The methods included in this training belong to a new paradigm in management theory and practice, which views organizations as adaptive evolving systems. These are recursive management models which encourage emergence and initiate self-organization.

 

The methods we examine are based on a solid scientific foundation of important research in the social sciences, cognitive processes, complexity theory, game theory and individual decision theory. The good thing is that this new knowledge has been rethought through the many years experience of leading management consultants such as Dave Snowden, creator of the Cynefin Framework and Yves Morieux, author of the "Smart Simplicity" method, who have transposed it into practical guidelines and simple rules.

 

The course presents in a concise and accessible form, both these two modern concepts as well the method of adaptive project management. The content is richly illustrated with examples and case studies on how exactly smart rules are applied in complex working environments to solve complex problems.

 

These models and methodologies are suitable for large and small companies that face the challenges of today's uncertain and volatile business environment. The course provides practical guidelines for any manager who has become aware that traditional methods and best practices are no longer effective.

 

In today’s complex organizations, cause and effect are discovered but only retrospectively; when situations are simplified and tried and tested procedures applied, they do not help, but are rather likely to cause failure; predictive methods are unreliable; projects do not develop along the known linear cycle; planning is hampered by changing requirements. For those who have encountered such issues, this course provides new effective sense-making tools to apply wherever complexity hinders progress and causes confusion.

 

But complexity should not be viewed as a burden, because it also acts as a catalyst to innovation and value creation. Working on the edge of chaos can be quite productive. In this state, there is enough freedom to keep the system vibrant and enough stability to prevent collapse.

 

The methods we introduce address complexity through internal principles and rules that do not aim to control, but only guide and shape the system through “safe mistakes” and similar strategies. These rules help cope with complexity and the key is in the balance of autonomy and cooperation.