Managing Construction Projects Using the Advanced Programmatic Risk Analysis and Management Model

Risk management is an important part of construction management, yet the risk-based decision support tools available to construction managers fail to adequately address risks relating to cost, schedule, and quality together in a coherent framework. This paper demonstrates the usefulness of the Advanced Programmatic Risk Analysis and Management Model (APRAM) originally developed for the aerospace industry, for managing schedule, cost, and quality risks in the construction industry. The usefulness of APRAM for construction projects is demonstrated by implementing APRAM for an example based on an actual building construction project and comparing the results with other risk analysis techniques. The results show that APRAM simultaneously addresses cost, schedule, and quality risk together in a coherent, probabilistic framework that provides the information needed to support decision making in allocating scarce project resources.     

Cost Contingency Management

In the practice of project management, cost contingencies have the objective of covering probable cost increases (risks) above target estimates. Due to the variable performance nature for a wide range of activities, contingencies not only should be properly calculated but also assigned in the budget estimation process and wisely controlled during project execution. In this article, the Monte Carlo simulation approach is recommended as part of a proposed methodology for cost contingency management, which also includes a heuristics for contingency assignment (allocation) among project activities, as long as the activities have some degree of uncertainty regarding their future costs.     

Achieving Multiple Project Objectives through Contingency Management

Project managers use budgets to satisfy multiple objectives such as cost control, short durations, and high quality. Contingency funds are included in project budgets to manage risk and achieve project goals. Understanding how managers use budget contingencies requires a dynamic information processing model of how managers bridge the gap between high project complexity and limited managerial capacity. The results of collecting contingency management practices of real estate development project managers is reported and a dynamic simulation model of contingency management described. The model is used to test hypotheses of the effectiveness of aggressive and passive management strategies on cost, timeliness, and facility value. Managers were found to pursue general project objectives in their management of contingency. An aggressive strategy was found to be more robust but performed poorer than a passive strategy. Conclusions include the prevalence of trade-offs between robust and high-performance contingency management policies in construction projects and the importance of incorporating uncertainty into project planning and management.     

Probabilistic Estimation and Allocation of Project Time Contingency

Project managers implement the concept of time contingency to consider uncertainty in duration estimates and prevent project completion delays. Some project managers also build a distribution of the project time contingency into the project activities to create a more manageable schedule. Generally, both the estimation and distribution of the project time contingency are conducted by using subjective approaches. Because the project schedule feasibility mainly depends on the variable behavior of the project activities, the estimate of project time contingency and its allocation at the activity level should be obtained by considering the performance variability of each activity rather than basing on human judgment. In this paper, the stochastic allocation of project allowances method, which is based on Monte?Carlo simulation, is proposed to estimate the project time contingency and allocate it among the project activities. The application of this method to a three-span bridge project results in a fair allocation of the project time contingency and provides practical means to control time contingencies at the activity level.     

Discriminant Analysis for Predicting Ranges of Cost Variance in International Construction Projects

Unanticipated market conditions as well as project-related risks can easily lead to cost overruns in international construction projects. For a contractor to be financially successful in international projects, a careful examination of the project is a prerequisite to understanding the cost variance characteristics. Based on the reasonably accurate characterization of the cost performance, the markup or contingency amount is determined to ensure both a decent level of profit and a good chance of winning the contract. This paper presents a classification model to categorize international construction projects, particularly faced by Korean contractors, into five cost-variation classes: extreme cost overrun, moderate cost overrun, neutral, moderate cost saving, and extreme cost saving. The model is able to characterize an international project for its cost performance prediction in comparison to the contractor’s initial cost estimate. A linear discriminant analysis is utilized to develop the predictive classification model with the support of the bootstrap method. Tests show that the proposed model is able to help cost estimators determine a proper level of cost contingency before bidding on an international project.     

