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Qing Wang

Improving the Cost Model Development Process using Neural Networks, Ph.D. Thesis (2000)

Research Project Outline

In order to achieve success in export markets through provision of high levels of product choice, manufacturing industry will need to develop and economically use many new materials and manufacturing processes. To support this development, it is expected that the quantity, type, accuracy and complexity of cost models will need to be greatly increased. It is essential under these changing conditions that the process of developing cost models is able to remain responsive to user requirements and effective in terms of the resources required to generate models.

This Ph.D. research project investigates existing methods of establishing 'cost estimating relationships' and identifies their relative benefits and limitations in terms of their effects on the overall cost model development process. The basic tasks involved in the cost model development process and the basic characteristics of cost models have been identified and used to evaluate the use of Artificial Neural Networks (ANNs) as alternative methods of establishing cost models. The problem of identifying suitable ANN structures has been resolved by the use of the Taguchi Methodology. The estimating accuracy and robustness of cost models developed using suitable ANN structures are then examined under varying conditions in order to simplifying data identification and collection tasks can be realised when compared with regression analysis and identify guidelines that could help to improve the efficiency and economics of the cost model development process.

Riham Khalil “Predicting the Effect of Variability on the Efficiency of the Flow Processing Systems” Ph.D. Thesis, 2005.