Modelling Complex Projects
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More About This Title Modelling Complex Projects
English
It is widely acknowledged that traditional Project Management techniques are no longer sufficient, as projects become more complex and client's demand reduced timescales. Problems that arise include inadequate planning and risk analysis, ineffective project monitoring and control, and uninformed post-mortem analysis. Effective modelling techniques, which capture the complexities of such projects, are therefore necessary for adequate project management. This book looks at those issues, describes some modelling techniques, then discusses their merits and possible synthesis.
* This is the only project management book that deals with Project Modelling.
* Features case studies throughout.
* Places the various approaches to Project Modelling within a coherent framework, and gives an objective overview.
* This is the only project management book that deals with Project Modelling.
* Features case studies throughout.
* Places the various approaches to Project Modelling within a coherent framework, and gives an objective overview.
English
TERRY WILLIAMS is Professor and Head of the Management Science Department at Strathclyde University. After studying at Oxford and Birmingham he lectured at Strathclyde University in Operational Research before joining Engineering Consultants YARD (now BAe) where he worked for 9 years developing Project Risk Management and as Risk Manager for major projects. He re-joined Strathclyde University in 1992 and continues research and consultancy modelling on major projects, particularly as one of a team supporting multi-million dollar post-project Delay and Disruption claims in Europe and North America.
Dr Williams is Editor of the Journal of the Operational Research Society. He is a frequent conference speaker, and has published widely in many academic and professional journals and books. He is MAPM, PhD and CMath.
Dr Williams is Editor of the Journal of the Operational Research Society. He is a frequent conference speaker, and has published widely in many academic and professional journals and books. He is MAPM, PhD and CMath.
English
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.
English
“…provides a lot of useful information about model building in general.” (Project Net, April 2004)
"...an essential resource for those required to model how a project may behave under certain circumstances..." (Jnl of the Operation Research Society, Vol 54(12), 2004)
"...well conceived, well written, and well produced..." (Chemistry World, 1 Feb 2004)
