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Uncertainty and Defining
Project Contingency Budget
for Canadian Department of
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Decision-Making and Quantitative Risk Analysis
Decision-Making and Quantitative Risk Analysis
Project Risk Assessment
Using Decision Trees with
ENGCOMP is a Saskatchewan-based structural, mechanical and cost engineering consulting firm. With structural engineering as its core business, ENGCOMP also specializes in risk analysis, cost estimation, and planning.
ENGCOMP was contracted to assist the Canadian Department of National Defence (DND) to define the budget for the fourth phase of construction of its ongoing Fleet Maintenance Facility Cape Breton (FMF CB) project located at the Canadian Forces Base Esquimalt, Victoria, BC. Using Palisade’s risk analysis software, @RISK, ENGCOMP conducted Monte Carlo simulations to quantify the uncertainty in defining the budget and schedule for this project.
Jason Mewis, President, ENGCOMP, says, “Risk analysis is crucial to the cost and schedule management of any project, and must include a scientific approach to contingency and risk reserve estimation. @RISK can help reduce uncertainty, greatly increasing the chances of project success.” ENGCOMP developed a system that breaks down the Monte Carlo simulation into two phases – a simulation for Contingency Analysis and one for Project Risk Analysis.
Mewis explains, “Based on our quantitative risk analysis, DND was able to clearly justify to the Federal Government’s Treasury Board why it should be allowed to get the capital appropriation for the project despite the level of uncertainty. This may not have been possible without the detailed and comprehensive analysis enabled by @RISK.”
Due to the success of the risk analysis undertaken by ENGCOMP, DND is talking with the company about possibly preparing a policy on performing this level of detailed Monte Carlo simulation-based analysis for all future DND projects.
ENGCOMP presented this case study at a previous Palisade Risk Conference. See similar customer case studies at upcoming conferences—more information below.
Now is the time to register for the 2012 Palisade Risk Conferences in London and Sydney. In its seventh year and attracting more people each time, the Palisade Risk Conference series has become the most important event of the year for quantitative risk and decision analysis. Over the course of two days, industry users and expert consultants will present dozens of real-world case studies and training sessions about innovative approaches to managing risk and uncertainty in a wide range of business applications. In addition, you’ll have the opportunity to meet one-on-one with technical consultants to address your personal modeling and risk problems.
Join us in London along with presenters from Unilever, Halcrow, Transatlantic Petroleum, and many more.
Join us in Sydney along with presenters from Aon Global Risk, the University of New South Wales, Lend Lease, and many others.
Lunches, breaks, and networking receptions are all included with your registration.
Some key features of new @RISK 6.0 are integration with Microsoft Project, improved graphs, better distribution fitting, and more. Other important new improvements in the Suite are Bayesian revision in PrecisionTree, the new OptQuest optimization engine in RISKOptimizer and Evolver, sensitivity analysis in NeuralTools, and scatter plot matrices in StatTools. There are many more enhancements, so we encourage you to take a look!
As a current Palisade software user, we would like to invite you to test new version 6.0 and send us your feedback. Your input will be help us improve version 6.0 as we prepare the final release. Please click the link below to download the DecisionTools Suite 6.0 beta:
The DecisionTools Suite Beta can be installed on the same system as a licensed copy of version 5.x of the DecisionTools Suite or @RISK. Both versions install to different directories and launch from separate icons. You cannot run both version 6 and 5.x in the same session of Excel; when running one version, exit Excel before launching the other version.
This Beta version expires on March 31, 2012.
We hope you find this to be another useful resource for networking, sharing ideas about models, and getting answers to your questions.
See Dr. Salling present at the 2012 Palisade Risk Conference in London.
This model, called SIMSIGHT, was developed to provide decision support using Monte Carlo simulation with @RISK for improving transport. The specific project analyzed is the replacement or upgrading of an airport in Greenland, a Danish territory. The analysis aims at shedding light on the robustness of the socio-economic feasibility relating to undertake such a major infrastructure investment.
The purpose of this presentation is to present an approach to explore the robustness of a decision about implementing a certain alternative. The relevance of examining robustness is related to the issues about uncertainty and risks, which may take a major role in connection with large scale projects, where factors such as construction costs and demand prognoses are uncertain for a number of reasons. Clearly, the variability relating to these will dominate and have high importance as regards the long-term socio-economic return or feasibility of the investment. A special interest in this context is to explore the concepts and belief of the “fat-tails” and “over-confidence” theory concerning input parameters as concerns MIN and MAX estimates. Specifically, three input distributions are investigated within @RISK namely the Trigen (Triangular), the Beta-PERT and the Erlang distributions, all relying on subjective measures corresponding to a minimum, most likely and maximum parameter value. However, how confident are we upon the latter? Is it merely guess work and speculations or is it possible to make actual decision support based upon subjective input parameters? And finally, how does @RISK cope with entries specifically concerning open-ended tails, thus, how are the extreme values represented in the Monte Carlo simulation?
A common decision faced by managers is whether to buy or enter into a short-term lease for equipment or to rent office space on a long or short-term lease. A short-term lease may be at a lesser rate, but a manager must worry about what happens when the lease expires such as replacing equipment or finding new office space. Moreover, the rate for a new short-term lease to replace the equipment or obtain new office space is not known. This worry can be avoided by entering into a long-term lease. But this also has its drawbacks. The equipment may become obsolete during the lease period. The equipment or the office space may no longer be needed or adequate to serve the needs of the company. These are imponderables in deciding whether to enter into long or short-term leases, but @RISK is designed to handle imponderables.
The example used here is a manager facing a decision of whether to enter into a single twenty-year lease for office space or a series of five year leases. Although the example is based on office space, the spreadsheet can be adapted for equipment buy or lease decisions. The model contains the impact of good and bad times on office space rentals, which need not be included in actual applications. The model also contains the possibility that office space may not be needed throughout the long-term lease, which should be included in actual applications. The possibility of not needing the office space over twenty years detracts from the desirability of ownership or entering into a long-term lease. The problem of what to do when a lease expires if the office space is still needed detracts from the desirability of a short-term lease. Thus it is not clear whether a company’s interests are better served by long or short-term leases. Analysis of the situation using @RISK should help to clarify a knotty problem facing managers.
Introduction a l’analyse des Risques et
Introducción al análisis de riesgo y
Introduction to @RISK with a Cost Estimation Focus
A Stochastic Simulation Model for
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