Risk Analytics Solution to Identify High-Risk Projects and Enable Mitigation and Prioritization for an Engineering Services Firm

Case Study

Risk Analytics Solution to Identify High-Risk Projects and Enable Mitigation and Prioritization for an Engineering Services Firm

Business Objective

Our client is a leading engineering services firm delivering integrated solutions for environmental remediation, sustainability, property redevelopment, energy and construction projects. The client had 1000+ active projects at any given time. They wanted a solution to assess risks associated with a project.

The main objective was to provide a solution to predict the probability of budget overshoot for each project. It should also be able to quantify the impact of project risk in monetary terms and thereby prioritize active projects in the portfolio for business action based on predicted riskiness and impact.

Challenges

  • Sheer number of project attributes that keep changing with time
  • Inconsistency and missing values for the number of attributes manually entered by the project managers

Solution Methodology 

  • Cleansed and combined project-associated attributes and derived financial attributes data to form the data set that was fed into the risk analytics engine.
    • The risk analytics engine used a combination of logistic regression for deriving risk probability and linear regression to quantify the dollar impact.
  • Built the models using historical data of completed projects.
  • Scored active projects in the portfolio using the two models and evaluated their predicted riskiness using a Risk-Impact Matrix framework.
  • Built an insight dashboard displaying portfolio risk and associated attributes on top of the model output using Tableau.
    • The dashboard also showed recommended possible actions to mitigate risks such as auditing, monitoring, and PM change among others.

Business Impact

  • Provided a semi-automated solution for the legacy platform involving complex data handling
  • Drastically reduced manual intervention and the turnaround time for the bid pricing process
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