White Paper March 14, 2002 - by Timberline Engineering, Inc.
Introduction
Cost estimation for an engineering project traditionally involves development of an “engineer’s estimate” of the project cost to which contingency is added to develop the “total project cost.” Contingency is used to reflect the fact that regardless of the diligence and competence of the estimator, there is uncertainty in the final cost of the project. Material costs change over time, markups and markdowns vary from job to job, field conditions arise that were unforeseen during design, and other factors add to the uncertain nature of cost estimating.
Contingency is added to account for these unforeseen conditions that are sure to occur. Traditionally, the contingency is a percentage of the engineer’s estimate. At a study level, a 25% to 30% contingency is typically added. At successive project phases smaller contingency numbers are typically used to reflect the fact that there are fewer unknowns in the estimate.
In recent years, the weakness of applying a percentage to the engineer’s estimate has been the subject of considerable discussion and research. The traditional approach does not provide decisionmakers with a clear indication of the likelihood that the project will be completed for the budgeted amount.
This approach does not allow managers to budget more money to accept less risk. And this approach tends to either overstate the costs, in a fairly typical project where there is little uncertainty, or understate the costs, in a project where there is significant uncertainty. Statistical techniques have been developed to address the shortcomings of the traditional approach and provide a clearer indication of the expected costs.
This white paper outlines a technique to model each line item that makes up the engineer’s estimate as a probability distribution. The probability distributions can then be combined to create a statistical view of the total project cost.
This approach shows the most likely construction cost and the probability that the cost will exceed any given contingency. This information allows decision makers to set budgets based on the amount of risk they are willing to accept.
................................................
Conclusion
This paper outlines an approach to cost estimation based on statistical risk analysis. This approach overcomes serious limitations of traditional approaches and results in a better understanding of the cost risk involved in a project. This technique has been used successfully by Timberline and other organizations for the past several years. The availability of powerful PC-based software for performing this analysis has made these techniques available to everyone.
*Source:
https://www.timberlineengineering.com/sites/default/files/risk_analysis.pdf
Jennifer S. Shane, Principal Investigator Department of Civil, Construction, and Environmental Engineering Construction Management and Technology
Iowa State University March 2015
Research Project Final Report 2015-10
Risk Monitoring and Control
Risk monitoring and control identifies and designates specific parties to take responsibility for each risk response. The aim is to track the identified, residual, and new risks, and to ensure the implementation of the risk response plans together with evaluating their effectiveness.
Risk monitoring and control also ensures that this becomes a continuous process during the project lifecycle, where risks are tracked whether new risks develop or anticipated risks occur or disappear.
The owner of each risk plan reports periodically to the project manager on plan effectiveness, unanticipated effects, and midcourse corrections that should be taken to mitigate risk (AASHTO 2009). Project risk reviews are scheduled regularly during the course of the project to repeat the risk management steps.
Project risk is also an agenda item in all project meetings. Risk ratings and prioritization commonly change during the project life cycle. This mainly assures that, if an unanticipated risk emerges, or a risk’s impact is greater than anticipated, planned response strategies are revised accordingly to control the risk.
Risk control, thus, involves choosing alternative response strategies, implementing contingency plans, taking any corrective actions required, and re-scoping the project (AASHTO 2009).
Suggested Guidance
While risk checklists are useful, project teams should not feel limited to the identified risks. The project team should consider each project individually and brainstorm risks that may be associated with the specific project that is not represented in any checklist.
Risk checklists from across MnDOT should be collected regularly and the checklist that is available through the Office of Project Management should be updated to reflect the current state of the checklists across the state.
One place to accumulate a number of risks is annually reviewing risk registers from across the state to provide updated checklist. The checklists may be divided into categories to make it easier to use. Risk registers should be reviewed regularly and the number of each risk should be counted.
An identified risk may only rarely appear in some districts and many more times in another, but based on the frequency it may be helpful to add the risk to the risk checklist as a way to communicate that this is a risk to be considered.
When updating a risk register, project teams should be sure to update all categories for the previously identified risks. Also, project teams should brainstorm and consider new risks that need to be added to the risk register. Project teams should consider risks beyond the cost, schedule, technical/scope risks, but should also consider risks associated with financing and risks outside of the MnDOT.
Risks should not only be tied to the cost of the project but also to the schedule of a project.
