Goonj: Using Data to Manage Logistics for Development

Logistics in the Social Sector

Industrial supply chain and logistics management solutions are a major application area of data-driven systems, and can be seen as a precursor to the big data revolution. However, you would be surprised how managing logistics in the social sector is an entirely different ball game altogether.

Imagine receiving more than 1,000 tons of material every year from the community, classifying them into various kits, converting them into usable material, and then distributing them to various organizations across the country.

Goonj, a renowned NGO, deals with this problem every year in its efforts to address the basic human need of clothing. Over the last sixteen years, they have pioneered the use of material – like clothes, shoes, stationary, and furniture, among other things – as a tool for empowerment and social change.

We spoke to Goonj’s co-founder Ms. Meenakshi Gupta to get a perspective on opportunities for enhancing data-driven decision making in the social sector.

Goonj’s Impact

In just sixteen years of its existence, Goonj has emerged as one of India’s best known NGOs. With over 250 partner organizations, including the Indian Army, they have a presence in 21 states in both urban and rural areas. They have also won several national and international awards, including Changemaker’s Innovation Award (which Goonj won three times) and the Edelgive Award. In addition, Goonj’s founder Anshu Gupta was chosen for the Ramon Magsaysay Award in 2015.

Through their innovative programs like Cloth for Work (CFW), they have managed to highlight the fundamental need for clothing. Through CFW, Goonj has channeled local knowledge by giving people clothes and other material in exchange for repairing roads, recharging water ponds, building bamboo bridges, digging wells, and more. Goonj has initiated over 1500 such activities in the past three years.

Their NJPC (Not Just A Piece of Cloth) campaign, which focuses on “opening up the most taboo and ignored subject of menstrual hygiene” by creating an affordable cloth napkin. The NJPC campaign has led to the delivery of over 3 million sanitary pads, produced out of waste cloth, to women in villages and slums.

A key aspect of Goonj’s programs and projects is their bottom-up, community-driven approach, which help them rework development ideas on material. Measuring the grassroots impact of this work holds exciting challenges for data-driven systems.

The Logistics Process

Goonj’s internal processing and logistics data is collected and managed through their daily internal reports, which are generated through the classification and sorting systems at their collection centers. Unlike industrial logistics, the workforce handling the logistics for Goonj might not be the most well educated. This lead to the creation of a classification system that is simple enough to be executed by local communities.

All material that Goonj collects goes through a chain of sorting, processing, and recycling processes. Then the material is grouped into kits, each having its own unique code based on the chain it follows. These unique codes are then recorded in a register.

These kits are dispatched in batches to organizations in different states across the country, the record of which is maintained in a separate register. This register is regularly cross-checked with the existing database to keep a tab on the current inventory of kits, and those that have been dispatched. The kits involved in each dispatch are also tracked, and a cumulative list is sent as a receipt to the organizations receiving the material.

The Bottleneck 

Goonj’s logistics processes are very streamlined, since they manage what is likely the highest volume of logistics in the Indian social sector.

However, even the most streamlined processes take their toll when they are tracked through paper-based data collection.

The amount of paper consumed, the time spent in data entry, and the effort spent in cross-checking, compiling, and sending the invoices … these are all areas that needed to be improved.

In an attempt to fine tune these processes, Goonj has recently turned to technology. Using a mobile data collection tool instead of paper will cut down on the time and effort spent in data entry. Cross-checking between the dispatch register and the kit register, then sending a material receipt are becoming automatic, digital processes.

Goonj has turned to SocialCops’s data collection tool Collect to digitize these processes and save time and effort that could be used in more important areas.

Measuring Impact in Logistics

Now measuring and managing material is being dealt with, but measuring social impact remains a tough data problem. Ms. Gupta remarked, “Our goal is to grow as an idea, not just as an organization. For that we need to make ourselves replicable, which in turn requires better impact assessment and evidence building.”

She added that they are looking forward to create a “replication kit” for their programs and projects to spread their approaches and learnings. They are also turning to technology again for better grassroots data collection. They plan to use Collect to process the data being generated through their NJPC program.

However, it is always perplexing how we can holistically “measure” social impact of dignity in clothing. How can we measure the impact on individuals, families and communities from giving women easy access to sanitary pads. How can we measure the impact of a bridge which helps people from a remote village cross a river safely?

At one level, we know that all these initiatives have led to immense impact. But how we can quantify this impact and use it to drive decision making remains an immense challenge. An industrial organization can measure their impact by revenue, sales, etc. However, measuring impact is still a challenge for organizations in the social sector. This is the key data problem to solve in the Indian context. But, as Goonj’s work exemplifies, sensitivity to grassroots realities can go a long way in solving this problem.

This article was co-authored by Sandeep Mertia and Avinash Kothuri.