Projects globally continue to get larger, more expensive, more expansive, more challenging and ultimately more complex.
Unfortunately… large-scaled, capital intensive and technically intricate projects (aka complex projects) fail to meet their investor sanctioned cost, schedule and benefit objectives 60 to 90% of the time. This includes; 9 of every 10 mega infrastructure projects, 5 of every 6 complex technology implementations, 4 of every 5 large dams, 3 of every 5 major road programs, 9 of every 10 major rail builds and so on. In fact, abnormally high failure rates have been observed across every major project sector, technical class and investment type with no demonstrable improvement for at least the past 70 years.
These observations are particularly concerning for the formal discipline of project
risk management as this specific discipline has long been hailed by both industry
and academia as being critical to a project’s ability to successfully meet its’
objectives. Such disproportionally high complex project failure rates however suggest that the traditionally accepted, project risk management practices have been mostly ineffective in helping complex projects succeed.
The Edge of Chaos takes a critical look at the industry accepted, project risk methods in order to better understand their validity, limitations and challenges within a complex project context. In so doing, the booklet presents an academically researched argument as to why conventional risk methods are not suited for environments of advanced complexity. Further, the booklet explores the complexity sciences (complex systems theory, network theory, chaos theory etc.) in search of new generation solutions which may help improve the manner in which risks are controlled within highly integrated, dynamic and adaptive project environments
The booklet concludes with a number of complexity science informed recommendations, to help practising risk officers improve their own understanding of what qualifies as effective risk management within projects of advanced complexity.