The Illusion of Control: Project Data, Computer Algorithms and Human Intuition for Project Management and Control (Management for Professionals) - Softcover

Vanhoucke, Mario

Softcover

ISBN 10: 3031317874 ISBN 13: 9783031317873

Publisher: Springer, 2024

Synopsis

This book comprehensively assesses the growing importance of project data for project scheduling, risk analysis and control. It discusses the relevance of project data for both researchers and professionals, and illustrates why the collection, processing and use of such data is not as straightforward as most people think. The theme of this book is known in the literature as data-driven project management and includes the discussion of using computer algorithms, human intuition, and project data for managing projects under risk.

The book reviews the basic components of data-driven project management by summarizing the current state-of-the-art methodologies, including the latest computer and machine learning algorithms and statistical methodologies, for project risk and control. It highlights the importance of artificial project data for academics, and describes the specific requirements such data must meet. In turn, the book discusses a wide variety of statistical methods available to generate these artificial data and shows how they have helped researchers to develop algorithms and tools to improve decision-making in project management. Moreover, it examines the relevance of project data from a professional standpoint and describes how professionals should collect empirical project data for better decision-making. Finally, the book introduces a new approach to data collection, generation, and analysis for creating project databases, making it relevant for academic researchers and professional project managers alike.

About this title

About the Author

Prof. Mario Vanhoucke is a Professor at Ghent University (Belgium), Vlerick Business School (Belgium) and UCL School of Management at University College London (UK). He teaches courses on Project Management, Applied Operations Research and Decision-making for Business. His research interests lie in the integration of project scheduling, risk management, and project control, which has led to more than 100 papers in international journals, five project management books published by Springer, and a PM Knowledge Center for online learning (www.pmknowledgecenter.com). His research has received multiple awards, e.g. from the PMI Belgium and the IPMA.

From the Back Cover

This book comprehensively assesses the growing importance of project data for project scheduling, risk analysis and control. It discusses the relevance of project data for both researchers and professionals, and illustrates why the collection, processing and use of such data is not as straightforward as most people think. The theme of this book is known in the literature as data-driven project management and includes the discussion of using computer algorithms, human intuition, and project data for managing projects under risk.

The book reviews the basic components of data-driven project management by summarizing the current state-of-the-art methodologies, including the latest computer and machine learning algorithms and statistical methodologies, for project risk and control. It highlights the importance of artificial project data for academics, and describes the specific requirements such data must meet. In turn, the book discusses a wide variety of statistical methods available to generate these artificial data and shows how they have helped researchers to develop algorithms and tools to improve decision-making in project management. Moreover, it examines the relevance of project data from a professional standpoint and describes how professionals should collect empirical project data for better decision-making. Finally, the book introduces a new approach to data collection, generation, and analysis for creating project databases, making it relevant for academic researchers and professional project managers alike.

Edition details

• Publisher: Springer
• Publication date: 2024
• Language: English
• ISBN 10: 3031317874
• ISBN 13: 9783031317873
• Binding: Paperback
• Number of pages: 344