The Price of Time

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

Chapter 4 in Data-Driven Project Management with Python, 2026, pp 51-68 from Springer

Abstract: Abstract This chapter examines how uncertainty and risk affect project duration and cost, introducing Monte Carlo simulation (MCS) as a key tool for sensitivity and schedule risk analysis. In Sect. 4.1, the fundamental problem of uncertainty in project scheduling is defined, emphasizing the limitations of deterministic planning approaches. In Sect. 4.2, Experiments 5 and 6 explain how statistical probability distributions and risk classes can be used to simulate thousands of project outcomes, generating probability-based insights into total duration and total cost. The use of random sampling reveals how small variations in activity durations can lead to large variations in overall project performance. In Sect. 4.3, the focus is on how schedule sensitivity metrics derived from MCS help identify the activities that have the greatest potential impact on overall project performance. The concept of control efficiency is introduced as a way to guide project managers toward more effective monitoring and corrective actions. Finally, Sect. 4.4 reflects on the interpretative nature of simulation results, highlighting both their power and their limitations. It argues that the use of probability distributions can be extended to represent real sources of risk and briefly introduces ongoing research that integrates project networks with risk networks to better understand how uncertainty propagates through complex projects, transforming uncertainty from a source of fear into a basis for informed judgment.

Keywords: Risk Analysis; Monte Carlo Simulations; Schedule Sensitivity; Probabilistic Durations (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-24556-4_4

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