OF INFRASTRUCTURE PROJECTS AND MITIGATION STRATEGIES
The causes of infrastructure projects’ cost escalation and time delays have been considered by many industries and previous research (Renuka et al., 2014, Cantarelli et al., 2013, Anderson et al., 2009, Flyvbjerg, 2007). This section presents an extant review of the causes of cost and time underestimation in infrastructure projects and mitigating measures provided by Flyvbjerg (2007) and Anderson et al. (2009). The established causes and proposed mitigations for the cost and time underestimation in infrastructure projects by Flyvbjerg (2007) and Anderson et al. (2009) are summarised in Table 2.1.
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Table 2.1: Causes and mitigation measures
Causes Mitigation measures Flyvbjerg
Technical
▪ Imperfect forecasting techniques ▪ Insufficient data
▪ Inexperienced forecasters
▪ Inherent difficulty in forecasting the future ▪ Honest mistakes Economic-Political ▪ Economic self-interest ▪ Economic public-interest Psychological ➢ Optimism bias
➢ Improved estimation tools ➢ Improved quality of data
➢ Employing experienced forecasters o Reference class forecasting ➢ Policy change o Transparency o Performance specifications o Regulatory regime o Risk capital ➢ Debiasing technique o Outside view Anderson Technical
▪ Project schedule changes
▪ Engineering and construction complexities ▪ Poor estimations
▪ Inconsistent application of contingencies ▪ Faulty execution ▪ Unforeseen events ▪ Unforeseen conditions Economic ▪ Effects of inflation ▪ Market conditions
▪ Delivery and procurement approach Economic-Political
▪ Bias
▪ Local concerns and requirements ▪ Scope change
▪ Scope creep Legal
▪ Ambiguous contract provisions ▪ Contract document conflicts
➢ Management strategy ➢ Scope and schedule strategy ➢ Off-prism strategy
➢ Risk strategy
✓ Identification ✓ Analysis
✓ Mitigation & Planning ✓ Allocation
✓ Monitoring & Controlling ➢ Delivery and procurement strategy ➢ Document quality strategy ➢ Estimate the quality strategy ➢ Integrity strategy
Source: Flyvbjerg (2007) and Anderson et al. (2009)
2.5.1 Causes of cost and time underestimation on infrastructure projects
Based on the previous research, Flyvbjerg (2007) identified causes of cost and time underestimation in infrastructure projects and classified them into technical, economic-political and psychological. Unlike the three causes of cost and time underestimation proposed by Flyvbjerg (2007), Anderson et al. (2009) identified 15 factors, classified into technical, economic, economic- political and legal, which cause cost escalation and time delays on infrastructure construction projects. (See Table 2.1).
The main difference in the identification of causes for cost escalation and time delays by Flyvbjerg (2007) and Anderson et al. (2009) are observed as the classification of causes. While Flyvbjerg (2007) determined technical, economic-political and psychological causes, Anderson et al. (2009) classification consisted of economic, economic-political, legal and technical factors and none considered the psychological aspect of optimism bias.
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Technical causes represented technical difficulties and errors in the process of project estimation. These difficulties and errors were caused by various factors, such as imperfect forecasting techniques, insufficient data, inexperienced forecasters, inherent difficulty in forecasting the future, honest mistakes, and other technical difficulties and errors. Due to the positive skew of cost escalation of infrastructure projects and the cost and time underestimation occurring in construction of infrastructure projects over time, the technical difficulties and errors are not the only cause of the frequent cost and time underestimation in infrastructure projects (Flyvbjerg, 2007). Anderson et al. (2009) associated the following seven factors with the technical cause of cost and time underestimation: project schedule changes due to project extensions, and budget constraints which cause additional costs depending on the two primary components of inflation and the time of the expenditure; engineering and construction complexities, which affect the internal coordination errors between project components and constructability problems; poor estimations including general errors, omissions, inadequacies and poor performance in planning and estimation procedures and techniques, inconsistent application of contingencies, including misuse and failure to define what costs and times contingencies cover; faulty execution; unforeseen events; and unforeseen conditions.
The economic self-interest and economic public-interest are two sorts of economic-political reasons for cost underestimation in infrastructure projects (Flyvbjerg, 2007). An example of economic self-interest is when of planners increase the chances of a project obtaining funding with the help of a favourable cost forecast. An example of economic public interest is the cost underestimations by planners and project promoters to encourage the minimisation of costs and saving of public finance (Flyvbjerg, 2007). Local concerns and requirements (perceived negative impacts of construction on the local societal and natural environment), scope changes, scope creep and bias: (deliberate underestimation of project costs to ensure that a project remains in the construction programme) are the four economic-political factors determined by Anderson et al. (2009).
Flyvbjerg et al. (2002) defined psychological reasons as optimism bias which is the tendency of planners and project promoters to be excessively optimistic by focusing on success scenarios and overlooking the chance of mistakes and failure. The optimism bias is very common among decision-makers in infrastructure projects (Cantarelli et al., 2013).
Anderson et al. (2009) recognised the effects of inflation (when the project estimates are not communicated in the year of construction costs), market conditions and delivery and procurement approach as the three factors of economics that cause cost and time underestimations. Two main factors of cost and time underestimation due to legal causes identified by Anderson et al. (2009) are ambiguous contract provisions and contract document conflicts.
