This paper was first published in the Canadian Journal of Civil Engineering, Vol. 21, 1994 pp 939-953, under the title "A Pragmatic Approach to Using Resource Loading, Production and Learning Curves on Construction Projects". It has been modified only to the extent necessary to make it presentable in web page format.

Published here October, 2001.

Abstract | Introduction | Resource Loading | S-curves | What can be Learned?
Productivity Improvement | Learning vs. Experience | Original Theory | Two Approaches
Illustration | Issues | Conclusions | References | Appendix 1 | Appendix 2 


A review of resource input and production output on construction work shows three separate stages in any activity. This is true whether viewed at the task, trade, subcontract or whole project level. These stages constitute "build-up", "steady-state" and "run-down". Each stage has distinctive features.

If data over these three stages are viewed as a histogram of period resources input over the duration of the work, a first approximation empirical profile can be articulated. That is: 40% of resource input occurs in the first 50% of the time, a further 40% input in the next 25% of the time and the remaining 20% in the last 25% of the time. This profile determines that peak loading will be 160% of the overall average.

If the same data is plotted as a running total on a percentage of total scale on both axes, the result is a typical S-curve. On a well-run project actual timing of this peak loading, i.e., Stage 2, appears to vary by only 10-15%.

As can be expected, production output follows a similar profile. However, if input and output S-curves are plotted to the same scales, the output S-curve will precede the input S-curve to the extent that productivity improvement is achieved. For the whole of this work to be optimized, it appears that productivity improvement must essentially be completed in Stage 1.

An empirical output or progress S-curve is suggested. This takes the form of one quarter of the progress in the first third of the time, another half in the next third and the final quarter in the final third of the time. A realistic productivity improvement ratio of 86% in Stage 1 would account for the difference between the two empirical S-curves of output and input.

Obviously, the best source of information for planning and estimating is derived from experience of very similar previous work. In the absence of specific experience, however, these empirical relations can be used as a first approximation, particularly for early planning.

Many construction projects offer various opportunities for repetitive work, though the total number of repetitions may be small compared to manufacturing processes. However, when carefully managed and tracked, such work provides distinct opportunities for productivity improvement. To optimize productivity gain, management energy must be focused on the first 25% of the series. The target must be to hit peak production within one-third of the planned total time.

Two approaches to productivity improvement calculations are described. The first focuses on the Cumulative Average Time for 'n' units. However, the second, a modification of the first but focusing on the time taken for the 'n'th unit, is more useful in most construction applications. In any case, it is suggested that the learning curve theory should not be carried further into the work than the first 25-30%.

Application of S-curve theory to construction work includes comparative estimating, forecasting, and quantifying the effects of delays upon performance. In these, the natural loss of productivity in the final 25% of the work should also not be over looked.

© 2001

Issues Regarding Total Time and Stage 1 Time  Issues Regarding Total Time and Stage 1 Time

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