Quantitative Project Risk Analysis for Construction Project: Introduction

Chapter 1 – Introduction

1.0 General

Construction projects grow larger and more complex day by day. Hence, effective risk management of such projects is essential for the mutual benefits of the client and the organisation involved. The time factor is crucial, as it defines not only schedules and pressing deadlines, but also extra costs and potential liquidated damages that may arise due to time slippage. As a result, the inherent uncertainty of construction projects may hinder the on-time completion and therefore the effective delivery to the client.

To capture the inherent uncertainty in project networks with respect to time, several risk-measuring scheduling tools have been developed over the years. By the late 1950s and onwards, extensive research was being conducted regarding the schedule performance of projects. This research produced the Critical Path Method (CPM), which is a simple scheduling tool for calculating the overall duration in a project network. Another such tool is the Programme Evaluation and Review Technique (PERT), which, in a simple manner of speaking, is a probabilistic scheduling tool for analysing project networks. PERT relies on an approximation of the Beta Distribution, to account for the variation in activity time estimates and thus calculates a more reliable time estimate than CPM of completing the project on time.

Since PERT’s appearance though, various researchers (Mohan et al. [1]; Hahn [2]) have generated considerable negative criticism, which mainly stems from the notion that the method produces optimistic results. More specifically, they advocated that the Beta Distribution is not the most suitable one, as it does not assign enough skewness to the activity duration estimates. Also, the method is approximate, as it does not account for near-critical paths, which may become critical should something go wrong. However, PERT is still widely used in construction projects, because of its simplicity and computational ease.

In the early 1960s, the Monte Carlo simulation (MCS) was developed. The MCS is a computer-aided simulation technique which has various applications in different industries. It can be used in quantitative risk analysis with respect to time as well, as it can generate the overall probability of completing projects on time, after having simulated numerous times the potential outcomes of the probabilistic models (Van Slyke [3]).  Without a doubt, MCS is more complex and computationally slower than PERT (Jun and El-Rayes [4]). However, MCS produces more accurate results, as it simulates all the possible scenarios precisely, accounts for near-critical paths and formulates an overall project duration probabilistic distribution.

Both of the scheduling tools mentioned above require the definition of the activity times estimates. According to the Project Management Institute’s (PMI) [5] Project Management Body of Knowledge (PMBOK) [6], a Work Breakdown Structure (WBS) is an “organisational tool that illustrates the entire scope of the project, broken down into manageable activities”. Such activities can be estimated with regards to cost and time. Therefore, the activity time estimates are computed on the lowest possible level, and if aggregated, the entire project schedule is formulated. However, what happens when these estimates are either erroneous or roughly approximate? Similarly, what happens if the probabilistic distributions in PERT are different from the Standard Beta?

1.1 Aims of the Study

The first aim of this project falls into the domain of quantitative project risk analysis with respect to the time factor. The criticality of defining accurate activity time estimates will be pointed out through the literature review. Further, the aspect of completing projects on time will be investigated through different methodologies apparent in the literature of quantitative project risk analysis. The results of the analysis will be compared against the widely accepted scheduling tools (PERT and MCS).

The second aim would be to implement such a risk analysis on two residential construction projects drawn from author’s work experience. The project networks will be formulated using MS Project, along with the activity duration estimates, essential for conducting the PERT analysis. Then, @Risk will be employed to perform the MCSs using various probabilistic distributions and a specific methodology drawn from the author’s literature review on the topic of quantitative project risk analysis.

1.2 Objectives of the Project

Taking into consideration the aims of the study, the objectives of this project are as follows:

1. To identify the importance of risk measuring scheduling tools in construction projects.
2. To investigate any proposed methodologies in the literature of construction project risk management, alternative or complementary to PERT and MCS.
3. To perform a quantitative risk analysis on two real-world construction projects to validate the alternative use of probabilistic distributions.
4. To apply the specified technique to two construction projects, to compare the resulting outcomes with those of PERT and MCS.
5. To formulate conclusions whether the findings drawn from the analysis could indicate that the employed methodology could be deemed more efficient and useful than the widely accepted risk measuring tools.

1.3 Structure of the Project

In Chapter 2, the insights of the literature review are presented through the author’s perspective. At first, an introduction in the field of project risk management is set out, followed by some research narrowed down to the fields of qualitative and quantitative risk analysis. Moreover, an extensive review is conducted regarding the PERT method along with the criticism it has received over the years.

In Chapter 3, the methodology adopted in the analytical part of the project is explained. The PERT method and the @Risk software are described for completeness. The main part of the methodology chapter focuses on the various types of probabilistic distributions equipped in the analysis, along with the Fast and Accurate Risk Evaluation (FARE) Technique by Jun and El-Rayes [4]. The latter is selected as an alternative to the MCS. All these theoretical aspects are applied to two construction projects, one with 50 activities and one larger with 110, which are outlined in detail in Section 4.2.

In Chapter 4, the two construction projects employed in the analysis are outlined. Further, the presentation of the analysis is illustrated using numerous figures and tables drawn by @Risk’s outputs. Also, a discussion of the results takes place by pointing out the key findings of the quantitative risk analysis. At that point, any validation of the employed methodology is indicated by comparing it with the already established scheduling tools.

Finally, in Chapter 5, the conclusions are extended, as drawn throughout the project and especially from the discussion conducted in Chapter 4.