Using Neural Networks with Limited Data to Estimate Manufacturing Cost

Document Type: Research Paper

Authors

1 Department of Industrial and Systems Engineering, Ohio University, Athens, Ohio, USA

2 School of Public Health Sciences and Professions, Ohio University, Athens, Ohio, USA

Abstract

Neural networks were used to estimate the cost of jet engine components, specifically shafts and cases. The neural network process was compared with results produced by the current conventional cost estimation software and linear regression methods. Due to the complex nature of the parts and the limited amount of information available, data expansion techniques such as doubling-data and data-creation were implemented. Sensitivity analysis was used to gain an understanding of the underlying functions used by the neural network when generating the cost estimate. Even with limited data, the neural network is able produced a superior cost estimate in a fraction of the time required by the current cost estimation process. When compared to linear regression, the neural networks produces a 30% higher R value for shafts and 90% higher R value for cases. Compared to the current cost estimation method, the neural network produces a cost estimate with a 4.7% higher R value for shafts and a 5% higher R value for cases. This significant improvement over linear regression can be attributed to the neural network ability to handle complex data sets with many inputs and few data points.

Keywords

Main Subjects


[1] Divelbiss D. (2005), Evaluation of the impact of product detail on the accuracy of cost estimates;
Thesis, Ohio University; Athens, Ohio.

[2] Fine T. (1999), Feedforward Neural Network Methodology; Springer Series in Statistics; 1st edition,
Springer.
[3] Günaydin, H. Murat, Do─čan, S. Zeynep. (2004), A neural network approach for early cost estimation
of structural systems of buildings; International Journal of Project Management 22; 595-602.
[4] Kim K., Han I. (2003), Application of a hybrid genetic algorithm and neural network approach in
activity based costing; Expert Systems with Applications 24; 73–77.
[5] Layer A., Brinke E.T., Houten F., Kals H., Haasis S. (2002), Recent and future trends in cost
estimation; International Journal Computer Integrated Manufacturing 15(6); 499-510.
[6] Millie D., Weckman G., Pigg R., Tester P., Dyble J., Litaker R.W., Hunter J.C., Carrick H.J.,
Fahnenstiel G.L. ( 2006), Modeling phytoplankton aAbundance in Saginaw Bay, Lake Huron: Using
artificial neural networks to discern functional influence of environmental variables and relevance to a
Great Lakes observing system; Journal of Phycology 42(2); 336-349.
[7] National Aeronautics and Space Administration (2004), NASA Cost Estimation Handbook;
Washington.
[8] Prechelt L. (1998), Automatic early stopping using cross validation: quantifying the criteria; Neural
Networks 11; 761–767.
[9] Principe J.C., Euliano E.R., Lefebvre W.C. (1999), Neural and adaptive systems: Fundamentals
through simulations with cd-rom; John Wiley & Sons; New York.
[10] Schenker B., Agarwal M. (1996), Cross-validated structure selection for neural networks; Computers
and Chemical Engineering 20(2); 175-186.
[11] Seo K.K., Park J.H., Jang D.S., Wallace D. (2002), Approximate estimation of the product life cycle
cost using neural networks in conceptual design; International Journal of Advanced Manufacturing
Technology 19; 461-471.
[12] Shlub A., Versand R. (1999), Estimating the cost of steel pipe bending, a comparison between neural
networks and regression analysis; International Journal of Production Economics 62; 201-207.
[13] Smith A.E., Mason A.K. (1997), Cost estimation predictive modeling: Regression versus neural
network; The Engineering Economist 42(2); 137–161.
[14] Swingler K. (1996), Applying neural networks, A practical guide; 3rd edition, Morgan Kaufmann.
[15] Walpole, Myers, Myers, Ye. (2002), Probability & Statistics for engineers & scientists; 7th edition,
Prentice Hall.
[16] West D. (2000), Neural network credit scoring models; Computers & Operation Research 27; 1131-
1152.
[17] West D., Dellana S., Qian J. (2005), Neural network ensemble strategies for financial decision
application; Computers & Operations Research 32; 2543-2559.
[18] Yamin H.Y., Shahidehpour S.M., Li Z. (2004), Adaptive short-term electricity price forecasting using
neural networks in the restructured power markets; Electrical Power and Energy Systems 26; 571-581.
[19] Yuval (2000), Neural network training for prediction of climatological time series, regularized by
minimization of the generalized cross-validation function; Monthly Weather Review 128(5); 1456-
1474.

[20] Zhang Y.F., Fuh J.Y.H. (1998), A neural network approach for early cost estimation of packaging
products; Computers and Industrial Engineering 34(2); 433-450.