Parametric Cost Estimating Relationships for Design Effort Estimation
The Canadian aerospace industry faces many challenges. One of them is the difficulty in estimating costs. In particular, the design effort required in a project impacts resource requirements and lead-time, and consequently the final cost. This paper presents the findings of a case study conducted for recognized global leader in the design and manufacturing of aircraft engines. The study models parametric cost estimation relationships to estimate the design effort of integrated blade-rotor low-pressure compressor fans. Several effort drivers are selected to model the relationship. Comparative analyses of three types of models are conducted. The model with the best accuracy and significance in design estimation is retained.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1087081Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2196
 AIAC, Aerospace Industries Association of Canada, 2012 http://www.aiac.ca/uploadedFiles/Resources_and_Publications/Ind ustry_Statistics/Revised%202010%20Stats_January%2025%202011_F V.pdf.Latest access 04/07/13
 Statistics Canada, 2012 http://www.servicecanada.gc.ca/eng/qc/job_ futures/statistics/2146.shtml, Latest access 04/07/13
 A. E. Smith, A. K. Mason, Cost estimation predictive modeling: regression versus neural network, The Engineering Economist, vol. 42, no. 2, pp. 137-161, 1997.
 M. C. Kocakülâh, A. D. Austill, Product development and cost management using target costing: a discussion and case analysis, Journal of Business & Economics Research, vol. 4, no. 2, 2006.
 A. Elragal, M. Haddara, The use of expert panels in ERP cost estimation research, Enterprise Information Systems: Communications in Computer and Information Science, vol. 110, no. 2, pp. 97-108, 2010.
 G. Bounds, The last word on project management, IIE Solutions, pp. 41- 43, 1998.
 G. Colmer, M. Dunkley, K. Gray, P. Pugh, and A. Williamson, Estimating the cost of new technology products, International Journal of Technology Management, vol. 17, nos. 7-8, pp. 840-846, 1999.
 G. Pahl, W. Beitz, H-. Schulz, U. Jarecki, Engineering Design: A Systematic Approach, Springer-Verlag, Germany, 2007.
 PMI, Project Management Institute, A guide to the project management book of knowledge, Project Management Institute Standards Committee, 2000.
 ISPA, International Society of Parametrics Analysis, Parametric estimating handbook, ISPA/SCEA Joint Office, Virginia, 2009.
 N. Fragkakis, S. Lambropoulos, G. Tsiambaos, Parametric model for conceptual cost estimation of concrete bridge foundations, Journal of Infrastructure Systems, vol. 17, no. 2, pp. 66-74, 2012.
 M. H. Kutner, C. J. Nachtsheim, J. Neter, and W. Li, Applied linear statistical models, McGraw-Hill, New York, 2004.
 D. C. Montgomery, Introduction to Statistical Quality Control, John Wiley, Ney York, 2005.
 K. Muralidhar, R. Parsa, R. Sarathy, A general additive data perturbation method for database security, Management Science, vol. 45, no. 10, pp. 1399-1415, 1999.
 S. Looney, T. Gulledge Jr., Use of the correlation coefficient with normal probability plots, The American Statistician, vol. 39, pp. 75-79, 1985.
 S. M. Ross, Introductory statistics, 3rd edition, Academic Press (Elsevier), Massachusetts, 2010.
 S. Weisberg, Transformation: applied linear regression, Wiley- Interscience, Hoboken, New Jersey, 2005.
 J. A. Snyman, Practical mathematical optimization: an introduction to basic optimization theory and classical and new gradient-based algorithms, Springer, New York, 2005.
 A. Salam, N. Bhuiyan, G. J. Gouw, S. A. Raza, Estimating design effort for the compressor design department: A case study at Pratt & Whitney Canada, Design Studies, vol. 30 no. 3, pp. 303-319, 2009.
 T. P. Wright, Factors affecting the cost of airplanes, Journal of Aeronautical Sciences, vol. 3, no. 4, pp. 122-128, 1936.