Cost Contingency Research Articles

Cost Escalation in Road Construction Contracts

This paper presents a study of cost escalation in unit price road construction contracts. The aim is to investigate why the final cost of contracts differs from the agreed contract cost following tendering, both to identify causes of observed discrepancies and to suggest measures that could improve the planning and delivery of future projects. Road projects often consist of several contracts and as they account for the biggest costs of the projects, cost escalation in the contracts may increase the risk of project cost overrun. Even if contract cost performance is an important indicator of project success, it may be too simplistic to equate this with project success. It is quite possible to deliver a project within budget even if contract costs escalate, as long as the project cost contingency is adequate to cover such escalations. However, escalations in contracts increase the risk of project overrun and may lead to other problems such as conflicts and delays. The results show that most of the studied contracts experienced cost escalation. The main cause of the escalation was change orders to the scope that were not covered by the original contract. In addition, the results indicate that complexity represented by contract size, duration, and urban location increases the risk and size of cost overrun. Based on these findings, the paper presents some recommendations on how contract delivery can be improved as well as some implications for future research.

Sorting Out Fuzzy Transportation Problems via ECCT and Standard Deviation

A well-organized arithmetical procedure entitled standard deviation is employed to find the optimum solution in this paper. This technique has been divided into two parts. The first methodology deals with constructing the entire contingency cost table, and the second deals with optimum allocation. In this work, the method of magnitude is used for converting fuzzy numbers into crisp numbers as this method is better than the existing methods. This technique gives a better optimal solution than other methods. A numerical example for the new method is explained, and the authors compared their method with existing methods such as north west corner method, least cost method, and Vogel's approximation method.

Towards improved cost evaluation of Carbon Capture and Storage from industry

This paper contributes to the development of improved guidelines for cost evaluation of Carbon Capture and Storage (CCS) from industrial applications building on previous work in the field. It discusses key challenges and factors that have a large impact on the results of cost evaluations, but are often overlooked or insufficiently addressed. These include cost metrics (especially in the context of industrial plants with multiple output products), energy supply aspects, retrofitting costs, CO2 transport and storage, maturity of the capture technology. Where possible examples are given to demonstrate their quantitative impact and show how costs may vary widely on a case-by-case basis.Recommendations are given to consider different possible heat and power supply strategies, as well as future energy and carbon price scenarios, to better understand cost performances under various framework conditions. Since retrofitting CCS is very relevant for industrial facilities, further considerations are made on how to better account for the key elements that constitute retrofitting costs. Furthermore, instead of using a fixed unit cost for CO2 transport and storage, cost estimates should at least consider the flowrate, transport mode, transport distance and type of storage, to make more realistic cost estimates. Recommendations are also given on factors to consider when assessing the technological maturity level of CCS in various industrial applications, which is important when assessing cost contingencies and cost uncertainties.Lastly, we urge techno-economic analysis practitioners to clearly report all major assumptions and methods, as well as ideally examine the impact of these on their estimates.

Towards Improved Cost Evaluation of Carbon Capture, Transport and Storage From Industry

This paper briefly summarises the development of improved guidelines for cost evaluation of carbon dioxide capture, transport and storage (CCS) from industrial applications [1] building on previous work in the field. It discusses key challenges and factors that have a large impact on the results of cost evaluations but are often overlooked or insufficiently addressed. These include cost metrics (especially in the context of industrial plants with multiple output products), energy supply aspects, retrofitting costs, CO2 transport and storage, and maturity of the capture technology. Where possible, examples are given to demonstrate their quantitative impact and show how costs may vary widely on a case-by-case basis. Recommendations are given to consider different possible heat and power supply strategies, as well as future energy and carbon price scenarios, in order to better understand CCS costs under various conditions. Since retrofitting CCS is especially relevant for industrial facilities, further recommendations are made on how to better account for the key elements that constitute retrofitting costs. Furthermore, instead of using a fixed unit cost for CO2 transport and storage, cost estimates should at least consider the CO2 flowrate, transport mode, transport distance and type of storage, to obtain more realistic cost estimates. Recommendations are also given on factors to consider when assessing the technological maturity level of CCS in various industrial applications, which is important when assessing cost contingencies and uncertainties. Lastly, we urge techno-economic analysis practitioners to clearly report all major assumptions and methods, as well as comprehensively examine the impact of uncertainties on their cost estimates.

