Buch, Englisch, 448 Seiten, Format (B × H): 155 mm x 231 mm, Gewicht: 816 g
A Handbook for Managing Technical Projects
Buch, Englisch, 448 Seiten, Format (B × H): 155 mm x 231 mm, Gewicht: 816 g
ISBN: 978-1-394-39584-2
Verlag: Wiley
A competency-based framework for leading complex engineering projects
Engineering projects require leadership that integrates technical depth with strategic management. This handbook provides a structured, competency-based framework designed specifically for high-pressure technical environments. Unlike traditional textbooks, this guide offers a non-sequential model for practitioners to find immediate, actionable answers to complex challenges.
It bridges the gap between standards and implementation, focusing on the core competencies essential for project success—from Project Controls to Knowledge Management. Whether you are an engineer transitioning into management or a veteran lead, this resource provides the technical specifics and leadership strategies needed to deliver value, manage high uncertainty, and align project outcomes with global strategic organizational goals. It is the essential reference for those seeking to enhance professional skills in a knowledge-intensive landscape.
Readers will also find: - Practitioner-Focused Design: Jump directly to the competency you need; every chapter is self-contained with curated reading lists for deeper study
- Integrated AI Guidance: Navigate the impact of artificial intelligence, IoT, and blockchain woven throughout the project lifecycle—grounded in engineering practice
- Engineering-Specific Mastery: Focus on Intellectual Property, Configuration Management, and Value Engineering—critical competencies often missing from generic project management frameworks
- 130+ Years of Collective Expertise: Leverage actionable strategies validated by a globally diverse editorial team and over 30 subject matter experts
Designed for engineering project managers and technical leads, this handbook connects competency-based frameworks with actionable strategies. It equips professionals to lead multidisciplinary teams through complex lifecycles while navigating the legal, ethical, and technological shifts reshaping the global landscape of modern engineering projects.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
List of Figures xx
List of Tables xxiv
List of Contributors xxv
About the Authors and Editors xxvii
Preface xxix
Acknowledgments xxxiii
1 Introduction 1
Artem Shushkov, CPEM and Valerie P. Denney, DBA, PMP
1.1 Purpose of This Handbook 1
1.2 EPMgrs Are Unique 1
1.3 Engineering Project 2
1.4 The Evolving Landscape of Project Management 4
1.5 Interrelations Between R&D, Technology, Engineering, and Innovation 5
1.6 Role of Technology in Corporate Competitiveness 7
1.6.1 Technology Strategy—Why It Matters for EPMgrs 7
1.6.2 Types of Technology Strategy 8
1.7 The Handbook: Competency Previews 9
1.7.1 The Handbook Competencies 9
1.8 How the Handbook Is Organized 13
Acronyms 14
Glossary 14
References 16
Recommended Reading List 16
2 Life Cycle Models and Tailoring 17
Artem Shushkov, CPEM
2.1 Introduction 17
2.2 Fundamental Concepts 18
2.3 Comparing Popular Life Cycle Models 19
2.3.1 Waterfall Model 20
2.3.2 V-shaped Model 21
2.3.3 Spiral Model 21
2.3.4 Iterative and Incremental Models 22
2.3.5 Agile Model 23
2.3.6 Hybrid Model 24
2.4 Selecting the Right Model: Key Factors 25
2.5 Scaling Models to Specific Project Needs 26
2.5.1 Scaling Up for Large Projects 27
2.5.2 Scaling Down for Small Projects 27
Acronyms 28
Glossary 28
References 30
Recommended Reading List 31
3 Project Controls 33
Artem Shushkov, CPEM and Terry R. Collins, PhD, PE, CPEM
3.1 Introduction 33
3.2 Quantitative Tools and Techniques 33
3.3 Qualitative Tools and Techniques 34
3.4 Fundamental Concepts 34
3.4.1 Project Controls Concept 34
3.4.2 KPIs and Milestones 37
3.4.3 Role of an EPMgr 39
3.4.4 KPIs and Types of a Project 39
