Predicting the number of bidders in construction competitive bidding using explainable machine learning models

Purpose: The number of bidders in upcoming tenders has important managerial implications for both construction clients and contractors in their decision-making in the competitive bidding process. However, there is a stagnation of research efforts on predicting the number of bidders with only a handful of studies over the past decades, which mainly focused on statistical distribution of the number of bidders. This study aims to provide a new perspective of predicting the number of bidders using machine learning (ML) algorithms. Design/methodology/approach: This study adopted a case study approach with a bidding dataset of public sector construction projects in Singapore. Six ML models were developed, and linear regression was used as a baseline model is assessing the predictive performance of ML models. Findings: The results show that ML models outperform the baseline linear regression model, in which XGBoost is the best performing model of R2 which is two times higher than the linear regression model. In addition, economic-related factors play a vital role in this prediction problem. Research limitations/implications: While the predictive performance of the developed ML models is relatively low, it indicates the challenges and complexities in this prediction problem, even with the use of artificial intelligent techniques. Originality/value: Being a pioneering work, this study sets a foundation for the use of ML models in this prediction problem and offers insights for future modelling attempts towards the development of a decision support system for construction clients and contractors.