*Source:
http://www.dot.state.mn.us/research/TS/2015/201510.pdf
International Context:
Introduction
Quiz
Risk-Based Estimating Overview
Process Stages
Goals and Benefits
*Source:
https://static.tti.tamu.edu/conferences/tsc14/presentations/proj-mgmt/huter.pdf
Indian Context:
STUDY AND NEED OF RISK MANAGEMENT FOR CONSTRUCTION PROJECTS
India is experiencing huge growth in the infrastructure and construction sector. With increase in scale of projects and finances, the need to address associated risks is paramount. Unaddressed risks can increase costs and time delays. Furthermore, can adversely affect the scope and quality of projects.
Risk management is a process that deals identification and mitigation of risks in projects and helps to complete them within budget and timeframe.
1. INTRODUCTION
The pace of change in the construction industry has imposed additional demands on construction project management to deliver projects to budget, timeframe, scope and quality. Risk management has become vital to a project's success: An important process and a control tool for reducing uncertainty and improving decision-making.
Enormous financial risk involved in large scale of projects, lays emphasis on the need for risk management in construction projects. Construction industry has a poor track record of coping with risks and as a result of which clients have been enduring agonizing outcomes of failure in the form of unnecessary delays in project completion, cost overrun and at times failing to meet quality standards & operational requirements (Wakjira, 2011).
Traditionally, during the pre-contract stage of project, most of these risks are not properly identified, not assessed for the likelihood of its occurrence and its impact on the project performance. Rather a 10% contingency is added to the total project cost in order to accommodate the effect of unforeseen circumstances. In most cases, the 10% contingency is based on intuitive guesswork and this explains the high cost overrun (Odenyinka 2000).
Thus the need to assess risk impact on construction projects (Awodele 2012).
This research study is attempted to understand the concept of risk management. Secondly, we have tried to state the need for risk management in the present construction industry. Also we bring to notice the current risk management practices carried out in the country.
2. RISK MANAGEMENT
Risk management process encompasses identifying factors that have potential to negatively impact a project’s cost, schedule, scope or quality; quantifying the associated potential impact of the identified risk; implementing measures to manage and mitigate the potential impact.
Risk management provides a structured way of assessing and dealing with future uncertainty. Risk management includes processes associated with identifying, analyzing and responding to the project risk. It includes maximizing the results
of positive events and minimizing the consequences of adverse events.
Qualitative and quantitative risk analyses are widely used.
Qualitative Risk Analysis relies on judgment, using criteria to determine outcome. A common qualitative approach is a precedence diagramming method, which uses ordinal numbers to determine priorities and outcomes. An example of qualitative risk analysis, is to list in descending order specific processes of a project, the risk or risks associated with each process, and the control or
controls that may or should exist for each risk.
Quantitative Risk Analysis relies on statistical calculation to determine risk, its probability of occurrence, and its impact on a project. A common example of quantitative risk analysis is:
Decision tree analysis, applying probabilities to two or more outcomes.
Another approach is Monte Carlo simulation, which generates a value from a probability distribution and other factors.
3. NEED FOR RISK MANAGEMENT
Construction projects can be extremely complex and fraught with uncertainty. Risk and uncertainty can potentially have damaging consequences for projects.
Therefore nowadays, risk analysis & management continue to be a major feature of construction project management in an attempt to deal effectively with uncertainty and unexpected events and to achieve project success.
Considering the complexity and the size and budget of construction projects
even small risks can have a greater impact on the project. Risks can cause delay in project delivery and monetary losses as well. Most of the times it is found that there is no systematic protocol for risk management in a project. Generally, risk management is carried out by brain storming sessions of senior engineers and the management.
This mostly depends on the experience of the said person and judgment can differ from person to person. when a risk event occurs this is not very useful
as the event has already occurred and the damage is done.
Hence it should be taken into consideration by the organisations that, there should be a dedicated risk management team to identify, analyze and mitigate
the risks. There should be a risk response plan ready before the risk event occurs. This not only reduces the monetary impact but also reduces delay in project and avoids degradation of quality.
4. CONCLUSIONS
As far as India is concerned risk management is still a new concept in the construction sector and this should be changed as soon as possible. Risk management should be considered a primary tool to assess the project.
From the study we can conclude that most organisations neither implement risk management nor implement it systematically. Risk mitigation plan should be in place at planning stage taking into account all possible future risks.
**Source
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 10 | Oct -2017 www.irjet.net p-ISSN: 2395-0072
Budget allocation for the Roads & Highways Sector: Rs 2.7 lakh crores
Source:
Barreras, A. J. (2011). Risk management: Monte Carlo simulation in cost estimating. Paper presented at PMI® Global Congress 2011—North America, Dallas, TX. Newtown Square, PA: Project Management Institute.
