By applying predictive analytics to an
organization’s historical data, project cost estimates and risk
assessments can be more accurate, improving forecasts and execution on a
multi-year investment plan. The challenge with using historical data is
that it is often dispersed throughout various financial and project
management software systems, requiring significant data analysis for the
comparison to be viable. This paper describes the steps involved in
developing a robust cost benchmarking tool that centralizes historical
project data into a user-friendly platform and predicts the cost of a
construction project with a high degree of confidence, using only a few
input variables. This paper will detail the four development steps: data
gathering, data classification, data analysis, implementation, and
refinement. Using the results from the tool, organizations can negotiate
project cost reductions and focus risk management processes to inform
contingency decisions. Across a portfolio, the changes can result in
significant savings and form the basis for setting achievable
performance targets.