Because the behaviour of complex projects is often puzzling, or counter–intuitive, we need models. This book presents a structured toolkit of techniques, developed gradually from the simple to the more complex, and provides examples to show where, when and why the techniques should be used. It looks at what causes project complexity, describes various aspects of project behaviour and develops modelling tools.
Starting with more traditional techniques modelling individual effects on projects, giving a full treatment (including some novel network concepts) the book enables readers to build breakdown – and network – type models. It also considers some of the more difficult aspects of modelling by moving into the "softer", more subjective, effects and then looking at systemic models of the effects as they come together. Finally, it looks at various methods of developing hybrid tools, to utilise the benefits of combinations of techniques.
Based on a wealth of practical experience and bringing together a range of tried and tested techniques, this book explains where the use of modelling can help estimate, monitor, control and analyse projects and thus lead to successful implementation.
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?
What is a project?
What are project objectives?
Basic project management techniques.
Projects referred to in this book.
What is a model?
Why do we model?
Modelling in practice.
4. What is a complex project?
What is complexity? Structural complexity.
What is complexity? Uncertainty.
What is complexity? Summary.
Tools and techniques–and the way ahead.
5. Discrete effects and uncertainty.
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.
6. Discrete effects: collecting data.
Collecting subjective data: identification.
Collecting subjective data: general principles of quantification.
Collecting subjective data: simple activity–duration models.
Effect of targets.
7. The soft effects.
Some key project characteristics.
Client behaviour and external effects on the project.
Subjective effects within the project.
Summary and looking forward.
8. Systemic effects.
A brief introduction to cause mapping.
Qualitative modelling: simple compounding.
Qualitative modelling: loops.
9. System dynamics modeling.
Introduction to system dynamics.
Using system dynamics with mapping.
Elements of models.
How effects compound.
10. Hybrid methods: the way forward?
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.
11. The role of the modeler.
What makes a good modeller?
Stages of project modeling.
Appendix: Extension of time claims.