Seeing the Future

Mario Vanhoucke ()
Additional contact information
Mario Vanhoucke: University of Ghent, Faculty of Economics and Business

Chapter 7 in Data-Driven Project Management with Python, 2026, pp 105-118 from Springer

Abstract: Abstract This chapter examines the principles, strengths, and limitations of forecasting project time and cost using Earned Value Management (EVM). In Sect. 7.1, the challenge of prediction is introduced, highlighting how EVM relies on average past performance to estimate future outcomes. In Sect. 7.2, Experiment 10, the final experiment of this book, uses Monte Carlo simulation to assess the accuracy of different time and cost forecasting methods, with the Mean Absolute or Relative Percentage Errors applied as the key metrics for evaluation. Section 7.3 introduces the performance factor concept as a bridge between objective data and human judgment, showing how forecasts can be fine-tuned yet may produce reliable, misleading, or even false predictions depending on the location of deviations between schedule and reality within the project network. Finally, Sect. 7.4 highlights empirical findings on real-world corrective actions, and explores how hybrid and data-driven methods, including reference class forecasting and machine learning, can improve predictive accuracy. The chapter concludes by presenting EVM forecasting as a foundation for more adaptive, data-rich approaches capable of capturing the true dynamics of modern projects.

Keywords: Project Forecasting; Early Warning Indicators; Probabilistic Scenarios; Performance Factor (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:mgmchp:978-3-032-24556-4_7

Ordering information: This item can be ordered from
http://www.springer.com/9783032245564

DOI: 10.1007/978-3-032-24556-4_7

Access Statistics for this chapter

More chapters in Management for Professionals from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().