Risk induced contingency cost modeling for power plant projects

The current practice of expert judgment-based contingency cost allocation by owners lacks a holistic understanding of project risks, their causal relationships, and impact on project costs. This study presents an integrated fuzzy set theory and fuzzy Bayesian belief network model for a rational, realistic determination of contingency costs for infrastructure projects. The application of the model is demonstrated for real-world power plant projects in Bangladesh. The model has promising results for its ability to establish the amount of contingency costs with a maximum error of 20% between the contingency cost predicted with the model and the actual contingency cost. It has the potential to assist both the owner and contractor to set aside a realistic amount of contingency cost in the preliminary phase of a project. The approach is also equally useful for monitoring and controlling project risks, and dynamically updates the contingency cost amount during project execution.

PREDICTING COST CONTINGENCY FOR PRIVATE PROJECTS: CASE STUDY IN VIETNAM

Actual cost in construction projects has tended to exceed the project budgets. Thus, cost contingency from the initial of the project is the significant budget to complete project activities. Cost contingency is an important element of cost estimation to protect project stakeholders such as owners, contractors, and architects from the risks of the project. This paper reviewed the previous studies to find the factors affecting cost contingency and proposed a regression model to predict the cost contingency for private projects in Vietnam. Through industry survey, project performance information from a total of 48 private projects in Vietnam was gathered. The research also compared with other studies to have an overview about cost contingency predicting methods. The key benefit of this research is that the project managers can use this model as another forecasting method to get a better quality cost management. The further research can be focused on developing the model for other project types in Vietnam.

Extend Cost Benefit Analysis of Bauxite Mining and Processing in the Central Highlands of Vietnam

The study focuses on assessing the effectiveness of bauxite mining and processing in the Central Highlands through the example of Tan Rai (Lam Dong) and Nhan Co (Dak Nong) bauxite-alumina complexes by the method of extended cost benefit analysis. External costs have been localized to be taken into account, including opportunity costs, environmental costs, corporate social responsibility costs and contingency costs for environmental incidents. The results showed that if calculating the environmental costs, the Tan Rai complex does not bring effectiveness for society with a negative NPV value (VND -5,167,422 million), IRR (6.27%) is lower than the discount (10%); Nhan Co complex is effective with positive NPV (VND 145,862 million), IRR (10.1%) is higher than the discount (10%), but the operation of this complex is risky when analyzing the sensitivity of the indicators. A ton of alumina will require an average from VND 0.7 to 0.9 million of environmental costs. In a year, two bauxite-alumina complexes in the Central Highlands create stable jobs for thousands of workers, contributing from VND 1,200 to 1,400 billion of taxes and fees for the State, equivalent to between VND 1.0 and 1.2 million per ton of alumina. From the lessons learned from Tan Rai and Nhan Co complexes, the future bauxite mining and processing projects need to be implemented on schedule and operate at 100% of the designed capacity right from the first year to achieve optimal efficiency. In the future, it is necessary to develop and evaluate the effectiveness of closed, full and chain options to improve the operational efficiency of bauxite projects, aiming to thoroughly solve the environmental issues by improving technological processes, implementing land restoration after mining, applying cleaner production solutions.

Making Construction Cost Estimate of Nuclear Power Plants Credible: Assessing Impact of Unknown Unknowns

