Modelling Complex Projects

1 This Book 1

Introduction to the book and the author 1

Why is there a need for this book? 3

The structure of this book 7

What do I need to know before I read this book? 8

Conclusion 11

2 Projects 13

What is a project? 13

What are project objectives? 15

Basic project management techniques 18

Projects referred to in this book 23

Conclusion 29

3 Modelling 31

What is a model? 31

Why do we model? 35

Modelling in practice 40

Validation 44

Conclusion 47

4 What is a Complex Project? 49

Introduction 49

What is complexity? Structural complexity 50

What is complexity? Uncertainty 55

What is complexity? Summary 58

Increasing complexity 59

Tools and techniques—and the way ahead 62

5 Discrete Effects and Uncertainty 65

Introduction 65

Uncertainty and risk in projects 66

Cost risk: additive calculations 78

Time risk: effects in a network 89

Analysing time risk: simulation 96

Criticality and cruciality 104

The three criteria and beyond 115

Conclusion 118

6 Discrete Effects: Collecting Data 119

Introduction 119

Collecting subjective data: identification 121

Collecting subjective data: general principles of quantification 123

Collecting subjective data: simple activity-duration models 126

Effect of targets 131

Conclusion 136

7 The Soft Effects 137

Introduction 137

Some key project characteristics 139

Client behaviour and external effects on the project 140

Management decisions 146

Project staffing 149

Subjective effects within the project 151

Summary and looking forward 154

8 Systemic Effects 155

The effects 155

A brief introduction to cause mapping 157

Qualitative modelling: simple compounding 158

Qualitative modelling: loops 161

Quantitative modelling 163

9 System Dynamics Modelling 167

Introduction to system dynamics 167

Using system dynamics with mapping 171

Elements of models 175

Production elements 176

Other elements 188

Managerial actions 189

How effects compound 193

Validation 195

Conclusion 196

10 Hybrid Methods: The Way Forward? 199

Introduction 199

Adapting standard models using lessons learned from SD 200

Using conventional tools to generate SD models 205

Using SD and conventional models to inform each other 206

Extending SD: discrete events and stochastic SD 208

The need for intelligence 210

Conclusion 212

11 The Role of the Modeller 215

Introduction 215

Project management 215

What makes a good modeller? 217

Stages of project modelling 219

Chapter summary 230

12 Conclusion 233

Appendix: Extension of time claims 235

References 249

Index 265