3.4.4.1 Examples of a Project and Relevant KPIs 40
3.4.5 Setting the Right KPIs 43
3.4.6 Balancing the Right KPIs 45
3.5 Artificial Intelligence in Project Controls 47
3.6 Global Considerations for Project Controls 47
3.7 Inter-competency Linkages 48
3.8 Practical Applications and/or Case Studies 49
Acronyms 51
Glossary 51
References 54
Recommended Reading List 55
4 Financial Management 57
Artem Shushkov, CPEM
4.1 Introduction 57
4.2 Quantitative Tools and Techniques 57
4.3 Qualitative Tools and Techniques 58
4.4 Fundamental Concepts of Project Selection 58
4.4.1 Financial Life Cycle of an Engineering Project 59
4.4.2 Forecasted Project Cash Flows 60
4.4.3 Project Cost Estimation 61
4.4.4 Valuation Techniques of an Engineering Project 61
4.4.5 Macro-parameters 64
4.4.6 Stage 1 Evaluation 65
4.4.7 Stage 2 Selection 65
4.4.7.1 Obtaining Value: High-certainty and High-uncertainty Projects 65
4.4.7.2 Technology Readiness Level and Probability of Success in High-uncertainty Projects 66
4.4.7.3 Obtaining Value: High-certainity and High-uncertainity Projects Metrics 67
4.4.7.4 Other Evaluation Techniques for High-complexity and High-uncertainty Projects 76
4.4.8 Stage 3 Definition 80
4.4.8.1 Sensitivity Analysis 81
4.4.9 Stage 4 Predevelopment Assessment 82
4.4.10 Stage 5 Development 83
4.4.11 Stage 6 Trials and Launch 83
4.4.12 Stage 7 Transfer 84
4.4.13 Stage 8.1 Maintenance and Upgrade 84
4.4.14 Stage 8.2 Termination 85
4.5 Artificial Intelligence in Financial Management 86
4.6 Global Considerations for Financial Management 87
4.7 Inter-competency Linkages 88
4.8 Practical Applications and/or Case Studies 88
4.8.1 Option “As-is” and $85,000,000 of CAPEX Invested in R&D 90
4.8.2 Option “As-to Be” and $66,000,000 of CAPEX Invested in R&D 90
4.8.3 Difference in Tax Liability 91
Acronyms 91
Glossary 92
References 95
Recommended Reading List 96
5 Requirements Management 97
James Marion, PhD, RMP, PMP and Valerie P. Denney, DBA, PMP
5.1 Introduction 97
5.2 Quantitative Tools and Techniques 98
5.3 Qualitative Tools and Techniques 98
5.4 Fundamental Concepts 98
5.4.1 Requirement Categories 98
5.4.2 The Product Life Cycle Model 101
5.4.2.1 Initiating Process Group: The Project Begins 101
5.4.2.2 Product Development 101
5.4.3 Integrating Research and Technology into New Products—Concept to Realization 102
5.4.4 Systems Engineering Method 102
5.4.4.1 Requirements Elicitation Process 103
5.4.4.2 Requirements Documentation and Storage Process 103
5.4.4.3 Requirements Tracing Process 103
5.4.4.4 Requirements Validation Process 103
5.4.4.5 Conversion of Requirements into Specifications Process 103
5.4.5 Project Stages 103
5.4.5.1 Stage 1: Evaluation 104
5.4.5.2 Stage 2: Selection 104
5.4.5.3 Stage 3: Definition 105
5.4.5.4 Stage 4: Predevelopment Assessment 105
5.4.5.5 Stage 5: Development 105
5.4.5.6 Stage 6: Trials & Launch 105
5.4.5.7 Stage 7: Transfer 106
5.4.5.8 Stage 8.1: Maintenance & Upgrade 106
5.4.5.9 Stage 8.2: Termination 106
5.4.6 Key Skills & Processes in Requirements Management 106
5.4.7 Tools, Techniques, and Methods Used in Requirements Management 107
5.4.7.1 Project Charter 107
5.4.7.2 CROPIS Analysis 108
5.4.7.3 SIPOC Analysis 109
5.4.7.4 Quality Function Deployment and House of Quality 109
5.4.7.5 Forecasting Methods 110
5.4.7.6 Document Analysis 110
5.4.7.7 Brainstorming 111
5.4.7.8 Interviews 111
5.4.7.9 Prototyping 111
5.4.7.10 Workshops 112
5.4.7.11 Survey 112
5.4.7.12 Mind Maps 112
5.4.7.13 User Stories 112
5.4.7.14 Use Case Diagrams 113
5.4.7.15 Process Flows 113
5.4.7.16 Context Diagrams 114
5.4.7.17 Mock-ups 116
5.4.7.18 Requirements Traceability Matrix 116
5.5 Artificial Intelligence in Requirements Management 117
5.6 Global Considerations for Requirement Management 118
5.6.1 Cultural and Communication Challenges 118
5.6.2 Language Barriers 118
5.6.3 Regulatory and Legal Considerations 118
5.6.4 Time Zone and Coordination Issues 119
5.7 Inter-competency Linkages 119
