Data Representation for Predicting Performance with Learning Curves
Publication: Journal of Construction Engineering and Management
Volume 123, Issue 1
Abstract
Mathematical learning curve models can be used to predict the time or cost required to perform future cycles in a repetitive construction activity. The analyst has a choice of several methods of representing the data, usually trading off between response and stability of forecasting information. Traditionally, learning curve data has been evaluated using either unit data or cumulative-average data. This paper evaluates those two methods and two other techniques: the moving average and the exponentially weighted average. For the 54 construction activities evaluated, unit data gives the most accurate prediction of the time or cost to complete the remaining cycles of the activity. Cumulative-average data gives the least accurate prediction. Compared to unit data, the exponentially weighted average can predict future performance with only a slight loss of accuracy early in the activity, but equal accuracy later in the activity. The exponentially weighted average may offer an improved combination of stability and response, depending on the smoothing parameter chosen.
Get full access to this article
View all available purchase options and get full access to this article.
References
1.
An improved rationale and mathematical explanation of the progress curve in airframe production. (1949). Stanford Res. Inst., Stanford, Calif.
2.
Carlson, J. G. H.(1973). “Cubic learning curves: precision tool for labor estimating.”Manufacturing Engrg. and Mgmt., 67(11), 22–25.
3.
“Effect of repetition on building operations and processes on site.” (1965). Rep. ST/ECE/HOU/14, United Nations Committee on Housing, Building, and Planning, United Nations, New York, N.Y.
4.
Everett, J. G., and Farghal, S. H.(1994). “Learning curve predictors for construction field operations.”J. Constr. Engrg. and Mgmt., ASCE, 120(3), 603–616.
5.
Everett, J. G., and Slocum, A. H.(1992). “CRANIUM: device for improving crane safety and productivity.”J. Constr. Engrg. and Mgmt., ASCE, 119(1), 23–39.
6.
Farghal, S. H., and Everett, J. G. (1994). “Learning curves as time and cost predictors for construction field operations.”Tech. Rep. 94–21, Ctr. for Constr. Engrg. and Mgmt., Dept. of Civ. and Envir. Engrg., Univ. of Michigan, Ann Arbor, Mich.
7.
Farghal, S. H., and Everett, J. G.(1997). “Learning curves: accuracy in predicting future performance.”J. Constr. Engrg. and Mgmt., ASCE, 123(1), 41–45.
8.
McClain, J. O., and Thomas, L. J. (1980). Operations management: production of goods and services. Prentice-Hall, Inc., Englewood Cliffs, N.J.
9.
McClure, R. M., Thomas, H. R., and Hehler, J. E. (1980a). “Construction of experimental segmental bridge.”Rep. to the Commonwealth of Pennsylvania Dept. of Transp., Pennsylvania Transp. Inst., Philadelphia, Pa.
10.
McClure, R. M., Willenbrock, J. H., and Henderson, J. D. (1980b). “Fabrication of segments for an experimental segmental bridge.”Rep. to the Commonwealth of Pennsylvania Dept. of Transp., Pennsylvania Transp. Inst., Philadelphia, Pa.
11.
Oglesby, C. H., Parker, H. W., and Howell, G. A. (1989). Productivity improvement in construction. McGraw-Hill, New York, N.Y.
12.
Thomas, H. R., Mathews, C. T., and Ward, J. G.(1986). “Learning curve models of construction productivity.”J. Constr. Engrg. and Mgmt., ASCE, 112(2), 245–258.
13.
Wright, T. P. (1936). “Factors affecting the cost of airplanes.”J. Aeronautical Sci., (Feb.), 124–125.
Information & Authors
Information
Published In
Copyright
Copyright © 1997 American Society of Civil Engineers.
History
Published online: Mar 1, 1997
Published in print: Mar 1997
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.