Estimating
is one of the key fundamental functions of the Quantity Surveyor. Estimates are
very vital to clients when making decisions and therefore, client expect from
estimator’s useful and objective information from estimators. These estimates
according to Morledge (2006), should have clear indications of the level of
information reliability and not subsequent explanations of inaccuracy
(Morledge, 2006).
Garret (2006) reported that the
sustainability and success of the construction industry depends greatly on the
level of accuracy in project estimates. This is strongly supported by the fact
that the construction industry statistics indicates that more than 50% of the
construction projects exceed their initial cost and time estimates (Garret,
2006). The consequences of adopting inaccurate estimates are quite enormous and
overwhelming. Garret (2006) ascertained that it is a straight forward equation
that if the price for the project is wrong then financial pressures, hardship,
supply chain conflict and quality problems will result. In addition, the
client’s vulnerability as the ultimate risk holder of the finished building can
be horribly exposed and no one may possibly gain (Morledge, 2006).
According to Ashworth (2002), the
common method of estimating the costs of construction works involves the
multiplication of unit rates and the measured quantities in the Bills of Quantities
(BOQ) .The calculation of the unit rates for the individual measured items in a
BOQ requires the collation of current cost information for labour, plant and
materials, as well as overhead and profit (Ayeni, 1999)
The estimation of the cost figures
of materials, plant and overhead and profit has never been a point of discourse
and contention. This is because they involve the quantitative estimation of the
cost values for plants and overheads while market survey research for materials
prices are the basis for the material cost estimation (Ashwort, 1999). The
aspect of labour pricing is usually done on the basis of the output constants collected
on each trade (Ayeni, 1999).The high degree of inaccuracy found in our BOQ
estimates is mostly attributed in the uncertainty of the accuracy of the labour
constants used in pricing labour costs. Ajia (2002) concluded that while most
of the outputs used by estimators are the British originated constants, some
contractors adopt outputs gotten from their realm of experience and hence
non-uniform outputs are widely in use. However, Yates and Swagata (1993) argued
that the productivity of workers is being influenced by factors which vary
according to geographical locations. Therefore, if these factors vary acutely
with location then how feasible realistic and accurate are the currently
adopted British originated outputs within the Nigerian context? Hence, this
research work intends to employ work study approach to empirically establish
labour outputs of some selected trades with the view of improving the accuracy
of construction cost estimates.
While it is very clear that the
materials, plants, overheads and profits ingredients of a unit rate of
construction work can be calculated on
the basis of quantitative estimations, the accuracy of the labour constants
commonly used for estimating labour costs still remain unclear and uncertain.
Similarly, the dynamics of the factors that affect the productivity of
construction workers in Nigeria are not well understood.
This research aims at using work study approach
to empirically establish labour outputs of some selected trades for the
Nigerian construction industry with the view of increasing the accuracy of
construction cost estimates.
1.
To investigate
and review some of the current labour outputs currently in use in Nigeria,
their basis and originations.
2.
To empirically
establish the output of some selected trades.
3.
To statistically
examine the influence of labour productivity factors on the established labour
outputs.
In order to conduct inferential test to
determine the influence of the labour productivity factors on the labour
outputs established, both null and alternative hypothesis were found deemly
necessary.
Hence,
the following research hypotheses are posited in terms of workers output and
productivity level:
·
Hypothesis(H1):The ages of workers have a significant
impact over their outputs on site
- Null hypothesis (H1o): There is no output
difference between the various age groups observed.
- Hypothesis (H2): The outputs of workers vary progressively with changes in the period
of work.
- Null hypothesis (H2o): there is no output
difference in all the different periods of observations.
- Hypothesis (H3): The level of outputs of workers depends on the type and level of
payment made to them.
- Null hypothesis (H3o): there is no output
difference in respect of the type and level of Payment to workers under
observation.
- Hypothesis (H4): The group of highly experienced workers has higher outputs than
those with low work experience.
- Null hypothesis (H4o): There is no output
difference between the highly experienced groups of workers and workers
with low experience level.
- Null hypothesis (H5): The well educated and
highly qualified group of workers has higher output than those with little
or no qualification.
·
Null hypothesis (H5o): There is no output difference between the well educated and
highly qualified group of workers and workers with little or no qualification.
SCOPE:
This research work will cover the following
selected trades:
·
Block work
·
Excavations
·
Plaster/Rendering
.
All the construction sites
considered are within the Kaduna
metropolis and Zaria town.
LIMITATIONS:
The following were the foreseeable limitations
of the study, which should affect the accuracy of the results:
1.
The Hawthorne
effect: a phenomenon whereby workers tend to improve upon their natural
productivity level when being directly observed.
2.
The difficulty
in assessing whether a worker is operating in full and natural capacity or not
during the period of observations.
3.
A non random
probability sampling technique was used for selecting the workers considered in
the research. Therefore, members of the population observed were selected with
some little element of bias.