Predicting the Status of Project Portfolios Using Markov Chains

The study formulates portfolio dynamics as transitions among a small set of empirically identified states (e.g., new, active, blocked, completed). Beyond short-term forecasts of portfolio mix, the Markov framework also computes long-run outcome distributions, offering executives a quantitative view of pipeline health and throughput. The results provide a repeatable, organization-wide mechanism to surface portfolio-level risks sooner and guide resource shifts where they matter most.

From a managerial standpoint, the framework is intentionally lightweight: It can be estimated from standard status histories and, thanks to its finite-state structure, scaled across business units with minimal overhead. Stress tests indicate that planning recommendations are stable under moderate model-identification error, which is key for adoption in data-sparse environments. Together, these features make the method a practical addition to project management offices (PMOs) seeking evidence-based steering of large project portfolios.

This work relates to a longstanding research interest in stochastic systems at the Chair of Operations, Economics and Strategy, for example, as it relates to the identification and optimization of self-exciting Hawkes processes [2–5].

Abstract

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.