Developing Sammaan’s Toolkit
While there has been a considerable amount of activity in the past few months on the tendering front for the toilet facilities in Bhubaneswar & Cuttack, we have also simultaneously started working on the toolkit. The toolkit is an essential part of Project Sammaan, aiming to put together guidelines for effective sanitation interventions in low-income urban contexts, based on our own experiences and learnings from Project Sammaan.
Through the detailed design exercises and fine-tuning our understanding of what works and what doesn’t work, an essential outcome of the project will be the toolkit, that will include a set of “blueprints” for future infrastructure projects that will be maximally effective and with minimal per toilet costs. In addition, the sanitation solutions determined to be effective through rigorous randomized controlled trial based evidence will be featured in the project’s toolkit for policy-makers who might be interested in replicating a packaged model of successful interventions elsewhere in South Asia. This will act as a resource for sanitation stakeholders throughout South Asia and, where applicable, any urban developing country setting.
The toolkit thus becomes as important to the success of Project Sammaan, as the successful implementation and running of the facilities in the communities of Bhubaneswar & Cuttack, since the project also seeks to enable the building of scalable models that can improve the urban sanitation situation in other cities as well.
Shreya Chakravarty, who is currently pursuing her Postgraduate Diploma in Graphic Design at NID, Ahmedabad has joined us to work on her diploma project which will be around Version 1 of the toolkit. This version will focus on the work-streams that Quicksand is anchoring- namely the hardware aspects of the project, including converting user insights from the Potty Project to design principles and product features, as well as learnings on Standard Operating Protocols in projects of this kind, gleaned from interactions with the government and experts, particularly during the hardware tendering process.
The intent is to build an effective decision-making tool, that will help government staff and private entities involved in infrastructure development projects of this kind, to be able to understand and consider the community users as key stakeholders. The toolkit would also help them evaluate various elements of the complex dynamics that are inherent to any attempt to solve the sanitation crisis and make optimal decisions.
Over the past couple of months, Shreya and other Quicksand folks have researched various other toolkits, decision-making tools, project management tools as well as product development tools. We have also built a detailed timeline as well as various phases of the project, as we felt that mapping out the various work-streams in detail would be immensely helpful for those responsible, to understand key activities and their scope, as well as identity current & required competencies to be able to carry out these activities well.
We are simultaneously working on converting user insights and design principles into a model that can aid further exploration and decision-making by stakeholders. We are also actively exploring and brainstorming around formats of presentation, ranging from print publications and decision-making cards to online tools that can help evaluate various scenarios. Shreya aims to finish at least a few phases of the toolkit in the next few months and we’ll share more of our learnings and deliverables from this project in the months to come. By honestly examining and learning from the Project Sammaan journey, we hope that we’ll be able create this toolkit that can lead towards a sustainable model for urban slum sanitation and that will be helpful for the very diverse stakeholders who currently work in this sector.
Post Quicksand’s work on the toolkit, J-PAL will be responsible for converting learnings from the software phase of the project into the toolkit for use by stakeholders, as well as disseminate learnings from the Randomized Control Trial experiment.