Constuction project Risk managment through Analytics

Brief

A leading Construction contractor in the US wanted a system to identify the factors –delaying the projects or causing the project budget overrun.

Challenge

The construction contractor, which was primarily into Mechanical services, was struggling to keep its cost in control. In addition, few projects had liquidated damages as company was not able to submit its projects on time.

Our Action

• Integrated the manual data from the  monthly/ weekly scanned copies of the project report submitted by construction manager from various sites
• Analyse key attributes  in the integrated data and its distribution across projects
• Identified the factors which impacted the project cost and time line significantly
• Carried the Correlation between  the cost overrun and  project delays with respect to identified factors
• Trained the machine learning predictive model in order to have quantitative impacts of the identified  factors on project delays or cost over-runs
• The predicted quantitative  impact were compared with actual numbers for the completed projects to check the model accuracy and to  improve the model further
• Prepared a dashboard that would predict risky project in advance& would provide the real time updates to project director on project health

Result

• -High risk projects were identified in early stage – giving management time to take corrective action.
• -Key attributes for delay/ cost overrun were identified and organisation level action was taken to control the same