Implementation of a neural network for the comparison of the cost of different procurement approaches

The choice of procurement system for a building project is a very significant one. However, there is currently very little comparative cost data to inform the selection of procurement system, especially the total cost to the client. This paper reports on a model that will make such a comparison possible. This model, which is currently under development at UMIST, is designed to consider 39 project variables, including the choice of procurement system, and estimate the final cost of the project using a neural network. The suitability of a neural network to model this problem has already been established by a pilot study. The advantage of using this type of model is that it permits comparisons between the different procurement methods to be made within the context of that particular project, rather than within projects as a whole. The classification and representation of the different variables to be considered within this model are discussed. In addition to this, the implementation of factor/cluster analysis to reduce the number of variables required by the neural network, and hence increase its accuracy, is explained. Furthermore, the level of confidence of the model and its implications for the implementation of a “What if?” analysis are discussed. This analysis would allow the client to assess how changing certain variables, including procurement, might affect the final cost of the project.