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This paper examines the effects of different factors on the success or failure of bids for infrastructure projects in Australia. Logistic regression analysis was used to empirically determine which factors have the largest effect on bid­ding success. Data was collected from 123 bids submitted by several infrastructure companies with subsidiaries in Aus­tralia. The analysis found that having a competitive advantage and a local partner, and also not competing against a local company were the most important factors; as they significantly increase the chances of success. However, four other factors, having relevant expertise, resource availability, a previous relationship with the client, and a previous relation­ship with consortium members, are ‘essential’ to be able to compete; as the absence of any of these four factors results in bid failure, although having them is not a guarantee of success. Results of this paper provide valuable information for any company considering the opportunity of entering into Australia’s infrastructure market as well as for companies that are already present in that country and are considering whether to bid for a specific project.

Research in Bid Tender Forecasting Models (BTFM) has been in progress since the 1950s. None of the developed models were easy-to-use tools for effective use by bidding practitioners because the advanced mathematical apparatus and massive data inputs required. This scenario began to change in 2012 with the development of the Smartbid BTFM, a quite simple model that presents a series of graphs that enables any project manager to study competitors using a relatively short historical tender dataset. However, despite the advantages of this new model, so far, it is still necessary to study all the auction participants as an indivisible group; that is, the original BTFM was not devised for analyzing the behavior of a single bidding competitor or a subgroup of them. The present paper tries to solve that flaw and presents a stand-alone methodology useful for estimating future competitors’ bidding behaviors separately.