Data-Driven Markovian Project Portfolio Tracking

We propose a finite-state Markov chain framework for tracking and forecasting the status of project portfolios. This approach enables forecasts of portfolio composition over time and the computation of long-run distributions of project outcomes. It supports strategic planning by identifying project success rates, average durations, and the balance of resource allocation between active and idle projects. From a managerial perspective, the model facilitates early detection of portfolio-level risks and provides a data-driven basis for adjusting resource deployment or re-prioritizing projects. We show that forecasts remain robust under moderate errors in model identification, enhancing the method’s practical applicability in environments with noisy or incomplete data. This work lays the foundation for a scalable, organization-wide mechanism to improve visibility into project dynamics and support evidence-based decision-making.