The construction estimation is an important content of the feasibility study of the projects. The accuracy of the cost estimation directly affects the decision of project, scale of construction, design scheme and economic effects, and affects the proceeding of projects. It is significant for the management and control of the project estimation to process the estimated practically, speedily, and exactly.
Construction cost is an important element that should be monitored at different phases of the process of a building construction. It is also a factual process designed to give a reliable estimation or prediction of its financial cost. Cost estimating is a fundamental activity which combines a mechanical process and a subjective expertise undertaken to assess and predict the total cost to execute the construction works. It consists of an application with an appropriate method to estimate and to measure the final quantities of the building. The purpose of construction cost estimation is to provide information for constructors which include areas in the procurement and pricing of construction, establishing contractual amount of payment, and quantity control.
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Cost models serve a variety of purposes. It can be defined as the symbolic representation for a system, expressing the content of that system in terms of the factors which influence its costs (1). It is a procedure developed to reflect, by means of derived processes, adequately acceptable output for an established series of input data (2). Besides that, techniques used in the cost modeling can also forecast the estimated cost of a proposed construction project (3). Therefore, every method, technique or procedure used by quantity surveyors for cost estimation or cost forecast may be termed as cost models. All procurement systems require a contractor to predict the cost of a project, and to determine a price for the work within the constraints of time and to maintain the required quality without compromising to the wellbeing of other projects. In the traditional procurement system, Bill of Quantities (BQ) is an essential part of tender document that lists all the items of work to be completed in a project which is to provide a mean of comparing bids from several contractors on a like for like basis. Since BQ has become an important tool for project costing and tendering it is also a document for architects and other consultants to have a sense of control on their projects in term of cost and finance. Today BQ are also used for cost planning, projected cash flows and budget, for valuation of interim payments and variation orders, and for settlement of final account.
The objective of this paper is to present a formal method, the Cost Significant Element (CSE) method to accelerate the tendering process in the construction industry. The CSE has existed in the industry for a long time and has been used by many project stakeholders to deliver the project and to meet the specified targets and objectives set by the clients.
The construction projects in Malaysia are getting more complicated and their scales are getting larger as the industrial development directly affects the construction sector. Hence it is getting more difficult to complete the projects within quality standards, budgeted cost limits and on time. Most of the time, decisions to be taken may be delayed due to the risk and uncertainties met by managers during the construction process and as a result some difficulties are arisen. One of these difficulties is caused by the lack of cost data whenever needed and in demanded quality. Therefore, the budgeted cost limits are often exceeded. However, it is well-known fact that the earlier cost planning is started on resulting in the more suitable outcome are obtained.
In construction industry, Cost significant Element (CSE) method is a way to build up a simple cost estimate model by using historical bills of quantities (BQ) which the document that states how much is to be spent on each functional element of a proposed building in relation to a defined standard of space and quality. It is also an organized breakdown of cost data into the standard elements of a building. CSE facilitate the comparison between projects and the development of recording cost data for future cost planning in a simple way.
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It is important to identify the cost significant item in the research on CSE method. A research project has been carried out in Istanbul Technical University Faculty of Architecture intending to estimate most probable building cost in the early stage of the construction process taking the advantage of recent developments in the information and communication technology (ICT) [Orhon, 1996], [Tas and Yaman, 2002]. The main objectives of this research were collecting building construction cost data, processing data and transforming it into building cost information, storing and retrieving the cost data and information, connecting private and public sector data as well as information through a database, saving time and minimizing mistakes, by avoiding reproduction of the information that has already been stored somewhere in the sector.
BQ is the traditional method that is commonly used by construction companies to predict the cost of a project in the detailed design phase and throughout the construction period. It will usually result in similar significant items being identified across different bills, but with each item having a minor difference. In order to overcome this problem, once the identification of cost of the elements, quantity or resource significant items is completed, the BQ items are aggregated into significant work elements. Whereas, it is one of the most important components of the tender documents along with technical drawings, specifications, conditions of contract, etc. Once a project is defined in detail, every item of work needed to complete the project is listed and priced by estimators. BQ for building work is counterproductive and leads to ambiguities and differences in interpretation, creating a potential for dispute. An acceptable level of simplification can be achieved through the aggregation of work items, presently required to be measured separately (Edwards and Edwards, 1995).
The process of developing cost significant models has tended to consists of several steps. These include finding the cost significant items, grouping similar items together, and calculating a cost significant factor. The cost significant items to be identify in a project uses a technique proposed by Shereef (1981) where items are considered as significant when their value is higher than, or equal to, the mean bill value. Other than that, research at the University of Dundee has consistently shown that BQ analyses using this technique are successful in identifying the 20% of significant items that constitute 80% of the cost (Horner and Zakieh, 1993).
However, it has long been recognized that in the value of the research work identified in the second stage of this process will not associate to the full cost of the trade. In Paretoââ‚¬â„¢s principle (commonly called 80:20 rule), when 80:20 rule is utilized the assumption is made that the 20 per cent of items are contributing 80 per cent of the value, and consequently the cost of the work packages identified in stage two equate to only approximately 80 per cent of the total cost of the trade. A model factor has to be applied to the total value of the work elements to determine the cost of the trade as a whole, to include a value for the 80 per cent of items not costed during this process. This factor is termed the cost model factor and is calculated by establishing the average proportion of the total cost of a trade that is accounted for by the significant work elements. The total cost of a trade can then be established by dividing the total cost of the significant work elements by the cost model factor (Munns and Al-Haimus).
In order to achieve the objectives of the study, there are two approaches to be carried out in this research. Firstly, primary data collected from a number of the construction companies in Johor Bahru area of Malaysia. Primary data are those data observed or collected directly from first-hand experience. It shows the advantage towards the accuracy of data since the specific information has been collected by the person who is doing the research. Twenty-five high cost double storey terrace housing projects historical BQ were collected in the data analysis.
In this particular research is to develop a building cost estimation model based on cost significant work elements. Several important steps came to establish this model. These were including finding the cost significant work items in BQ, grouping similar work items together to select work packages, and calculating a cost significant value factor (CSVF). Cost significant element models rely on the well documented finding that 80 per cent of a billââ‚¬â„¢s value is contained within the 20 per cent of the items which are most expensive (Barnes 1971; Skinner 1981; Ashworth & Skitmore 1983; PSA 1987). Projects which have similar features, the cost significant items are roughly the same. This model has covered the criteria that need to be considered and getting the result to prove the 20:80 rules in the estimating practice.
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In developing the model, literature reviews are adopted as secondary sources, and as such, it does not report any new or original experimental work. Cost-significant items can be grouped together, using a variety of techniques, into a smaller number of cost-significant element work packages, which within any given category of project consistently represent a fixed proportion of the total cost, usually close to 80 per cent. The total value of the project can then be calculated simply by multiplying the total price of the cost-significant work packages by an appropriate factor. The value of the factor varies slightly that to determine from an analysis of historical data (Poh, P.S.H. & Horner, R.M.W.). Accuracy of the result in this model can be varied by coarsening or refining to match the quality of data available at each stage in the projectââ‚¬â„¢s life when calculating of the cost significant model factor.