.3. Step 3 – Quantitative deterministic risk analysis 74

V.4. Step 4 – Quantitative probabilistic risk analysis 76

V.5. Step 5 – Risk treatment options (Decision making: Phase II) 80

V.6 Step 6 – Risk treatment options (Decision making: Phase III) 82

V.7 Suggested risk management approach 87

V.8 General Conclusion of the study – summary of the major

findings and data collection methodology 87

V.9 Scope of further exploration 88

VI. REFERENCES 89

VII. QUESTIONNAIRE 110

VII1. INTERVIEW SCHEDULE 114

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LIST OF FIGURES

Figure 1: Impacts related to schedule of a mega-project 04

Figure 2: The five steps of the risk-based decision making process 11

Figure 3: Risk management process according to ISO 31000:2009 15

Figure 4: General process of risk management 17

Figure 5: Risk management model 28

Figure 6 Risk based decision-making framework for a multi-billion-railway program

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Figure 7: Risk Data Population 54

Figure 8: Decision tree for the evaluation of risk treatment options 65

Figure 9: Risk Factors and their relative significance 86

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LIST OF TABLES

Table 1: Selected types of contracts and risk sharing 23

Table 2: The contractor’s risk for two types of contract 24

Table 3: The risk matrix 45

Table 4 Risk Treatment Description per Level 62

Table 5: Risk Treatment Options 63

Table 6: Summary of Qualitative Deterministic Analysis of Risk 70

Table 7: Summary of Qualitative Deterministic Analysis of Risk 72

Table 8: Criteria for Prioritizing the Risk Factors 74

Table 9: Quantitative Deterministic Analysis of Risk Prioritizing the Risk Factors 75

Table 10: Quantification of Risks and Finding the Risk Degrees 77

Table 11: Risk Treatment Description per Level 79

Table 12: Risk Treatment Options 81

Table 13: Prioritized risk factors 82

Table 14: Prioritized risk factors 84

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I. AN OVERVIEW OF THE RESEARCH

The management of large-scale investment projects is extremely risky and

challenging. One of the key success drivers of such (mega) projects is a proper risk

management strategy, whereby effective measures can be taken early enough to

mitigate any negative impact on schedule, cost or quality. This Research deals with the

application of the Risk-Based Decision-Making (RBDM) approach used to prevent

schedule slippage and cost overruns on mega-projects. According to the RBDM

Guidelines (USCG, 2013), the RBDM “is a process that organizes information about the

possibility for one or more unwanted outcomes to occur into a broad, orderly structure

that helps decision makers make more informed management choices”. It is well

established that such a process, based on adequate models can drastically improve the

decision-making process. However, applying the existing models to large (mega)

projects, such as a Multi-Billion Railway Program (MBRP) in Qatar, introduces new

challenges that should be addressed by developing new methods of approach and fine-

tuned large-scale RBDM models.

Because of their large scale and long time-frames, mega-projects are inherently

risky, carrying their own unique risk potential. Risk management and risk-based decision-

making are presently becoming more and more important. However, as many more and

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much larger mega-projects are being proposed and built around the world, it is becoming

clear that such (mega)projects have strikingly poor performance records in terms of

economy, environment and public support (Haimes, 2007). Due to complexity and lack of

information on the MBRPs, particularly on the construction of their underground

structures, there are many challenging aspects to be addressed during the design,

planning and construction works, including their interaction with all other “work

packages” thus affecting the overall cost and schedule of a MBRP.

There are also different stakeholder interests in place that can impact the risks to a

great extent. For example, the MBRP in Qatar and many other multi-billion programs

worldwide are sponsored by the governments and play an important strategic and

political role to the states. Hence, the multi-billion programs involve many stakeholders

with different and changing requirements, while their acceptability threshold for timely

delivery of the projects cannot be negotiated. However, the existing RBDM models are

not suitable to address complex MBRP schedule problem, the RBDM approach to mega-

projects introduces new methodological challenges in managing risks in order to enable

such risks to be either reduced, or eluded, transferred from one place to another, or

diversified and allocated to the parties best able to handle them. To develop a RBDM

model suitable for a MBRP, relevant studies will need to be conducted first in order to

investigate which elements of the existing models may be appropriate for the

development of an enhanced model specific for a MBRP and thus meet the main

objective of my research project.

As a rule, there is very little or no reliability of the overall experience from the past

projects, so that each particular project requires a tailored approach. Therefore, for

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testing the model to be developed, the case of Doha Metro construction in of Qatar will

be used. For that purpose, both, quantitative and qualitative (mixed) methods will be

used for analyzing the risk data collected both through my field work and through the

search of academic literature and other sources. Following the stakeholders preference,

priority is given to the overall project schedule criterion, but without compromising cost

and other criteria. The schedule slippage of the underground works due to many

uncertainties in place can be transferred to the other “work packages”, of a MBRP.

In early phase of planning of any mega-project, several alternatives are commonly

considered. These alternatives can include different layouts of the infrastructure, different

combinations of tunnel and bridges or different construction technologies. The early

design phase and the decisions taken at that time have the decisive role on the cost of

the MBRPs and mega-projects in general. The optimal solution is commonly selected

based on a cost benefit analysis, which appraise costs and benefits expected during the

project life (Lee Jr., 2000; HM Treasury, 2003; Flanagan and Jewell, 2005; Nishijima,

2009). For including the non-monetary factors such as traffic safety and social or

environmental impacts into the decision-making, the multi-criteria analysis (MCA) can be

utilized, which includes the economic efficiency as one of the criteria (Morisugi, 2000;

Vickerman, 2000).

One of the most important factors influencing the decision whether and how a

MBRP is to be built is the estimated time and costs of construction (Reilly, 2000).

Realistic estimate of construction time is equally important. The construction time

significantly influences the construction costs, because substantial part of the costs

comprises of the labor and machinery costs, which are time dependent. Additionally, the

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construction often requires restrictions in operation of existing infrastructure and

therefore causes secondary costs and is negatively perceived by pubic. Delays of

opening of a MBRP operation are in general economically and politically problematic.

However, to manage the construction schedule of a mega-project is extremely difficult.

From the complexity of a mega-project presented in Figure 1 as presented by Smith

(1998), it is clear that too many (direct and indirect) impacts must be taken into account

to be able to estimate the schedule slippage, for example.

Figure 1: Impacts related to schedule of a mega-project

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The evaluation of uncertainties is crucial information for making decisions. Knowing

them, the decision maker can decide whether the uncertainty is acceptable, whether

some measures to reduce the uncertainty must be taken or whether to select another

option. The need of probabilistic prediction of construction time and costs and their

communication with the stakeholders has gradually been recognized and the demand for

applicable probabilistic models is apparent.

There are only few methods and models for quantification of uncertainty in

construction time and cost prediction for infrastructure in general (Flyvbjerg, 2006), or for

tunnels in particular, e.g. the Decision Aids for Tunneling (DAT) developed at MIT

(Einstein, 1996), an analytical model presented by Isaksson and Stille (2005) or a model

combining Bayesian networks and Monte Carlo simulation proposed by Steiger (2009).

Probabilistic models have not been widely accepted in the practice so far. A first reason

is that there was not real demand for the quantitative modeling of uncertainties and risk,

because decision makers were not used to work with such information. A second reason

is that the existing models often did not provide a realistic estimate of the uncertainties

and they therefore did not gain acceptance among the practitioners. However, this

situation seems to be changing in the recent years and both the demand and the

reliability of the model results have increased.

For reliable predictions, it is essential to realistically estimate the parameters of the

probabilistic model. At present, such estimates mostly rely on expert judgment. However,

these can be strongly biased and unreliable. Therefore, the expert estimates should be

supported by analysis of data from previous projects. Špačkova (2012) introduces an

advanced Dynamic Bayesian Networks (DBNs) model which takes over some modeling

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procedures from existing models and extends the scope of the modeled uncertainties

and aims at developing models for quantification of uncertainties in the construction

process of a linear infrastructure such as rail or roads.

In focus of my research is a schedule-driven mega-project, MBRP in Qatar. This

Research is related to the development of an enhanced RBDM model applicable for

selection of the most suitable risk treatment option(s) for meeting the schedule of a

MBRP without jeopardizing the other criteria of the program. As the MBRPs, as well as

any other typical mega-project, encompass many contractual, physical, and other

components, and stakeholder interests, their design, planning and construction have

proven to be very risky due to high costs and longtime spans. Historically, many of such

projects had enormous cost overruns and schedule slippages, and thus avoidance of

these risks through a well-established process for making decisions is a specific

challenge for my DBA research.

The proposed research is based on a combination of empirical data and literature

review, leading to the development of an analytical (quantitative) framework, followed by

a case-study survey. Relevant literature and data are for this research are proposed to

be collected authentic sources, like Government publications are reports, including

online sources like EBSCO, JSTOR and such other academic databases.

The research process of building a new RBDM model applicable to the MBRPs will

be, based on my previous and current experience in MBRP management. I will continue

to investigate the relevant RBDM models applicable in the current MBRP. Next, based on

my review of the existing decision-making models, especially by taking reference from

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several RBDM models and by combining them, the new RBDM model, applicable for a

MBRP will be built. In the meantime, I will collect the internal and external risk data to

validate the model.

II. POSITIONING OF THE RESEARCH PROGRAM

II.1 Research Objectives

The objective of this research is to provide tools for the analysis of MBRP

construction uncertainties and risks. The particular aims are to exploit the RBDM

methodology to arrive at the models that are the best suited for mega-projects. For the

above purpose, relevant studies will be conducted to enhance the existing RBDM

models or to develop new ones, with the mandated objective of this project viz.

addressing the question of selecting the most suitable risk treatment options; for the

particular case of meeting the schedule of a MBRP without jeopardizing the other criteria

of the program. The major objectives of this research are as follows:

1. Investigate in detail the existing RBDM models and use them to develop an

enhanced model for risk management of a schedule-driven MBRP.

2. Using the proposed RBDM model, evaluate and select the most suitable risk

treatment options for meeting the schedule of a MBRP without jeopardizing the other

criteria of the program.

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3. Assess the validity of the proposed model by using the Qatar Rail implementation

project as a case study.

This research aims at developing models for quantification of uncertainties in the

construction process of a MBRP. Specifically, the models are to be developed for

probabilistic assessment of the construction time. However, probabilistic models have

not been widely accepted in practice so far because the existing models often did not

provide a realistic estimate of the uncertainties and they therefore did not gain

acceptance among the practitioners. But, this situation is changing in the recent years

and both the demand and the reliability of the model results have increased. However,

only a few methods and models can be used for quantification of uncertainty in

construction time and cost prediction for MBRPs (Flyvbjerg, 2006), or for their

components such as tunnels in particular, for which an analytical model was developed

by Isaksson and Stille (2005) or a model combining Bayesian networks and Monte Carlo

simulation proposed by Steiger (2009) and recently a Dynamic Bayesian network (DBN)

model of tunnel construction process developed by Špačkova (2012).

II.2. Research Questions.

The main research question is as follows:

“How to select the most suitable risk treatment options for meeting the

schedule of a MBRP without jeopardizing the other criteria of the program?”

In order to answer the main question, the following sub-questions, which are linked

to the research objectives, should be addressed first:

Research sub-questions linked to the objective #1:

1. What are the risks, which may prevent or deter the timely completion of a MBRP?

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2. Which of the existing RBDM models may be risk-specific for a MBRP?

3. What are the limitations of the existing RBDM models when used for a MBRP, and

what could be the possible solutions to overcome these limitations?

4. Which benefits of the existing RBDM models can be used to design a new

enhanced RBDM model for a MBRP?

Research sub-questions linked to the objective #2:

5. How should the schedule-specific risks be valuated within particular risk treatment

options?

6. How should the proposed RBDM model be used to select the most suitable

(optimal) risk treatment option for meeting the schedule of a MBRP?

Research sub-questions linked to the objective #3:

7. Which all data collection mechanisms and validation techniques should be used

while going for validation of the proposed RBDM model for a MBRP?

8. How should the validity of the proposed model for a MBRP be assessed?

II.3. Research Hypotheses

The proposed RBDM model will be based on three hypotheses.

Hypothesis 1: The optimal risk treatment option provided by the developed model

can minimize the risks associated with the MBRP measures in terms

of the variance from the scheduled figures and actual figures collected

through a case study of MBRP.

Hypothesis 2: The optimal risk treatment option provided by the developed model in

respect of “meet the schedule” criterion will not have significant

adverse impact as regards the optimal results based on other criteria.

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Hypothesis 3: The proposed RBDM model can be validated when applied in practice

through a case study on the current MBRP in Qatar.

II.4. Expected Contribution

The main scientific contribution which is expected from this research project will be

an enhanced RBDM model and computer tool applicable to schedule-driven mega-

projects such as the MBRP in Qatar for selection of the most suitable risk treatment

option(s) for meeting the required project be schedule without jeopardizing the other

criteria. The RBDM will employ a Multi-Criteria (MC) approach, based on the probability

of occurrence and consequences of each potential risk during design, planning and

construction activities, with both internal and external impacts taken into account. The

RBDM model applicable for MBRPs will be validated on a current MBRP in Qatar, which

is believed to suitable to serve as a realistic criterion to validate the model, given that the

main stakeholders, including the owner, the general design contractor, the strategic

program management contractor, and the construction contractors, are highly project

schedule driven and are ready to adopt RBDM techniques. Although the model will be

designed to be project-agnostic, the case study will both demonstrate the accuracy and

effectiveness of the proposed methodology and help in fine-tuning the model steps with

an aim make it applicable for other mega-projects. The validation of the performance of

the proposed model will also identify its limitations, which could then open the door for

future improvements. A comprehensive comparison with the existing models will be also

included.

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III. LITERATURE REVIEW

This section is a review of the literature relevant to the research area under study.

Accordingly, it starts with the literature related to risk-based decision making process,

various models that are used for decision making by ensuring optimal levels of various

risks involved, process of risk management including the step-by-step procedure

involved, the relevant ISO standards applicable in project management setting etc. are

discussed in this section.

III.1. Risk-Based Decision-Making Process

The risk-based decision making (RBDM) process is composed of five major steps, as

shown in the Figure 2, adapted from Mayers et al (2002).

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Figure 2: The five steps of the risk-based decision making process

As a good guidance through my future research, these steps are briefly outlined below:

Step 1— Establish the decision structure

Understanding and defining the decision that must be made is critical. This first

component of risk-based decision making is often overlooked and deserves more

discussion. The following steps must be performed to accomplish this critical component:

Step 1a — Define the decision. Specifically describe what decision(s) must be made.

