Changing and Transitioning Systems

For sustainable development to succeed, we need to take into account all the critical areas of interest a system may consist of, and have an effect on. This is what SiD calls "integrated analysis," or "thinking in the full spectrum." Without a guiding structure, this process leads to an overwhelming overload of information. The SNO hierarchy solves this.

The SNO Hierarchy as a Working Framework

The three layers of SiD categorize indicators in a different way at each level:

• Object level (bottom): Uses the ELSI categorization system to explore all physical aspects of the world around us, bottom-up. This enables you to make custom indicator sets for your area of application while still working in the full spectrum.
• Network level (middle): Offers a full set of tools to explore relations in a system and unravel system dynamics. The network parameters (CRAFTDCCV, SSCNE, PEAIE) are powerful lenses to look at the dynamics of the network and connect object and system level.
• System level (top): Where we can determine sustainability. Its three main indicators of Resilience, Autonomy, and Harmony are uniform and generically applicable. Building insight by analyzing through this system from the bottom up yields unparalleled insight into the complexities, opportunities, pitfalls, and solution pathways of challenges.

Indicators, Properties, and Parameters

These three terms are distinct and important to understand:

• Indicators are specific measures that "indicate" a certain performance aspect of a system. We define these for each project specifically. With the right indicators, we can tangibly express our project goals in measurable performance parameters. For example, the time it takes you to run a specific flight of stairs can be an indicator of your fitness. You can set a measurable goal for that. Measuring how tall you are (an object property) does not tell you much about your health and is therefore not a great health indicator.
• Properties are directly measurable physical aspects: size, mass, temperature, energy consumption. They are the raw data.
• Parameters are used at the network level. They are not always directly measurable but can usually be derived from a variety of properties. They are neutral measurement points about aspects of the network that help evaluate its nature, either quantitative or qualitative.

The SiD Mountain

Working with this system on concrete cases is the subject of the SiD Method chapters. The SiD Mountain diagram shows a representation of the mental process of stepping through SiD from start to finish. It is an iterative process: you go through the cycle several times, in various depths, from fast reconnaissance to thorough analysis. Each cycle moves through goal-setting, system mapping, system understanding, solutions, and evaluation.

Going from A to B: Overstating Your Goal

Consider a system in state A. We want the system to go to state B. In order to transition the system from A to B, we need to apply an action. If we do not take action, or not the right one, the system continues to move on its own and will end up in state A' (wherever its own momentum takes it).

Since we are talking about complex systems (not linear ones), the system may not actually go to B, or not quickly enough, as a result of various system dynamics such as historical momentum or the rebound effect. If we apply the action on A but do not account for these dynamics, we will not arrive at B but at B', or perhaps somewhere between A' and B'. This may not yield the desired outcome.

In order to arrive at B, we may have to apply action to try to make the system go to B+ (beyond B) in order to make it reach the area of B. Since B+ is "beyond" B, we are essentially over-aiming at our goal.

As a simple illustration, consider a long-distance shooter aiming at targets over great distances. The bullet is affected by wind, gravity, and the curvature of the earth. The shooter needs to aim a distance away from the target to counteract these dynamics. With systems, the difference is that the time delay between cause and effect can be large. We cannot apply trial and error to get a feeling for how much further away we need to aim. However, we do know that we need to make adjustments for the presence of system dynamics. This adjustment is usually to aim higher in ambition than where you hope to end up.

This is equally true when setting goals for the performance of a company, city, or production system. When we set goals for the transition of these systems, we need to overstate the goals to arrive near our intended goal. The usual practice, unfortunately, is to aim lower so as not to disappoint anyone or to prevent making empty promises. Therefore we recommend anyone working on these challenges to push and argue for ambitious goals each and every time. Be mindful that going too much over the top with goals may also backfire: agents in the system may not believe in an absurdly overstated goal and will not even try.

Transition Dynamics: The Fashion Industry Example

The DRIFT (Dutch Research Institute for Transitions) model provides a useful framework for mapping, analyzing, and optimizing transition pathways. It identifies phases that a system passes through during transition:

1. Experimentation: New approaches are tested at small scale.
2. Acceleration: Successful experiments gain momentum.
3. Emergence: New patterns become visible.
4. Breakdown: The old regime begins to fail.
5. Optimization/Chaos: Turbulence as old and new coexist.
6. Destabilization: The old regime loses its hold.
7. Phase out: Old practices are retired.
8. Stabilization: The new regime settles.
9. Institutionalization: New practices become the norm.

The DRIFT model was applied in 2018 to support the acceleration of the fashion industry's transition, supported by the C&A Foundation's program Fashion for Good. The analysis mapped both the current transition state and a suggested improved transition path. Using these insights, new actions, focus areas, and programs were derived to boost the transition to circularity in the fashion industry. These maps are a great example of "before and after" mapping, a useful tool for any systemic transition effort.