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From Plan to Reality

In: Data-Driven Project Management with Python

Author

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  • Mario Vanhoucke

    (University of Ghent, Faculty of Economics and Business)

Abstract

This chapter introduces Earned Value Management (EVM) as a foundational approach to data-driven project control. It begins in Sect. 6.1 by outlining the central challenge of translating project progress into meaningful, quantitative signals that reveal whether a project is on track in terms of time and cost. Section 6.2 introduces a new Python experiment consisting of two parts, one through a deterministic simulation and a second one through a Monte Carlo-based approach. This experiment uses three projects with distinct network topologies to illustrate how project control systems can transform baseline schedules into dynamic feedback tools for performance monitoring and forecasting. The analysis of results in Sect. 6.3 offers a deeper reflection on why EVM sometimes succeeds and other times fails. Using the alarm—focus—shoot metaphor, the chapter explains how EVM’s warning signals (alarm) can guide managerial attention (focus) and corrective actions (shoot). Drawing on the concept of control efficiency, the section revisits earlier research to show the effectiveness of EVM depends on project structure and how combining sensitivity-based and performance-based control information can significantly improve efficiency. Finally, Sect. 6.4 briefly discusses the future of project control beyond classical EVM and concludes by positioning EVM not as an endpoint, but as a first step toward a more data-driven understanding of how projects evolve, deviate from the plan, and can ultimately be steered toward success.

Suggested Citation

  • Mario Vanhoucke, 2026. "From Plan to Reality," Management for Professionals, in: Data-Driven Project Management with Python, chapter 6, pages 89-104, Springer.
    • Handle: RePEc:spr:mgmchp:978-3-032-24556-4_6
    DOI: 10.1007/978-3-032-24556-4_6
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