5.8 Practical Applications and/or Case Studies 120
5.8.1 Case 1: Evolving Requirements in Agile Hardware Software Integration 120
5.8.1.1 Summary 120
5.8.1.2 Supporting Information 120
5.8.1.3 Challenges 121
5.8.1.4 Lessons Learned 121
5.8.2 Case 2: Balancing Engineering, Regulatory, and Environmental Requirements 121
5.8.2.1 Summary 121
5.8.2.2 Supporting Information 121
5.8.2.3 Challenges 122
5.8.2.4 Lessons Learned 122
5.8.3 Case Study 3: Managing Conflicting and Evolving Requirements 122
5.8.3.1 Summary 122
5.8.3.2 Supporting Information 122
5.8.3.3 Challenges 123
5.8.3.4 Lessons Learned 123
Acronyms 124
Glossary 124
References 125
Recommended Reading List 127
6 Value Engineering 129
Artem Shushkov, CPEM
6.1 Introduction 129
6.2 Quantitative Tools and Techniques 129
6.3 Qualitative Tools and Techniques 130
6.4 Fundamental Concepts 130
6.4.1 Function and Cost 131
6.4.2 Stage 2 Selection 133
6.4.3 Stage 3 Definition 133
6.4.4 Stage 4 Predevelopment Assessment 137
6.4.5 Stage 5 Development 138
6.4.6 Stage 6 Trials and Launch 140
6.4.7 Stage 7 Transfer 141
6.4.8 Stage 8.1 Maintenance and Upgrade 141
6.4.9 Stage 8.2 Termination 143
6.4.10 Sustainability 143
6.4.10.1 Life Cycle Sustainability Analysis 143
6.4.10.2 Sustainable Engineering 144
6.5 Artificial Intelligence in Value Engineering 145
6.6 Global Considerations for Value Engineering 146
6.7 Inter-competency Linkages 147
6.8 Practical Applications and/or Case Studies 147
Acronyms 148
Glossary 149
References 151
Recommended Reading List 153
7 Risk and Safety Management 155
James Marion, PhD, RMP, PMP and Valerie P. Denney, DBA, PMP
7.1 Introduction 155
7.2 Quantitative Tools and Techniques 156
7.3 Qualitative Tools and Techniques 156
7.4 Fundamental Concepts 156
7.4.1 Elements of Risk Management 157
7.4.1.1 Elements of Success in Risk Management 157
7.4.1.2 Purpose of Risk Management 158
7.4.1.3 Risk Appetite and Risk Tolerance 159
7.4.1.4 Iterative Risk Identification 159
7.4.1.5 Distinguishing Risks, Issues, and Assumptions 159
7.4.1.6 Contingency Reserves and Management Reserves 160
7.4.1.7 Purpose of Risk Reassessment 160
7.4.2 Elements of Safety Management 161
7.4.2.1 Elements of Success in Safety Management 161
7.4.2.2 Understanding Safety Regulations 162
7.4.2.3 Incident Management 162
7.4.2.4 Emergency Preparedness 162
7.4.3 Planning: Understanding Project Scope and Objectives 163
7.4.3.1 Impact of Project Environment on Risk and Safety Management 163
7.4.3.2 Components of Risk and Safety Management Plans 163
7.4.4 Identification: Systematically Identify Potential Risks and Safety Concerns 164
7.4.4.1 Assumptions Analysis 164
7.4.4.2 Brainstorming 164
7.4.4.3 Checklists 164
7.4.4.4 Historical Information Reviews 164
7.4.4.5 Project Document Reviews 165
7.4.4.6 Risk Breakdown Structures 165
7.4.4.7 SWOT Analysis 166
7.4.4.8 Risk Register 166
7.4.4.9 Assumption Log 168
7.4.4.10 Issue Log 168
7.4.5 Qualitative Analysis and Prioritization Tools 168
7.4.5.1 Bubble Chart 169
7.4.5.2 Probability and Impact Matrix 169
7.4.5.3 Risk Categorization Assessment 171
7.4.5.4 Risk Data Quality Assessment 171
7.4.5.5 What-if Analysis 171
7.4.6 Quantitative Risk Analysis and Prioritization Tools 173
7.4.6.1 Cost Benefit Analysis 173
7.4.6.2 Critical Chain Project Management 173
7.4.6.3 Decision Tree Analysis 174
7.4.6.4 Failure Mode and Effects Analysis 175
7.4.6.5 Monte Carlo Simulation 175
7.4.6.6 Multi-criterion Selection Techniques 175
7.4.6.7 Program Evaluation and Review Technique 176
7.4.6.8 Root Cause Analysis 177
7.4.6.9 Sensitivity Analysis 178
7.4.7 Response Planning and Execution 179
7.4.7.1 Contingency Response Strategies 179
7.4.7.2 Risk Handling Techniques 179
7.4.7.3 Safety Measures 181
7.4.8 Monitoring and Control 181
7.4.8.1 Components of a Risk Report 181
7.4.8.2 Risk Control Tools and Techniques 182
7.4.8.3 Monitoring and Enforcement in Safety Management 183
7.4.9 Primary Project Stages and Application 183