Major category ies of decisions include (1) accepting or rejecting a proposed facility or

operation, (2) determining who and what to inspect, and (3) determining how to best

improve a facility or operation.

Step 1b — Determine who needs to be involved in the decision. Identify and solicit

involvement from key stakeholders who (1) should be involved in making the decision or

(2) will be affected by actions resulting from the decision-making process.

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Source: Mayers et al (2002)

Step 5

Step 1 Step 3Step 2 Step 4

Step 1c — Identify the options available to the decision maker. Describe the choices

available to the decision maker. This will help focus efforts only on issues likely to

influence the choice among credible alternatives.

Step 1d — Identify the factors that will influence the decisions (including risk factors).

Few decisions are based on only one factor. Most require consideration of many factors,

including costs, schedules, risks, etc., at the same time. The stakeholders must identify

the relevant decision factors.

Step 1e — Gather information about the factors that influence stakeholders. Perform

specific analyses to measure against the decision factors. Some common decision

analysis tools could help to structure the overall decision-making process.

Step 2 — Perform the risk assessment

The risk assessment is the process of understanding (1) What bad things can happen,

(2) How likely they are to happen and (3) How severe the effects may be. The key to risk

assessment is choosing the right approach to provide the needed information without

overworking the problem. The following steps must be performed to assess risk:

Step 2a — Establish the risk-related questions that need answers. Decide what

questions, if answered, would provide the risk insights needed by the decision maker.

Step 2b — Determine the risk-related information needed to answer the questions posed

in the previous step. For each information specify (1) Information type needed, (2)

Precision required, (3) Certainty required and (4) Analysis resources (staff-hours, costs,

etc.) available

Step 2c — Select the risk analysis tool(s). Select the risk analysis tool(s) that will most

efficiently develop the required risk-related information.

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Step 2d — Establish the scope for the analysis tool(s). Set any appropriate physical or

analytical boundaries for the analysis.

Step 2e — Generate risk-based information using the analysis tool(s). Apply the selected

risk analysis tool(s). This may require the use of more than one analysis tool and may

involve some iterative analysis (i.e., starting with a general, low-detail analysis and

progressing toward a more specific, high-detail analysis).

Step 3 — Apply the results to risk management decision making

To reduce a risk, actions must be taken to manage it in a way to provide more

benefit than they cost. They must also be acceptable to stakeholders and not cause

other significant risks. The following steps must be performed to manage risk:

Step 3a — Assess the possible risk management options. Determine how the risks

can be managed most effectively. This decision can include (1) accepting/rejecting the

risk or (2) finding specific ways to reduce the risk.

Step 3b — Use risk-based information in decision making. Use the risk-related

information within the overall decision framework to make an informed, rational decision.

This final decision-making step often involves significant communication with a broad set

of stakeholders.

Step 4 — Monitor effectiveness through impact assessment

Impact assessment is the process of tracking the effectiveness of actions taken to

manage risk. The goal is to verify that the organization is getting the expected results

from its risk management decisions. If not, a new decision-making process must be

considered.

Step 5 — Facilitate risk communication

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Risk communication is a two-way process that must take place during risk-based

decision making. At every step in the process, encourage stakeholders to identify the

issues of importance to them and present their views on how each step of the process

should be performed. Stakeholders should agree on the work to be done in each phase

of the risk-based decision-making process. They can then support the ultimate decisions

The above is in line with the standardized risk management process according to

ISO 31000:2009 (2009), Figure 3. The first, essential step of the process is establishing

of the context, which consists of (1) defining scope and aims of the risk management

process, (2) describing criteria of success and (3) explaining the constraints and

limitations, all based on the stakeholders’ objectives. The risk assessment contains three

steps: First, phenomena and events, which might influence the stakeholders’ objectives

in either positive or negative way, are identified (risk identification). Second, the causes

and likelihood of the events and their impacts are analyzed on a qualitative or

quantitative basis (risk analysis). Third, the results of the risk analysis are compared with

the acceptance criteria and with the objectives and decisions are made how to treat the

risks (risk evaluation).

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Figure 3: Risk management process according to ISO 31000:2009

For risk treatment, four general strategies (also known as “4Ts of risk response”,

Špačkova, 2012) can be applied: Tolerate the risk if the risk is acceptable, Treat the risk

(take measures to decrease the risk), Transfer the risk to another stakeholder or

insurance company or Terminate the activity or project, if the risk is unacceptable and

other strategies are not applicable. The implementation of the selected risk management

strategy must be properly controlled. At each stage of the process, the findings must be

properly communicated with the stakeholders. The findings and decisions should be

repeatedly revised whenever some new information is available or when the conditions

change.

Many decisions must be made regarding design, project financing and type of

contract. These decisions are made under high uncertainty, such as uncertainty in

construction cost, time of completion, impact on third party property or maintenance

costs. Assessment of these uncertainties is crucial for making the right decisions. Often,

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the solutions that seem to be cheaper and faster based on deterministic estimates are

associated with higher uncertainties and risks. Making decisions based on deterministic

values is therefore insufficient. The construction process is affected by different types of

uncertainties. It is therefore important to distinguish between the common variability of

the construction process and the uncertainty on occurrence of extraordinary events, such

as failures of the construction process

Application of risk management in mega-projects has particularly been motivated by

increasing complexity of the construction projects and by pressure for cost savings and

for construction time reduction. Identification of risks in early design phase allows

significant reduction of life-cycle costs through improvements of the design and planning

and through appropriate treatment of the risk in the later phases. Some manuals have

been developed specifically for the underground construction and tunneling projects

(Clayton, 2001; Eskesen et al., 2004; Staveren, 2006). In these manuals, a special

attention is paid to the geotechnical risks, which play a crucial role in the underground

construction (Špačkova, 2012). The risk management system for the Gotthard Base

Tunnel (GBT) has been discussed by Lieb and Erhbar (2011). The overall risk

management process applied to this mega-project is presented in Figure 4.

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Figure 4: General process of risk management

III.2. Multi-Criteria Decision-Making

Decision making about mega-projects should normally include identifying objectives

and possible options for achieving the objectives, as well as the criteria to be used to

compare the options. Then follow analysis of the options, making choices, and finally

feedback. The actual outcome of each of the individual decisions at each stage is not

known with certainty. An ever-increasing complexity of mega-projects such as MBRPs

offers new and exciting scientific and technological challenges. Sen and Yang (1998)

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Source: Lieb and Erhbar (2011)

examine some of the underlying issues on multi-criteria decisions and related modeling

strategies, with a view to exploring a generalized multiple-criteria approach to the

decision-making in mega-projects. Multi-Criteria Decision Analysis (MCDA) methods are

used to reduce complex problems in selection of a preferred alternative. However, Linkov

and Steevens (2008) claim that these methods do not necessarily weight the relative

importance of criteria and combine the criteria to produce an aggregate score for each

alternative.

Major transportation projects require huge physical and financial resources and are

usually funded by government or public private partnerships. In the conventional

approach to project development, government is the project promoter and financier, and

private firms, who actually conduct the project, are intended to do the best-case

feasibility studies, produce the designs, and earn additional profits by numerous change

orders later on. It is going to be harder and harder to get public and political support for

mega-projects unless they come up with better-performing delivery models. Another

critical approach is to incorporate risk analysis into early project development stage,

such as feasibility studies. Therefore, management of large scale projects presents a

special challenge for executive politics and of government more widely. Jennings (2012)

claims that, by assessing the role of executive politics in the adoption and management

of mega-projects, it is possible to undertake comparative analysis of sources of their

under-performance in relation to the role of high politics and institutions, executive

politics and the consequences of the design of project financing and administration (such

as in the balance of risk between the public and private sectors), as well as biases in

decision-making about project risks, and uncertainties that can impact upon technical

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and economic issues of mega-projects. Priemus (2010) claims that “many of the

notorious interim rises in the costs of mega-projects are the result of fresh insight by

political bodies who change the scope of the project along the way to make it fit more

neatly into the spatial planning and to protect the environment against noise and air

pollution, blots on the landscape and other negative effects” and concludes that “missed

deadlines and deteriorations in cost-benefit ratios are often the result of changing

political views or of a perceived need for political compromises”. However, Nijkamp, P.

(2008) argues that “it is not so easy to draw transparent and unambiguous conclusions

from these various findings, but in general it seems plausible that uncritical beliefs in

mega-projects increase the probability of disappointments in a later stage”.

Evidently, all of the direct or indirect participants tend to maintain different interests

in the same project, making it extremely difficult to properly align them for project

success. In the pursuit of successful project performance, time control is one of the most

important functions, especially in megaprojects where various risk variables cause

schedule delays. Schedule delays have been a source of great distress to both owner

and contractor mainly because time overruns are directly or indirectly connected with

cost overruns (Flyvberg et al. 2003). If a mega project is delayed, claims are often filed

between owner and contractors. In certain cases, the claims among contracting parties

escalates into severe disputes. For this reason, the analysis of schedule delays in mega

projects has received continuous interests from both researchers and practitioners.

It is very important to make distinction between the cases whether a single or

multiple criteria are to be employed. A decision problem may have a single criterion or a

single aggregate measure like cost. Then the decision can be made implicitly by

28

determining the alternative with the best value of the single criterion or aggregate

measure. The case with a finite number of criteria and infinite number of feasible

alternatives meeting the requirements belongs to the field of multiple criteria

optimization. Also, techniques of multiple criteria optimization can be used when the

number of feasible alternatives is finite but they are given only in implicit form (Steuer, R.

E. 1986). The decision-making problems when the number of the criteria and alternatives

is finite, and the alternatives are given explicitly, are called multi-attribute decision

making (MADM) problems

Franco and Montibeller (2009) note that "it is surprising that much of the MCDA

literature has paid relatively minor consideration to the processes of articulating and

defining a multi-criteria problem". In many multi-criteria models, particularly so in multi-

attribute utility/value models, the objectives are organized as a value tree, which

decomposes the overall objective of an evaluation into operational objectives for a more

easy assessment of decision alternatives. They also claim that “the process of creating,

evaluating and implementing strategic decisions is typically characterized by the

consideration of high levels of uncertainty“. Xu (2012) suggests implementation of the

Evidential Reasoning (ER) approach to be generally applied for MCDA when analyzing

multiple criteria decision making (MCDM) problems under uncertainties. Keeney (1996)

cautions, however, against over-emphasis on alternative focused thinking, distinguishing

this from value focused thinking. Liu et al. (2011) proposed a risk multi-attribute decision-

making (risk MADM) method based on prospect theory for risk decision making problems

with interval probability in which the attribute values take the form of the uncertain

variables.

29

Multi-attribute decision making techniques can partially or completely rank the

alternatives: a single most preferred alternative can be identified or a short list of a

limited number of alternatives can be selected for subsequent detailed appraisal. This

theory allows complete compensation between criteria, when the gain on one criterion

can compensate the lost on another (Keeney and Raiffa 1976). After having determined

for each pair of alternatives whether one alternative outranks another, these pairwise

outranking assessments can be combined into a partial or complete ranking. In most of

the approaches based on the Multi-attribute Utility Theory (MAUT), the weights

associated with the criteria can properly reflect the relative importance of the criteria only

if the scores aij are from a common, dimensionless scale. The basis of MAUT is the use

of utility functions. Utility functions can be applied to transform the raw performance

values of the alternatives against diverse criteria, both factual (objective, quantitative)

and judgmental (subjective, qualitative), to a common, dimensionless scale. In practice,

intervals [0,1] or [0,100] are used for this purpose.

Besides the above simple additive model, Edwards (1977) also proposed a simple

method to assess weights for each of the criteria to reflect its relative importance to the

decision. First, the criteria are ranked in order of importance and 10 points are assigned

to the least important criterion. Then, the next-least-important criterion is chosen, more

points are assigned to it, and so on, to reflect their relative importance. The final weights

are obtained by normalizing the sum of the points to one. However, as Edwards and

Barron (1994) pointed out, the comparison of the importance of attributes is meaningless

if it does not reflect the range of the values of the alternatives as well. They proposed a

variant that in the course of the comparison of the importance of the criteria also

30

considers the changes from the worst utility value level to the best level among the

alternatives.

In view of the foregoing it may be summarized that MDCA offers a meaningful

approach towards taking holistic and integrative project management decisions that

considers multifarious individual decisions, but at the same time ensures proper

coordination between all such decisions so that optimal performance of the project as a

whole. An integrative approach to treatment of risks of all sorts is adopted here for so

that optimal performance is ensured throughout the project management cycle.

III.3. Allocation of Risks

Due to the various natures of risks which may be encountered in a major

construction project and the differing weights which may attach to their consequences

(and the differing ‘treatments’ which they may entail), it is not uncommon to break the

risks down into commercial (business or project prerequisite and sustainability) risks,

construction (and/or operational) risks and third-party (act of God/government). However,

one of the dangers of adopting such an approach is that it can tend to reinforce an

assumed allocation of risk dependent upon the project delivery method being proposed

and the respective interests of the various stakeholders. Table 1 shows how the risk is

shared between the employer (also called: client, principal, owner) and the contractor,

depending upon the type of contract, as adapted from Flanagan and Norman (1993).

31

Table 1: Selected types of contracts and risk sharing

By way of example, a contractor assessing the risks involved in bidding on a

straightforward ‘construct only’ commercial office tower project may assume that so-

called ‘project risks’, such as the availability of requisite planning approvals or the

principal’s financing, the impact of latent conditions, risks of delay and so on. While

contractors, principals and financiers will, however, each attach varying levels of

importance to various risks, a consideration of the totality of risks which may be

encountered is essential in order to determine their impact and ‘knock on’ effect (Ritchie,

2007).

The context in which the contractor undertakes a risk assessment at the tender

phase is in accordance with corporate limits documented, for example in tendering

guidelines which may also detail limits of liability for key commercial risks. The contracts

will then be considered against a number of criteria, such as financial and funding risks,

construction performance risks and design risks. The issues for consideration under the

financial and funding risks include payment risk and may also extend to issues such as

maintaining positive cash flow through the life of the project; payment for on- and off-site

materials; and the possible impact of security of payment legislation. Construction

32

Source: Špačkova (2012)

performance risks, on the other hand, relate to the willingness or otherwise of the

contracting party to accept general damages and consequential damages; liquidated

damages; the provision of parent company guarantees; the requirement for operating

company performance guarantees; guarantees for long-term performance of materials or

equipment; and industrial relations risk.

