Katie Shaw: From Opaque to Open: Untangling Apparel Supply Chains with Open Data

The intro from the conference website:

The tragic Rana Plaza building collapse in 2013 revealed to the world how little many global brands knew about where their products were being made. Following the disaster, demand for supply chain disclosure in the apparel sector was heightened. However, in response to these calls for greater transparency, supply chain disclosure has been inconsistent, inaccessible, of poor and varied quality, and stored in siloed databases. The Open Apparel Registry (OAR) was built to address all these data challenges. At its heart, the OAR exists to drive improvements in data quality for the benefit of all stakeholders in the apparel sector. Powered by a sophisticated name- and- address-matching algorithm, the tool creates one common, open registry of global facility names and addresses, with an industry standard facility ID.

Join us to learn more about the challenges facing the apparel sector, including low levels of technical exposure and understanding of open data; collaborative work that’s being done to educate the sector on the power of open data, including the launch of the Open Data Standard for the Apparel Sector (ODSAS), and examples of how data from the Open Apparel Registry being freely shared and used is creating meaningful changes in the lives of some of global society’s most oppressed people.

[Note from Maurits: apparel is a fancy word for clothes. I had to look it up.]

So what is the trouble with fashion anyway? Not high fashion, I just mean your daily clothes.

Fashion generates 2.5 trillion dollar in global annual revenues. Two of the richest people in the world are fashion industry magnates. "It takes a garment worker 18 months to earn what a fashion brand CEO earns during lunchtime."

In 2013 a garment factory collapsed in Dhaka. Workers were forced to go to work earlier on that day, although many saw cracks in the building.

Supply chains are complex. The label in your T-shirt may say "made in Vietnam" but parts of it may have been made in a completely different location. Maybe simply the buttons are from an entirely different country.

How can better and open* data help? There are databases with addresses of factories, that we cannot match to an actual address. Visiting the factory to check working conditions, or train people, is not possible if you cannot even find the building.

See the tool at https://openapparel.org

Each unique facility in the OAR (Open Apparel Registry) is allocated an ID number.

Technically, the biggest issue was: the data. (It's always the data...)

• no industry-wide standards
• often extracted from PDF
• non-structured addresses (5 kilometers after the post office)

We had 50,000 existing facilities. Any new facility uploaded needed to be checked: maybe it is already there. This took far too long. We now use the dedupe Python package for this, which uses fuzzy matching. Much faster.

We made an Open Data Standard for the Apparel Sector (ODSAS). We call for Open Data Principles in EU corporate sustainability reporting directive legislation.

Sign on and join: Clean Clothes Campaign, Open Appparel Registry, WikiRate, and more.

I want to share stories of the OAR in action.

Clean Clothes Compaign, where Plonista Paul Roeland works, is a global alliance dedicated to improving working conditions of workers in the apparel supply chain. It uses the OAR data in its Urgent Appeal work, in which it responds to concrete violations reported by workers and unions. For example, a union leader was sacked, but after an appeal by CCC he was restored after five days.

Our data is used to map which apparel facilities will be underwater in 2030. Researchers combined our data with sea level projections from the climate panel. The OAR provided a unique data-set for this work.

See our code here: https://github.com/open-apparel-registry

It's time to untangle supply chains!

For further reading, there are also books, especially Fashionopolis by Dana Thomas.

BHRRC used our data in a case where workers did not get paid. In our data they found which brands were using this factory, they contacted them, and the case got solved.