7.5 Artificial Intelligence in Risk and Safety Management 183
7.6 Global Considerations for Risk and Safety Management 184
7.6.1 Cultural and Behavioral Differences in Risk Perception 184
7.6.2 Regulatory and Legal Variability 185
7.6.3 Recommendations for Global Risk and Safety Management 185
7.7 Inter-competency Linkages 185
7.8 Practical Applications and/or Case Studies 185
7.8.1 Case Study 1: Boeing 737 MAX—Engineering Risk, Oversight, and Safety Failures 186
7.8.1.1 Summary 186
7.8.1.2 Supporting Information 186
7.8.1.3 Challenges 186
7.8.1.4 Lessons Learned 187
7.8.2 Case Study 2: Nord Stream Pipeline Sabotage—Infrastructure Vulnerability and Risk Preparedness 187
7.8.2.1 Summary 187
7.8.2.2 Supporting Information 187
7.8.2.3 Challenges 187
7.8.2.4 Lessons Learned 187
7.8.3 Case Study 3: Baltimore Key Bridge Collapse—Structural Risk and Maritime Safety 187
7.8.3.1 Summary 187
7.8.3.2 Supporting Information 188
7.8.3.3 Challenges 188
7.8.3.4 Lessons Learned 188
Acronyms 188
Glossary 189
References 190
Recommended Reading List 192
8 Leadership 193
Gene Dixon, PhD, CPEM, FASEM and Daryl Watkins, DM, PMP, PCC
8.1 Introduction 193
8.2 Quantitative Tools and Techniques 194
8.3 Qualitative Tools and Techniques 194
8.4 Fundamental Concepts 194
8.4.1 Is Leadership Different for EPMgrs (or Should It Be Different)? 196
8.4.2 How Has Leadership Theory Evolved 198
8.4.2.1 Understanding Complexity 198
8.4.2.2 Rethinking Leadership: From Control to Collaboration 198
8.4.2.3 Essential Skills for Complexity Leadership 199
8.4.3 Leadership as a Process 201
8.4.4 EPMgr Duties 204
8.4.4.1 KPIs 205
8.4.4.2 Delegation 206
8.5 Artificial Intelligence in Leadership 207
8.6 Global Considerations in Leadership 208
8.7 Inter-competency Linkages 209
8.8 Practical Applications and/or Case Studies 210
8.8.1 Case Study: The Boeing 787 Dreamliner 210
Acronyms 211
Glossary 211
References 212
Recommended Reading List 215
9 Communications 217
Gene Dixon, PhD, CPEM, FASEM
9.1 Introduction 217
9.2 Qualitative Tools and Techniques 218
9.3 Fundamental Concepts 218
9.3.1 Principles of Effective Communication 221
9.3.2 Barriers to Effective Communication 223
9.3.3 Meetings and Communications 225
9.3.4 Listening 226
9.3.4.1 Advanced Listener Skills 227
9.3.5 Communication Management 228
9.3.5.1 Preventing Miscommunications 229
9.3.5.2 Efficient Project Documentation 229
9.3.6 Communications Management Planning 230
9.3.7 Laws of Project 231
9.3.8 Technical Presentations 233
9.3.9 Principles of Rhetoric 235
9.3.9.1 Principle 1: Invention 236
9.3.9.2 Principle 2: Arrangement 236
9.3.9.3 Principle 3: Style 237
9.3.9.4 Principle 4: Memory 238
9.3.9.5 Principle 5: Delivery 238
9.4 Artificial Intelligence in Communications 240
9.5 Global Considerations for Communication 241
9.5.1 Stakeholder Engagement 242
9.6 Inter-competency Linkages 244
9.7 Practical Applications and/or Case Studies 244
Acronyms 245
Glossary 245
References 247
Recommended Reading List 248
10 Configuration Management 249
Gene Dixon, PhD, CPEM, FASEM
10.1 Introduction 249
10.2 Quantitative Tools and Techniques 249
10.3 Qualitative Tools and Techniques 250
10.4 Fundamental Concepts 250
10.4.1 Application 251
10.4.2 cm and Information Risks 253
10.4.3 Configuration Management Plan 254
10.4.4 Configuration Identification 257
10.4.5 Configuration Control 257
10.4.6 Configuration/Change Control Board (CCB) 259
10.4.7 Security Impact Analysis (SIA) 260
10.4.8 Configuration Accounting 260
10.4.9 Configuration Quality Verification 261
10.4.10 Training 261
10.5 Artificial Intelligence in cm 261
10.6 Global Considerations for cm 262
10.7 Inter-competency Linkages 263
10.8 Practical Applications 263
10.8.1 Case 1: Remediation Without Records 263
10.8.2 Case 2: Compliance Through Configuration Audits 264
Acronyms 264
Glossary 265
References 266
Recommended Reading List 267
11 Resource Management 269
Artem Shushkov, CPEM
