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Goh Bee Hua, Chu Yee Leen

Developing national standards for the classification of construction information in Singapore

Abstract: Despite lagging far behind countries which have started developing construction information classification systems over the last 30 to 50 years, Singapore is fast catching up in this area of development via the formation of the Construction Industry IT Standards Technical Committee (CITC) in 1998. The Government's intention is to create Singapore into a business and IT hub, and the National IT Standards Committee (NITSC) was formed in 1990 to spearhead the development of national IT standards in all sectors of the economy. To date, the CITC has initiated and established standards in the areas of CAD, cost and resources information, and specification. The paper discusses the developmental process for one published standard, the Singapore Standard Code of Practice for Classification of Construction Cost Information (SS CP 80: 1999), and one standard which is in preparation, the Proposed Singapore Standard Code of Practice for Classification of Construction Resources Information. The intention is to share the Singapore experience with countries which are embarking on a similar programme. The next challenge for CITC is to manage change and promote widespread adoption of these standards by the industry. Results from the questionnaire survey and interviews indicate a positive attitude towards standards development but less positive towards full adoption. Lack of incentives, little immediate benefits, cost to be incurred from re-classifying historical data and cross-disciplinary differences are some of the findings. The key pointers for intended standard developers are: make a conscious effort of involving industry players in the development of the standards in order to help bring down barriers to change; adopt a two-pronged approach so as to achieve a win-win-win result; identify leaders in the industry who can drive the developed standard/technology in order to convince other players to follow suit; and develop assistance schemes to help small firms embrace standardisation and IT.

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Full text: content.pdf (75,350 bytes) (available to registered users only)

Series: w78:2002 (browse)
Cluster: papers of the same cluster (result of machine made clusters)
Class: class.economic (0.030731) class.synthesis (0.018880) class.impact (0.014794)
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Permission to reproduce these documents have been graciously provided by the Aarhus School of Architecture, Denmark. The assistnace of the editor, Prof. Kristian Agger, is gratefully aprecciated.


Lin Chao, Goh Bee Hua

Process modelling of E-procurement in the Singapore construction industry

Abstract: The E-commerce, as a new trading and business mode through the Internet, has started to penetrate all industry sectors aggressively in Singapore. As value of construction procurement usually accounts for about 70%-80% of the contract value, more and more portals are being set up by local and overseas companies to target the market, especially in building resources procurement, real estate marketing, etc. This research will focus on the process modelling of the E-procurement in construction, i.e. the reengineering of the E-procurement process in construction, based on the current traditional procurement practice, the survey in local construction industry, and the survey in local construction portal providers. The objectives of the research are to promote IT in Singapore and to reduce the duplication of work in current construction procurement. The ideal model will be proposed after the analysis of the above surveys and the final Eprocurement process modelling will be in a format of IDEF0, which is a typical diagramming method for process modelling.

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Full text: content.pdf (88,385 bytes) (available to registered users only)

Series: w78:2002 (browse)
Cluster: papers of the same cluster (result of machine made clusters)
Class: class.strategies (0.080943) class.collaboration (0.060178) class.commerce (0.037604)
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Permission to reproduce these documents have been graciously provided by the Aarhus School of Architecture, Denmark. The assistnace of the editor, Prof. Kristian Agger, is gratefully aprecciated.


Mao Zhi, Goh Bee Hua, Wang Shouqing, Ofori G

Forecasting construction industry-level total factor productivity growth using neural network modeling

Abstract: Total Factor Productivity (TFP) is widely recognised as a better indicator than Labour Productivity and Multi-Factor Productivity to represent industry-level productivity performance. Productivity is the key determinant of a nation's standard of living and an industry's competitiveness. As such, the ability to predict trends in TFP growth in the construction industry is very important. The factors influencing TFP growth in the construction industry are complicatedly interrelated. This fact made the conventional regression method highly inadaptable to such complex multi-attribute nonlinear mappings. As an AI information-processing tool, the artificial neural network (ANN) system has been proven to be a powerful approach to solving complex nonlinear mappings with higher accuracy than regression methods. However, so far, there has been little application of ANNs in predicting TFP growth in the construction field. This study will for the first time, apply the concepts of ANNs to develop a model to forecast the TFP growth in the case of the construction industry of Singapore. Macro-level information processing models are useful in monitoring and predicting the performance of the construction industry as a whole. With the need to manage construction performance information at all three levels, namely, industry, firm and site, this study looks specifically at developing an 'intelligent' model for forecasting industry-level productivity. Meanwhile, using the same set of data, a model developed by the Multiple Linear Regression method will serve as a benchmark to judge the performance of the ANN model. The ANN model, compared with the traditional regression model, would be expected to have better forecasting ability for TFP growth in the construction industry, in terms of accuracy.

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Full text: content.pdf (79,002 bytes) (available to registered users only)

Series: w78:2002 (browse)
Cluster: papers of the same cluster (result of machine made clusters)
Class: class.analysis (0.037562) class.processing (0.012219) class.legal (0.002318)
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Permission to reproduce these documents have been graciously provided by the Aarhus School of Architecture, Denmark. The assistnace of the editor, Prof. Kristian Agger, is gratefully aprecciated.


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