The illusion of control : project data, computer algorithms and human intuition for project management and control / / Mario Vanhoucke

• Record Nr.: UNINA9910734824803321
• Autore: Vanhoucke Mario
• Pubbl/distr/stampa: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
• ISBN: 3-031-31785-8
• Edizione: [1st ed. 2023.]
• Descrizione fisica: 1 online resource (331 pages)
• Collana: Management for Professionals, , 2192-810X
• Disciplina: 658.4040285
• Soggetti: Computer algorithms; Data mining; Project management - Data processing
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Intro -- 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 &amp -- 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&amp -- S Project Datasets -- References.
• Sommario/riassunto: 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.