Case Study: Predictive Analytics for Risk Management

An energy company developing solar plants sought to understand the factors driving project risk. Avicado employed machine learning techniques to identify key influencers of project risk. The insights gained included:
- Utility Sales: Projects intended for sale to utilities were found to have a sevenfold increase in the likelihood of being high-risk or having a fatal flaw
- Battery Energy Storage Systems (BESS): Projects that included BESS were four times more likely to be high-risk compared to those without
By quantifying these risk factors, the client could better anticipate challenges and allocate appropriate contingencies. The use of machine learning provided concrete data to support intuitive knowledge, allowing the company to make more informed decisions about project planning and risk management.
These case studies highlight the importance of reliable, accessible data and how it has helped clients navigate the complexities of modern construction project management. By adopting PMIS solutions, creating effective data warehouses, and developing insightful dashboards, you too can unlock valuable insights to enhance project performance.
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