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.

• Adil Salam
• Nadia Bhuiyan
• Gerard J. Gouw

• Effort estimation
• design
• aerospace.

<p>[1] 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
[2] Statistics Canada, 2012 http://www.servicecanada.gc.ca/eng/qc/job_
futures/statistics/2146.shtml, Latest access 04/07/13
[3] 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.
[4] 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.
[5] 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.
[6] G. Bounds, The last word on project management, IIE Solutions, pp. 41-
43, 1998.
[7] 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.
[8] G. Pahl, W. Beitz, H-. Schulz, U. Jarecki, Engineering Design: A
Systematic Approach, Springer-Verlag, Germany, 2007.
[9] PMI, Project Management Institute, A guide to the project management
book of knowledge, Project Management Institute Standards Committee,
2000.
[10] ISPA, International Society of Parametrics Analysis, Parametric
estimating handbook, ISPA/SCEA Joint Office, Virginia, 2009.
[11] 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.
[12] M. H. Kutner, C. J. Nachtsheim, J. Neter, and W. Li, Applied linear
statistical models, McGraw-Hill, New York, 2004.
[13] D. C. Montgomery, Introduction to Statistical Quality Control, John
Wiley, Ney York, 2005.
[14] 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.
[15] S. Looney, T. Gulledge Jr., Use of the correlation coefficient with
normal probability plots, The American Statistician, vol. 39, pp. 75-79,
1985.
[16] S. M. Ross, Introductory statistics, 3rd edition, Academic Press
(Elsevier), Massachusetts, 2010.
[17] S. Weisberg, Transformation: applied linear regression, Wiley-
Interscience, Hoboken, New Jersey, 2005.
[18] J. A. Snyman, Practical mathematical optimization: an introduction to
basic optimization theory and classical and new gradient-based
algorithms, Springer, New York, 2005.
[19] 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.
[20] T. P. Wright, Factors affecting the cost of airplanes, Journal of
Aeronautical Sciences, vol. 3, no. 4, pp. 122-128, 1936.</p>