11.1 Introduction 269
11.2 Quantitative Tools and Techniques 269
11.3 Qualitative Tools and Techniques 269
11.4 Fundamental Concepts 270
11.4.1 Corporate Assets 270
11.4.2 Intangible Assets 271
11.4.3 Tangible Resource Estimation 272
11.4.4 Intangible Resource Estimation 274
11.4.5 Resource Breakdown Structure 275
11.4.6 Resource Allocation Matrix 276
11.4.7 Execution Responsibilities and Decision-making Roles 277
11.4.8 Resource Performance Report 279
11.4.9 Procurement 280
11.5 Artificial Intelligence in Resource Management 282
11.6 Global Considerations for Resource Management 283
11.7 Inter-competency Linkages 284
11.8 Practical Applications and/or Case Studies 284
Acronyms 285
Glossary 286
References 288
Recommended Reading List 290
12 Quality Management 291
James Marion, PhD, PMP, PMI-RMP and Valerie P. Denney, DBA, PMP
12.1 Introduction 291
12.2 Quantitative Tools and Techniques 291
12.3 Qualitative Tools and Techniques 292
12.4 Fundamental Concepts 292
12.4.1 Key Principles in QM 295
12.4.2 Tools Related to QM 296
12.4.2.1 QM Plan (QMP) 296
12.4.2.2 Activity Network Diagram 297
12.4.2.3 Affinity Diagram 297
12.4.2.4 Interrelationship Diagram 297
12.4.2.5 Matrix Diagram 298
12.4.2.6 Prioritization Matrix 300
12.4.2.7 Process Decision Program Chart (PDPC) 300
12.4.2.8 Tree Diagram 300
12.4.2.9 Pareto Charts 300
12.4.2.10 Cause-and-effect Diagrams (Fishbone or Ishikawa Diagrams) 303
12.4.2.11 Scatter Charts 303
12.4.2.12 Control Charts 303
12.4.2.13 Project Reviews 304
12.4.2.14 Design Reviews 304
12.4.2.15 Engineering Project Checklists 305
12.4.2.16 Root Cause Analysis/Five Whys 305
12.4.2.17 Flow Chart 305
12.4.2.18 PDSA Cycle (Plan-do-study-act) 309
12.4.2.19 Failure Mode and Effects Analysis (FMEA) 309
12.4.2.20 Design of Experiments (DOE) 309
12.4.2.21 Quality Function Deployment (QFD) 309
12.5 Artificial Intelligence in QM 310
12.6 Global Considerations for QM 311
12.7 Inter-competency Linkages 312
12.8 Practical Applications and/or Case Studies 312
12.8.1 Interstate 95 Philadelphia Rapid Rebuild—Emergency Design-build QA Under 312
12.8.2 F-35 Modernization Delays—Software-integration and Supply-chain Quality Risks 313
12.8.3 Boeing Starliner OFT-1–End-to-end Software-testing and Integration Gaps 314
Acronyms 315
Glossary 316
References 318
Recommended Reading List 320
13 Ethics and Professional Responsibility 321
Valerie P. Denney, DBA, PMP
13.1 Introduction 321
13.2 Qualitative Tools and Techniques 322
13.3 Fundamental Concepts 322
13.3.1 Stakeholders and Ethics 323
13.3.2 Stages of Ethical and Moral Development 324
13.3.3 Ethical Theories 325
13.3.4 Tools and Techniques in Ethics in Professional Responsibility 326
13.3.4.1 Ethical Decision-making Framework (EDMF) 326
13.3.4.2 Giving Voice to Values (GVV) 326
13.3.4.3 Professional Codes of Conduct 327
13.3.4.4 Ethics Escalation Principles and Whistleblowing 328
13.4 Artificial Intelligence in Ethics and Professional Responsibility 328
13.5 Global Considerations Ethics and Professional Responsibility 329
13.6 Inter-competency Linkages 330
13.7 Practical Applications and/or Case Studies 330
Acronyms 332
Glossary 333
References 333
Recommended Reading List 335
14 Regulatory Responsibility and Intellectual Property Management 337
Artem Shushkov, CPEM
14.1 Introduction 337
14.2 Quantitative Tools and Techniques 337
14.3 Qualitative Tools and Techniques 338
14.4 Fundamental Concepts 338
14.4.1 Regulatory Responsibility 338
14.4.2 Contract Law 338
14.4.3 Regulatory Requirements, Codes, and Standards 339
14.4.4 Warranties, Liability, and Insurance Issues 340
14.4.5 Environmental and Safety Issues 341
14.4.6 USA and Europe’s AI and Data Privacy Regulations 341
14.4.7 IP Management 342
14.4.7.1 Patents 343
14.4.7.2 Trademarks 346
14.4.7.3 Copyright 349
14.4.7.4 Trade Secrets 350
14.4.8 IP Management by Stage 351
14.4.8.1 Stage 1: Evaluation 351
14.4.8.2 Stage 2: Selection 352
14.4.8.3 Stage 3: Definition 355
