Название: Data-Driven Project Management with Python: Optimizing Schedules, Simulating Risk and Analyzing Project Performance through 10 Example Experiments
Автор: Mario Vanhoucke
Издательство: Springer
Серия: Management for Professionals
Год: 2026
Страниц: 168
Язык: английский
Формат: pdf (true), epub
Размер: 21.1 MB

This book explores how project scheduling, risk analysis and control can be understood, tested and taught through data-driven experimentation. It presents 10 Python-based example experiments that guide readers from fundamental scheduling techniques to advanced project control methods. All project data and code are provided, allowing readers to reproduce, modify, and extend every analysis.

The first part introduces the Critical Path Method as the foundation for structured scheduling and extends it to time–cost optimization and resource-constrained scheduling through heuristics and integer programming. The second part employs Monte Carlo simulation to capture schedule uncertainty and to measure activity sensitivity for both unconstrained and resource-limited projects. The third part focuses on project control, using Earned Value Management (EVM) to replicate forecasting accuracy studies from academic literature.

The book’s distinctive contribution lies in linking theoretical scheduling principles with executable Python models, enabling a transparent exploration of how data can drive project decisions. It raises questions about the adequacy and complexity of project data, the measurement of uncertainty and the balance between simplicity and realism, offering both conceptual insight and a practical laboratory for data-driven project management.

The book offers an educational yet forward-looking approach, combining clear explanations with ten reproducible Python-based experiments. Readers are encouraged not only to understand, but to experiment, i.e. test and extend the models themselves. By bridging theory and practice, it provides a hands-on and reproducible framework to explore how data shapes scheduling, risk analysis, and project control. The book is particularly suited for use in courses on project management, operations research or decision analytics, as well as for self-learners eager to build technical and analytical data-driven project management skills in a structured way.

In this book, I present 10 example experiments using project data from my previous books. This allows readers to consult the original sources for a deeper understanding of the concepts and methods while seeing, in the current book, how the outcomes are generated and visualized using easy-to-use Python code. The code serves as an interactive laboratory, letting readers modify data, explore alternative scenarios, and experiment with the models. By engaging with these experiments, readers will not only see how data-driven project management works in practice but also gain the confidence to test their own scenarios, explore alternative approaches, and better understand the dynamic nature of planning, risk analysis and project control. The code builds on example data from my previous books but goes one step further by translating the concepts into graphs and visual results that make the outcomes more tangible. Moreover, the Python scripts are easily adaptable: users can modify the data, experiment with variations, and thereby deepen their understanding through direct interaction. Real learning comes from doing.

All project data used in each experiment are taken from my previous books. This allows readers to consult those sources for a more detailed explanation of the concepts and methods behind each experiment, while seeing in this book how the outcomes are generated and visualized using the Python code. Each experiment demonstrates key aspects of data-driven project management, from planning and risk analysis to project control, allowing readers to explore these concepts hands-on, understand how results are generated, and experiment with variations using the provided Python code.

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