2.5.2 Mitigation measures
In order to counteract the discussed causes of cost underestimation, Flyvbjerg et al. (2002) and Flyvbjerg (2007) recommended that cost escalation errors due to technical difficulties are eliminated or reduced by applying better forecasting techniques, improved quality of data, and
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employing experienced forecasters. However, Flyvbjerg et al. (2002) do not reflect the following important factors in the study of cost underestimation of infrastructure projects due to the technical causes: infrastructure project scope and new technology. Scope creep causes increases in the construction cost, as well as in the construction cost variance. New technologies often add to the technical complexity of projects, which is the main driver of cost escalation (Flyvbjerg, 2007). Flyvbjerg (2007) suggested that cost underestimation due to both types of economic-political they identified are limited with measures of accountability and proposed applying simple reality checks and debiasing techniques to eliminate cost underestimation due to psychological factors.
2.5.2.1 Reference Class Forecasting (RCF)
Flyvbjerg (2007) developed reference class forecasting techniques to counteract cost underestimation and time delay due to technical and psychological causes in infrastructure projects by estimating the performance of project cost and time from an outside view (statistical analyses of past projects) rather than the inside view (project specifics).
RCF improves the accuracy of project cost and time estimations by reducing the optimism bias (Batselier and Vanhoucke, 2017) through applying the techniques systematically to infrastructure projects by identifying the reference class of past similar infrastructure projects, creating the probability distribution of the project cost and time performance measure based on empirical data from the reference class projects, and estimating the most likely outcome by comparing the project considered to the reference class distribution
Flyvbjerg et al. (2004) calculated the probability of cost escalations due to the optimism bias in three categories of infrastructure projects namely: rails, roads, and fixed links by applying RCF, as shown in Table 2.2.
Table 2.2: Applicable cost escalation due to optimism bias
Infrastructure category Cost escalation 50th percentile 80th percentile Roads 15% 32% Rails 40% 57% Fixed links 23% 55%
Source: Flyvbjerg et al. (2004)
Table 2.2 shows that the rail projects had the largest cost escalation due to optimum bias at both 50th and 80th percentile, fixed link also lay above 55% cost escalation at 80th percentile. Based on the evaluated cost escalation due to optimism bias, Flyvbjerg et al. (2004) suggested raising the constant budget of these infrastructure projects to produce more realistic forecasts for the individual projects’ capital expenditure as presented in Table 2.3.
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Table 2.3: Applicable capital expenditure increases
Infrastructure category Cost escalation 50th percentile 90th percentile Roads 15% 45% Rails 40% 68% Fixed links 23% 63%
Source: Flyvbjerg et al. (2004)
RCF as an estimation technique contains two limitations: it is difficult to find comparable projects and it may be impossible to predict extreme outcomes. In addition to the RCF limitations, the use of RCF has a major disadvantage, in that it may lead to too large construction cost estimates, with diverging consequences (Batselier and Vanhoucke, 2017).
Although Flyvbjerg (2006) has emphasised the capability of RCF and the outside view estimation technique to reduce or eliminate the optimism bias in the estimation of cost and time of infrastructure projects, Flyvbjerg (2006) also acknowledged the usefulness of other estimation techniques based on the inside view, such as Monte Carlo simulations and the Estimate Validation Process (EVP).
2.5.2.2 Change in Policy
Flyvbjerg et al. (2003) recommended a policy change based on accountability to counteract the cost underestimation due to economic-political causes, which could be executed through four mechanisms: transparency (public discussion of projects involving stakeholders and civil society), performance specifications (shift the perspective from a technical solution-driven approach to a goal-driven approach), specifying the regulatory regime (compound of economic rules), and risk capital (participation of private investors in the construction of an infrastructure project without a sovereign guarantee).
Anderson et al. (2009) proposed the following eight strategies as solutions to the identified 15 factors causing cost escalation and time delays on infrastructure projects: management strategy (manage the cost and time estimation process during the entire infrastructure project development), scope and schedule strategy (create processes to control changes in project scope and project scheduling), off-prism strategy (proactive engagement of external stakeholders and assessment of macro-environmental conditions possibly influencing project costs and times), risk strategy (identification, analysis, mitigation and planning, allocation, monitoring, and controlling), delivery and procurement strategy, document quality strategy (improve accuracy and consistency of estimate), estimate quality strategy (improved accuracy and consistency of estimate through uniform approaches and qualified personnel) integrity strategy (minimise the influence of outside pressures that can cause biases).
A focus on the tools of the first two phases of the risk strategy, identification and analysis, is needed since the uncertainty model proposed by the researcher allows one to identify and analyse
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uncertainties. Thus, there is an interest in giving a complete overview of the analysis techniques used by Anderson et al. (2009) before introducing the new tool.
The main focus of the research is on the debiasing technique (outside view) and risk strategy, from among other proposed mitigations by Flyvbjerg (2007) and Anderson et al. (2009) to counteract cost escalation and time delays in infrastructure projects. Risk strategy includes five phases: identification; analysis; planning and mitigating; allocation; and controlling, and monitoring. The focus on the risk strategy is needed to prepare the basis of the uncertainty model. Before discussing risk strategy in detail, it is necessary to define the concept of risk.