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
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.
FICE, FEIC, FCSCE, FPMI