Cost Prediction and managment Research Papers (original) (raw)

The use of an effective feedback system is considered to influence the quality of estimates. Similarly, it is held that a means of monitoring performance should be incorporated into any forecasting system. Moreover, on an individual basis, systematic reflection is considered crucial for effective experiential learning. It is suggested that only through some process of systematic reflection may a professional achieve growth and self-renewal.
This paper examines the application of effective feedback systems and systematic reflection by early stage design cost estimators. The findings from a fully structured interview survey of experienced early stage design cost estimators (n = 84) and a questionnaire survey of student quantity surveyors (n = 331) are presented.
Despite the recommendations of previous studies, many practitioners still have inadequate feedback systems. Many either did not systematically reflect on the outcomes of estimates, or used self-assessment as the sole means of evaluation. Also, practitioners had significantly lower Reflective Observation learning style scores when compared to the student sample, while their declared approach to learning exhibited a reluctance for self-assessment or self-appraisal. Finally, on an organisational basis both practitioners and student quantity surveyors gave a low rating to the provision of constructive feedback by the organisation on their performance.

- by David J Lowe
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- Quantity Surveying, Reflection, Experiential Learning, Feedback
- by Allen Y. Chang
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- Computational Geometry, Pure Mathematics, Cost Model, Ray Tracing
- by Allen Chang
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- Algorithms, Cost Model, Ray Tracing, General Terms

There is a dearth of information on the comparative costs of projects carried out using the main procurement building systems. This paper reports the feasibility study of a research programme to produce a computer-based neural network cost model to show the effect on client costs of ‘inter alia’ using different procurement approaches.
A literature search identified 39 cost-significant project variables. Data were collected from collaborating QS practices, resulting in 46 project data-sets with which to test various modelling approaches.
Evaluation of the data and model objectives identified multiple regression and neural networks as potential model forms. Investigation and trials of both have shown that regression and neural networks can provide effective representation of the client costs model but neural networks, due to their greater ability in modelling interdependencies between input variables, modelling non-linear relationships, and handling incomplete data sets, will probably be the better choice with which to analyse the very much larger volume of data planned for the next phase of the research.
The results have demonstrated that such a model can be developed, that data to support it can be obtained and, additionally, that the utility of the model may be significantly greater than had been envisaged at the start of the study.