Consultancy

Every step of the way.

What’s in a process of building systemic applications.
Translating Systems Thinking to useable real-world applications can be a real challenge. How do we collaboratively form meaningful outcomes that are engaging enough to be adopted in policy making? How can we use these holistic systemic maps as blueprints? Or quite simply put, how do we ‘do’ Systems thinking? At the core of every systems project is a similar process to follow.

In essence, several steps are followed, each contributing to a comprehensive and effective outcome. From a blank canvas, to causal maps and eventually, visual data-driven interfaces. By following these steps, we come to outcomes that can systematically analyze and address complex problems.

This approach turns Systems Thinking into real-world impact, ready to influence policy and practice.

To get there, take a look at my services:

Follow The Steps.

1. Defining the concept

You start your process all about clearly articulating the problem or concept to set the foundation for your problem. This step ensures that the issue is well understood and framed for subsequent modeling.
2. Defining the framework

With the concept in place, the next step is to establish the SD model’s framework. Here, we identify key variables, relationships, and feedback loops that govern system behavior. This framework serves as the structural backbone of the model.
3. Group Sessions

Collaboration is essential in SD. Group sessions bring together diverse perspectives and expertise. Stakeholders, experts, and team members engage in discussions, brainstorming, and knowledge sharing to refine the model’s framework and parameters.
4. Simulation

Simulation involves converting the conceptual framework into a functional simulation model using differential equations. This step allows us to observe how the system behaves over time under various inputs and scenarios. It is an important step in testing hypotheses and understanding system dynamics.
5. Monitor Use

Regularly check the model’s performance against real-world data. Use these comparisons to make adjustments and improvements, ensuring the model remains accurate and useful for decision-making.