“There is nothing in a caterpillar that tells you it will be a butterfly — R. Buckminster Fuller
The mission
Water and waste infrastructures are long-lived, capital-intensive investments with long payback periods. Being fixed in place and technologically embedded, they are often costly or slow to retrofit — which leaves them vulnerable to becoming stranded assets if the operational context evolves faster than they can adapt. Stakeholders’ expectations on the one hand and climate and environmental factors on the other hand are drivers of prime importance. In many cases, contextual change occurs incrementally and gets unnoticed until the operational landscape is affected.
The adaptation process is governed by many interactions which form a complex system and generate a great deal of divergent perceptions. This often results in conflicts and loose compromise that prevent the adaptation process from progressing and delivering tangible results.
The mission is to provide the intelligence services that organizations need to learn how water and waste utilities are responding to societal expectations for higher resource efficiency and identify the leverage points they can use to influence contextual change. The ultimate goal is to avoid service disruption and take advantage of organizational synergies.
The service
Our distinctive contribution lies in equipping organizations with the intelligence tools needed to set up learning loops on issues related to the transition to Circular Economy in water and waste management. Emphasis is placed on testing hypotheses on cross-sectoral causal links and building scenarios for how their operational context might evolve. By benchmarking these scenarios against common archetypes in the utilities sector, we help develop models that capture the dynamics governing interactions across the whole system. This, in turn, surfaces the leverage points — technology, management tools, institutional set-up — needed to steer the transition.
This learning architecture unfolds through four concrete steps, each building on the last, following an inside-out logic of abductive reasoning and iteration.
- Knowledge Mapping: based on the analysis of the demand, we run a systemic process to research the available information on the components of the system (actors, borders, regulation loops, etc) and their evolution in time. We use primary and secondary sources as well as AI tools.
- System Modelling: We extract evidence from the database and articulate them according to models reflecting how the system is presumed to work. Missing information is extrapolated through hypotheses in line with the models. The need to verify the hypotheses guides further research.
- Sense Making: Models are used to gain insights and identify key variables in order to elaborate a narrative on the issues at stake that incorporates a plurality of legitimate views. Priority is given to highlighting the interactions between the different components of the system and retrieving leverage points to drive the transition. It often triggers the need to reframe the initial demand.
- Capacity Building: : Scenarios on the future lead to formulating and implementing adaptation projects that can mobilise stakeholders around new organisational frameworks. These frameworks must be supported by a range of capacity tools (training, procedures, information system, institutional set-up, etc).
The added value :
- Overcome cognitive biases
- Avoid emotional traps
- Facilitate stakeholder engagement
- Detect weak signals that may reveal a tipping point
- Capitalize on knowledge
- Design innovative organizational schemes