Credibility requires predictability. Nuclear power plant (NPP) construction projects tend to be large and expensive, sometimes with high cost overruns far beyond those that might have been expected or predicted due to usual and recognized uncertainties and variations (e.g., in labor and materials costs combined with multiyear duration and complex construction logistics). This unaccounted for uncertainty brings the credibility of new NPP build projects into question and may prevent future projects from going forward. It is believed that the high initial capital cost of nuclear power is less of a hindering factor than the uncertainty about that cost. For nuclear power to regain credibility and enable future NPP construction projects, this unexpected uncertainty, or unknown unknown, needs to be assessed. Regular (expected) uncertainties (known unknowns) were addressed previously in a paper where the Iman-Conover method was used to account for correlated uncertainties. This paper addresses the impact of unexpected events (unknown unknowns), such as the Three Mile Island Unit 2 (TMI-2) accident. For this purpose, NPP construction in the United States is divided into two periods: pre-1979 (NPPs completed before the 1979 TMI-2 accident), and post-1979 (NPPs under construction when the accident happened and completed later). The latter group experienced significant schedule and budget overruns due to the change in regulation imposed after NPP construction was already under way. Analyzed a posteriori, this event and the escalated cost for the second group of NPPs was used to study the impact of a representative unexpected event. An approach was developed to assess the range of potential risks, including those due to such unexpected events, and thus enable assigning appropriate contingencies. A traditional large four-loop pressurized water reactor [PWR12-Better Experience (BE)] was considered. With the inputs derived from the pre-1979 data, the expected total capital investment cost (TCIC) mean value for the PWR12-BE is found to be $3.3 billion, with a contingency of $1.3 billion, which corresponds to 39.4% of the TCIC mean. If the unknown unknowns are taken into account based on the post-1979 data, the TCIC mean value increases to $9.4 billion, with a cost contingency that is 108% of the TCIC mean derived for the pre-1979 NPPs. Based on the experience-based assumed probability of unexpected events with large financial impact, it is then possible to derive an adequate contingency. The presented analysis offers a possible approach to treat unknown unknowns and to assess their impact on cost, providing the required contingency, as well as uncertainty in the construction time. In a broader context, this may provide quantitative tools to support making long-term energy policy decisions of new considered nuclear power projects.

Representation of Uncertainty in Electric Energy Market Models: Pricing Implication and Formulation

Modern market management systems continue to evolve due to the intentions to improve system security and reliability. This evolution has been leading to a transition of market auction models from a deterministic structure with approximations on the reliability criteria (e.g., acquisition of contingency reserve through proxy reserve policies) to explicit representation of contingencies (e.g., estimation of postcontingency states via participation factors and stochastic programming). This aricle proposes a comprehensive framework to establish various procedures for evaluating: (i) transparency and incentive compatibility of different contingency modeling approaches, and (ii) efficiency of two possible stochastic market designs. First, the concept of securitized locational marginal price is presented to solve the issue of how market participants should be compensated for providing N-1 reliability services. Then, pricing implications and settlements of three market models are compared: a deterministic market model with proxy serve policies; state-of-the-art market models with estimated postcontingency states; and (iii) a two-stage stochastic market model. Second, this article evaluates two stochastic market models while accounting for potential adjustments from day-ahead scheduling to real-time operation: (i) minimizing expected operating cost of all N-1 scenarios, and (ii) minimizing the base-case (or no contingency) cost. These analyses are conducted on IEEE 118-bus test system.

Cost Performance and Cost Contingency during Project Execution: Comparing Client and Contractor Perspectives

AbstractCurrent literature shows that poor cost performance in projects has become routine. Research on cost performance has mostly focused on one of the involved parties, either the client or the ...

Prediction of simulated cost contingency for steel reinforcement in building projects: ANN versus regression-based models

If cost contingency is too high, it might cause the construction project to be uneconomic, aborted, and lock-up funds not available for other organizational activities; if too low, it may result in unsatisfactory performance outcomes. To predict the simulated cost contingency (SCC), a Monte Carlo Simulation (MCS) is applied. However, the use of simulation in most cases is difficult, cumbersome and rarely used in the construction industry. The objective of the current research is to develop ANN and regression based (RB) models to predict the SCC for building projects due to the variability of steel reinforcement (SR) prices. For all the adopted scenarios in dealing with the independent variables, the results revealed that on the average, ANN models are more powerful than RB models in theoretical modeling of SCC when comparing its predicted value with its actual simulated value. On the contrary, RB models outperform ANN models from practical point of view when comparing predicted SCC with its real value. Thus, contractors are advised to adopt RB models in predicting SCC for the practicality in estimating cost contingency. However, the developed models are expected to be highly beneficial for contractors in making SCC calculations easier.

