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Adapting Bottoms-up Cost Estimating Relationships to New Systems

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For several years, Ohio University has been engaged in research to develop improved cost estimating procedures for jet engine components. The resulting methodology uses bottoms-up cost estimating relationships to estimate the cost for each of the manufacturing processes needed to create the features on the part, but these CERs are implemented in a parametric framework.
This means that most of the user-entered attributes are high-level descriptions of part geometry and these attributes are used to map appropriate values to the attributes in the feature-level CERs.
Once the improved methodology had been successfully applied to most of the parts found in a typical jet engine, the methodology was adapted for use in gas turbines for generating electricity. The advantage of this transition is that gas turbine architecture is very similar to that of a jet engine. This made it possible to reuse many of the geometric models and CERs that were originally developed for jet engine components. However, because gas turbine components are much larger, the CERs needed to be evaluated for use on the new components.
In general, each feature CER contains a scaling factor that is calibrated for the manufacturing process used to create the feature. This factor is based on the operating rate of the process, such as the material removal rate of a machining process, and is adjusted to account for the machinability of the material, so that the same CER can be used across different materials.
The scaling factor is multiplied by the size of the feature to estimate the labor hours required to create the feature. The units of measure for feature size depend on the process being performed; examples include area, length, and volume.
One issue addressed in this paper is the recalibration of the scaling factors used in the CERs. In general, an advantage of a bottoms-up approach to cost estimation is the reusability of CERs without recalibrating them. However, this was necessary for some CERs because gas turbine parts are much larger than the corresponding jet engine components, and therefore the features are generally larger. Since the CERs were originally developed for features in the range of sizes found on jet engines, they did not necessarily produce accurate estimates when they were linearly extrapolated to the size of features on gas turbines. The situations in which recalibration was needed and its impact (i.e., the potential error from simply extrapolating the original CER) are discussed. This provides insight on the extent to which feature based CERs can be reused to accurately estimate cost in new situations.
This paper also discusses the mapping from part attributes to feature attributes. Because of differences in the design practices for gas turbines (due to the larger size of the parts as well as less emphasis on minimizing the weight of the parts), the relationships between part attributes and feature attributes that were identified for jet engines are not necessarily appropriate for gas turbines.


Dale T. Masel
Department of Industrial and Systems Engineering
Ohio University
Dale Masel is an Associate Professor of Industrial and Systems Engineering at Ohio University, where he has worked for ten years. He received his PhD in Industrial Engineering from Penn State University. During the past 10 years, he has been part of a project team supported by General Electric to develop improved cost estimation methods, with applications to a variety of product lines. He is a member of the Institute of Industrial Engineers and the Society of Cost Estimating and Analysis.

Robert P. Judd
Department of Industrial and Systems Engineering
Ohio University
Robert Judd is Chair of the Department of Industrial and Systems Engineering at Ohio University and is also a Professor of Electrical Engineering at Ohio University. He came to Ohio from Oakland University in 1992, where he was a member of the faculty after receiving his PhD in Systems Engineering from Oakland.
Since 2000, Robert has been co-PI of a research project sponsored by General Electric to develop improvement methods for manufacturing cost estimation. Prior to his work for GE, he was co-PI of a team that developed of the FIPER (Federated Intelligent Product EnviRonment) Cost Estimator. Other participants in the project included General Electric, Parker Hannifin, Goodrich and Engineous Software and it was funded in part by the National Institute for Science and Technology (NIST) Advanced Technology Program.

John D. Dowler
Department of Industrial and Systems Engineering
Ohio University
John is a Research Engineering in the Department of Industrial and Systems Engineering at Ohio University, with a focus on and the development of methodologies for cost estimation. He has worked on developing cost estimation methods for a variety of products, including jet engines, gas turbines, and steam turbines. He received his BS and MS degrees from Ohio University in Industrial and Systems Engineering.