LEADER 06050oam 22005415 450 001 9910734824803321 005 20231106214100.0 010 $a3-031-31785-8 024 7 $a10.1007/978-3-031-31785-9 035 $a(CKB)27451731000041 035 $a(MiAaPQ)EBC30618345 035 $a(Au-PeEL)EBL30618345 035 $a(DE-He213)978-3-031-31785-9 035 $a(OCoLC) 1389613855 035 $a(PPN)272252115 035 $a(EXLCZ)9927451731000041 100 $a20230704d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 14$aThe illusion of control $eproject data, computer algorithms and human intuition for project management and control /$fMario Vanhoucke 205 $a1st ed. 2023. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023. 215 $a1 online resource (331 pages) 225 1 $aManagement for Professionals,$x2192-810X 311 0 $a9783031317842 327 $aIntro -- 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. 327 $a12.1 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. 330 $aThis book comprehensively assesses the growing importance of project data for project scheduling, risk analysis and control. It discusses the relevance of project data for both researchers and professionals, and illustrates why the collection, processing and use of such data is not as straightforward as most people think. The theme of this book is known in the literature as data-driven project management and includes the discussion of using computer algorithms, human intuition, and project data for managing projects under risk. The book reviews the basic components of data-driven project management by summarizing the current state-of-the-art methodologies, including the latest computer and machine learning algorithms and statistical methodologies, for project risk and control. It highlights the importance of artificial project data for academics, and describes the specific requirements such data must meet. In turn, the book discusses a wide variety of statistical methods available to generate these artificial data and shows how they have helped researchers to develop algorithms and tools to improve decision-making in project management. Moreover, it examines the relevance of project data from a professional standpoint and describes how professionals should collect empirical project data for better decision-making. Finally, the book introduces a new approach to data collection, generation, and analysis for creating project databases, making it relevant for academic researchers and professional project managers alike. 410 0$aManagement for Professionals,$x2192-810X 606 $aComputer algorithms 606 $aData mining 606 $aProject management$xData processing 615 0$aComputer algorithms. 615 0$aData mining. 615 0$aProject management$xData processing. 676 $a658.4040285 676 $a658.4040285 700 $aVanhoucke$b Mario$0963281 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910734824803321 996 $aThe Illusion of Control$93404564 997 $aUNINA