Cost Contingency and Cost Evolvement of Construction Projects in the Preconstruction Phase

The current literature discusses the methods to estimate the costs and cost contingency. The literature also distinguishes “known unknowns” and “unknown unknowns” contingencies. Little is written, however, about the evolvement of total project cost estimates during the preconstruction phase of construction projects. Moreover, not many studies are investigating the “known unknowns” and “unknown unknowns” contingencies in real construction projects. Practice expressed the need for getting more insight into the development of the estimated costs of the projects in the preconstruction phase. This paper, therefore, discusses the estimate of the total project costs (and cost contingency) in the preconstruction phases of 29 Dutch flood defense projects using a case study approach. Altogether, the projects have experienced an 11.51% increase in the estimated costs compared to the initial estimates, which is low compared to previous studies. This increase in the cost estimates of the flood defense projects can be explained by “technical” reasons. The investigation of “known unknowns” and “unknown unknowns” contingencies shows that the percentage of the “unknown unknowns” contingency has increased in the preconstruction phase while a reduction was expected. This increase suggests that the projects were not confident about their estimates and the increase can be explained by a lack of experience, organizations’ culture, or the phenomenon of “pessimistic bias.” Practitioners can avoid “pessimistic bias” behavior by asking for opinions about their estimates and using historical project data. Further research is suggested into realized cost contingency after project execution.

A Suggested Module for Risk Framework of Water Control Construction Projects using Qualitative and Quantitative Techniques

Managing risk is an integral part of sound management and risk management helps to achieve projects objectives. Although the process of risk management is standardized to a great extent, risk control strategies depend on project circumstances and feature. One of the essential humanity’s challenges in the future, is surely ensuring water needs. Equitable and sustainable management of water resources is a major global challenge, and supply of clean fresh water is decreasing around all nations. Egypt is one of the countries hardest affected by climate change. Also challenges include population growth, increase in food demand and Lack of water and energy Leads to let managing risk for construction of water control structures projects on water streams in Egypt becomes more challenging and essential than ever before. Moreover, this study aims to show the effect of risk on cost and time targets for water control structure projects in Egypt .This study mainly identify a list of significant risk factors effecting on cost and time for these type of projects in Egypt through studying pervious publishing for risk management in construction projects and a questionnaire survey , then conducting a qualitative analysis using a Severity and Probability matrix (S/P matrix) techniques to assess the influence of risk factors and to isolate critical risk factors for a quantitative analysis. Finally, conducting a quantitative analysis by mean of Analytical Hierarchy Process (AHP) to determine the cost and time contingency, s the conclusion of this study shows that the cost contingency needed to account for the different critical risk factors is to increase the estimated cost by 12.35 % on the total estimated budget of the project. In addition that the time contingency needed to consider for different critical risk factors is to increase the scheduled time by 11.25% over the total originally scheduled of the project.

Risk quantification using fuzzy-based Monte Carlo simulation

Estimating cost contingency of construction projects depends largely on data captured from previous projects and/or experience and judgment of members of project team. Mote Carlo simulation is commonly used in estimating contingency, where its accuracy was reported to depend on number of iterations used in the simulation process, probability density functions associated with each project cost item being considered and the correlation among these cost items. The literature reveals that the latter is the most important issue for accurate estimate of contingency. It, however, requires the calculation of coefficients of correlation among cost items based on captured historical records of cost data. Subjective correlation was introduced to alleviate the difficulties associated with the calculation of these coefficients. This paper presents a newly developed method for cost contingency estimation that considers subjective correlations and allows for contingency estimation with and without computer simulation. Unlike the methods reported in the literature, the present method considers uncertainty associated with the coefficients of correlation and utilizes earlier work of the first author in calculating the variance of total project cost. It also allows for assessing the impact of variable covariance matrix on the estimated project cost using a simple and user-friendly computational platform. The application of the developed method on cost data captured from two databases demonstrates its use and accuracy in estimating cost contingency. The results are compared to those produced by others using Monte Carlo Simulation with and without correlation using an actual project data.