14.4.8.4 Stage 4: Predevelopment Assessment 355
14.4.8.5 Stage 5: Development 359
14.4.8.6 Stage 6: Trials and Launch 359
14.4.8.7 Stage 7: Transfer and IP Compliance 360
14.4.8.8 Stage 8.1: Maintenance and Upgrade 361
14.4.8.9 Stage 8.2: Termination 361
14.5 Artificial Intelligence in Regulatory Responsibility and IP Management 362
14.5.1 Artificial Intelligence in Regulatory Responsibility 362
14.5.2 Artificial Intelligence in IP Management 363
14.6 Global Considerations for Regulatory Responsibility and IP Management 364
14.6.1 Global Considerations for Regulatory Responsibility 364
14.6.2 Global Considerations for IP Management 365
14.7 Inter-competency Linkages 367
14.8 Practical Applications and/or Case Studies 367
14.8.1 Practical Applications and/or Case Studies: Regulatory Responsibility 367
14.8.2 Practical Applications and/or Case Studies: IP Management 368
14.8.2.1 New Type of Pump 368
14.8.2.2 Trademark Obstacle 368
Acronyms 369
Glossary 370
References 372
Recommended Reading List 374
15 Knowledge Management 375
Artem Shushkov, CPEM
15.1 Introduction 375
15.2 Quantitative Tools and Techniques 375
15.3 Qualitative Tools and Techniques 376
15.4 Fundamental Concepts 376
15.4.1 Knowledge Differentiation 376
15.4.2 Social Nature of KM 377
15.4.3 Organizational Learning 378
15.4.4 Effective KM System 378
15.4.5 KM in an Engineering Project 380
15.4.6 Sharing Tacit Knowledge in an Engineering Project 381
15.4.7 Lessons Learned in an Engineering Project 382
15.4.8 When to Perform Lessons Learned Extraction? 383
15.4.9 Lessons Learned Techniques 385
15.4.9.1 Lessons Learned Techniques: 6M 385
15.4.9.2 Lessons Learned Techniques: Five Whys 387
15.4.9.3 Lessons Learned Techniques: Peer Review 387
15.4.9.4 Lessons Learned Techniques: Gap Analysis 388
15.4.9.5 Lessons Learned Techniques: After-action Review 388
15.5 Artificial Intelligence in KM 389
15.6 Global Considerations for KM 390
15.7 Inter-competency Linkages 391
15.8 Practical Applications and/or Case Studies 392
15.8.1 Inefficiencies in Knowledge Retrieval Through the “Library Approach” 392
15.8.2 Embedding Lessons Learned into Work Processes and OL 392
Acronyms 393
Glossary 393
References 394
Recommended Reading List 395
Appendix A - Summary of Generative Artificial Intelligence Sections 397
Appendix B - Summary of Case Studies and Practical Applications 401
Appendix C - Summary of Global Considerations 405
Index 409
Figure 1.1
Figure 1.2
Figure 1.3
Figure 1.4
Figure 1.5
Figure 1.6
Figure 1.7
Figure 1.8
Figure 1.9
Figure 1.10
Figure 1.11
Figure 1.12
Figure 1.13
Figure 1.14
Figure 1.15
Figure 1.16
Figure 1.17
Figure 1.18
Figure 1.19
Figure 2.1
Generalized engineering project stages
Decision-making criteria for engineering development options
Example of PM adaptation across companies
Interrelations between the four disciplines
Types of technology strategies
Key questions covered in an engineering project, project controls
competency
Key questions covered in an engineering project, financial management
competency
Key questions covered in an engineering project, requirements
management competency
Key questions covered in an engineering project, value engineering
competency
Key questions covered in an engineering project, risk and safety
management competency
Key questions covered in an engineering project, leadership competency
Key questions covered in an engineering project, communications
competency
Key questions covered in an engineering project, configuration
management competency
Key questions covered in an engineering project, resource management
competency
Key questions covered in an engineering project, quality management
competency
Key questions covered in an engineering project, ethics and professional
responsibility competency
Key questions covered in an engineering project, regulatory
responsibility competency
Key questions covered in an engineering project, IP management
competency
