 Modelling Complex Projects
ISBN: 978-0-471-89945-7
Hardcover
284 pages
October 2002
US $110.00
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1. This book.
Introduction to the book and the author.
Why is there a need for this book?
The structure of this book.
What do I need to know before I read this book?
Conclusion.
2. Projects.
What is a project?
What are project objectives?
Basic project management techniques.
Projects referred to in this book.
Conclusion.
3. Modelling.
What is a model?
Why do we model?
Modelling in practice.
Validation.
Conclusion.
4. What is a complex project?
Introduction.
What is complexity? Structural complexity.
What is complexity? Uncertainty.
What is complexity? Summary.
Increasing complexity.
Tools and techniques-and the way ahead.
5. Discrete effects and uncertainty.
Introduction.
Uncertainty and risk in projects.
Cost risk: additive calculations.
Time risk: effects in a network.
Analysing time risk: simulation.
Criticality and cruciality.
The three criteria and beyond.
Conclusion.
6. Discrete effects: collecting data.
Introduction.
Collecting subjective data: identification.
Collecting subjective data: general principles of quantification.
Collecting subjective data: simple activity-duration models.
Effect of targets.
Conclusion.
7. The soft effects.
Introduction.
Some key project characteristics.
Client behaviour and external effects on the project.
Management decisions.
Project staffing.
Subjective effects within the project.
Summary and looking forward.
8. Systemic effects.
The effects.
A brief introduction to cause mapping.
Qualitative modelling: simple compounding.
Qualitative modelling: loops.
Quantitative modeling.
9. System dynamics modeling.
Introduction to system dynamics.
Using system dynamics with mapping.
Elements of models.
Production elements.
Other elements.
Managerial actions.
How effects compound.
Validation.
Conclusion.
10. Hybrid methods: the way forward?
Introduction.
Adapting standard models using lessons learned from SD.
Using conventional tools to generate SD models.
Using SD and conventional models to inform each other.
Extending SD: discrete events and stochastic SD.
The need for intelligence.
Conclusion.
11. The role of the modeler.
Introduction.
Project management.
What makes a good modeller?
Stages of project modeling.
Chapter summary.
12. Conclusion.
Appendix: Extension of time claims.
References.
Index.
Introduction to the book and the author.
Why is there a need for this book?
The structure of this book.
What do I need to know before I read this book?
Conclusion.
2. Projects.
What is a project?
What are project objectives?
Basic project management techniques.
Projects referred to in this book.
Conclusion.
3. Modelling.
What is a model?
Why do we model?
Modelling in practice.
Validation.
Conclusion.
4. What is a complex project?
Introduction.
What is complexity? Structural complexity.
What is complexity? Uncertainty.
What is complexity? Summary.
Increasing complexity.
Tools and techniques-and the way ahead.
5. Discrete effects and uncertainty.
Introduction.
Uncertainty and risk in projects.
Cost risk: additive calculations.
Time risk: effects in a network.
Analysing time risk: simulation.
Criticality and cruciality.
The three criteria and beyond.
Conclusion.
6. Discrete effects: collecting data.
Introduction.
Collecting subjective data: identification.
Collecting subjective data: general principles of quantification.
Collecting subjective data: simple activity-duration models.
Effect of targets.
Conclusion.
7. The soft effects.
Introduction.
Some key project characteristics.
Client behaviour and external effects on the project.
Management decisions.
Project staffing.
Subjective effects within the project.
Summary and looking forward.
8. Systemic effects.
The effects.
A brief introduction to cause mapping.
Qualitative modelling: simple compounding.
Qualitative modelling: loops.
Quantitative modeling.
9. System dynamics modeling.
Introduction to system dynamics.
Using system dynamics with mapping.
Elements of models.
Production elements.
Other elements.
Managerial actions.
How effects compound.
Validation.
Conclusion.
10. Hybrid methods: the way forward?
Introduction.
Adapting standard models using lessons learned from SD.
Using conventional tools to generate SD models.
Using SD and conventional models to inform each other.
Extending SD: discrete events and stochastic SD.
The need for intelligence.
Conclusion.
11. The role of the modeler.
Introduction.
Project management.
What makes a good modeller?
Stages of project modeling.
Chapter summary.
12. Conclusion.
Appendix: Extension of time claims.
References.
Index.
