Achieving ‘best value’ for the community by deployment of risk based cost estimation using Monte-Carlo Simulation to rate-payer-funded capital intensive road projects.

• Main Authors: Mahender Rao, Harshavardhan Vijay Ranade
• Format: Article
• Language: English
• Published: UTS ePRESS 2014-09-01
• Series: Organisational Project Management
• Subjects: Risk- based cost estimation; road projects; Monte Carlo simulation; budget forecasting; infrastructure capital projects
• Online Access: http://epress.lib.uts.edu.au/journals/index.php/opm/article/view/4112

This paper presents the application and validation of a new tool developed by the first author for accurate risk-based estimation of project budgets. Typical capital intensive projects to which this tool can be applied include road reconstruction, road resheet and road rehabilitation projects. Quantitative risk analysis and stochastic modeling using Monte -Carlo simulation is embedded in the algorithms of the computer code. The tool forecasts a range of possible project costs and the probability of the occurrence of those costs by taking into account uncertainties and associated risks. Application of the tool to capital intensive road projects designed by the second author and constructed in 2011 & 2012 demonstrates its validity and utility. Comparisons of forecasted estimates using this tool with actual costs and with traditional deterministic methods of cost estimation (such as --point base-case estimates inclusive of contingency) provide valuable insights that can aid management in evaluating alternatives and in making informed decisions when estimating and allocating budgets to a portfolio of road projects.