The specific research approach is shown in FIGURE 4 below. This study used the basic cost estimate development approach recommended by the RAND Corporation for the development o f military weapon systems (Novick, 1956). Although RAND’s study is over 44 years old, its basic principles are still sound and relevant to today’s cost
estimating efforts. Novick’s (1956) study clearly provides a conceptual framework for the basic procedures o f weapon system cost analysis and estimating.
' Separate Investment and Operations Costs Develop CERs Develop the Cost Model Screen/Convert Data Verify/Validate Cost Model Acquire Data From
OSMIS Database Develop Pool o f Ground
Combat Systems to Analyze Determine Qualitative Characteristics to Analyze Determine Technical Parameters/Spedfications to Analyze
FIGURE 4. Cost Estimating Methodology
The first objective is to identify the major sub-elements o f the weapon systems in the OSMIS database. This allows each sub-element to be analyzed individually so that an inference can be drawn about its impact on the total operating costs for the system. To achieve this goal, data collection focuses on maintenance costs, repair parts costs, and petroleum costs for each system. For this study, the twelve standard WBS elements identified above are the sub-elements analyzed for cost impacts. Some elements, such as WBS 11 (NBC Equipment), are treated as qualitative elements and others, such as WBS 02 (Suspension/Steering) are treated as technical elements.
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The second objective is to determine the impact o f the assigned mission o f the weapon system on the total cost. This study deals with the assigned mission by focusing on the utility o f the system. For example, the M2 Bradley Fighting Vehicle (BFVj and the M l A1 Abrams tank have similar combat missions, “close with and destroy the enemy by using direct fire and maneuver,” (Command and General Staff College Battle Book,
1996). However, the M2 BFV is more suited for supporting mounted and dismounted infantry operations than the M l tank. Therefore, the two systems have differing utility functions and extremely different associated costs. The vehicle utility is analyzed using a dummy variable (Mission), which has values o f 0 (combat service support and combat support vehicles) or 1 (combat indirect fire and direct fire vehicles).
The third objective is to distinguish between the one-time investment costs o f the weapon system and the recurring yearly costs o f operating the system. In essence, this amounts to determining which are short-term costs and which are long-term costs for the weapon system. However, the decision to build a weapon system is inherently a
commitment to fully invest in its future operating and support costs. Therefore, this study focuses on operating resources that can be directly attributed to being consumed by the weapon system.
These three objectives represent the foundation that the study is built on.
Completing these tasks allowed the total cost o f each element to be aggregated up to develop a total system yearly operating cost. Additionally, this allowed each element to be analyzed to determine potential cost risks associated with that element. Aggregation o f the data also allowed the development o f a CER for each cost element.
The CERs play an important role in estimating the cost risk associated with the parametric model. As with any known complex system, the “system darkness principle” applies to the weapon systems in this study. The system darkness principle simply states that no system can be completely known (Skyttner, 1996). This general systems theory principle indirectly applies to the parametric model in this study. The model will have a certain degree o f uncertainty associated with it and will not perfectly estimate the future cost o f operating known or yet to be developed ground combat systems. However, cost models that provide some quantitative means o f estimating the cost risks are more effective in communicating the total cost o f the system and providing data for making more informed decisions regarding system cost expectations. One method o f quantifying risk is using probability distributions to identify elements that have large cost variances (Uher, 1996). This study identifies cost risk factors, which are included in the model, and uses probability distributions to analyze the impact o f each WBS sub-element on the total system operating cost.
The model is developed using two statistical tools, (1) correlation analysis, and (2) multiple linear regression. The correlation analysis allows the identified variables to be compared to each other and the response variable (cost). This analysis helps to eliminate variables that are highly dependent on each other and avoid multicollinearity problems in the final model. Multiple linear regression allows the formulation o f a mathematical model useful in predicting the operating costs o f current and future combat systems within the range o f the systems used to develop the model.
Lastly, the study uses several verification and validation testing techniques. The verification process verifies that the model is indeed an unbiased estimator. This is
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accomplished by analyzing the residuals o f each the actual cost to the model’s estimate. The residuals o f each factor and their probability distributions are also analyzed for normality. The validation process uses one ground combat system, independent o f the model, from the OSMIS database to test the predicted average yearly operating costs against the actual average yearly operating cost o f the system.