Define the scope: clearly outline the parameters and objectives of the predictive modeling project. Conduct data analysis: deep dive into our rich repository of product development data. This involves understanding the dataset’s structure, performing necessary data cleaning, and preparing the dataset for analysis. Model Development: develop or apply a suitable algorithm that leverages machine learning to predict whether key performance metrics will be met on time. The model should account for various factors that influence the project lifecycle. Model Validation: test the accuracy and reliability of the predictive model, ensuring its efficacy in real-world scenarios. Knowledge Transfer: present the findings and the model to the SS&P PLM team, explaining its functionality, benefits, and potential impacts on our project management practices. Future Roadmap: Propose how this predictive model can be integrated into our ongoing and future project management frameworks, including suggestions for further refining the model and exploring additional use cases within our company.