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Parametric Cost Estimation Method


A cost estimating methodology using statistical relationships between historical costs and other program variables such as system physical or performance characteristics, contractor output measures, or manpower loading.

General Information

The parametric, or statistical, method uses regression analysis of a database of two or more similar systems to develop cost estimating relationships (CERs) which estimate cost based on one or more system performance or design characteristics (e.g., speed, range, weight, thrust). The parametric method is most commonly performed in the initial phases of product description, such as after Milestone B when the program is in the Engineering and Manufacturing Development (EMD) phase. Although during this phase an acquisition program is unable to provide detailed information (like drawings and standards), the program can specify top-level system requirements and design characteristics. In other words, estimating by parametrics is a method to show how parameters influence cost.

Parametric estimating is used widely in government and industry because it can yield a multitude of quantifiable measures of merit and quality (i.e., probability of success, levels of risk, etc.). Additionally, CERs developed using the parametric method can easily be used to evaluate the cost effects of changes in design, performance, and program characteristics. Note that the parametric method, which makes statistical inferences about the relationship between cost and one or more system parameters is very different from drawing analogies to multiple systems.


A critical consideration in parametric cost estimating is the similarity of the systems in the underlying database, both to each other and to the system which is being estimated. A good parametric database must be timely and accurate, containing the latest available data reflecting technologies similar to that of the system of interest (design, manufacturing/assembly, material). Of course, a general rule when collecting data for statistical analysis is the more data, the better. Finally, as with estimating by analogy, parametric data must be normalized to represent a given economic year and remove any quantity effects.


For example, attempting to estimate the cost of a “today” computer (electronic memory chips) using a database of older computers (magnetic core memory) would yield an estimate much higher than the actual cost of the current system because the memory chips are much cheaper to produce and install than the old core memory. In addition, changes in manufacturing technology or processes have occurred, such as the use of automatic insertion equipment instead of hand insertion of components onto printed circuit boards (PCBs). This has led to major reductions in the labor content associated with the assembly of PCBs.


Additionally, the database must be homogenous. A data element entry for one system must be consistent with the same data element entry for every other system included in the database. For example, in a rocket motor database where there is an element called the "motor weight", each weight entry should be based on the same assumptions for each system. Assume that each motor is defined to include the rocket grain, motor case, and nozzle. If some systems report a motor weight that does not include one or more of these components (or includes additional components), then the database is not homogenous and CERs developed from the database are questionable. Too often a database is built over time, with inputs from various sources, without any one individual responsible for insuring the homogeneity of the data.


The validity of a CER is usually judged by its regression statistics, which measure the accuracy of the fit of the CER to the sample data points used in developing the CER. The most commonly used regression statistic is the coefficient of determination (R2), although there are serval other regression statistics such as Standard Error (SE) and Coeficient of Variation (CV).


Analysts need to ensure that the value of the new system’s parameters fall inside the range of the parameter values for the existing systems. If not, it may not be a good estimate regardless of how good the regression statistics are. For example, a CER developed from data on aircraft that travel at less than the speed of sound may not predict costs well for a system which is to travel at supersonic speeds.


Estimating by the parametric method is appropriate relatively early in the program life cycle when a detailed design specification is not available, but a database of like systems and a performance specification are available. The parametric method is also useful as a check against an estimate made using another method.


Estimating by the parametric method has many advantages over other estimating methods. Because the CER is based upon more than a single data point, estimating by parametrics is less risky than estimating by analogy. A major benefit of applying statistical methods is that one can also measure error from a derived CER and readily perform cost sensitivity analysis based on parameters within each derived CER. The biggest downside of estimating by parametrics is that such a technique is constrained by the amount and quality of the data. Many times an analyst unknowingly incorporates flawed data into the database, in effect producing inaccurate CERs. For this reason and a variety of other reasons, the resulting statistics can be misleading. By providing a much more detailed view of what is being estimated, estimating by engineering circumvents the necessity for statistical analysis.