Predicting implementation cost contingencies for residential construction projects in flood-prone areas

PurposeThe purpose of this paper is to predict implementation cost contingencies for residential construction projects in flood-prone areas, where floods with storms frequently cause serious damage and problems for people.Design/methodology/approachExpert interviews are conducted to identify the study variables. Based on bills of quantities and project documents, historical data on residential construction projects in flood-prone areas are collected. Pearson correlation analysis is first used to check the correlations among the study variables. To overcome multicollinearity, principal component analysis is used. Then, stepwise multiple regression analysis is used to develop the cost prediction model. Finally, non-parametric bootstrap method is used to develop range estimation of the implementation cost.FindingsA list of project-related variables, which could significantly affect implementation costs of residential construction projects in flood-prone areas, is identified. A model, which is developed based on an integration of principle component analysis and regression analysis, is robust. Regarding range estimation, 10, 50 and 90 percent cost estimates, which could provide information about the uncertainty levels in the estimates, are established. Furthermore, implementation cost contingencies which could show information about the variability in the estimates are determined for example case projects. Such information could be critical to cost-related management of residential construction projects in flood-prone areas.Originality/valueThis study attempts to predict implementation cost contingencies for residential construction projects in flood-prone areas using non-parametric bootstrap method. Such contingencies could be useful for project cost budgeting and/or effective cost management.

Stochastic economic analysis of coal-alternative fuel production from municipal solid wastes employing hydrothermal carbonization in Zimbabwe.

Hydrothermal carbonization (HTC) is a promising technology for converting high moisture municipal solid waste (MSW) to a safe low-chlorine hydrochar. The key objective is to assess the economic viability of an HTC based MSW management system in Zimbabwe. Previous studies have only used deterministic estimates of hydrochar production costs disregarding uncertainties in their model parameters. Herein, a probabilistic economic analysis is introduced to quantify the uncertainty concerning costs. The goal is to determine factors that will consolidate the venture to achieve a certain level of return. The effectiveness of different investment strategies, namely, a government or private sector-run operation will be tested using Monte Carlo simulations. Results indicate a 55% and 18% probability for a positive Net Present Value (NPV) for a state-run and private operation respectively. A specific investment cost of US$54 - 67 per Mg of MSW treated, a return on investment (ROI) of 5.4-29.0% and internal rate of return (IRR) of 5.2-22.9% can be expected if the project is undertaken by government. The private sector can expect an ROI of -0.8-18.2% at a 90% confidence level and a lower IRR of -2.1-16.2% from US$57 - 71 per Mg of MSW invested. Contingency costs are US$25 per Mg and US$38 per Mg of MSW for the government and private sector respectively at a 100% confidence interval. A 70% rise in collection tariffs or a minimum selling price of US$91/Mg of the hydrochar would ensure a positive NPV for the government-run operation.

Renewable energy contingencies in power systems: Concept and case study

This paper introduces the concept of renewable energy contingencies that represent long-term/extended variability of variable renewable energy (VRE) resources, namely, significant periods (e.g., days/weeks) of low wind/solar availability. These contingencies have not received much attention to date but are likely to emerge as a major issue in some countries such as India as the share of VRE increases. Using 38 years of climate model reanalysis data for wind over India, we demonstrate that low periods of wind contingency below long-term (Indian) national average of 5 m/s can extend for more than 100 days in several zones some of which are deploying large wind farms. Even in some of the best wind resource areas in India with long term average wind speed close to 7 m/s, low wind days (e.g., 5 m/s which is substantially below average) can extend up to 60 days. We propose a four-step methodology around a co-optimization based energy-ancillary services dispatch model to assess the impact of renewable contingency and implemented it for the state of Tamil Nadu, the most wind-rich state of India. We have estimated that annual renewable contingency cost impact of 5 GW additional wind in Tamil Nadu to be in the range of US$27–76 million pa. Planning analysis should embrace the concept of renewable contingency to recognize these costs and put in place necessary spinning reserve and back-up generation resources.

