A new approach for product cost estimation using data envelopment analysis

Abstract: Cost estimation of new products has always been difficult as only few design, manufacturing and operational features will be known. In these situations, parametric or non-parametric methods are commonly used to estimate the cost of a product given the corresponding cost drivers. The parametric models use priori determined cost function where the parameters of the function are evaluated from historical data. Non-parametric methods, on the other hand, attempt to fit curves to the historic data without predetermined function. In both methods, it is assumed that the historic data used in the analysis is a true representation of the relation between the cost drivers and the corresponding costs. However, because of efficiency variations of the manufacturers and suppliers, changes in supplier selections, market fluctuations, and several other reasons, certain costs in the historic data may be too high whereas other costs may represent better deals for their corresponding cost drivers. Thus, it may be important to rank the historic data and identify benchmarks and estimate the target costs of the product based on these benchmarks. In this paper, a novel adaptation of cost drivers and cost data is introduced in order to use data envelopment analysis for the purpose of ranking cost data and identify benchmarks, and then estimate the target costs of a new product based on these benchmarks. An illustrative case study has been presented for the cost estimation of landing gears of an aircraft manufactured by an aerospace company located in Montreal, CANADA.

How to cite this paper

 Defersha, F., Salam, A & Bhuiyan, N. (2012). A new approach for product cost estimation using data envelopment analysis.International Journal of Industrial Engineering Computations , 3(5), 817-828.

Refrences

Adler, N., Friedman, L., & Sinuany-Stern, Z. (2002). Review of ranking methods in the data envelopment analysis context. European Journal of Operational Research, 140, 249–265.

Andersen, P., & Petersen, N. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39, 1261–1294.

Bode, J. (1998). Decision support with neural networks in the management of research and development: Concepts and applications to cost estimation. Information & Management, 34, 33–40.

Bode, J. (2000). Neural networks for cost estimation: Simulation and pilot applications. International Journal of Production Research, 38, 1231–1254.

Caputo, A. C., & Pelagagge, P. M. (2008). Parametric and neural methods for cost estimation of process vessels. International Journal of Production Economics, 112, 934–954.

Cavalieri, S., Maccarrone, P., & Pinto, R. (2004). Parametric vs. neural network models for the estimation of production costs: A case study in the automotive industry. International Journal of Production Economics, 91, 165–177.

Chan, K. Y., Kwong, C. K., & Tsim, Y. C. (2010). A genetic programming based fuzzy regression approach to modeling manufacturing processes. International Journal of Production Research, 48, 1967–1982.

Charnes, A., Cooper, W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European Journal of Operations Research, 2, 429–444.

Dean, E. P. (2005). Parametric cost deployment. Proceedings of the Seventh Symposium on Quality Function Deployment.USA, 27–34.

Dura, O., Rodriguez, N., & Consalter, L. A. (2009). Neural networks for cost estimation of shell and tube heat exchangers. Expert Systems with Applications, 36, 7435–7440.

Foussier, P. M. M. (2006). From Product Description to Cost: A Practical Approach. Vol. 2, Building a Specific Model. Berlin: Springer.

Layer, A., Brine, E. T., Van, H. F., Kals, H., & Haasis, S.(2002). Recent and future trends in cost estimation.International Journalof Computer Integrated Manufacturing, 15, 499–510.

Pandya, A. S., & Macy, R. B.(1996).Pattern Recognition with Neural Networks in C++.Florida: CRC Press.

Phaobunjong, K. & Popescu, C. M. (2003). Parametric cost estimating model for buildings. AACE International Transactions.

Seo, K. K., Park, J. H., Jang, D. S., & Wallace, D. (2002). Prediction of the life cycle cost using statistical and artificial neural network methods in conceptual product design. Journal of Computer Integrated Manufacturing, 15, 541–554.

Verlinden, B., Du?ou, J. R., Collin, P., & Cattrysse, D.(2008). Cost estimation for sheet metal parts using multiple regression and artificial neural networks: A case study. International Journal of Production Economics, 111, 484–492.

Zhang, Y. F., Fuh, J. Y., & Chan, W. T.(1996).Feature-based cost estimation for packaging products using neural networks. Computers in Industry, 32, 95–113.

Zhang, Y. F. & Fuh, J. Y. H.(1998). A neural network approach for early cost estimation of packaging products. Computers & Industrial Engineering, 34, 433–450.

Zheng, G. & Zhang, P.(2006).Meta-heuristic algorithms for parameter estimation of semi-parametric linear regression models. Computational Statistics & Data Analysis, 51, 801–808.