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Project cost risk is the uncertainty in final project cost, contingent on initial budget, requirements and schedule. For a proposed mission, a dynamic simulation model relying for some of its input on a simple risk elicitation is used to identify and quantify systemic cost risk.
In most cost estimating applications at the NASA Langley Research Center (LaRC), it is desirable to present predicted cost as a range of possible costs rather than a single predicted cost. A cost risk analysis generates a range of cost for a project and assigns a probability level to each cost value in the range. Constructing a cost risk curve requires a good estimate of the expected cost of a project. It must also include a good estimate of expected variance of the cost. Many cost risk analyses are based upon an expert's knowledge of the cost of similar projects in the past. In a common scenario, a manager or engineer, asked to estimate the cost of a project in his area of expertise, will gather historical cost data from a similar completed project. The cost of the completed project is adjusted using the perceived technical and economic differences between the two projects. This allows errors from at least three sources. The historical cost data may be in error by some unknown amount. The managers' evaluation of the new project and its similarity to the old project may be in error. The factors used to adjust the cost of the old project may not correctly reflect the differences. Some risk analyses are based on untested hypotheses about the form of the statistical distribution that underlies the distribution of possible cost. The usual problem is not just to come up with an estimate of the cost of a project, but to predict the range of values into which the cost may fall and with what level of confidence the prediction is made. Risk analysis techniques that assume the shape of the underlying cost distribution and derive the risk curve from a single estimate plus and minus some amount usually fail to take into account the actual magnitude of the uncertainty in cost due to technical factors in the project itself. This paper addresses a cost risk method that is based on parametric estimates of the technical factors involved in the project being costed. The engineering process parameters are elicited from the engineer/expert on the project and are based on that expert's technical knowledge. These are converted by a parametric cost model into a cost estimate. The method discussed makes no assumptions about the distribution underlying the distribution of possible costs, and is not tied to the analysis of previous projects, except through the expert calibrations performed by the parametric cost analyst.
Cost risk for new technology development is estimated by explicit stochastic processes. Monte Carlo simulation is used to propagate technology development activity budget changes during the technology development cycle.
This paper discusses the JPL Cost Engineering Group (CEG) cost risk analysis approach that accounts for all three types of cost risk. It will also describe the evaluation of historical cost data upon which this method is based. This investigation is essential in developing a method that is rooted in engineering realism and produces credible, dependable results to aid decision makers.
The geometric viewpoint identifies the choice of a correlation matrix for the simulation of cost risk with the pairwise choice of data vectors corresponding to the parameters used to obtain cost risk. The correlation coefficient is the cosine of the angle between the data vectors after translation to an origin at the mean and normalization for magnitude. Thus correlation is equivalent to expressing the data in terms of a non orthogonal basis. To understand the many resulting phenomena requires the use of the tensor concept of raising the index to transform the measured and observed covariant components into contravariant components before vector addition can be applied. The geometric viewpoint also demonstrates that correlation and covariance are geometric properties, as opposed to purely statistical properties, of the variates. Thus, variates from different distributions may be correlated, as desired, after selection from independent distributions. By determining the principal components of the correlation matrix, variates with the desired mean, magnitude, and correlation can be generated through linear transforms which include the eigenvalues and the eigenvectors of the correlation matrix. The conversion of the data to a non orthogonal basis uses a compound linear transformation which distorts or stretches the data space. Hence, the correlated data does not have the same properties as the uncorrelated data used to generate it. This phenomena is responsible for seemingly strange observations such as the fact that the marginal distributions of the correlated data can be quite different from the distributions used to generate the data. The joint effect of statistical distributions and correlation remains a fertile area for further research. In terms of application to cost estimating, the geometric approach demonstrates that the estimator must have data and must understand that data in order to properly choose the correlation matrix appropriate for a given estimate. There is a general feeling by employers and managers that the field of cost requires little technical or mathematical background. Contrary to that opinion, this paper demonstrates that a background in mathematics equivalent to that needed for typical engineering and scientific disciplines at the masters or doctorate level is appropriate within the field of cost risk.
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Accurate cost estimates help managers understand critical cost-risk information, which improves their perception of the impact project changes have on the budget and schedule. This research project examined buffer reserves, a key aspect of cost estimation. The main focus of this project was to utilize historical data to calculate the extent to which NASA programs typically exceed their original buffers.
This paper documents a set of recommendations for reducing flight software cost risk as derived from a detailed analysis of the underlying causes of the observed cost growth.
This paper summarizes the final results of the follow-up study updating the estimated software effort growth for those projects that were still under development and including an evaluation of the roles versus observed cost risk for the missions included in the original study which expands the data set to thirteen missions.
Given limited budget, the problem of prioritization among Engineering Support Requests (ESR's) with varied sizes, shapes, and colors is a difficult one. At the Kennedy Space Center (KSC), the recently developed 4-Matrix (4-M) method represents a step in the right direction as it attempts to combine the traditional criteria of technical merits only with the new concern for cost-effectiveness. However, the 4-M method was not adequately successful in the actual prioritization of ESRs for the fiscal year 1995 (FY95). This research identifies a number of design issues that should help us to develop better methods. It emphasizes that given the variety and diversity of ESR's one should not expect that a single method could help in the assessment of all ESR's. One conclusion is that a methodology such as Costs, Risks, and Values Evaluation (CRAVE) should be adopted. It also is clear that the development of methods such as 4-M requires input not only from engineers with technical expertise in ESR's but also from personnel with adequate background in the theory and practice of cost-effectiveness analysis. At KSC, ESR prioritization is one part of the Ground Support Working Teams (GSWT) Integration Process. It was discovered that the more important barriers to the incorporation of cost-effectiveness considerations in ESR prioritization lie in this process. The culture of integration, and the corresponding structure of review by a committee of peers, is not conducive to the analysis and confrontation necessary in the assessment and prioritization of ESR's. Without assistance from appropriately trained analysts charged with the responsibility to analyze and be confrontational about each ESR, the GSWT steering committee will continue to make its decisions based on incomplete understanding, inconsistent numbers, and at times, colored facts. The current organizational separation of the prioritization and the funding processes is also identified as an important barrier to the pursuit of cost-effectiveness. Perhaps the greatest barrier is that, at the working level, KSC's culture is so preoccupied with technical concerns that it seems almost oblivious to any cost concerns, let alone cost-effectiveness concerns. It is recommended that we must urgently begin to change that culture and seek a better balance between these two concerns.
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This paper reports on the results of a follow up study conducted on seven JPL missions completed on or near launch since 1999. The objective is to determine to what extent the recommendations were implemented and whether they had any impact.
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The Jet Propulsion Laboratory (JPL) in Pasadena, California is a US Government Federally-Funded Research and Development Center that is run by the California Institute of Technology for the National Aeronautics and Space Administration (NASA) JPL's primary role is to conduct unmanned, robotic missions from Explorer to Voyager, to Mars Pathfinder.
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