Methodology for Integrated Risk Management and Proactive Scheduling of Construction Projects

An integrated methodology is developed for planning construction projects under uncertainty. The methodology relies on a computer supported risk management system that allows for the identification, analysis, and quantification of the major risk factors and the derivation of their probability of occurrence and their impact on the duration of the project activities. Using project management estimates of the marginal cost of activity starting time disruptions, a heuristic procedure is used to develop a stable proactive baseline schedule that is sufficiently protected against the anticipated disruptions that may occur during project execution and that exhibits acceptable makespan performance. We illustrate the application of the methodology on a real life construction project and demonstrate that our proactive scheduler generates baseline schedules that outperform the schedules generated by commercial software packages in terms of robustness and timely project completion probability.     

CTAN for Risk Assessments Using Multilevel Stochastic Networks

Measuring projects’ cost and schedule risks in an integrated framework using simulation has several modeling challenges that have yet to be addressed by researchers. This paper presents a multilevel network modeling approach that aims to integrate a combination of different networks in one framework, and presents a computer simulation implementation to the cost and time risk assessment network (CTAN). The CTAN is an integrated network that includes uncertainties in the realization of the schedule logic, in activities durations, in project scope, and in cost. The simulation model is a decision support simulation system (DSSS) that currently consists of three modules: the CTAN, the stochastic decision trees, and the stochastic shortest/longest rout network. The CTAN-DSSS may be used in cost and schedule risk assessment. It completely integrates with other DSSS networks and deals with risks associated with cost, time, and scope at equal importance. The DSSS was verified by conducting several tests and validated by its extensive use for both undergraduate and graduate courses in Civil Engineering at the University of Calgary over the last three years.     

Evaluation of Risk Factors Leading to Cost Overrun in Delivery of Highway Construction Projects

Accurate owner budget estimates are critical to the initial decision-to-build process for highway construction projects. However, transportation projects have historically experienced significant construction cost overruns from the time the decision to build has been taken by the owner. This paper addresses the problem of why highway projects overrun their predicted costs. It identifies the owner risk variables that contribute to significant cost overrun and then uses factor analysis, expert elicitation, and the nominal group technique to establish groups of importance ranked owner risks. Stepwise multivariate regression analysis is also used to investigate any correlation of the percentage of cost overrun with risks, together with attributes such as highway project type, indexed cost, geographic location, and project delivery method. The research results indicate a correlation between the reciprocal of project budget size and percentage cost overrun. This can be useful for owners in determining more realistic decision-to-build highway budget estimates by taking into account the economies of scale associated with larger projects.     

Using Risk Analysis to Determine Construction Project Contingencies

A contingency allowance is an amount of money used to provide for uncertainties associated with a construction project. Traditionally, it is a percentage addition on top of the base estimate. Estimating using risk analysis (ERA) is a methodology that can be used to substantiate the contingency by identifying uncertainties and estimating their financial implications. A study of the effect of ERA was carried out to compare the variability and consistency of the contingency estimates between non-ERA and ERA projects. This paper presents results of a survey that compares a total of 287 non-ERA and 45 ERA projects. The results show a highly significant difference in variation and consistency between these groups. It indicates successful use of the ERA method for public works projects to reduce unnecessary and exaggerated allowance for risk. However, the contingency allowance for ERA projects was still considered high. Improvement and refinement of the ERA method as well as recommendations on capital budgeting policy are suggested.     

Hierarchical Structuring of PPP Risks Using Interpretative Structural Modeling

Project risk management emphasizes the need to rank and prioritize risks in a project to focus the risk management efforts. This risk prioritization is of special significance in public-private partnership (PPP) projects, since project success depends upon the efficient allocation of risks to the party who can best manage it. Previous studies on risk identification and assessment of PPP project risks have only produced an unstructured list of such risks and prioritizing them on the basis of probability and impact. This paper suggests the use of interpretative structural modeling (ISM) to prepare a hierarchical structure as well as the interrelationships of these risks that would enable decision makers to take appropriate steps. MICMAC (matrice d'impacts croises-multiplication appliqué a un classemen) analysis is also done to determine the dependency and driving power of the risks. ISM, along with MICMAC analysis, provides a useful hierarchy of risks whose individual relationships are unambiguous but whose group relationships are too complex to organize intuitively and can help practitioners better understand risk dependencies and prioritize risk-mitigation efforts. This study identified 17 risks encountered during the development phase of PPP projects in Indian road sector and found that fourteen risks were weak drivers and weak dependents. Delay in financial closure, cost overrun risk, and time overrun risk have been found to have the highest dependence on other risks. The analysis can be extended by practitioners for risk analysis in other infrastructures such as railways, seaports, airports etc.     

