The Illusion of Control: Project Data, Computer Algorithms and Human Intuition for Project Management and Control
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Cham
Springer
[2023]
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| Series: | Management for Professionals
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xiv, 330 Seiten Illustrationen, Diagramme |
| ISBN: | 9783031317842 |
| ISSN: | 2192-8096 |
Staff View
MARC
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| 245 | 1 | 0 | |a The Illusion of Control |b Project Data, Computer Algorithms and Human Intuition for Project Management and Control |c Mario Vanhoucke |
| 264 | 1 | |a Cham |b Springer |c [2023] | |
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| 650 | 4 | |a Big Data | |
| 650 | 4 | |a Algorithms | |
| 650 | 4 | |a Production management | |
| 650 | 4 | |a Project management | |
| 650 | 4 | |a Operations research | |
| 650 | 4 | |a Big data | |
| 650 | 4 | |a Algorithms | |
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Record in the Search Index
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| adam_text | Contents Part I 1 Data-Driven Project Management 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 4 Understanding.................................................................................................. Wisdom ............................................................................................................. 5.1 5.2 15 16 23 25 27 29 29 32 35 39 47 What Academics Do 4.1 Measuring Time ............................................................................... 4.2 Shedding New Light ........................................................................ 4.3 Thank You, Tony............................................................................... References...................................................................................................... 5 З 3 8 10 13 13 Tolerance Limits ............................................................................. Control Points .................................................................................. 53 54 59 63 64 65 68 74 xi
Contents xii 5.3 Signal Quality .................................................................................. 5.4 Mission Accomplished.................................................................... References...................................................................................................... 6 85 6.1 Schedule ............................ 87 6.2 Risk .................................................................................................. 93 6.3 Control .............................................................................................. 98 6.4 Torture.......... ..................................................................................... 102 References...................................................................................................... 104 Learning............................................................................................................. Part III 7 What Professionals Want Control Efficiency........................................................................................... 7.1 Effort of Control.............................................................................. 7.2 Quality of Actions............................................................................ 7.3 Accuracy Pays Off................................................................... 7.4 Empirical Evidence................................... 7.5 The Control Room............................................................................
References...................................................................................................... 107 108 116 119 122 124 127 129 8.1 Project Control Methods (Revisited).................................. 130 8.2 Best of Both Worlds......................................................................... 137 8.3 The Signal (Not the Noise).............................................................. 144 8.4 Hope and Dream............................................................................... 151 References...................................................................................................... 152 8 Analytical Project Control............................................................................. 9 Reference Class Forecasting.......................................................................... 155 9.1 Outside View..................................................................................... 157 9.2 Construction Project (Study 1)........ 160 9.3 Hybrid Approach (Study 2).............................................................. 164 9.4 Similarity Properties (Study 3)........................................................ 170 9.5 Thank You, Bent............................................................................... 176 References...................................................................................................... 177 Part ГѴ 10 79 82 84 About Project Data Project Data...................................................................................................... 10.1 Where Are We
Now?....................................................................... 10.2 Two Types of Project Data.............................................................. Reference...................................................................... 11 Artificial Projects............................................................................................. 11.1 11.2 11.3 11.4 Random Data..................................................................................... Structured Data.................................................................................. Generating Data................................................................................ Twilight Zone................................................................................... 181 181 184 187 189 191 192 197 199
xiii Contents 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..................................................................................................... 203 205 208 212 217 222 222 12 Progress Data ................................................................................................ 225 226 229 232 235 240 241 12.1 Imagination....................................................................................... 12.2 Variation Model............................................................................... 12.3 Risk Model....................................................................................... 12.4 Scenario Model............................................................................... 12.5 Fiction.............................................................................................. References..................................................................................................... 243 13.1
Curiosity........................................................................................... 244 13.2 Classification.................................................................................... 245 13.3 New Library..................................................................................... 247 13.4 Reality.............................................................................................. 252 References..................................................................................................... 255 13 Empirical Projects.......................................................................................... 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 16 283 283 286 286 287 287 288 289 Afterword The Perfect Researcher................................................................................. 16.1 16.2 257 259 264 266 270 277 280 281 Doubt............................................................................................... Ignorance......................................................................................... 293 295 296
Contents xiv 16.3 Wildness........................................................................................... 297 16.4 Serendipity....................................................................................... 298 References...................................................................................................... 300 A Operations Research Scheduling Group.............................................. 301 Earned Value Management (Glossary)..................................................... 305 C Properties of Similarity............................................................................... 307 В D Patterson Format.......................................................................................... 311 E Network and Resource Indicators...................................... F Network x Resources ss NetRes................................................................ 319 313 G Example Project Card................................................................................. 323 H OR S Project Datasets................................................................. 327 References...................................................................................................... 329
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| adam_txt |
Contents Part I 1 Data-Driven Project Management 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 4 Understanding. Wisdom . 5.1 5.2 15 16 23 25 27 29 29 32 35 39 47 What Academics Do 4.1 Measuring Time . 4.2 Shedding New Light . 4.3 Thank You, Tony. References. 5 З 3 8 10 13 13 Tolerance Limits . Control Points . 53 54 59 63 64 65 68 74 xi
Contents xii 5.3 Signal Quality . 5.4 Mission Accomplished. References. 6 85 6.1 Schedule . 87 6.2 Risk . 93 6.3 Control . 98 6.4 Torture. . 102 References. 104 Learning. Part III 7 What Professionals Want Control Efficiency. 7.1 Effort of Control. 7.2 Quality of Actions. 7.3 Accuracy Pays Off. 7.4 Empirical Evidence. 7.5 The Control Room.