Key questions covered in an engineering project, knowledge
management competency
Evolution of life cycle models
Figure 2.2
Figure 2.3
Figure 2.4
Figure 2.5
Figure 2.6
Figure 2.7
Figure 2.8
Figure 3.1
Figure 3.2
Figure 3.3
Figure 3.4
Figure 3.5
Figure 3.6
Figure 4.1
Figure 4.2
Figure 4.3
Figure 4.4
Figure 4.5
Waterfall model
Stage-gate model
V-shaped model
Spiral model
Iterative and incremental models
Agile model
Hybrid models
Key questions covered in an engineering project, “project controls”
competency
Example of the critical areas through classic triangle’s parameters
Nature of KPIs and milestones in an engineering project
The dimensions of earned value management
An example of the project’s team and appropriate focus areas and KPIs
The Arthur D. Little innovation metrics framework and representative
metrics
Key questions covered in an engineering project, financial management
competency
Typical financial structure of a project life cycle
Examples of positive and negative cash flow drivers
An example of cost estimate applicability at different stages of an
engineering project
Valuation techniques in relation to the level of project complexity and
uncertainty
Example of a TRL Example of organizing project stages, TRL, and success probability High certainty and high-uncertainty project metrics NPV: Expected cash inflows are discounted and compared to outlays An example of a decision tree Example of a Monte Carlo simulation Example of a real options valuation Figure 4.6
Figure 4.7
Figure 4.8
Figure 4.9
Figure 4.10
Figure 4.11
Figure 4.12
Figure 4.13 An example of a project EMV factor analysis, $M Figure 4.14 An example of a project cost factor analysis, $M Figure 4.15 Example of a sensitivity analysis with a class estimate +/- 15% Figure 4.16
Figure 4.17
Figure 4.18
Figure 5.1
Figure 5.2
Figure 5.3
Figure 5.4
Figure 5.5
Figure 5.6
Figure 5.7
Figure 5.8
Figure 5.9
Value dynamics of an engineering project
Expenses capitalization “as-is”
Expenses capitalized “as-to be”
Key questions covered in an engineering project, requirements
management competency
Project charter example
Example of CROPIS analysis
SIPOC analysis example
QFD example
Mind map example
User stories example
Use case example
Context diagram example
Figure 5.10 RTM example
Figure 6.1 Key questions covered in an engineering project, value engineering
competency
Figure 6.2 Total project life cycle cost
Figure 6.3 Setting an appropriate cost of an outcome
Figure 6.4 Setting a target cost reduction objective and cost-reduction challenge
Figure 6.5 Setting a target cost for an outcome’s functions
Figure 6.6 Value engineering and value analysis in an engineering life cycle
Figure 7.1 Key question covered in an engineering project, risk and safety
management competency
Figure 7.2 Key factors of project risk and safety management
Figure 7.3 RBS example
Figure 7.4 Deriving threats and opportunities using SWOT analysis
Figure 7.5 Risk register example-a
Figure 7.6 Risk register example-b
Figure 7.7 Assumption log example
Figure 7.8 Issues list example
Figure 7.9 Bubble chart example
Figure 7.10 Probability and impact matrix showing both threats and opportunities Figure 7.11 Example of definitions for levels of probability and impact Figure 7.12 Prompt list examples used for Risk Categorization Figure 7.13 Risk data quality assessment example Figure 7.14 What-if analysis example Figure 7.15 Cost benefit analysis example Figure 7.16 CCPM example Figure 7.17 Decision Tree Analysis Example 1
Figure 7.18 Decision tree analysis example 2
Figure 7.19 FMEA example
Figure 7.20 Monte Carlo histogram example
Figure 7.21 Multi-criterion selection technique example
Figure 7.22 PERT example
Figure 7.23 Root cause analysis example
Figure 7.24 Sensitivity analysis example—cost growth parameters
Figure 7.25 Risk report example
Figure 8.1 Key questions in an engineering project, leadership competency
Figure 8.2 An all-leader organization
