Seeing the Future

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.