Table 2: The contractor’s risk for two types of contract

Design and construct building contracts Joint mining and civil construction

contractsDelay in award of tender/access to site Mining leaseSite conditions Purchase of fleet

Design responsibility Interface risk

Ambiguities in documentation Wall design

Extensions of time Scope of works/fit for purpose

Interface risk, fit-out works Cultural heritage

Adapted from Ritchie (2007)

There are many types of contracts made for mega-projects, such as “Turn-key”,

“Split packages”, “Cost plus”, “Build-Operate-and –Transfer” (BOT) etc., each with their

contract-specific risk management approach. It will therefore be necessary for modeling

purpose to take into account specific features of risks attributed to each type of contract.

A contractor assessing the risks involved in bidding on a straightforward ‘construct

only’ project may assume that so-called ‘project risks’, such as the availability of requisite

planning approvals or the principal’s financing, are matters solely the concern of the

principal and, accordingly, focus on so-called ‘construction risks’, such as the impact of

latent conditions, risks of delay and so on. While contractors, principals and financiers

will, however, each attach varying levels of importance to various risks, a consideration

33

of the totality of risks which may be encountered is essential in order to determine their

impact and ‘knock on’ effect (Ritchie, 2007)

Collaborative contracting models have been studied by Cordi et al (2012) on an

example of three railway project cases. Such a partnering practice is considered to be a

learning process as the mutual experience increases. However, when complexity

increases more sophisticated management becomes inevitable, calling also for

integration with core project processes, but partnering tools and systems do not seem to

provide much guidance.

To be successful, a project must meet financial, technical and safety requirements

and it must fulfill a time schedule. The criteria of project success from the point of view of

different stakeholders can be contradicting and finding an optimal solution is a

challenging task. Many decisions must be made regarding design, project financing and

type of contract. These decisions are made under high uncertainty, such as uncertainty

in construction cost, time of completion, impact on third party property or maintenance

costs. Assessment of these uncertainties is crucial for making the right decisions.

The stakeholders consider every risk which they identify as being relevant to the

project as a whole, and thereafter seek to categorize those risks by the manner in which

they are proposed to be ‘treated’, rather than seeking to ‘fit’ risks into general categories

or seek to allocate them at the outset to the respective stakeholders as matters of

concern only for the other project participants. Instead of simply pricing for risks, there

are other opportunities for mitigating risks either by their elimination, retention, reduction

or transference. Often these mitigation strategies, particularly risk transference, are given

effect contractually via the use of such means as contractual exclusions, limitations of

34

liability, indemnity clauses, risk transference, guarantees, performance bonds and

insertion of a risk premium.(Mead, 2006.,Mead et.al, 2007.)

Often, the solutions that seem to be cheaper and faster based on deterministic

estimates are associated with higher uncertainties and risks. Making decisions based on

deterministic values is therefore insufficient (Spackova,Olga., 2012).All phases of a

MBRP construction are influenced by numerous uncertainties that can be categorized in

two groups: usual uncertainties in the course of design, construction and operation and

occurrence of extraordinary events (failures) causing significant unplanned changes of

the expected project development. Distinguishing between the two types of uncertainties

is necessary, because the principal divergence of their nature requires different

approaches to their analysis. It is further evident that the usual uncertainties influence

the occurrence of extraordinary events, (Špačkova, 2012). These dependences must

therefore be considered in the quantitative risk analysis.

III.4. Risk Management

The mega-projects such as multi-billion railway programs (MBRPs) are multifunctional,

enormous in size, lengthy in life time, expensive and highly uncertain. This makes the

management of such projects extremely risky and challenging. Therefore, one of the key

success drivers of mega-projects is a proper risk management strategy whereby

effective measures can be taken early enough to mitigate any bad impact on cost,

schedule, or quality. Sen and Yang (1998) claim that "The risk-based decision-making

(RBDM) can improve the decision-making process". However, applying them on mega-

projects, such as MBRPs, introduces new methodological challenges that should be

addressed.

35

For that purpose, an integrated methodology is needed to establish the

interrelationship between schedule and cost risk factors in risk-and capital-intensive

mega-projects, as well as to develop strategies, allocate capital resources, as well as to

manage the risks, and analyze critical decisions. Analyzing the uncertainties and risk is

crucial for identifying optimal solutions in all phases of a project, and categorization of

uncertainties influencing the project is important for their proper analysis and modeling.

However, the transfer of experiences between different projects is not a straightforward

task and it cannot be easily automated. At present, the deterministic estimates are used

in the majority of cases. However, the construction community recently recognized the

limitations of the deterministic approach and more attempts are made to quantify the

uncertainties and risks.

The foundation of a risk management system is essential to evaluate risk mitigation

efforts, set priorities for risk monitoring, and create a contingency planning procedure.

Such an approach includes understanding the full complexity, resource requirements,

long time horizons, and exposure to interrelated and pervasive drivers of risk in order to

enable managers to better anticipate and manage the risks of their mega-projects and

lock in the full value of their investments. The interests of many shareholders in

megaprojects are typically very strong, which is easy to understand given the enormous

sums of money at stake, the many jobs, the national prestige, etc. Therefore, the

approach to risk management of mega-projects is differentiated from management of

smaller projects by an emphasis on those decisions and commitments that have the

greatest risk exposure and therefore requires a comprehensive identification and

36

rigorous quantification of the uncertainties that represent the biggest threats to on-time

and on-budget project delivery, Figure 5.

Figure 5: Risk management model

An effective risk management requires a detailed understanding of how the risks

relate to one another; how they will respond to different management approaches; and

how much time, effort, and money will need to be invested before a meaningful impact

on the risk is achieved. Andy Jordan (2013) argues that the first step is to understand

how each individual risk interacts with others—the relationships between risks: “A

change in one risk can have a wide-ranging effect elsewhere in the organization and

understanding the relationships is a vital part of an organizational risk management

process”. He claims that there are two types of relationship between risks that need to be

37

Source: Westney (2007)

considered: Risk-driven relationships and Action-driven relationships. In the Risk-driven

relationships the risk itself is driving associated risks, so as one risk changes its profile, it

drives change in associated risks. In case of Action-driven relationships the actions are

taken to control the risk drive changes to related risks. This requires a compromise in

risk control activities. Of course, both situations may exist for the same risk. He

concludes that “the risks that have the most risk-driven relationships are often the most

serious.” Therefore, the only way that decisions can be made with any degree of

confidence is with a solid understanding of these relationships between the risks. In

recent years the risk based decision-making (RBDM) need has increased concurrently

with new and unprecedented mega-project development (Bruzelius et al., 2002; Priemus

et al., 2008). The success parameters for any mega-project are in time completion within

a specific budget, which meets the required technical performance (Dey, 2002), the so-

called golden or iron triangle of project management (Polydoropoulou et al., 2009).

III.5. Risk Assessment Tools

An overview of commonly used risk assessment tools was presented by Mayers, J

(2002): Among the many risk assessment tools he included Pareto analysis, Checklist

analysis, Relative ranking/risk indexing, Preliminary risk analysis (PrRA), Change

analysis, What-if analysis,. Failure modes and effects analysis (FMEA), Hazard and

operability (HAZOP) analysis, Fault tree analysis (FTA), Event tree analysis (ETA), Event

and causal factor charting, and Preliminary hazard analysis (PrHA). However, all of these

exhibit certain limitations, particularly if applied to mega-projects, such as MBRPs.

38

Pareto analysis is a ranking technique based only on past data that identifies the

most important items among many. This technique uses the 80-20 rule, which states that

about 80 percent of the problems are produced by about 20 percent of the causes. It can

be used for any type of system, process, or activity as long as enough historical data are

available. Usually used to find the most important risk contributors so that more detailed

risk assessments can be performed later. Pareto analysis, however, has several

limitations. It focuses only on the past and offers a valuable look at key contributors to

past problems, but the exclusive reliance on historical data can be misleading. Because

the data under-represent events that have not happened yet or have occurred rarely, this

can skew decisions and resource allocations, especially when a relatively small total

number of problems has occurred. Also, recent changes may invalidate historical trends,

or at least reduce their accuracy.

Checklist analysis is an evaluation against existing guidelines in the form of one or

more risk checklists. It is useful for any type of system, process, or activity, especially

when, suitable checklists exist. Checklist analysis, however, is likely to overlook

potentially important weaknesses. Also, most checklist reviews produce only qualitative

results, with no quantitative estimates of risk-related characteristics. Such approach

offers great value for minimal investment, but it can answer more complicated risk-

related questions only if some degree of quantification is added, possibly with a relative

ranking/risk indexing approach. (Marhavilas et. al., 2011., Learningace, 2013., Osha,

2013).

Relative Ranking / Risk Indexing Technique uses measurable features of a facility to

calculate index numbers that are useful for comparing risks of different options. The

39

relative ranking/risk indexing technique can provide a high-level assessment of the risks

associated with a range of activities, which is suitable when only relative priorities are

needed, as long as a proper scoring tool exists. However, these results can be difficult to

tie to absolute risks as the relative ranking/risk indexing technique uses various indexing

tools to derive risk scores for particular activities. However, the tools are typically focused

on a particular type of risk and for broader, standardized applications; considerably more

development and validation time may be needed. Also, relative ranking/risk indexing

tools are specifically designed to focus on a particular type of risk which makes it difficult

to account for situations outside the scope of the particular tool. (Mayers, 2012, ABS

Consulting, 2010, ABS Consulting 2008).

Preliminary Risk Analysis (PrRA) is a technique used to define the risk related to

important high-risk accident scenarios and to identify the risk of the accidents. However,

because the PrRA focuses on potential accidents, the failures leading to accidents are

not explored in much detail, so that it introduces a level of uncertainty in the results. Also,

the resulting recommendations for reducing risk are typically general in nature instead of

focused on attacking specific issues.

Change analysis is used for any situation in which change from normal setup,

operations, or activities is likely to affect risks. It relies on comparisons of two systems or

activities to identify weaknesses in one of the systems in relation to the other However; it

is highly dependent on points of comparison. Also, it does not inherently quantify risks

and is strongly dependent on the expertise of those participating in the analysis, i.e. on

their ability to recognize and evaluate notable differences between the system or activity

of interest and the point of comparison.

40

What-if analysis is a problem-solving approach used to suggest upsets that may

result in accidents or system performance problems and make sure the proper

safeguards against those problems are in place. It is generally applicable for almost

every type of risk assessment application, especially those dominated by relatively

simple failure scenarios. However, it is likely to miss some potential problems, because it

relies exclusively on the knowledge of the participants and is likely to overlook potentially

important weaknesses, being difficult to audit, because there is no formal structure

against which to audit. It also gives only qualitative results and no quantitative estimates

of risk-related characteristics.(Brown et. al., 1999, Heldman, 2002)

Failure modes and effects analysis (FMEA) is a technique that generates qualitative

descriptions of potential performance problems and lists of recommendations for

reducing risks, but it can also provide quantitative failure frequency or consequence

estimates. It is generally used as a system-level and component-level risk assessment

technique, applicable to any well-defined system. However, examination of human error

is limited as it addresses potential human errors only to the extent that human errors

produce equipment failures of interest, while the miss-operations that do not cause

equipment failures are often overlooked. Also, its focus is on single-event initiators of

specific equipment failures, which are analyzed one by one, so that important

combinations of equipment failures may be overlooked. The, examination of external

influences is limited to those events only that produce equipment failures of interest and

accounts for possible effects of equipment failures only during one mode of operation or

a few closely related modes of operation.

41

Hazard and Operability (HAZOP) analysis is used mostly as a system-level risk

assessment technique and generates primarily qualitative results, although some basic

quantification is possible. The HAZOP technique requires a well-defined system or

activity as it is a rigorous analysis tool that systematically analyzes each part of a system

or activity. It is rather time consuming as it systematically reviews credible deviations,

identifies potential accidents that can result from the deviations. However, such detailed

analysis focuses on one-event causes of deviations so that, if it objective is to identify all

combinations of events that can lead to accidents of interest, more detailed techniques

should be used.

Fault Tree Analysis (FTA) is a technique that visually models how logical

relationships between equipment failures, human errors, and external events can

combine to cause specific accidents of interest. The probabilities and frequencies can be

added to the analysis to estimate risks numerically. It is generally applicable for almost

every type of risk assessment application, but examines only one specific accident of

interest so that, to analyze other types of accidents, other fault trees must be developed.

Also, quantification requires significant expertise because using fault tree analysis results

to make statistical predictions about future system performance is complex, so that

analysts often become so focused on equipment and systems that human and

organizational issues are not taken adequately in their models. (GRI, 2011, FAA &

Eurocontrol, 2007).

Event Tree Analysis (ETA) is a technique that logically develops visual models of the

possible outcomes of an initiating event by the use of decision trees. Probabilities and

frequencies can be added to the analysis to estimate risks numerically. It is suited to

42

almost every type of risk assessment, but it is limited to one initiating event, leading to

overly optimistic estimates of risk.

Event and Causal Factor Charting is used to understand how an accident occurred

by finding the underlying root causes of the key contributors and to make

recommendations for fixing the root causes. It is most commonly used when the accident

scenario is complicated, involving a chain of events or a number of root causes.

However, it does not necessarily ensure that the root causes have been identified,

unless the causal factor is the root cause. Also, using event charting can overwork

simple problems.

Preliminary Hazard Analysis (PrHA) is a technique that focuses on finding hazards,

assessing the severity of accidents that could occur involving the hazards, and on finding

protective features for reducing the risks of the hazards. This technique is typically

conducted early in the process, before other analysis techniques are practical, and thus

requires additional follow-up analyses. Also, the quality of the results of a PrHA is highly

dependent on the knowledge of the team. At the time, there are few or no fully developed

system specifications and little or no detailed design information and therefore, the risk

assessment relies heavily on the knowledge of subject matter experts. If these experts

do not participate in the risk assessment, or if the system is a new technology having

little or no early operational history, the results of the PrHA will reflect the uncertainty of

the team in many of its assessments and assumptions.(Mullai, A., 2006., ERC, 2013).

