Parametric Cost-Estimation of an Assembly using Component-Level Cost Estimates
There are two approaches that can be taken for estimating the manufacturing cost of a product assembly. One approach is to separately estimate the cost of each of the individual components from the work breakdown structure (WBS) and summing these component costs to determine the total cost. Another approach is to derive a parametric model that uses information about the overall assembly to produce a cost estimate.
The advantage of the former approach is accuracy, since detailed information about each component is used; the advantages of the latter approach is speed and simplicity, since only a limited number of parameters are used to generate the estimate.
This paper describes a hybrid methodology for generating a cost estimate for an assembly that combines these two approaches. The cost is estimated for each element in the WBS, but only a limited number of parameters from the assembly are used as inputs. This is accomplished by first generating detailed part-level cost-estimating relationships (CERs) that can estimate the cost for each component in the assembly. Then once these CERs have been developed, values are mapped from the assembly parameters to be used as inputs for these CERs.
This approach retains most of the accuracy of the detailed cost models while greatly simplifying the modeling process. The accuracy is maintained because the parts being modeled are part of the same assembly and share geometric relationships. Although the model appears parametric to the user, it is more accurate and robust than a parametric model because it is derived from CERs for each component.
This methodology was applied to generate cost models for aircraft engine assemblies. The accuracy of the models was almost the same as the accuracy from the detailed models, but the number of attributes required to generate the estimate was reduced by approximately 90%.
Keywords: Hybrid Cost Estimation, Parametric Cost Estimation, Bottoms-up Cost Estimation
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 8 years, he has been part of a project team supported by GE Aviation to develop improved cost estimation methods for jet engine components. He is a member of the Institute of Industrial Engineers and the Society of Cost Estimating and Analysis.
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.
Prior to his work in cost estimation for GE Aviation, he was involved in the development 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.