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.
Author(s): Mario Vanhoucke
Series: Management for Professionals
Publisher: Springer
Year: 2023
Language: English
Pages: 330
City: Cham
Preface
Acknowledgements
Contents
Part I Data-Driven Project Management
1 About This Book
1.1 Theory and Practice
1.2 Data and People
1.3 Book Outline
1.4 Keep Reading
References
2 Each Book Tells a Story
2.1 Bookstore
2.2 Only a Click Away
2.3 Keep Writing
References
3 The Data-Driven Project Manager
3.1 Three Components
3.2 A Reference Point
3.3 The Beauty of Details
3.4 Literature (in a Nutshell)
References
Part II What Academics Do
4 Understanding
4.1 Measuring Time
4.2 Shedding New Light
4.3 Thank You, Tony
References
5 Wisdom
5.1 Tolerance Limits
5.2 Control Points
5.3 Signal Quality
5.4 Mission Accomplished
References
6 Learning
6.1 Schedule
6.2 Risk
6.3 Control
6.4 Torture
References
Part III What Professionals Want
7 Control Efficiency
7.1 Effort of Control
Top-Down Project Control
Bottom-up Project Control
7.2 Quality of Actions
7.3 Accuracy Pays Off
7.4 Empirical Evidence
7.5 The Control Room
Afterthought
References
8 Analytical Project Control
8.1 Project Control Methods (Revisited)
8.2 Best of Both Worlds
8.3 The Signal (Not the Noise)
8.4 Hope and Dream
References
9 Reference Class Forecasting
9.1 Outside View
9.2 Construction Project (Study 1)
9.3 Hybrid Approach (Study 2)
9.4 Similarity Properties (Study 3)
9.5 Thank You, Bent
References
Part IV About Project Data
10 Project Data
10.1 Where Are We Now?
10.2 Two Types of Project Data
Reference
11 Artificial Projects
11.1 Random Data
11.2 Structured Data
11.3 Generating Data
11.4 Twilight Zone
11.5 Data and Algorithms
11.6 Diverse Data
11.7 Core Data
11.8 Equivalent Data
11.9 From a Distance
11.10 Final Words
References
12 Progress Data
12.1 Imagination
12.2 Variation Model
12.3 Risk Model
12.4 Scenario Model
12.5 Fiction
References
13 Empirical Projects
13.1 Curiosity
13.2 Classification
13.3 New Library
13.4 Reality
References
14 Calibrating Data
14.1 Calibrating Data
14.2 Partitioning Heuristic
14.3 Human Partitioning (the rider)
14.4 Automatic Partitioning (the horse)
14.5 Calibration Results
14.6 Conclusion
References
15 More Data
15.1 Resources
15.2 Modes
15.3 Subgraphs
15.4 Skills
15.5 Reality
15.6 Portfolio
References
Part V Afterword
16 The Perfect Researcher
16.1 Doubt
16.2 Ignorance
16.3 Wildness
16.4 Serendipity
References
A Operations Research & Scheduling Group
B Earned Value Management (Glossary)
C Properties of Similarity
D Patterson Format
E Network and Resource Indicators
F Network Resources = NetRes
G Example Project Card
H OR&S Project Datasets
References