III.6. Decision-Making Models

Throughout the advancement of the construction sector more and more decision-

making models have appeared to help construction projects to evolve toward informed

43

decision-making. Decision-making models have been applied to various areas in the

construction sector, such as metropolitan construction projects (Kuo et al., 2012), project

contractual commitment (Nguyen et al., 2010), project risk identification and assessment

(Mojtahedi et al., 2009), construction on transport project outcome (Polydoropoulou et

al.,2009), mega projects cost-benefit analysis, planning and innovation (Priemus et al.,

2008), portfolio balancing of engineering (Zeng et al., 2007)and contracting projects

(Caron et al., 2007), construction project risk assessment (Deelstra et al., 2003;

Špačkova, 2012), project facility design, construction, and life-cycle performance (Kam et

al., 2003), and project risk management (Dey, 2002).

Besides the decision-making models specific for the construction sector, the

decision-making models are also broadly used in other industries and specific areas.

Therefore the existing decision-making models can be grouped in two categories, ones

that are generally acceptable, and others that can only be employed in specific cases.

Zhou et al. (2008) presented a study, which enable decision-making and mitigation

strategy planning of information systems in the public sector. A research on errors in

criteria’s weights to rank high risks in BOT (build-operate-transfer) projects is presented

by Ebrahimnejad et al. (2010). Liu et al. (2009) claim that risk evaluation of construction

program is far more complex than that of weighting risks criteria in a single construction

project, and presented Gray-AHP methods, which also consider correlation between the

projects in the program. However, a research done by Giezen (2012) shows that

reduction of complexity has a positive effect on the planning of mega infrastructure

projects.

44

Decision making involves the use of decision treesto help identify good strategies

for planning a response to a set of interdependent decisions sequenced through time.

Golub (1997) and Targett (1996) claim that decision trees have as their prime focus the

question of uncertainty about the outcomes of decisions. For problems that merit it, it is

also possible to combine decision tree modeling with the appraisal principles and in this

way develop contingent decision strategies based on multi-criteria assessment.

Appropriate analysis of the decision tree allows the decision maker to develop, from the

outset of the decision process, a contingent decision strategy (Dey, 2002).

For the modeling purposes it is convenient to distinguish uncertainties, ie. Ones that

cannot be reduced and ones resulting from incomplete knowledge of the system, but can

be reduced when additional information is available. Der Kiureghian and Ditlevsen

(2009) claim that distinguishing between the two types of uncertainties depends on the

judgment whether or not the uncertainty can be reduced in later analysis.

The risk is not a universal quantity, because the objectives of individual stakeholders

can differ and can develop during the project, and the perception of the consequences of

not meeting the objectives is also individual. Therefore, the risk must always be analyzed

with regard to the context and objectives. There are only few methods and models for

quantification of uncertainty in construction time and cost prediction for infrastructure in

general (Flyvbjerg, 2006), or for tunnels in particular (Einstein, 1996). Probabilistic

models have not been widely accepted in the practice because neither “there was not

real demand for the quantitative modeling of uncertainties and risk” nor “the existing

models often did not provide a realistic estimate of the uncertainties and they therefore

did not gain acceptance among the practitioners” (Špačkova, 2012). However, this

45

situation seems to be changing in the recent years and both the demand and the

reliability of the model results have increased. The need of probabilistic prediction of

construction time and costs and their communication with the stakeholders has been

recognized in recent years (Grasso et al., 2006; Edgerton, 2008) and the demand for

applicable probabilistic models is apparent.

III.7. Applicability of the Existing Models.

A particular emphasis is placed herewith on the existing risk-based decision-making

(RBDM) models that could help meeting my research objectives listed in paragraph. 2.1

of this Research, bearing in mind the need these models to be applied to mega-projects,

such as MBRP in Qatar. Mega-projects in general tend naturally to fail. Their

performance has historically been poor. However, the track records show that, while the

exposure of mega-projects to risk cannot be avoided, the risk can be managed. The

track record of transportation mega-projects reveals that the costs are usually

significantly underestimated. There are some cases of enormous cost overruns, such as

Suez Canal (1,900%), but an order of magnitude less (200%) Panama Canal (Teglasi,

2012). Seung Heon Han et al. (2009) reported that the construction of a 412 km long

high-speed railway in Korea (KTX) met numerous uncertainties and challenges during

planning and managing phase which resulted in schedule delays of 5.5 years and cost

overruns of 3.17 times over the planned budget. They discovered the following major

delay causes: (1) lack of abilities to manage hi-tech mega project; (2) changes of routes;

(3) inappropriate project delivery system; (4) a lack of scheduling tool tailored for a mega

project; and (5) redesign and changes in main structures and tunnels.

46

Merrows (1988) has developed a database on performance of 52 mega-projects

worldwide, ranging in cost from $500 million to over $10 billion (in 1984 $). From it he

developed statistical relationships of actual to estimate the cost overruns and schedule

slippage that involve different issues that may influence the cost and schedule of mega-

projects of different kinds. However, for more reliable predictions it is essential to

realistically estimate the parameters and, instead of or in addition to such a deterministic

approach, employ a probabilistic approach which combines the probability of occurrence

of unwanted events and their consequences.

At present, such deterministic estimates mostly rely on expert judgment, but these

can be strongly biased and unreliable. Therefore, the expert estimates should be

supported by analysis of data from previous projects. Three different approaches are

available for estimating the failure rates: expert judgment, reliability analysis or a

statistical approach using data from constructed tunnels. Each of the approaches has its

strengths and weaknesses. Ideally, multiple approaches should be employed and results

should be compared and critically examined

Estimates of construction time and costs and of other performance parameters are

highly uncertain. Obviously, the earlier in the project the estimates are made, the higher

is the uncertainty. In spite of that, most stakeholders require a deterministic estimate of

costs and time in the current practice: these deterministic estimates are used as a basis

for decision-making and they are communicated with public. However, this approach

creates false expectations, which are unlikely to be fulfilled, and it can lead to wrong

decisions, (Špačkova, 2012).

47

Taghavifard et al. (2009), claim that the domain of decision analysis models falls

between two extreme cases, one deterministic and the opposite pure uncertainty.

Between these two extremes are problems at risk". Dudenhoefer et al (2006) examine

the issues of interdependency modeling, simulation and analysis and provide formalism

for these dependencies. Liu et al. (2011), proposed a risk multi-attribute decision-making

(risk MADM) method based on prospect theory for risk decision making problems with

interval probability, in which the attribute values take the form of the uncertain linguistic

variables. Their risk MADM model can support my skills for the development of a new

RBDM model, but, as it presents a method which is more in line with the actual decision-

making behavior in all sectors, it does not entirely address the RBDM in MBRP process.

Kester et al. (2009) address portfolio management based on project selection,

termination and deletion decisions. Their model is specific for portfolio and does not

address RBDM MBRP process; however it can partially contribute to this research in

identifying whether an integrative portfolio decision-making model can provide more

success in the long run by integrating strategic and quantitative criteria consideration into

the portfolio decision-making. A case study by Giezen (2012) presents the advantages

and disadvantages of reducing complexity in mega project planning.

Belton and Stewart (2002) introduce the concept and meaning of the conflict between

different goals, objectives and criteria in decision-making involving multiple stakeholders.

This model is specific to risk based design criteria, but not suitable for the RBDM MBRP

process. However, it may direct the selection of risk data in step one of development of a

new enhanced model by addressing multiple stakeholders and criteria of a MBRP. Zhou

et al. (2008) presented a study, which enable decision-making and mitigation strategy

48

planning of information systems in the public sector. Although their methodology has not

be designed for the construction industry, the checklist presented in the study can be

enhanced and used as part of the proposed model to help a MBRP identify and assess

risks.

The risks are different if considered from the viewpoint of investor and contractor,

taking into account the contractor’s aversion to higher losses. When the risk is analyzed

from the viewpoint of the investor, a majority of the financial losses due to failures (i.e.

reconstruction of the tunnel and the overburden, damage to the third party property,

compensations to people) is transferred to the contractor. However, in spite of the

transfer of the risks, the delay of the commencement of tunnel operation leads to

additional costs to the investor includes costs of traffic disruption and costs of debt

financing. When the risk is analyzed from the perspective of the contractor, the

contractor is insured against the direct financial loss caused by the construction failures.

The insurance covers costs for reconstruction of the tunnel and the overburden, damage

to the third party property and compensations to the injured people. However, the

prolongation of the construction brings additional costs, which are not covered by the

insurance. These costs consist in costs of labor and machinery, which is bound to the

project and in the penalty the contractor, must pay to the investor in case of delay. The

financial loss of the contractor thus contains both the additional costs and contractor’s

deductible.

The human factor reflects the influence of common factors, which systematically

influence the construction process and thus introduce strong stochastic dependences

among the performance in each segment of the tunnel. These can be the quality of

49

design and planning, organization of construction works or other external influences.

Also, the quality of the construction company and the appropriateness of the technology

are uncertain in the planning phase. The uncertainty in these common factors increases

the uncertainty in estimates of the total construction time.

Research on errors in criteria’s weights to rank high risks in build-operate-transfer

(BOT) projects by Ebrahimnejad et al. (2010) shows that (MADM) model can help

weighting the criteria of the enhanced RBDM MBRP model. However according to Liu et

al. (2009), risk evaluation of construction program is far more complex than that of

weighting risks criteria in a single construction project, and they presented Gray-AHP

method, which involves correlation between the projects in the program. Their approach,

like the approach by Ebrahimnejad et al. (2010), can help defining criteria and, when

combined, can be used to develop an enhanced RBDM model applicable for MBRPs.

Not many of the existing decision-making models are expected to be suitable to

address my research objectives. Nevertheless, the researcher expects some of these

models to be useful when dealing with the research objectives; e.g. The work done

Lechner et al. (2002), could be suitably replicated in the proposed study. Haimes (2007)

and ISO 31000 (2009), Kester et al., (2009), Poole and Samuel (2011). Many models

have been developed to simulate the decision making process, such as those by Targett

(1996), Alarcón and Ashley (1996),US Coast Guard R&D Center (2013), Tagfavifard et

al. (2009), Liu et al. (2011), Zhou et al. (2007), Keeney (1996), etc. These models are

sought to be incorporated or replicated in the proposed study, after suitable modifications

so as to make them fit into the MBRP framework under study. For multi-criteria decision

analysis (MCDA), available models are of Belton et al. (2002) and Franco and

50

Montibeller (2009). For BOT projects in particular there is a model of Ebrahimnejad et al.

(2010), Gray-AHP methods developed by Liu et al. (2009).

For the research on the decision-making opportunities to improve MBRP design,

construction, and life-cycle performance the model applied by Kam and Fischer (2003)

might be used. The risks treatment options in MBRP construction will refer to Mead

(2007). Standardization principles and risk management guidelines will be according to

international standards such as SM ICG (2013), ISO 31000, 2009 and ASB (2012). Risk

analysis will be carried out by the use of methodologies for industrial plants (Tixier et al,

2002) and interdependent technical infrastructures (Johansson 2010). Monte Carlo

simulation will be carried out similarly as done by Whitlock and Kalos (2008) and Centre

for Traffic and Transport. Decision-making approach will refer to these of Borchardt

(2010), Montibeller and Franco (2010), Risktec (2005), Birnbaum (2008). Environmental

risk will be modeled as presented by the U.S. EPA (2009), and Kiker et al. (2005). For

decision analysis the approach used will be similar to these of Goodwin and Wright,

(2009), Golub (1997). For the risk management in particular situations such as strategic

alliance making process, customer relationship or research the approach will be similar

to these applied by Das and Teng (1998), Papadopoulos et al. (2012) and Wood and

Welch (2010) respectively. If needed, the reliability of railway systems will be modeled

like was done by Vromans (2005). Also, an integrated regional risk assessment will be

similar to that done by Nicolet-Monnier (1996) for the situation in Switzerland.

In conclusion, the review of the literature above highlights that existing research on

RBDM in the construction industry has not specifically addressed the RBDM for mega-

51

projects such as present MBRPs have. The existing models are mainly characterized as

follows:

1. They are usually applicable for a small scale projects, or for one organization or

for one activity, and cannot directly be used to address the RBDM of a MBRP;

2. They usually lack a clear and deep understanding of the uncertain nature and

high risk required for RBDM in a complex organization such as a MBRP;

3. In probabilistic models, the decision maker is concerned not only with the

outcome value but also with the amount of risk each decision carries. However,

the probability is generally used to measure the likelihood of occurrence of a

single risky event, while in MBRPs such events may be combined.

4. They are usually too specific, either too simple and applicable for all the

organizations or too narrow i.e. they can only be employed in certain areas. As a

result, they cannot provide direct, explicit RBDM guidance to a MBRP; and

5. The existing models are mainly intended to improve the informed decision-making

of a detailed project, or specific process group in the life cycle of the project,

rather than to a comprehensive RBDM of the whole schedule-driven MBRP.

In continuing my search through the academic literature, I will look for any additional

RBDM models that could be applicable to the schedule driven mega-projects, and thus

enable my final selection of the reference ones that could support development of a new

RBDM model for MBRP, as well as to select the most suitable risk treatment options for

meeting the schedule of a MBRP without jeopardizing the other criteria of such a

program. With the above mentioned general characteristics of the existing models in

mind, those RBDM models will be selected that are most likely suitable to be enhanced

52

and combined in order to design a new RBDM model for a MBRP, which than will be

fine-tuned in a case study. The new RBDM MBRP model will be developed from a

combination of the existing frameworks for data input, process of data and data output.

Among them are the Standardization Workgroup of the Safety Management International

Collaboration Group (SM ICG 2013), Risk Treatment in Enterprise Risk Management of

the Actuarial Standards Board (ASB, 2012) and the research done by Tixier et al, (2002)

on 62 risk analysis methodologies of industrial plants. The generic framework of the SM

ICG (2013) will be used to develop the input, process and output, as well as validation of

the model. Treatment options for risks in construction, civil and mining projects (Mead,

2007) will be used to take account of the special features of MBRPs.

III.8. Requirements for a new RBDM for MBRPs

III.8.1. Qualitative risk analysis

Both, qualitative and quantitative methods will be applied. The qualitative risk

analysis aims at identifying the hazards threatening the MBRP construction project, to

evaluate the consequent risks and to determine the strategy for risk treatment. This

analysis serves as a basis for preparation of contracts, for management of the project

and for allocation of responsibilities amongst the stakeholders or their employees and

representatives. To evaluate the risks, varying classification and rating systems

describing the probability of occurrence of a hazard and its expected consequences. The

rating of the probability and consequences is combined in a risk index or by means of a

risk matrix (Eskesen et al., 2004). The risk indices are usually calculated as product of

the probability rating and consequence rating, as presented in Table 3. The individual

53

risk for each hazard is estimated as product of the frequency and consequence. The

total projects risk is determined as the sum of individual risks. However, this approach is

likely to lead to an incorrect estimation of risk (Špačkova, 2012).