Statistical Analysis on the Cost and Duration of Public Building Projects

A probabilistic model is proposed to predict the risk effects on time and cost of public building projects. The research goal is to utilize a real history data in estimating project cost and duration. The model results can be used to adjust floats and budgets of the planning schedule before project commencement. Statistical regression models and sample tests are developed using real data of 113 public projects. The model outputs can be used by project managers in the planning phase to validate the schedule critical path time and project budget. The comparison of means analysis for project cost and time performance indicated that the sample projects tend to finish over budget and almost on schedule. Regression models were developed to model project cost and time. The regression analysis showed that the project budgeted cost and planned project duration provide a good basis for estimating the cost and duration. The regression model results were validated by estimating the prediction error in percent and through conducting out-of-sample tests. In conclusion, the models were validated at a probability of 95%, at which the proposed models predict the project cost and duration at an error margin of ±0.035% of the actual cost and time.     

Programmatic Cost Risk Analysis for Highway Megaprojects

Highway megaprojects (construction projects over $100 million) are fraught with uncertainty. These projects have historically experienced increases in project costs from the time that a project is first proposed or programmed until the time that they are completed. Persistent cost underestimation reflects poorly on the industry in general but more specifically on engineers. Traditional methods take a deterministic, conservative approach to project cost estimating and then add a contingency factor that varies depending on the stage of project definition, experience, and other factors. This approach falls short, and no industry standard stochastic estimating practice is currently available. This paper presents a methodology developed by the Washington State Department of Transportation (WSDOT) for its Cost Estimating Validation Process. Nine case studies, with a mean cumulative value of over $22 billion, are presented and analyzed. Programmatic risks are summarized as economic, environmental, third party, right-of-way, program management, geotechnical, design process, construction, and other minor risks. WSDOT is successfully using the range cost output from this procedure to convey project costs to management and the public.     

Risk Allocation and Risk Handling of Highway Projects in Taiwan

Risks always exist in construction projects and often cause schedule delay or cost overrun. Risk management is a key issue in project management. The first step of risk management is risk identification. It includes the recognition of potential risk event conditions in the project and the clarification of risk responsibilities. We conducted multiple-case studies using a systematic analytical procedure to identify risks in highway projects in Taiwan, to recognize risk allocation by contract clauses, and to analyze the influence of risk allocation on the contractor’s risk handling strategies. The results show that the owner allocates risks by stipulating specific contract clauses into five kinds of risk allocation conditions. If a risk is more controllable by the contractor, the owner has a greater tendency to allocate the risk to the contractor. Risk allocation determines which kinds of risks the contractor would take and influences the contractor’s risk handling decisions. The analysis furthermore indicates that, if the probability of a certain risk event condition is uncontrollable, then with the increasing possibility of taking the risk, the contractor’s tendency of risk handling changes from actively transferring the risk to passively retaining the risk. In contrast, if a risk is controllable and certainly allocated to the contractor, the contractor tends to take the initiative to reduce the impact caused by the risk event rather than retain the risk.     

Resource Management in Construction

Resource plannirtg and management is one of the most important ingredients for competitiveness and profitability in today's construction industry. In order to control costs, equipment and labor should be utilized in the most efficient way possible. This can be achieved by minimizing the total cost of leased resources under the constraint of maximum and most efficient use of owned equipment and contracted labor force. This paper presents a mixed‐integer linear programming model for the management of resources throughout the project life. Based on the Critical Path Method time analysis, the model derives the schedule for equipment rentals and transient resources, as well as the utilization scheme for owned equipment and other available resources. The model can be used as an estimating tool for multi‐project resource planning and sharing, and as a means to implement the most efficient utilization of resources throughout the duration of the whole project.     