ABSTRACT
Monte Carlo simulation is a practical tool used in determining contingency and can facilitate more effective management of cost estimate uncertainties. This paper lays out the process for effectively developing the model for Monte Carlo simulations and reveals some of the intricacies needing special consideration. This paper starts with a discussion on the importance of continuous risk management practice and leads right into the why and how a Monte Carlo simulation is used to establish contingency. Given the right Monte Carlo simulation tools and skills, any size project can take advantage of the advancements of information availability and technology to yield powerful results.
*Source:
https://www.pmi.org/learning/library/monte-carlo-simulation-cost-estimating-6195
John J. Reilly, John Reilly International ■ www.johnreilly.us
Philip Sander, RiskConsult GmbH ■ www.riskcon.at
A. Moergeli, Moergeli Consulting, llc ■ www.moergeli.com
ABSTRACT
Every cost estimate is uncertain. Underestimating construction costs by owners in the planning or design phase or by contractors in the bidding phase, and the possibility of low probability/high impact “black swan” events, can lead to disputes, claims, and litigation.
A better understanding of potential costs can help owners budget and secure authorization for projects, with a reduced chance of cost overruns.
A better understanding of potential costs can help contractors in determining an appropriate base cost and margin for bidding, strategies to secure the work in a low bid environment, and construction management strategies to maximize profit, to avoid loss, and to better manage and recover costs of construction changes and claims.
This paper will address cost estimating methods focused on construction. It will address the uncertainty inherent in predicting the value of any future project element or process as well as identifying risk (threats or opportunities) that can impact outcomes.
It will address risk-based methods that can improve our understanding of the cost of uncertainty and potential risk events.
*Source:
Mahender Rao - Maribyrnong City Council
Harshavardhan Vijay Ranade - Maribyrnong City Council
ABSTRACT
This paper presents the application and validation of a new tool developed by the first author for accurate risk-based estimation of project budgets. Typical capital intensive projects to which this tool can be applied include road reconstruction, road resheet and road rehabilitation projects.
Quantitative risk analysis and stochastic modeling using Monte -Carlo simulation is embedded in the algorithms of the computer code. The tool forecasts a range of possible project costs and the probability of the occurrence of those costs by taking into account uncertainties and associated risks.
Application of the tool to capital intensive road projects designed by the second author and constructed in 2011 & 2012 demonstrates its validity and utility.
Comparisons of forecasted estimates using this tool with actual costs and with traditional deterministic methods of cost estimation (such as --point base-case estimates inclusive of contingency) provide valuable insights that can aid management in evaluating alternatives and in making informed decisions when estimating and allocating budgets to a portfolio of road projects.
Author Biographies
Mahender Rao, graduated as a Civil Engineer from Osmania University in India.
He worked both as a construction and contracts engineer for Larsen & Toubro (L&T), one of the largest engineering firms in India having a turn-over of 13.5 billion US$. He specialized in activity based costing, tendering, contract management, etc. and won several prestigious residential, industrial and infrastructure contracts for the organisation.
With a view to drive-in efficiencies in estimation practices he teamed up with private software experts and with their help, developed innovative Invoice Management, Project Finance Management and Risk Based Estimation Systems. Currently he is working for Maribyrnong City Council (MCC) as a Construction Engineer. He is now involved in testing those products by customizing them for MCC.
Harshavardhan Vijay Ranade holds a PhD in Structural Engineering from RMIT, Australia.
He has a Master’s degree in Information Technology from Edith Cowan University in Perth, Western Australia and a Bachelors Degree in Civil Engineering from the University of Pune, India. He is a professional engineer with more than eight years of international experience in civil design of road and related infrastructure and structural design/analysis of buildings and bridges.
He has worked in the public and private sectors in Australia and in the private sector in India. He currently holds the position of Design/Project Engineer at Maribyrnong City Council since 2009. He has developed new and innovative tools during and after his PhD which involved significant applied research, software development and finite element analysis of multi-storey buildings. The tools were validated on building designs for projects.
*Source:
https://epress.lib.uts.edu.au/journals/index.php/opm/article/view/4112/4550
CASE STUDY Risk Management, Estimating 16 July 2008
Napier, Z. & Liu, L. (2008). The predictive validity and performance drivers of risk-based estimating: case studies of Australian water infrastructure projects.