Improving the efficacy of cost contingency plans for construction projects in South Africa

The use of cost contingency in construction projects provides a lucid and lively recognition of upcoming/impending challenges of cost overrun in delivering the construction project thereby giving room for project delivery to cost. The aim of this study is to evaluate how the efficacy of cost contingency plans for construction projects can be improved. This study adopted the use of a quantitative research approach with the use of questionnaire structured to capture the perceived measures of improving the efficacy of cost contingency plans for construction projects. The study harnessed information from Construction Managers/Project Managers, Contract Managers and Quantity Surveyors in Gauteng Province, South Africa. Data retrieved was analysed using mean item score, standard deviation and Kruskal-Wallis H-test. This study was limited to Gauteng province in South Africa due to time and cost constraint. The findings showed that the three most effective measures of the efficacy of cost contingencies in construction projects are assessing risk possibilities, assessing risk impact, and identifying project risk and uncertainties. The study concluded that cost contingency plan can be improved when risks are adequately assessed together with their impact on the construction project. By recommendation, identification; definition; and evaluation of risks is a paramount activity which project professionals must embark upon before a construction project can commence.

Economic evaluation of the Austrian telephone-based triage system

Abstract Background In order to reduce avoidable consultations but also avoidable self-referrals to hospital outpatient departments, the Austrian authorities agreed to establish a voluntary telephone-based triage system in the course of the health reform 2013. Three regions piloted the system in early 2017. Methods An economic evaluation aimed to assess the impact on health service demand after having consulted the telephone-based system. For analysing the impact, we used the conceptual model of “shift cases” from one particular service setting such as outpatient clinic to another (e.g. GP office) and calculated savings realised through patient shifts. Based on potential savings in private and public costs and running costs of the service, we identified threshold values for cost-effective operation. Results In total, approximately 45,000 completed telephone consultations were registered in the pilot phase. 2,500 persons were advised to conduct self-care whereas more than 40,000 were recommended to contact a health service provider with differing levels of priority. Adherence to the initial recommendation of the provider setting was 70%, the level of priority was met in 90%. With regard to the economic impact, public savings of shift cases range from €31 (self-care instead of GP consultation) to €198 (GP/specialist instead of hospital outpatient clinic). Public costs ranged between €10 and €50 depending on the degree of capacity utilisation, contingency costs and duration of calls. Therefore, economic net gains can be realized if approximately 15% to 25% of callers choose a lower care setting due to the consultation service. Conclusions The tele-triage service has shown to be a potentially cost effective tool but largely depends on user uptake, patient adherence and local maintaining costs. In order to exploit the full potential of the system, policy makers are advised to promote the use of the system in general and to evaluate the used algorithm. Key messages Telephone-based triage systems are a potentially cost-effective strategy in order to reduce avoidable encounters both on a primary care level but also at hospital outpatient clinics. The public savings of a shift in the provider setting vary substantially depending on the level of service delivery with diminishing savings for shifts in lower levels.

Integrated priority decision index for risk assessment in chaos: cost overruns in transport projects

Purpose Cost overrun is inherent to project chaos, which is one of the key drivers of project failure. The purpose of this paper is to explore the critical elements of complexity-risk interdependency for cost-chaos in the construction management domain by utilizing a multi-criteria decision model. Design/methodology/approach A total of 12 complexity and 60 risk attributes are initially identified from the literature and using expert’s judgements. For the development of a structured hierarchy of key complexity and risk drivers, a real-time Delphi process is adopted for recording and evaluating the responses from experts. Afterwards, a pair-wise comparison using analytical network processing is performed to measure complexity-risk interdependencies against cost alternatives. Findings The findings of the integrated priority decision index (IPDI) suggest that uncertainties related to contingency and escalation costs are the main sources of cost overrun in project drift, along with the key elements such as “the use of innovative technology,” “multiple contracts,” “low advance payment,” “change in design,” “unclear specifications” and “the lack of experience” appear to be more significant to chaos in complexity-risk interdependency network. Research limitations/implications This study did not address the uncertainty and vulnerability exit in the judgment process, therefore, this framework can be extended using fuzzy logic to better evaluate the significance of cost-chaos drivers. Practical implications These results may assist the management of cost overrun to avoid chaos in a project. The proposed model can be applied within project risk management practices to make better-informed technical decisions in the early phases of the project life cycle where uncertainty is high. Originality/value This research addresses the importance of cost overruns as a source of project chaos in dynamic systems where projects reach the edge of chaos and progress stops. A new IPDI index contributes toward evaluating the severity of complexity and risk and their interdependencies which create cost-chaos in infrastructure transport projects.