Stochastic Modeling for Pavement Warranty Cost Estimation

This paper presents a cost estimation model for long-term pavement warranties with multiple distress indicators. One application area for such warranties involves performance-based specifications (PBSs). In contrast to traditional approaches, PBS gives contractors the flexibility to select construction methods, materials, and even design. However, the contractors then must warrant the performance of their work for a specified period of time. Therefore, an accurate estimation of the risks associated with the warranty is a significant cost issue for any contractor to cover potential risks while still being competitive in bidding. Quantitative evaluation of the cost of risk incurred by the warranty has several difficulties. The deterioration of a highway project is a complex process, which is affected by pavement structure, material, traffic load, and weather conditions. Based on a probabilistic risk analysis of failures of performance indicators, the resulting model can estimate the warranty cost at a detailed level. The application of the model has been demonstrated via a numerical case study using long-term pavement performance data.     

Ontology for Relating Risk and Vulnerability to Cost Overrun in International Projects

Risk management is about identifying risks, assessing their impacts, and developing mitigation strategies to ensure project success. The difference between the expected and actual project outcomes is usually attributed to risk events and how they are managed throughout the project. Although there are several reference frameworks that explain how risks can be managed in construction projects, a major bottleneck is the lack of a common vocabulary for risk-related concepts. Poor definition of risk and patterns of risk propagation in a project decrease the reliability of risk models that are constructed to simulate project outcomes under different risk occurrence scenarios. This study aims to extend previous studies in risk management by presenting an ontology for relating risk-related concepts to cost overrun. The major idea is that cost overrun depends on causal relations between various risk sources (namely, risk paths) and sources of vulnerability that interfere with these paths. Ontology is used to develop a database system that represents risk event histories of international construction projects and to construct a model for estimation of cost overrun. It will form the basis of a multiagent system that can be used to simulate the negotiation process among project participants about sharing of costs considering the risk allocation clauses in the contract, sources of vulnerability, and causal relations between risk events and their impacts. The ontology is constructed by interaction with Turkish contractors working in international markets and extensive literature review on risk-related concepts. The validation test results provide evidence that the ontology is fairly effective to help Turkish contractors to assess cost overrun by considering sources of vulnerability and risk in international construction projects.     

Evaluating Risk in Construction–Schedule Model (ERIC–S): Construction Schedule Risk Model

Research was undertaken to develop a method to assist in the determination of the lower and upper activity duration values for schedule risk analysis by program evaluation and review technique analysis or Monte Carlo simulation. A belief network was the modeling environment used for this purpose, and the resulting model was named Evaluating Risk in Construction–Schedule Model. The development of the belief network model consisted of four steps. First, construction schedule risks were identified through a literature review, an expert review, and a group review by a team of experts. Second, cause effect relationships among these risks were identified through an expert survey. This led to the development of the structure of belief network model. Third, probabilities for various combinations of parents for each risk variable were obtained through an expert interview survey and incorporated into the model. Finally, sensitivity analysis was performed. The model was tested using 17 case studies with very good results.     

Float Consumption Impact on Cost and Schedule in the Construction Industry

Quantifying and minimizing the risks associated with delays in the construction industry are the main challenges for all parties involved. Float loss impact in noncritical activities is one of the complicated delays to assess on a project’s duration and cost. This is due to the fact that the deterministic critical path method cannot cope with such delays unless they exceed the total float values. Further, stochastic analysis, which is used in this research to assess the impact of such delays, is perceived by many planners to be complicated and time consuming. This paper presents a method to control the risks associated with float loss in construction projects. The method uses a recently developed multiple simulation analysis technique that combines the results of cost range estimates and stochastic scheduling, using Monte Carlo simulation. The proposed method quantifies the float loss impact on project duration and cost. Least-squares nonlinear regression is used to convert the stochastic results into a polynomial function that quantifies the float loss impact by relating directly the float loss value to project duration and cost at a specified confidence level.