Table 3: The risk matrix

Consequence

Frequency Disastrous Severe Serious Considerable Insignificant

Very likely Unacceptable Unacceptable Unacceptabl

e

Unwanted Unwanted

Likely Unacceptable Unacceptable Unwanted Unwanted Acceptable

Occasional Unacceptable Unwanted Unwanted Acceptable Acceptable

Unlikely Unwanted Unwanted Acceptable Acceptable Negligible

Very unlikely Unwanted Acceptable Acceptable Negligible Negligible

III.8.2. Quantitative analysis of uncertainty and risk

The quantitative risk analysis aims to numerically evaluate the risk in order to provide

valuable information for decisions-making under uncertainty such as the selection of

appropriate design or construction technology, efficient communicating the uncertainties

with stakeholders, as well as determination of the bid price, the time of completion and

insurance premium with a required level certainty on an objective and quantitative basis.

The selection of tools for quantitative risk analysis depends on the objectives of the risk

analysis and on the type of uncertainties, which are analysed. Compared to the

qualitative risk analysis, the quantitative risk analysis requires a clearer structuration of

54

the problem, detailed analysis of causes and consequences and description of the

dependences among considered events or phenomena.

The approaches to quantification of uncertainty and risk include Monte Carlo (MC)

simulation, Fault tree analysis (FTA), Bayesian Networks (BN), Poisson model, Analytical

solution, Event tree analysis (ETA), Bayesian Networks (BN) and others. FTA and

Poisson model is commonly used for assessment of probability of a hazard occurrence

and ETA for analysis of consequences. Several approaches for analyzing the

extraordinary events have been used in the literature and practice. Eskesen et al. (2004)

and Aliahmadi et al. (2011) assess the expected value of construction risk for different

tenderers in order to select the best contractor. Sturk et al. (1996) use a Fault tree

analysis (FTA) for the estimation of the probability that the tunnel construction harms

trees in a park, in order to select an optimal construction strategy. Jurado et al. (2012)

estimate the probability of ground water related hazards using the FTA. Šejnohaet al.

(2009) presents a methodology combining FTA and Event tree analysis (ETA) for

quantification of the risk of extraordinary events in the course of tunnel construction.

Sousa and Einstein (2012) and Špačkova (2012) introduce a dynamic Bayesian

networks (DBN) model for modeling the risk of construction failure

Other models have been developed for modeling the usual uncertainties. In

Ruwanpura and Ariaratnam (2007), tools for simulation of the tunnel drilling process are

presented, which include Monte Carlo (MC) simulation for the evaluation of the usual

uncertainties in predicting construction time and costs. In Chung et al. (2006) and in

Benardos and Kaliampakos (2004) observed (actual) advance rates are used for

updating the predicted (computed) of advance rates and consequently the respective

55

excavation time; in respect of the remaining part of the tunnel project under their study.

While Chung et al (2006) used Bayesian analysis for their study; artificial neural

networks were employed by Benardos and Kaliampakos (2004). A well-known Decision

Aids for Tunneling (DAT) model developed at MIT for probabilistic quantification of risks

of the tunnel construction processes, uses MC simulation for probabilistic prediction of

construction time and costs, taking also into account the geotechnical uncertainties,

which are modeled by means of a Markov process (Chan, 1981), as well as the

uncertainties in the construction process.

In response to the research sub-questions mentioned before, a literature review has

been conducted to investigate to what extent the existing RBDM models could be

suitable for a MBRP. In recent years the need for RBDM has increased concurrently with

new and unprecedented multi-billion programs developed in countries like China

(Guangshe et al., 2011) and worldwide (Haimes, 2007, Flyvbjerg et al., 2003, Merrow,

1988, Jennings, 2012). Multi-billion or mega programs are multifunctional, enormous in

size, lengthy in life time, expensive and highly uncertain (Bruzelius et al., 2002; Priemus

et al., 2008, Westney, 2007,Kwakand Smith, 2009, Tagfavifarel et al., 2009, Riabacke,

2006). The success parameters for any multi-billion programs are in time completion

within a specific budget, which meets the required technical performance (Dey, 2002,

Lechner et al., 2002, Sjoberg, 2002), the so-called golden or iron triangle of project

management (Polydoropoulou et al., 2009).

Decision making models have been applied to various areas in the construction

sector, such as metropolitan construction projects (Kuo et al., 2012, Vromans, 2005,

Kamand Fisher, 2003, Poole and Samuel, 2011, Liu & Wang, 2009), project contractual

56

commitment (Nguyen et al., 2010, Maytorena et al., 2007, Flyvbjerg et al., 2003) or

project risk identification and assessment (Mojtahedi et al., 2009, Jordan, 2013, Sen&

Yang, 1995). The project performance model for decision making by Alarcon et al.,

(1996) presents an early methodology for modeling project performance. Throughout the

advancement of the construction sector, more and more decision-making models have

appeared to help construction projects to evolve toward informed decision-making

(Kam& Fisher, 2003,Liu &Wang, 2009, Poole & Samuel, 2011, Vromans, 2005). Besides

the decision-making models specific for the construction sector, decision-making models

are also broadly used in other industries and specific areas (Mc Kenaand Wilczynski,

2006, McDaniel et al., 1999).

Therefore the listed at the end and additional literature on the existing decision-

making models relevant to this research will be grouped and evaluated in order to

address each of the research questions listed in paragraph II.2. However, it is necessary

to develop additional modules which would fit into the existing research gaps in the

current academic literature; and these are considered to be of added importance for the

risk assessment in respect of the MBRP and such other mega-projects. To that group

belong impacts of the number and particularly the type of contracts, relationships

between the risk ranking criteria used to select the most suitable risk treatment option(s),

involvement of the high politics and other possible risk initiating events in MBRP design,

planning, and construction.

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IV. RESEARCH DESIGN AND METHODOLOGY

IV.1 Research Design

In view of the foregoing discussions, it may be pointed out that the model suggested by

Tixier et al (2002) [Figure 6] augers well with MBRP because of the fact that it combines

the benefits of a comprehensive generic framework (SM ICG, 2013) with the specificity

of a large railways project like MBRP, and hence has got utmost similarity in the

objectives and research context. Hence the development of the proposed model will

follow the six steps and the methodologies by Tixier (2002), which are considered fairly

relevant to my RBDM MBRP Model (Figure 6):

The first step is the qualitative deterministic analysis of risk data – during this

stage a selection of risks data will undergo a preliminary risk analysis. This step

will initially be supported by PRA theory of Nicolet-Monnier, (1996) and the

multiple criteria decision-making model by Belton and Stewart. (2002).

The step two (Decision-making I) is the selection of risk data for further analysis.

The risks will be prioritized for treatment by evaluating them against pre-

established criteria, which will be supported by the decision-making model of

Ebrahimnejad et al. (2010).

58

During the step three a quantitative deterministic analysis of risk data will be

using scenario or similar theory, based on a quantitative distribution of each risk’s

time impact will be further analyzed (e.g. single point, uniform or triangle

distribution). Selection of the proper distribution will be similar to that done by the

Centre for Traffic and Transport in Denmark by the use of the Monte Carlo

Simulation method for the risk analysis.

In the step four the quantitative probabilistic analysis of risk data will be

conducted - during this stage a quantitative approach to the risks data will be

undertaken by using a distribution of all risk’s time impact through Monte Carlo

Simulation as presented by Whitlock and Kalos, (2008).

Step five is the selection of risk treatment options (Decision-making II) – during

this stage decision should be made on how to minimize potential downside but

also maximize the potential upside of opportunities by using a pre-established

criteria with the support of ISO 31000 (2009).

Step six evaluates the treatment options through Decision Tree Theories

(Decision-making III) - during this stage the models of ISO 31000 (2009)

combined with Risktec (2005) will be applied to evaluate the available treatment

options through a decision tree analysis.

IV. 2 Methodology to be applied

In order to address my second research objective (to propose an enhanced RBDM

model for a MBRP and select the most suitable risk treatment options for meeting the

59

schedule of a MBRP without jeopardizing the other criteria of the program) and to select

among the existing RBDM models, those that could be used in the steps needed to

create a new model for MBRP, a framework is designed and presented in Figure 6.

As shown in Figure 6, the framework is composed of three sections. Section one

contains the risk data input. In Section two the process steps of the development of an

enhanced RBDM model are given. Section three presents the output of the enhanced

model. It is based on a combination of the Standardization Workgroup of the Safety

Management International Collaboration Group (SM ICG 2013) and a research done by

Tixier et al, (2002) on 62 risk analysis methodologies of industrial plants. For that

purpose the generic framework of the SM ICG (2013) will be used as guide to create the

input, process and output, as well as for validation of the model, while the development

of the model will follow the six steps and methodologies by Tixier (2002), which are

relevant in the sense of guiding the design of the RBDM MBRP model.

Figure 6: Risk based decision-making framework for a multi-billion-railway program

60

61

IV.3. Input Data

To meet objective 3 of this Research, the risk data will be thoroughly collected, after

being standardized using standard and time-tested procedures like the ‘six times

evaluating, six party management and an unified platform’ method proposed by Liu et.al

(2014) or the one used by Sarkar, D (2010) to meet requirements of the RBDM model for

MBRPs as follows:

Standardize risk data for collection:

In order to collect, aggregate and combine risk data from different sources, it is

necessary to standardize the risk data before collecting (SM ICG, 2013). The quality of

the risk data used for the RBDM model will also be assessed. All risk data used will be

checked for credibility and should be completely unbiased. Hence standard risk manual,

plans, procedures, processes and taxonomies will be developed for the case study on

MBRP in Qatar according to the international standards such as COSO (2004),ISO

31000 (2009)and Project Management Institute, Inc. (2013) as well as implemented

across the organization (CMS, 2013) and configured in the risk database (ARM, 2013).

Example standardization needed for the risk data collection: all risk data will be captured

into the risk database which auto generates fields for risk id, risk treatment plan id,

fallback plan id and allocation of risk owner and / or treatment owner. A particular

62

emphasis will be placed at the Stakeholder’s involvement in the MBRP risks. For that

purpose, relevant data (e.g. high policy, national prestige, etc.) will also be taken into

account.

Collect risk data:

As it is unknown in advance which single risk and risk factor can have an impact on the

program schedule, a sample risk data, including external and internal risk data sample

on all level of the organization and during all phases and stages, will be collected from

the risk data population, as shown in Figure 7:

Figure 7: Risk Data Population

The risk data will include:

Rail Sector risk data [best practice, academic research such as Zhou et al.

(2007)].

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Sample Risk Data

Enterprise risks data (e.g. policies, procedures, processes, internal lessons

learned, Incident / accident investigations, audits);

Business functions risks data and Program risks data, bearing in mind that the risks

within a program are greater than the sum of risks from the individual projects. This

magnification occurs because of the potential compounding risks created by the on-

going and accumulating effects of the multiple projects within the program.. To

handle this, a thorough risk analysis of the Program will be conducted, with

particular attention paid to those risks that affect the schedule. For instance, a delay

in one project can ripple through the entire Program and magnify the time impacts

upon subsequent or related projects (PMI, 2013);

Projects and Business Functions Departments risks data; and

Contractors and sub-contractors risks data.

IV.4. RBDM Processes

Standardized risk data that would have been collected will be used for the RBDM

process through steps 1 to 6 (Section 2 in Figure 6)

Step 1 Qualitative deterministic analysis of risk data

The standardized risk data that will be collected in the Section 1 require a selection

through preliminary risk analysis (Nicolet-Monnier, 1996) against the multiple criteria

decision-making model (Belton et al., 2002). Preliminary risk analysis is a qualitative

technique, which involves a disciplined analysis of the event sequences, which could

materialize a risk. In this technique, the possible undesirable events will be identified first

and then analyzed separately. For each undesirable event, possible treatment actions

will then be formulated. The result from this methodology provides a basis for

64

determining which risks should be looked into more closely and which analysis methods

are most suitable. With the aid of a probability / consequence diagram or risk matrix, the

identified risks will be qualitatively scored and ranked against the MBRP multi criteria,

allowing measures to be prioritized to prevent the risk to materialize. All criteria,

quantitative as well as semi-qualitative, will be evaluated even though the schedule

impact is the preferred criterion as mentioned in the introduction.

Qualitative Risk Assessment of risk data will be carried out with an objective to

priorities the identified risks for further action in the next step. All risks will be assessed

by defining their probability of occurrence and the corresponding impact to the multiple

criteria: schedule, cost, reputation, health & safety, security, environment, legal, and

quality & performance. The Inputs to Qualitative Risk Assessment could be too large, but

the following will be addressed in the qualitative risk assessment process only:

Organization-wide factors, including published information such as commercial

databases, academic studies, benchmarking, or other industry studies;

Program/project scope statement such as program/project assumptions, which

generally, by their uncertain nature, should be evaluated as potential causes of

program/projects risks;

Organizational process assets from which data about risks on past

program/projects and the lessons learned is obtained;

Program/project plans; and

Program/project implementation plan from which an understanding of the

schedule, cost and quality management plans may be obtained as well as the

work breakdown structure (WBS) and assumptions used.

65

A pre-defined rating system will be used in the probability and impact assessment

process to perform the qualitative analysis to prioritize the risks. The level of probability

for each risk and its impact on each criteria will be evaluated based on documents, event

records, or verbal information obtained during the interviews or meetings with

responsible teams/persons and on the assumptions recorded which justify the levels

assigned (Liu et al., 2009; Ebrahimnejad et al., 2010; Giezen, 2012).

Step 2: Decision Making I; Select risk data for further analysis

After quantifying the risk data in the previous step, this step is carried out to refine the

prioritization of risk data for treatment or quantitative analysis by evaluating them through

the Multi-Attribute Decision-Making (MADM) process against pre-established criteria

such as those used by Liu et al., (2009); Ebrahimnejad et al., (2010); Giezen, (2012) and

other authors. Ranking the risks will be made to facilitate the risk analysis in the risk

treatment process, which is important for understanding the level of risk exposure

relative to the MBRP risk appetite and tolerance. Decisions about risk treatment can be

made so to optimize risk taking and maximize the likelihood of achieving the Program

primary objective of meeting the schedule. Because it is too expensive or ineffective to

respond to all risks, decision-makers need to know which of their risks are most critical

and prioritize accordingly. Usually, it is found that the treatment of a few critical risks

results in dramatic reductions in residual risk, whereas the treatment of each following

risk results in nominal incremental reductions in residual risk. Before deciding which risks

to accept, the risks will need to be prioritized. To ensure objectivity and transparency, the

risks will be prioritized against agreed criteria because the results may give a different

outcome from the initial thinking.

