Predicting significant contract risks at the tender evaluation stage

This paper presents a mathematical model for predicting the significant potential contract risks at the tender evaluation stage for construction projects in the Republic of Trinidad and Tobago. The author demonstrates that statistical tools such as Monte Carlo simulation, linear/logistic regression and the programme evaluation and review technique can be used to predict correctly the significant potential contract risks, namely: price risk; schedule risk; quality risk; health, safety and welfare risk; and logistics risk. The author has incorporated these tools in the mathematical model in order to assess the total risk of each submitted tender in a tender evaluation process. The author is of the view that the tenderer with the lowest overall risk should be awarded the contract as compared to the historical method of awarding tenders – that is, use of the highest score. The analysis of 126 data sets is presented along with emerging trends and results.