Introduction
Engineering infrastructure in Australia accounts for an increasing share of the construction market. In the 2005–2006 financial year, construction of infrastructure was valued at approximately A$40 billion, accounting for more than 12 percent of Australian GDP and employ 6.5 percent of the workforce (Engineers Australia, 2005).
It has been estimated that, due to years of neglect, the immediate needs for infrastructure investment in Australia is about A$400 billion. The growth in infrastructure investment over the five years since 1999/2000 has been at 15.2% compounded annually (Engineers Australia, 2008). However, studies on infrastructure projects have found that large infrastructure projects are plagued by delays and large cost overruns.
For example, studies have found that inaccuracy in cost estimation ranges from 20.4% to 44.7% depending on the type of projects (Flyvbjerg, Bruzelius, and Rothengatter, 2003; Flyvbjerg, Holm, and Buhl, 2002; 2005). Similarly, it has been reported that overruns of 50–100% in fixed prices are common for major infrastructure projects, and overruns above 100% are “not uncommon,” with the magnitude of cost overrun unchanged over the past 70 years (Bruzelius, Flyvbjerg, and Rothengatter, 2002).
Literature on the management of infrastructure projects point to 2 main culprits for the inaccuracy in cost forecasting for infrastructure projects--optimism bias and strategic misrepresentation (Bruzelius, Flyvbjerg, & Rothengatter, 2002; Flyvbjerg, 2006).
Optimism bias occurs due to the tendency for people to be over-optimistic by overestimating benefits and underestimating costs (Lovallo & Kahneman, 2003), while strategic misrepresentation occurs when people deliberately misrepresent project costs and risks due to political, economic, and/or organisational pressures (Flyvbjerg, 2006).
The main consequence of both optimism bias and strategic misrepresentation in cost estimation is that, blinded by their belief that “results are determined purely by their own actions and those of their organisations” (Lavollo & Kahneman, 2003) and the ignorance or discounting of risks/uncertainties in the estimates, the project owners are unable to fully account for the uncertainty or risks in their estimates.
Failure to fully account for risks will result in flawed planning, possible breakdown in relationship between the client and contractors, difficulties in delivering the project, and the inevitable project delays and cost overruns.
Flyvbjerg, Holm, and Buhl (2002; 2005) proposed using reference class forecasting (RCF) as a way to overcoming the effects of optimism bias and strategic misrepresentation in cost forecasting. RCF uses actual and estimated cost data from similar projects to determine the probability distribution of these types of projects. Based on the project estimators' preference for risk overrun, the cost estimates for new projects are then compared with the distribution curve from the RCF to determine the most likely outcome (Flyvbjerg, 2006).
The main challenge for applying RCF method is the accumulation of a sample of similar projects with large enough sample size and accurate cost information. It may take a very long time for develop such a database. For some types of projects that are relatively rare in a country (e.g. nuclear power plants or large-scale desalination plants), it may never be possible to have a sample size large enough for statistical analysis.
The problem is further exacerbated by the fact that private companies may not be willing to share such commercially sensitive information with competitors or the governmental agencies.
In the absence of such data sets for RCF forecasting, outsiders' view, a key contributing factor to the effectiveness of RCF, could still be brought in through a collective decision-making process that could potentially mitigate the effects of optimism bias and strategic misrepresentation.
In this study, we examine the predictive validity of a cost estimation method that has been adopted by an increasing number of construction and consulting firms in their bids to improve estimating accuracy. The estimating method is called Risk Based Estimating (RBE). Despite its popularity, there is little empirical evidence on the predictive validity of RBE and there is a lack of understanding on how it improves estimating performance. The findings from this study identify the main performance drivers and suggest that the RBE method has good predictive validity.
This study is based on case studies of 11 water infrastructure projects in the Sydney region. The overall cost overrun/under-run data from the case projects provide a good indicator for the predictive validity of RBE. Further, through a series of interviews with selected people experienced in RBE, we have identified the main factors that drive improvement in RBE's predictive validity (hereafter referred to as performance drivers).
In the next sections, relevant literature is reviewed and research questions defined, followed by a description of the research methodology and analysis process. Then, results are analyzed and conclusions are drawn. Finally, implications, validity threats, and further studies are discussed.
*Source:
https://www.pmi.org/learning/library/australian-water-infrastructure-projects-cost-7122
Copyright © 2015 - 2024 PMax - All Rights Reserved.
Powered by GoDaddy
We use cookies to analyze website traffic and optimize your website experience. By accepting our use of cookies, your data will be aggregated with all other user data.