Cover Image

Not for Sale



View/Hide Left Panel
Click for next page ( 104


The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 103
103 What does it do? Most Probable Cost (m) The communication of a range of values representing the possible array of ultimate project costs creates a better under- Average Cost (always >m) standing of estimate precision. The optimistic and pessimistic Probability values at the ends of the range do not necessarily represent the very least or the very most that the project will cost, but typ- Highest Credible Cost (p) ically the most probable range of project costs. The size of the range will be determined by the identified uncertainties. The interpretation and use of the range depends on how aggres- sive the agency is with the results. Cost When to use it? Lowest Possible Cost (o) Ranges may be considered throughout project develop- Figure R3.4-1. Caltrans three-point estimate to generate estimate range. ment, but should be utilized on projects in earlier stages of development to communicate the level of knowns and un- knowns (risks) about the project. most optimistic situation, the work package will have a cost that is greater than zero. At the other end of the scale, there is no highest-possible cost. It is always possible to How to use it? spend more money. Federal planning regulations indicate that a three-point es- timate or cost ranges/cost bands in the outer years of the met- ropolitan transportation plan are acceptable. Therefore, sin- Tips gle point estimates should be avoided before sufficient detail about the project is known, when it is unrealistic to prepare a While estimate ranges transparently convey the uncer- reasonably accurate single-point estimate. A three-point esti- tainty involved in a project, they can be misunderstood. The mate is prepared at any point during this period by estimat- range theoretically shows the highest probable cost for a proj- ing the lowest possible, the most likely, and the highest prob- ect. If people focus on the high end of the range, the project able cost estimate based on a combination of available project can be slowed or stopped. The range should be used as part data and informed judgment. of a comprehensive risk management plan. If the risks and uncertainties that are driving the range can be understood, they can likely be mitigated and the project can be completed Example at the lowest possible cost. Caltrans uses three-point estimates for some elements of project costs and is planning to make wider use of this Resources technique (Figure R3.4-1). Although the math may appear Caltrans Office of Statewide Project Management Improvement (2007). complex at first glance, it is easy to implement with a simple Project Risk Management Handbook: Threats and Opportunities, spreadsheet. The three point estimating process uses these 2nd ed., May 2007, Caltrans, Sacramento, CA. http://www.dot.ca. steps: gov/hq/projmgmt/guidance_prmhb.htm. Have subject matter experts develop three estimates for R3.5 Estimate Ranges-- each item of work: Monte Carlo Analysis An optimistic estimate (o): the lowest credible cost assum- ing that everything goes right. Expressing a cost estimate in terms of an estimate range A most-likely estimate (m): the expert's best guess of transparently communicates the uncertainty associated with the cost. an estimate. Monte Carlo analysis is part of a sophisticated A pessimistic estimate (p): the highest credible cost, as- probabilistic model process that can be used to generate a suming that virtually everything goes wrong. range estimate through simulation methods. The use of Monte The average cost of the item is (o+4m+p)/6. The average Carlo analysis is typically facilitated by experts in this field is always greater than the most likely estimate. This is be- who work closely with estimators, project team members, and cause there is a finite lowest-possible cost. Even in the subject matter experts.