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Step 3 Quantitative deterministic analyses of risk data

Further analysis of risks data will be carried out through the so-called “scenario

theory” based on a quantitative distribution of each risk’s time impact (e.g. single point,

uniform or triangle distribution) can be chosen (Risk Analysis and Monte Carlo

Simulation within Transport Appraisal, Centre for Traffic and Transport). Selection of the

distribution will be based on the risk data available. The single point, uniform or triangle

distributions (Hesse, 2000) do not depend on large numbers or mature risk data unlike

the probabilistic distribution (Bernoulli, 2013).

Once the impact range has been obtained, the distribution of the time impact within a

range can be selected based on the following three options:

If a single (one only) impact within the range is provided, it will be recorded as “most

likely” and the default distribution will be discrete (e.g. “The time delay will be X

weeks” as considered relevant to MBRPs).

If minimum and maximum impacts are provided to cover the entire range, then the

default distribution should be uniform (e.g. “The time delay could be anywhere

between Xminimum weeks and Xmaximum weeks”).

If all, minimum, maximum and most likely impact values are obtained then the so

called “triangle distribution” will be considered (e.g. “The time delay is likely to be

X weeks but could be anywhere between Xminimum weeks and Xmaximum weeks”).

The distribution represents the alternative time a project or program element can

take. The uncertainty in time that an element might take reflects all of the risks that could

impinge on that program/project element.

Step 4 Quantitative probabilistic analyses of risk data

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Next collection of risks data will be through probabilistic theory, which is a

quantitative approach to risks data by using normal distribution of all risk’s time impact

through Monte Carlo Simulation (Whitlock and Kalos, 2008). In case the data collected is

not normal or near normal, then other methods (eg. Poisson distribution) are proposed to

be used depending on their suitability. Quantitative risk analysis attempts to assign

adequate numerical values (weighting factors) to risks attributed either by using collected

empirical data, or by quantifying qualitative assessments. The number of risks on which

quantitative risk analysis is performed varies depending if it concerns the entire MBRP or

one project within it only.

Quantitative risk analysis will be carried out for all identified risks from the qualitative

risk assessment. Inputs into the quantitative risk assessment process will be provided

from the following sources:

Organization-wide factors including published information such as commercial

databases, academic studies, benchmarking, or other industry studies;

Program/projects scope statement, which defines the boundaries and limitations

of the scope of the program/projects;

Risk management plan where the procedure to be used for quantitative risk

analysis on the program/projects will be described and the roles and

responsibilities for conducting risk management, budgets, and to schedule

activities for risk management, risk categories, the risk categories and revised

stakeholders’ risk tolerances will be detailed;

Risk register showing the risk categories that require quantitative risk analysis and

their relative priority ranking to identify high priority risks;

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Program/project implementation plan that provides details on the program/project

schedule management plan and the program/project cost management;

Data gathered from interviews with members of the program/project teams and

other parties with specialist knowledge on the different areas of the

program/project; and

Correlation, dependency or combined dependency between the projects. For

example, if one project goes over schedule, others might be more likely to go over

schedule (Liu et al., 2009).

The program/projects quantitative risk analysis will be implemented stepwise

depending on the urgency of risk treatment, as well as on the required level of details

and reliability including confidence intervals of the data. As more data are becoming

available, a detailed quantitative schedule risk analysis will be carried out to assess the

probability of achieving the pre-determined program/project completion date. Such a

more complex form of quantitative risk analysis will be carried out by using schedule risk

analysis software which are popularly used in Project Management (like, Oracle’s

Primavera)and by consulting relevant experts on the particular subject matters to

validate the risk data and techniques used. The risk data from the risk database and the

schedule data from the MBRP schedule will be imported into the schedule risk analysis

software for further probabilistic analysis through Monte Carlo analysis. The quantitative

schedule risk analysis (QSRA) will then be carried out for different scenarios such as

significant schedule change, emergence of significant schedule risk and other major

changes. The Qatar rail project data will be used for the case study to validate the

model.

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The Monte Carlo simulation (Whitlock et al., 1978) involves random sampling of

each probability distribution by running a number of iterations using the frequency

ranges, probabilities and impact ranges. Each iteration will pick a value from the impact

range based on the values and distribution provided.

The risk register, updated with the results of the quantitative risk analysis, will be the

main output, along with a prioritized list of those risks that pose the greatest threats or

opportunities to a single project or the program through influence on the critical path of

the schedule. Other outputs from more comprehensive quantitative analysis include the

following:

More detailed quantification of the time and cost contingency reserves if required

within a given level of confidence, e.g. 70th and 95th percentile level of confidence,

which means 70% or 95% probability that the schedule or budget will be met.

Calculations of the probability distribution and ascending cumulative distribution of

achieving time and cost objectives at a given level of confidence; and

Any trends in quantitative risk analysis results that could lead to conclusions

affecting risk treatments.

After completing this step, the risks which are likely to jeopardize the program will

become known. This information is needed for the next step in order to define those risks

which are necessary to be treated.

Step 5. Decision Making II - Select risk treatment options

Risk treatment not only seeks to minimize potential downside, but also maximize the

potential upside of opportunities. Too much treatment is as undesirable as too little, and

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therefore the objective is to find the right balance to optimize returns to the business by

maximizing gains from opportunities while minimizing losses from threats. Evaluation of

downside risk involves the consideration of non-financial as well as financial impacts

(COSO, 2004; ISO 31000, 2009; PMBOK 2013). This step delivers qualitative and

quantitative information about residual risk to the risk management processes so that

management can make informed strategic decisions about how to allocate resources to

program/projects. Once risks have been evaluated using the probability impact diagram,

I shall determine treatment options for each risk level (low, medium or high) based on the

definitions provided in Table 4.

Table 4 Risk Treatment Description per Level

Risk Level Description Timeframe

High

The risk shall be mitigated to As Low As Reasonably

Practicable (ALARP), unless the risk reduction cost

significantly outweighs the risk reduction benefit.

Otherwise, the risk is eliminated.

Arbitrary

Medium

The risk is accepted if adequate controls and other

internal measures are in place. Risk control measures

may be implemented to further mitigate the risk if a

strong effectiveness case exists.

Arbitrary

LowThe risk is accepted but if possible removes risk cause

or reduces likelihood and/or impacts.

Arbitrary

On a very rare basis, exceptions may be noted. Depending on the risk appetite of

the authorities risks that qualify under Green category (Low risk) do not require any

treatment, while risks under Blue category (Medium risk) require suitable treatment (like

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terminate, tolerate or transfer such risks as deemed relevant), and risks under Red

category (High risk) require risk control on a priority basis. Exceptions rated as “High,”

which cannot be eliminated will be handled on a case-by-case basis by management as

an outlier from the normal. The maximum timeframe to address a risk treatment and to

implement its mitigation measures depends on the risk. It will be assumed that all risk

treatment owners (responsible decision-makers at all levels of MBRP management) shall

adhere to the timeframe defined in the Table 4 unless it can be justified why the risk

treatment and mitigation should have a timeframe beyond the limitations.

Treatment of individual risks rarely occurs in isolation, and thus it is important to

have a clear understanding of a complete treatment strategy to ensure that critical

dependencies and linkages are not compromised. For this reason, the development of

an overall treatment strategy is a top-down decision-making process within the

organizational management structure. This process is driven by the need to achieve the

program objective while controlling each uncertainty and bringing it within tolerable limits

for each such uncertainty. Table 5 presents the four risk treatment options (Avoid,

Accept, Transfer or Mitigate) that decision-makers should choose in order to continue

treating a risk.

Table 5 Risk Treatment Options

Risk Treatment Options

AvoidAvoiding the risk by deciding not to start or continue with the activity that

gives rise to the risk

AcceptTaking or increasing risk in order to pursue an opportunity. Retaining the risk

by informed decision

Transfer Sharing the risk with another party or parties including contracts and risk

72

financing.

Mitigate Removing the risk source or changing the likelihood or impact

When a decision has been made which risks to avoid, accept, transfer or mitigate,

an evaluation of the treatment options by the use of decision tree needs to be made,

which is the last step in the RBDM process.

Step 6. Decision Making III. Evaluate treatment options through Decision

Tree Theories

As described above, based on ISO 31000 (2009) and Risktec (2005), the options

available for the decision tree will be based on: Avoid, Mitigate, Accept, or Transfer. The

designed risk response portfolio will not only focus on reducing the likelihood of a risk

occurring, but will also include plans for balance and recovery to ensure crisis

management, business continuity and reputation management. All four risk treatment

options (Avoid, Mitigate, Accept and Transfer) for MBRP will be evaluated, taking into

account their costs, benefits and views of relevant stakeholders such as the strategic

objectives and other criteria. Whilst risk treatment options, which are not cost-effective

(i.e. the value of any reduction in risk is out-weighted by the cost of the control) would

normally be discarded, there will be mandatory requirements or preferred criteria

imposed by internal standards or external stakeholders.

The evaluation of each risk treatment option will be based on the decision tree, such

as one used in a RBDM research done by Risktec (2005). The evaluation will be carried

out for each of four risk treatment options against Compliance, Objectives, Risk appetite

and Schedule benefit, as shown in Figure 8.

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Figure 8. Decision tree for the evaluation of risk treatment options

Eventually, a decision can be made. In order to decide which alternative to select for

a schedule driven MBRP, a decision criterion will be applied that takes into account both

the possible outcomes for each decision alternative and the probability that each

outcome will occur. Sometimes the outcome is clearly worthwhile or not, which makes

the decision easy. Sometimes, however, there is no clear answer, which needs further

analysis or a simple consensual decision. An assessment is also needed to identify

whether the “residual” risk is acceptable, given the risk appetite or not.

IV.5 Output

The ultimate output of my research will be a multi-criteria RBDM model adapted for

a schedule driven mega-project such as MBRP. As a result of the case study will be an

informed RBDM report with a recommendation on how to select the most suitable risk

treatment option to meet the project schedule without jeopardizing other criteria.

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IV.6 Validation of the RBDM Model

The enhanced RBDM model of a MBRP as described in Figure 7will be tested and

validated in a case study carried out for the Qatar Rail MBRP. There, a Program risk

management team is formed to manage risks in the construction of the five metro lines,

four long distance high-speed lines and a light rail transit line. According to the program

schedule, changes to the design of the infrastructure are identified as a critical event to

meet the target date of completion. The time overrun of one work package or a project is

identified as the major risk needed to be assessed for securing the success of the entire

Program. At the moment preliminary risk assessment through projects, program and

strategic risk workshops has identified a number of risks, which can lead to the delay of

the program, such as scope change, ineffective interface and integration, delay of tender

process, availability of required plant and equipment, lack of required number and level

of skilled resources, alignment and Rights of Way not legally protected. These risks

prove to be difficult to measure due to the lack of risk data and uncertainties involved. As

there are no adequate practical data yet to support a traditional risk analysis needed to

test the proposed model, it will be tested in phases following the steps as mentioned in

the quantitative deterministic analysis of risk data.

IV.7 Data Collection

A carefully drafted and pre-tested Questionnaire encompassing all the risk factors as

identified in the risk tree has been administered for data collection. For design of the

Final Questionnaire a Pilot study was done by discussing with 10 superior officers of the

project during August 2014. Afterwards, the refined (final) Questionnaire was prepared

and sent to 350 principal project staff (officers and above) so as to collect the data in the

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form of their responses / feedbacks during September 2014. Only 117 Questionnaires

were replied as of December 2014, even after two reminder letters. Of these 117

Questionnaires only 100 alone were complete in all respects. These 100 (fully complete)

Questionnaires were used for further analysis during January-March 2015. For selecting

the 350 respondents as above random sampling (lottery method) was adopted using the

list of regular Officers as per the official records of Qatar Railway Project. Besides, expert

opinions were collected regarding the general trend in technology, structure and nature

of the market, relative significance as well as risk associated with various aspects of the

project etc. during November-December 2014. For this an Interview schedule was

prepared and opinions of 8 highly reputed experts (selected using purposive or

deliberate sampling) in this field were collected using face to face (structured) interviews.

Each interview took 25 to 30 minutes’ time, on an average. Accordingly, the risk weights

were assigned meaningfully and risk assessments done for the individual project

activities and for the project as a whole.

IV.8 Conclusion

A comprehensive literature review has been conducted in order to identify whether

the existing risk based decision-making models can be used to address the RBDM of a

MBRP. The reviewed models have the advantage of addressing mainly one single item

applicable to the RBDM model of a MBRP, but not all constituents of the model. Such

items that can be used to design only particular parts of a RBDM model specific for a

MBRP, are, for example the errors in criteria’s weights (Ebrahimnejad et al., 2010) or

correlation between projects in a program (Liu et al., 2009),. There is not, however, any

RBDM model suitable to address the MBRP as a whole.

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Development, testing and validation of a new RBDM model applicable to the entire

MBRP is presented in this Dissertation is based mainly on elements selected from the

existing models. The new model will be tested by the use of data collected during current

construction of the Qatar Rail projects. The model will then be used to evaluate, compare

and select the most appropriate risk treatment option against multiple criteria of the

program by considering particularly those risks that may influence the program schedule,

but without disregarding any other risks and criteria. To complete and address the three

elements of the “iron triangle” in the project management, further research into the cost

and quality aspects of a MBRP program will be required.

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v. ANALYSIS OF RISK DATA – A MULTIPLE CRITERIA MODEL

V.1 STEP 1 – QUALITATIVE DETERMINISTIC ANALYSIS

This is the first step in the analysis of risk data. A qualitative and deterministic approach

is adopted in this step which involves a preliminary analysis of the risk. The standardized

risk data that will be collected require a selection through preliminary risk analysis

against the multiple criteria decision-making model. As already noted, preliminary risk

analysis is a qualitative technique that involves a disciplined analysis of the event

sequences which could materialize a risk. In this technique, the possible undesirable

events are identified first and then analyzed separately. For each undesirable event,

possible treatment actions are then formulated. This methodology provides a basis for

determining which risks should be looked into more closely and which analytical methods

are most suitable. With the aid of a probability / consequence diagram (risk matrix) the

identified risks are qualitatively scored and ranked against the MBRP multi-criteria,

allowing measures to be prioritized to prevent the risk to materialize. Qualitative Risk

Assessment of risk data is carried out in order to prioritize the identified risks for further

action in the next step. All risks are assessed by defining their probability of occurrence

and the corresponding impact to the multiple criteria.

