Estimating project overheads rate in bidding: DSS approach using neural networks

Project overheads estimation by applying a selected rate as a percentage of direct cost is used widely in bidding in construction, but the rate is prone to inaccuracy if it is selected subjectively. An improved approach is developed, a decision support system (DSS) based on a construction firm's cost data and using a neural network model for mapping of overheads rates from project attributes. The estimating ability of the proposed DSS is continually updated by retraining the neural networks with accumulated cost data in an expanding project database. An illustrative example is provided, in which the creation and updating of a prototype neural network model were simulated using cost data for projects spanning six years. The model explains the effects of duration and direct cost on overheads rates that the regression method fails to account for. The results also give empirical evidence that the DSS is capable of improving accuracy through annual model updating and may be used as a means for implementing organizational learning. The methods for assessing the loss risk for a bid incorporating an estimate from the DSS are provided.