Figure 8.3 The leadership triad
Figure 9.1 Key questions in an engineering project, communication management
competency
Figure 9.2 Communications process
Figure 9.3 Noise in the communications process
Figure 10.1 Key questions covered in an engineering project, CM competency
Figure 10.2 The CM process flow chart
Figure 10.3 Design change package inputs
Figure 11.1 Key questions covered in an engineering project, resource management
competency
Figure 11.2 Example assets of an organization
Figure 11.3 The value of the S&P 500 companies
Figure 11.4 Average royalty rate by industry
Figure 11.5 An example of a resource breakdown structure (RBS) Figure 11.6 An example of a RACI matrix Figure 11.7 An example of a RAPID matrix Figure 11.8 Typical transfer pricing essence Figure 12.1 Key questions covered in an engineering project, QM competency Figure 12.2 QM plan example Figure 12.3 Activity network diagram example Figure 12.4 Affinity diagram example 1
Figure 12.5 Affinity diagram example 2
Figure 12.6 Interrelationship diagram example
Figure 12.7 Y-shaped matrix diagram example
Figure 12.8 Priority matrix sample
Figure 12.9 PDPC example
Figure 12.10 Tree diagram example
Figure 12.11 Pareto chart example
Figure 12.12 Fishbone diagram example
Figure 12.13 Scatter chart example
Figure 12.14 Control chart example
Figure 12.15 Project review checklist example
Figure 12.16 Design review checklist example
Figure 12.17 Engineering project checklist example
Figure 12.18 Five whys example
Figure 12.19 Flow chart example
Figure 12.20 FMEA example
Figure 13.1 Key questions covered in an engineering project, ethics and professional
responsibility competency
Figure 14.1 Key questions covered in an engineering project, regulatory
responsibility competency
Figure 14.2 Key questions covered in an engineering project, IP management
competency
Figure 14.3 Example of a patent landscape in nanotechnology
Figure 14.4 Project outcome decomposition on the example of software
Figure 14.5 An example of sales coverage by a mix of IPs for sustainable protection
Figure 14.6 Possible options to exploit IP
Figure 14.7 Core components of a commercialization strategy
Figure 14.8 Breaking-down management issues on the example of a patent
Figure 15.1 Key questions covered in an engineering project, KM competency
Figure 15.2 Importance of components in a KMS
Figure 15.3 Types of project completions
Figure 15.4 Combination of the 6M technique and the Ishikawa diagram
Figure 15.5 Example of five Whys technique
Figure 15.6 Peer review technique
Table 3.1
Table 3.2
Table 3.3
Table 3.4
Table 4.1
Table 4.2
Table 6.1
Table 6.2
Table 8.1
Table 8.2
Table 9.1
Table 10.1
Table 11.1
Table 13.1
Table 13.2
Table 13.3
Table 14.1
Table 14.2
Table 14.3
Table 14.4
Table A. 1
Table B. 1
Table C. 1
Examples of the identified critical decision-making areas
Examples of KPIs and milestones in an engineering project
An example of the final goals and objectives for an EPMgr
The home building EVM report for April
High-uncertainty project. Example of EMV, EPVI, and J calculation
High-uncertainty project. Example of EDPP calculation
Miles’s cost and functionality of a proposed outcome questions
Differences between value engineering and value analysis
The evolution of leadership theories
Complexity leadership case studies for EPMgrs
5S for efficient document control
Project stage vs. CMP
An example of a resource allocation matrix (RAM)
Key engineering project management stakeholders and ethical decision
considerations
Key moral values for EPMgrs
Some common ethical problems and solutions for EPMgrs
The cooperative patent classification
The main advantages and disadvantages of IP types
Options for IP commercialization: advantages and disadvantages
Global IP variations: the example of a patent
Summary of GenAI applications by competency
Summary of case and practical applications by competency
Summary of global considerations by competency