OCR for page 103
104 What is it? analysis can be used to determine project cost and schedule ranges and the most likely values for each. Figure R3.5-1 shows Monte Carlo analysis is a computerized probabilistic simu- typical probability outputs from a Monte Carlo analysis. The lation modeling technique that uses random number genera- histogram is useful for understanding the mean and dispersion tors to draw samples from probability distributions. Monte of the results. The cumulative chart is useful for determining Carlo analysis uses repetitive trials to generate overall probabil- project budgets and contingency values at specific levels of cer- ity distributions for project cost or schedule. It relies upon mul- tainty or confidence. In addition to graphically conveying in- tiple inputs of probabilities for risk events and for uncertainty formation, Monte Carlo analyses produce numerical values for in cost and duration of line items. A trial consists of the simula- common statistical parameters, such as the mean, standard de- tion engine selecting a value for each of the line items based on viation, distribution range, and skewness. their probabilities and generating a final estimate based on that trial. This process is repeated many times (usually several thou- sand) to generate a distribution for the total cost or schedule. When to use it? Monte Carlo analysis is applied on complex projects and is Why use it? used as the basis for a Type III risk analysis. The tool requires that the project team be familiar with all project risks and be Monte Carlo analysis has many advantages. It can determine able to quantitatively describe the risks. Application of Monte risk effects for cost and schedule models that are too complex Carlo analysis requires knowledge and training for successful for common analytical methods. The output of a Monte Carlo implementation. Input to Monte Carlo analysis requires the simulation can provide a graphical distribution of project cost user to know and specify probability distribution information, or schedule. This distribution can be used to generate an esti- mean, standard deviation, and distribution shape. While com- mate range. It also can be used to calculate a contingency. plex and requiring significant modeling experience, Monte Monte Carlo analysis can explicitly incorporate the risk knowl- Carlo analyses are the most common tool for quantitative risk edge and judgment of the estimators, project team, and subject analysis because they provide detailed, illustrative information matter experts for both cost and schedule risk events. The tech- about risk impacts on the project cost and schedule. nique can reveal, through sensitivity analysis, the impact of specific risk events on the project cost and schedule. How to use it? Monte Carlo analysis can be used to generate a number of What does it do? different decision-making tools for the project team. In order The tool allows the project team to visualize the uncertainty to produce these tools, the input must be assessed to accu- relating to the total project cost and schedule. Monte Carlo rately model project risks. Each risk can be given a different Distribution for Total Project Costs Cumulative Total Project Costs (Current $) (Current $) 0.020 1.00 Mean = 499.57 Mean = 499.57 0.015 0.75 0.010 0.50 0.005 0.25 0.000 0.00 400 500 600 700 400 500 600 700 5% 90% 5% 5% 90% 5% 437.98 566.93 437.98 566.93 Figure R3.5-1. Typical Monte Carlo output for total costs.

OCR for page 103
105 Figure R3.5-2. Example tornado diagram output from a Monte Carlo analysis. risk profile indicating where the most likely and least likely can be developed to produce simple Monte Carlo analyses. values are. Among these different distributions are Triangular, For example, the WSDOT has developed its own Monte Uniform, Normal, BetaPert, BetaPert modified, LogNormal, Carlo analysis package in Microsoft Excel using macros. Ad- Discrete, Trigen, and any custom-defined distribution. ditionally, some stand-alone software exists to generate cost In addition to the total cost ranges shown in Figure R3.5-1, and schedule Monte Carlo simulations. The most common an additional output of a Monte Carlo analysis is a tornado stand-alone software is "Pertmaster." diagram. The tornado diagram is a graphic depiction of a sen- sitivity analysis. The tornado diagram can be used to show Example which risks will have the greatest positive or negative effect on project cost and schedule. Figure R3.5-2 indicates the corre- WSDOT has developed a risk-based approach to cost esti- lation that project risks have to the total project schedule. The mating in CEVP. CEVP is used to convey project cost through risks with the longest bars have the largest impact on the over- estimate ranges. Figure R3.5-3 provides an example of how all cost or schedule variability. CEVP is used to convey an estimate range. The project rep- Several commercial software packages exist to help teams resented has a 10 percent chance of being completed for run Monte Carlo analyses. As well as software that integrates $651 million or less, while there is a 90 percent chance that the within existing spreadsheet programs, spreadsheet macros project will cost $693 million or less. However, there is a chance 10% Cost Mean Cost $651 $668 0.14 0.12 90% Cost $693 0.10 Probability 0.08 0.06 0.04 0.02 720 700 710 690 670 680 650 660 640 Distribution for Cost to Completion (2002 $ million) Figure R3.5-3. Example of an estimate range.