References. 107 108 116 119 122 124 127 129 8.1 Project Control Methods (Revisited). 130 8.2 Best of Both Worlds. 137 8.3 The Signal (Not the Noise). 144 8.4 Hope and Dream. 151 References. 152 8 Analytical Project Control. 9 Reference Class Forecasting. 155 9.1 Outside View. 157 9.2 Construction Project (Study 1). 160 9.3 Hybrid Approach (Study 2). 164 9.4 Similarity Properties (Study 3). 170 9.5 Thank You, Bent. 176 References. 177 Part ГѴ 10 79 82 84 About Project Data Project Data. 10.1 Where Are We
Now?. 10.2 Two Types of Project Data. Reference. 11 Artificial Projects. 11.1 11.2 11.3 11.4 Random Data. Structured Data. Generating Data. Twilight Zone. 181 181 184 187 189 191 192 197 199
xiii Contents 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. 203 205 208 212 217 222 222 12 Progress Data . 225 226 229 232 235 240 241 12.1 Imagination. 12.2 Variation Model. 12.3 Risk Model. 12.4 Scenario Model. 12.5 Fiction. References. 243 13.1
Curiosity. 244 13.2 Classification. 245 13.3 New Library. 247 13.4 Reality. 252 References. 255 13 Empirical Projects. 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 16 283 283 286 286 287 287 288 289 Afterword The Perfect Researcher. 16.1 16.2 257 259 264 266 270 277 280 281 Doubt. Ignorance. 293 295 296
Contents xiv 16.3 Wildness. 297 16.4 Serendipity. 298 References. 300 A Operations Research Scheduling Group. 301 Earned Value Management (Glossary). 305 C Properties of Similarity. 307 В D Patterson Format. 311 E Network and Resource Indicators. F Network x Resources ss NetRes. 319 313 G Example Project Card. 323 H OR S Project Datasets. 327 References. 329 |
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| illustrated | Illustrated |
| index_date | 2024-07-03T22:29:11Z |
| indexdate | 2024-07-10T09:54:59Z |
| institution | BVB |
| isbn | 9783031317842 |
| issn | 2192-8096 |
| language | English |
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| physical | xiv, 330 Seiten Illustrationen, Diagramme |
| publishDate | 2023 |
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| publisher | Springer |
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| series2 | Management for Professionals |
| spelling | Vanhoucke, Mario Verfasser (DE-588)171868773 aut The Illusion of Control Project Data, Computer Algorithms and Human Intuition for Project Management and Control Mario Vanhoucke Cham Springer [2023] © 2023 xiv, 330 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Management for Professionals 2192-8096 Operations Management Project Management Operations Research and Decision Theory Big Data Algorithms Production management Project management Operations research Big data Erscheint auch als Online-Ausgabe 978-3-031-31785-9 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034349913&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Vanhoucke, Mario The Illusion of Control Project Data, Computer Algorithms and Human Intuition for Project Management and Control Operations Management Project Management Operations Research and Decision Theory Big Data Algorithms Production management Project management Operations research Big data |
| title | The Illusion of Control Project Data, Computer Algorithms and Human Intuition for Project Management and Control |
| title_auth | The Illusion of Control Project Data, Computer Algorithms and Human Intuition for Project Management and Control |
| title_exact_search | The Illusion of Control Project Data, Computer Algorithms and Human Intuition for Project Management and Control |
| title_exact_search_txtP | The Illusion of Control Project Data, Computer Algorithms and Human Intuition for Project Management and Control |
| title_full | The Illusion of Control Project Data, Computer Algorithms and Human Intuition for Project Management and Control Mario Vanhoucke |
| title_fullStr | The Illusion of Control Project Data, Computer Algorithms and Human Intuition for Project Management and Control Mario Vanhoucke |
| title_full_unstemmed | The Illusion of Control Project Data, Computer Algorithms and Human Intuition for Project Management and Control Mario Vanhoucke |
| title_short | The Illusion of Control |
| title_sort | the illusion of control project data computer algorithms and human intuition for project management and control |
| title_sub | Project Data, Computer Algorithms and Human Intuition for Project Management and Control |
| topic | Operations Management Project Management Operations Research and Decision Theory Big Data Algorithms Production management Project management Operations research Big data |
| topic_facet | Operations Management Project Management Operations Research and Decision Theory Big Data Algorithms Production management Project management Operations research Big data |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034349913&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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