A pre-defined rating system is used in the probability and impact assessment

process to perform the qualitative analysis to prioritize the risks. The level of probability

for each risk and its impact on each criteria is evaluated based on documents, event

records, or verbal information obtained during the interviews or meetings with

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responsible teams/persons and on the assumptions recorded which justify the levels

assigned.

Table 6: Summary of Qualitative Deterministic Analysis of Risk

Type of

Risk

1st Tier 2nd Tier Risk Involved withMBRP Conventional

railSocial

and

Political

Regulatory Compliances

– Licenses, Permits,

Clearances etc.

Environmental Impact

Statement

High Low

Transportation Impact

Statement

High Low

Energy Use Statement High Medium Technology Transfer High MediumProject Feasibility Long-term Viability High High

Political Situation High LowPlanning Reasonableness of Project’s

Scope, Schedule, Cost

High High

Technical Constraints High MediumComplexity of Project High Medium

Public Perception on Safety Higher HighDecision Making Process High High

Enginee

r-ing /

Constru

ction

Design Standards / Code High LowComplexity High MediumCompleteness of Design High LowSystem Integration High Low

Construction

Infrastructure

Procurement

Safety Standard High HighQuality Control High MediumType of Contract Medium MediumContracting Arrangement Medium LowLabor Medium Medium

System Procurement Specification High LowScope of Procurement High LowProcedure of Procurement High Low

Financia

l

Funding Funding Source High LowInflation Medium MediumAccuracy of Cost Estimate High High

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Exchange Rate High LowCost Overrun High HighDelay Cost (Time Overrun / Schedule Slippages) High High

Based on Qualitative analysis it is noted that high risks are involved in Schedule

Slippages, Cost overrun, maintaining safety standards, ensuring the viability of the

project in the long run as well as putting in place a sound decision making

system(Table6)

V.2 STEP 2 – DECISION-MAKING: PHASE I

The step two (Decision-making I) is the selection of risk data for further analysis. In this

step, the risks are prioritized for treatment based on pre-established criteria.

After identifying the risk data in the previous step, this step is carried out to refine

the prioritization of risk data for treatment or quantitative analysis by evaluating them

through the Multi-Attribute Decision-Making (MADM) process against pre-established

criteria. Ranking the risks will be made to facilitate the risk analysis in the risk treatment

process, which is important for understanding the level of risk exposure relative to the

MBRP risk appetite and tolerance. Decisions about risk treatment can be made so to

optimize risk taking and maximize the likelihood of achieving the Program primary

objective of meeting the schedule. To ensure objectivity and transparency, the risks will

be prioritized against agreed criteria because the results may give a different outcome

from the initial thinking.

Accordingly, the various factors identified as shown in Table 7 are assigned

relative scores (in the range 1 to 10) depending on the perceived severity or criticality of

the risks and these risks are arranged for prioritization. Table 9 shows the risks arranged

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in the order of priority, based on the above methodology. Depending on the scores

allotted (1 to 10) it is noted that the priorities of the various risks vary from 1 to 10 in the

reverse order, as shown in Table 7.

Table 7: Summary of Qualitative Deterministic Analysis of Risk

Type of

Risk

1st Tier 2nd Tier Risks of MBRP – Priority Quantum of

Risks

Priority

Social

and

Political

Regulatory

Compliances

– Licenses, Permits,

Clearances etc.

Environmental Impact Statement Low 1 10Transportation Impact Statement Low 3 8Energy Use Statement Medium 5 6

Technology Transfer Medium 6 5Project Feasibility Long-term Viability High 7 4

Political Situation Low 2 9Planning Reasonableness of Project’s

Scope, Schedule, Cost

High 8 3

Technical Constraints Medium 5 6Complexity of Project Medium 6 5

Public Perception on Safety High 7 4Decision Making Process High 7 4

Enginee

r-ing/

Constru-

ction

Design Standards / Code Low 3 8Complexity Medium 5 6Completeness of Design Low 3 8System Integration Low 3 8

Construction

Infrastructure

Procurement

Safety Standard High 7 4Quality Control Medium 6 5Type of Contract Medium 5 6Contracting Arrangement Low 3 8Labor Medium 5 6

System Procurement Specification Low 3 8Scope of Procurement Low 3 8Procedure of Procurement Low 3 8

Financia

l

Funding Funding Source Low 4 7Inflation Medium 6 5Accuracy of Cost Estimate Low 8 3

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Exchange Rate High 7 4Cost Overrun High 9 2Delay Cost (Time Overrun / Schedule Slippages) High 10 1

V.3 STEP 3 – QUANTITATIVE DETERMINISTIC RISK ANALYSIS

During the step three a quantitative deterministic analysis of risk data is done using

probabilistic grades to each of the risk factors identified. The prioritized risks as shown in

Table 7 are quantified using probability grades as shown in Table 8.

Table 8: Criteria for Prioritizing the Risk Factors

Criticality of Risk

Factors

Largest Larger Medium Smaller Small

Range of the Score (1-10) 9-10 7-8 5-6 3-4 1-2

Risk Weight assigned 1.0 0.8 0.6 0.4 0.2

Further analysis of risks data will be carried out through the so-called “scenario

theory” based on a quantitative distribution of each risk’s time impact can be chosen.

Selection of the distribution is done based on the risk data available. The single point,

uniform or triangle distributions do not depend on large numbers or mature risk data

unlike the probabilistic distribution. Once the impact range has been obtained, the

distribution of the time impact within a range can be selected based on the following 3

options: (i) If a single impact within the range is provided, it will be recorded as “most

likely” and the default distribution will be discrete; (ii) If minimum and maximum impacts

are provided to cover the entire range, then the default distribution should be uniform; (iii)

If all, minimum, maximum and most likely impact values are obtained then the so called

“triangle distribution” will be considered.

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The distribution represents the alternative time a project or program element can

take. The uncertainty in time that an element might take reflects all of the risks that could

impinge on that program/project element.

Table 9: Quantitative Deterministic Analysis of Risk – Prioritizing the Risk Factors

Risk Type Risk Factors WeightSocial and

Political

Environmental Impact Statement C11 0.2 Transportation Impact Statement C11 0.4Energy Use Statement C11 0.6Technology Transfer C11 0.6Long-term Viability C11 0.8Political Situation C11 0.2Reasonableness of Project’s Scope, Schedule, Cost C11 0.8Technical Constraints C11 0.6Complexity of Project C11 0.6Public Perception on Safety C11 0.8Decision Making Process C11 0.8

Engineering /

Construction

Standards / Code C21 0.4Complexity C22 0.6Completeness of Design C22 0.4System Integration C22 0.4Safety Standard C22 0.8Quality Control C22 0.6Type of Contract C22 0.6Contracting Arrangement C22 0.4Labor C22 0.6Specification C22 0.4Scope of Procurement C22 0.4Procedure of Procurement C22 0.4

Financial Funding Source C32 0.4Inflation C32 0.6Accuracy of Cost Estimate C32 0.8Exchange Rate C32 0.8Cost Overrun C32 1Delay Cost (Time Overrun / Schedule Slippages) C32 1

V.4 STEP 4 – QUANTITATIVE PROBABILISTIC RISK ANALYSIS

In the step four the quantitative probabilistic analysis of risk data is done. In this stage a

quantitative approach to the risks data is undertaken by using a distribution of all risk’s

83

time impact through Monte Carlo Simulation, the data being normally distributed. Use of

other distributions (that are not normal, eg. Poisson), though not required here, is noted

to provide quite identical results. Quantitative risk analysis attempts to assign adequate

numerical values (weighting factors) to risks attributed using quantifying qualitative

assessments as noted earlier. Quantitative risk analysis is carried out for all identified

risks from the qualitative risk assessment.

The program/projects quantitative risk analysis is to be implemented stepwise

depending on the urgency of risk treatment, as well as on the required level of details

and reliability including confidence intervals of the data. As more data are becoming

available, a detailed quantitative schedule risk analysis is to be carried out to assess the

probability of achieving the pre-determined program/project completion date. Such a

more complex form of quantitative risk analysis is carried out by using schedule risk

analysis software which is popularly used in Project Management viz. Oracle’s

Primavera. Besides, consultation with relevant experts in the subject field is also made in

order to validate the risk data and techniques used. The risk data from the risk database

and the schedule data from the MBRP schedule is imported into the schedule risk

analysis software for further probabilistic analysis through Monte Carlo analysis. The

quantitative schedule risk analysis (QSRA) is then carried out for different scenarios

such as significant schedule change, emergence of significant schedule risk and other

major changes. The Qatar rail project data is used for the case study to validate the

model.

Table 10: Quantification of Risks and Finding the Risk Degrees

Risk Factors Probability Influence on the Risk

84

Pij overall project Wij degree

Environmental Impact Statement C11 0.3 0.2 0.06 Transportation Impact Statement C11 0.5 0.4 0.2Energy Use Statement C11 0.5 0.6 0.3Technology Transfer C11 0.5 0.6 0.3Long-term Viability C11 0.5 0.8 0.4Political Situation C11 0.3 0.2 0.06Reasonableness of Project’s Scope, Schedule,

Cost C11

0.4 0.8 0.32

Technical Constraints C11 0.45 0.6 0.27Complexity of Project C11 0.45 0.6 0.27Public Perception on Safety C11 0.4 0.8 0.32Decision Making Process C11 0.45 0.8 0.36Standards / Code C21 0.35 0.4 0.14Complexity C22 0.3 0.6 0.18Completeness of Design C22 0.3 0.4 0.12System Integration C22 0.35 0.4 0.14Safety Standard C22 0.25 0.8 0.2Quality Control C22 0.45 0.6 0.27Type of Contract C22 0.2 0.6 0.12Contracting Arrangement C22 0.35 0.4 0.14Labor C22 0.4 0.6 0.24Specification C22 0.35 0.4 0.14Scope of Procurement C22 0.45 0.4 0.18Procedure of Procurement C22 0.35 0.4 0.14Funding Source C32 0.25 0.4 0.1Inflation C32 0.3 0.6 0.18Accuracy of Cost Estimate C32 0.4 0.8 0.32Exchange Rate C32 0.25 0.8 0.2Cost Overrun C32 0.5 1 0.5Delay Cost (Time Overrun / Schedule Slippages)

C32

0.5 1 0.5

Overall Project Risk Degrees =

Probability of occurrence of various risk factors is assessed through a systematic

process of collecting feedback from the respective stakeholders using carefully designed

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and pre-tested Questionnaires / Interview schedules. Based on such feedbacks from

multiple respondents, a reasonable (most likely) probability is arrived at. (Table 10).

Now, the overall project risk is computed using the following formula:

Overall Project Risk Degrees =

From Table 10, it is noted that the overall project risk as above works out to 6.67

in the present case. It may be noted that the above risk is in the ‘High Risk’ category as

per the Risk Assessment Standards Matrix shown in Table 11.

Table 11: Risk Assessment Standards Matrix

Risk degree range Risk level

00 - 4.0 Low risk

4.0 – 6.0 Medium risk

6.0 – 8.0 High risk

8.0 – 10.0 Higher risk Source: As per the Research Design for the study

It may be stated that an overall risk of 6.67 as above though in the High Category

is in the lower half (6 to 7) within that range (Table 11). This is turn suggests a focused

approach towards the most risky activities of the project rather than all projects elements;

because, some elements are much more risky than the ‘just high’ overall project risk.

V.5 STEP 5 – SELECTION OF RISK TREATMENT OPTIONS

(DECISION MAKING II)

Step five is the selection of risk treatment options (Decision-making II) – during this stage

decision is made on how to minimize potential downside but also maximize the potential

upside of opportunities by using pre-established criteria. Risk treatment not only seeks to

86

minimize potential downside, but also maximize the potential upside of opportunities. Too

much treatment is as undesirable as too little, and therefore the objective is to find the

right balance to optimize returns to the business by maximizing gains from opportunities

while minimizing losses from threats. This step delivers qualitative and quantitative

information about residual risk to the risk management processes so that management

can make informed strategic decisions about how to allocate resources to

program/projects.

Depending on the risk appetite of the authorities risks that qualify under Green

category (Low risk) do not require any treatment, while risks under Blue category

(Medium risk) require suitable treatment (like terminate, tolerate or transfer such risks as

deemed relevant). Risks under Yellow category (High risk) and Red category (Higher

risk) require close monitoring and control of risks on a priority and top priority bases

respectively. Exceptions rated as “High,” which cannot be eliminated will be handled on a

case-by-case basis by management as an outlier from the normal. The maximum

timeframe to address a risk treatment and to implement its mitigation measures depends

on the risk.

The risk treatment options for each risk level (low, medium, high, and higher) are

suggested based on the definitions provided in Table 12.

Table 12: Risk Treatment Description per Level

Risk Level Description of the Risk Treatment Required Periodicity

Higher

(Red)

The risk shall be mitigated to As Low As Reasonably

Practicable (ALARP), unless the risk reduction cost

significantly outweighs the risk reduction benefit. Otherwise,

the risk is eliminated.

Very frequent

and close

monitoring

87

High

(Yellow)

Close monitoring of risks is required for these risks at

frequent intervals. Each kind of risk needs to be monitored

and minimized / mitigated appropriately.

Frequent

monitoring and

control

Medium

(Blue)

The risk is accepted if adequate controls and other internal

measures are in place. Risk control measures may be

implemented to further mitigate the risk if a strong

effectiveness case exists.

Regular and

Periodical

monitoring

Low

(Green)

The risk is accepted but if possible removes risk cause or

reduces likelihood and/or impacts.

Periodical

reviews

Treatment of individual risks rarely occurs in isolation, thus, it is important to have a

clear understanding of a complete treatment strategy to ensure that critical

dependencies and linkages are not compromised. For this reason, the development of

an overall treatment strategy is a top-down decision-making process within the

organizational management structure. This process is driven by the need to achieve the

program objective while controlling each uncertainty and bringing it within tolerable limits

for each such uncertainty.

Table 13.Presents the four risk treatment options (Avoid, Accept, Transfer or

Mitigate) that decision-makers should choose in order to continue treating a risk during

the entire life cycle of the project concerned viz. MBRP of Qatar Railways.

Table 13: Risk Treatment Options

Options Procedure involved in exercising the option

AvoidAvoiding the risk by deciding not to start or continue with the activity that gives rise

to the risk

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AcceptTaking or increasing risk in order to pursue an opportunity. Retaining the risk by

informed decision

Transfer Sharing the risk with another party or parties including contracts and risk financing.

Mitigate Removing the risk source or changing the likelihood or impact

When a decision has been made which risks to avoid, accept, transfer or mitigate,

an evaluation of the treatment options by the use of decision tree needs to be made,

which is the last step in the RBDM process.

V.6 STEP 6 – RISK TREATMENT OPTIONS (DECISION MAKING III)

Step six evaluates the treatment options through Decision Tree Theories (Decision-

making III). Here, the models of ISO 31000 combined with Risktec (2005)are applied to

evaluate the available treatment options through a decision tree analysis. Based on ISO

31000 (2009) and Risktec (2005), the options available for the decision tree will be

based on: Avoid, Mitigate, Accept, or Transfer. The designed risk response portfolio not

only focuses on reducing the likelihood of a risk occurring, but also includes plans for

balance and recovery to ensure crisis management, business continuity and reputation

management. All four risk treatment options (Avoid, Mitigate, Accept and Transfer) for

MBRP will be evaluated, taking into account their costs, benefits and views of relevant

stakeholders such as the strategic objectives and other criteria. Whilst risk treatment

options, which are not cost-effective would be discarded, mandatory requirements or

preferred criteria imposed by internal standards or external stakeholders.

Eventually, a decision can be made. In order to decide which alternative to select for

a schedule driven MBRP, a decision criterion will be applied that takes into account both

the possible outcomes for each decision alternative and the probability that each

89

outcome will occur. Sometimes the outcome is clearly worthwhile or not, which makes

the decision easy. Sometimes, however, there is no clear answer, which needs further

analysis or a simple consensual decision. An assessment is also needed to identify

whether the “residual” risk is acceptable, given the risk appetite or not.

The various risk factors as in Table 10 could now be arranged in the descending

order of their significance (criticality). Thus, it is noted that factors like Cost overrun,

Delay cost have the have the highest significance (0.5 each) followed by long term

viability (0.4) decision making process (0.36) and so on. The least significant items were

observed to be environmental impact statement and political situation (0.06 each). (Table

13). Based on the prioritized risk factors as above, a diagram showing the risks and their

relative significance is drawn up as shown in Figure 9. These risks are categorized into 4

groups (red, yellow, blue and green) depending on their significance (Table 14, Figure 9).

Table 14: Prioritized risk factors

Cost Overrun C32 0.5

Red

Category

Delay Cost (Time Overrun / Schedule Slippages) C32 0.5Long-term Viability C11 0.4Decision Making Process C11

0.36Accuracy of Cost Estimate C32 0.32Public Perception on Safety C11 0.32Reasonableness of Project’s Scope, Schedule, Cost C11 0.32Energy Use Statement C11 0.3Technology Transfer C11 0.3Complexity of Project C11 0.27Quality Control C22 0.27

90

Yellow

Category

Technical Constraints C11 0.27Labor C22

0.24Transportation Impact Statement C11 0.2

Blue

Category

Exchange Rate C32 0.2Safety Standard C22 0.2Scope of Procurement C22 0.18Complexity C22 0.18Inflation C32 0.18Contracting Arrangement C22 0.14Procedure of Procurement C22 0.14Specification C22 0.14Standards / Code C21 0.14System Integration C22

0.14Completeness of Design C22 0.12

Green

Category

Type of Contract C22 0.12Funding Source C32 0.1Political Situation C11 0.06Environmental Impact Statement C11 0.06

91

Figure 9. Risk Factors and their relative significance (Source: based on Table 14)

92

V.7 SUGGESTED RISK MANAGEMENT APPROACH

93

In view of the foregoing, it is suggested that a highly focused and systematic effort

towards management of risks based on their severity (as shown in Table 12 and Figure

9) needs to be followed. Prioritization of risk management efforts is very much essential,

and hence resources are used in a specific and selective manner based on the above

rationale. This in turn results in optimization in resource utilization as well as outcome

from the project.

V.8 GENERAL CONCLUSION OF THE STUDY – SUMMARY OF THE MAJORFINDINGS AND DATA COLLECTION METHODOLOGY

Based on the foregoing discussions, based on an objective and systematic procedure

involving six major steps, it is revealed that of the 29 different variables under study four

are in the most important category (Red category) deserve maximum attention. These

factors include Cost overrun and Delay cost, both with 0.5 weights each, followed by

Long term viability (weight 0.4) and then by Decision making process (weight 0.36).

These four factors in the red category are followed by nine factors in the Yellow category

which need close monitoring but with significance lesser than the four in the Red

category. These have weights ranging from 0.32 to 0.24 and these include Accuracy of

Cost Estimate; Public perception on safety; Reasonableness of Project’s Scope,

Schedule and Cost; Energy Use Statement; Technology Transfer; Complexity of Project;

Quality Control; Technical Constraints; and Labour in that order. Next in significance is

eleven factors in the Blue category with weight score ranging from 0.2 to 0.14 and these

factors deserve only lesser monitoring and control. These ‘Blue category’ factors are

Transportation Impact Statement, Exchange rate, Safety standard, Scope of

procurement, Complexity, Inflation, Contracting arrangement, Procedure for

94

procurement, Specification, Standards/Code, and System integration in that order. These

eleven factors in the Blue category are followed by five factors in the Green category.

These five factors have risk weights in the range 0.12 to 0.06 and these include

Completeness, Type of contract, Funding source, Environmental impact statement, and

Political situation, in that order.

Given the overall project risk in the High risk (6.67) category, it may be noted that

the four Red category factors as noted above have to be very closely monitored so as to

optimize the overall project risk. Next in severity is the nine factors in the Yellow

category, and then by the eleven factors in the Blue category. Lastly comes the five in

the Green category which need the least attention among all. The above priority is

required in all risk management endeavours.

The data collection for the study was been done using random sampling from

among principal project officers using Questionnaire and also from experts in the field

using Interview schedule through face-to-face structured interviews. The methodology of

the study being conceptual supported by empirical study involving data collected from

practitioners and experts in the field, the findings of the study are suitable for informed

project management decisions in practical situations. This adds up to the relevance and

utility of the research.

V.9 SCOPE FOR FURTHER EXPLORATION

(i) Computer-based Decision Making Model for Cost Control

(ii) Computer-based Model for Control of Time Overruns

(iii) Computer-based Simulation Models for Decision making on various aspects etc.

95

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VII . QUESTIONNAIRE (As part of an Academic Research Study entitled, “Multi-Criteria Risk-Based Decision-Making (RBDM) Model for a Multi-Billion-Railway Program (MBRP)”. The data collectedwill be strictly confidential and will be used for academic purpose alone)---------------------------------------------------------------------------------------------------------------------

PROFILE OF THE RESPONDENT (REGULAR OFFICERS ONLY)

(Please fill in the details)

Name and Designation

Length of service in the organization (as on 31.08.2014) ___Years & ___ Months

Details of the responsibilities handled in managing projects (i)

(ii)

(iii)

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Particulars Responses [Use the scaling : 5– Yes, Very

True; 4 – Yes; 3 – Neutral; 2 – No; 1 – No; Not

at all.]

Has the project leadership of MBRP of Qatar Railways has planned for compliancewith all requisite regulatory / legal norms?

1 2 3 4 5

Have all the cost impacts to the MBRP been duly documented? [eg. Cost Overruns]

1 2 3 4 5

Have the impacts to the daily operations of MBRP been documented?

1 2 3 4 5

Have the impacts to existing processes of Qatar Railways been documented?

1 2 3 4 5

Have the schedule impacts to MBRP been identified? [eg. Time Overruns /Slippages]

1 2 3 4 5

Have the benefits of the MBRP project beenfully identified?

1 2 3 4 5

Will MBRP require funds above that originally allocated to the project? [Cost Overrun]

1 2 3 4 5

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Has MBRP included the State procurement Processes and time frames as well as the Project certification/release of funds process?

1 2 3 4 5

Has sufficient funding been approved for the MBRP of Qatar Railways?

1 2 3 4 5

Have qualified project managers been assigned to the MBRP?

1 2 3 4 5

Has MBRP identified all the relevant stakeholders?

1 2 3 4 5

Is the scope of the MBRP within the agencymission

1 2 3 4 5

Are there business owners assigned to the MBRP who have the time and understand responsibilities for the MBRP?

1 2 3 4 5

Has a MBRP steering committee been established within a MBRP governance plan?

1 2 3 4 5

Is there adequate agency staffing resourcesmade available for the MBRP?

1 2 3 4 5

Is there a “change management” process established?

1 2 3 4 5

Has the MBRP team documented the initial business requirements for the MBRP beyond the business objectives listed in theMBRP charter?

1 2 3 4 5

Have the stakeholders approved all documented business requirements?

1 2 3 4 5

Are documented requirements defined in measurable terms?

1 2 3 4 5

Are requirements prioritized – i.e. essential,conditional, or optional?

1 2 3 4 5

Has an initial impact analysis been performed on the business requirements – i.e., cost, operations, support

1 2 3 4 5

Is there a well-defined and institutionalized change control process for requirements?

1 2 3 4 5

Has the MBRP established the ability to verify that business requirements can be traced through technical design, system building or software coding/ configuration

1 2 3 4 5

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and test phases to verify that the system performs as intended and contains no unnecessary elements?

Is the MBRP planning to use an existing technology?

1 2 3 4 5

Is the MBRP planning to implement a COTS (Commercial Off the Shelf”) solution?

1 2 3 4 5

If COTS solution is anticipated will it requiresignificant customization, other than built in set-up/start up configurations

1 2 3 4 5

Does the agency or State (DoIT) have experience with the underlying technology or hardware/operating system environment?

1 2 3 4 5

Is a test environment part of the MBRP or isthere an existing test environment that mirrors the production environment?

1 2 3 4 5

Is there at least a tentative plan for Business Continuity?

1 2 3 4 5

Does the plan include the appropriate time and cost contingency?

1 2 3 4 5

Does the plan include the appropriate time and cost estimates?

1 2 3 4 5

Has a product planning methodology been chosen appropriate for the complexity of thesolution and the knowledge of the MBRP team?

1 2 3 4 5

Has the MBRP adequately identified the constraints and assumptions of the MBRP?

1 2 3 4 5

Has the MBRP adequately identified the MBRP risks, with probability, impact, mitigation and or contingencies?

1 2 3 4 5

Are there significant dependencies on otherMBRPs or staff resources?

1 2 3 4 5

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Please assign risk weights to the following risk elements depending on their criticality to MBRB

Low Risk, Green

(0 to 2.5)

Medium Risk, Blue

(2.6 to 5.0)

High Risk, Yellow

(5.1 to 7.5)

Very High Risk, Red

(7.6 to 10)

Accuracy of Cost Estimate ………..(Risk Weight……)

Complexity of Project ……….(Risk Weight…….)

Cost Overrun ……..(Risk Weight……..)

Delay Cost (Time Overrun / Schedule Slippages) ………..(Risk Weight……..)

Decision Making Process ………..(Risk Weight…….)

Energy Use Statement ………..(Risk Weight…….)

Exchange Rate ………..(Risk Weight…….)

Labor ………..(Risk Weight……..)

Long-term Viability ………..(Risk Weight……..)

Public Perception on Safety ………..(Risk Weight……..)

Quality Control ………..(Risk Weight……..)

Reasonableness of Project’s Scope, Schedule, Cost ………..(Risk Weight……..)

Safety Standard ………..(Risk Weight……..)

Scope of Procurement ………..(Risk Weight……..)

Technology Transfer ………..(Risk Weight……..)

Technical Constraints ………..(Risk Weight……..)

Transportation Impact Statement ………..(Risk Weight……..)

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VIII. INTERVIEW SCHEDULE(FOR USE AT THE TIME OF DATA COLLECTION THROUGH FACE TO FACE INTERVIEW)

SECTION I: GENERAL INFORMATION REGARDING THE EXPERT INTERVIEWED

NAME AND DESIGNATION:

EXPERIENCE IN THE FIELD:

DATE AND TIME OF THE INTERVIEW:

SECTION II: OVERALL OPINION OF THE INDUSTRY AND BUSINESS ENVIRONMENT

Narrate briefly your overall opinion regarding the current scenario of Railway projects and theinfluence of the socio-economic, political, legal, governmental and such other environmental factorson mega Railway Projects, please:

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SECTION III: SPECIFIC OPINION REGARDING QATAR RAILWAYS MEGA PROJECT

Narrate briefly your specific opinions regarding the Multi-Billion Railway Program (MBRP) of theQatar Railways. Also, mention briefly the major Strengths, Weaknesses, Opportunities and Threats(SWOT) in respect of the said project, please:

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121

SECTION IV: RELATIVE CRITICALITY OF VARIOUS RISK FACTORS

Low Risk, Green

(0 to 2.5)

Medium Risk, Blue

(2.6 to 5.0)

High Risk, Yellow

(5.1 to 7.5)

Very High Risk, Red

(7.6 to 10)

Accuracy of Cost Estimate ………..(Risk Weight……..)

Complexity of Project ………..(Risk Weight……..)

Cost Overrun ………..(Risk Weight……..)

Delay Cost (Time Overrun / Schedule Slippages) ………..(Risk Weight……..)

Decision Making Process ………..(Risk Weight……..)

Energy Use Statement ………..(Risk Weight……..)

Exchange Rate ………..(Risk Weight……..)

Labor ………..(Risk Weight……..)

Long-term Viability ………..(Risk Weight……..)

Public Perception on Safety ………..(Risk Weight……..)

Quality Control ………..(Risk Weight……..)

Reasonableness of Project’s Scope, Schedule, Cost ………..(Risk Weight……..)

Safety Standard ………..(Risk Weight……..)

Scope of Procurement ………..(Risk Weight……..)

Technology Transfer ………..(Risk Weight……..)

Technical Constraints ………..(Risk Weight……..)

Transportation Impact Statement ………..(Risk Weight……..)

122