The Promise of Machine Learning

In today’s global economy, multinational companies must be able to manage networks that span across continents and include thousands of suppliers. Without some degree of automation, it’s a near impossible task. It simply takes too much time and costs too much money.

Artificial intelligence- machine learning in particular- allows companies to scan a much larger universe of available data in a far shorter period of time. This data can be aggregated and analyzed at a scale and speed unmatched by humans, enabling companies to conduct more comprehensive and exhaustive assessments of risk in their supply chains. Though a finding of forced labor should ultimately rely on more careful and detailed information, such as social audits or worker voice, FLARE and other machine learning tools serve as critical decision support tools. Where risk exists in every tier of a supply chain, machine learning can help companies prioritize their efforts in the absence of any other good information.

Machine learning solutions are only as good as the data that trains them.
To train a machine learning model, there must be at least two categories of firms in a dataset – for example, low-risk and high-risk. If a tool can only respond to the data it is trained with, then a dataset that includes only high-risk firms gives the model nothing to learn.

However, gathering data in contexts where it is extremely limited, poorly structured, and scattered across sites that do not bear a clear relationship to one another, can be extremely challenging. This was the case for India’s garment industry.

Envisage confronted similar challenges collecting data on labor violations in India’s shrimp processing sector. Finding officially reported cases of forced labor difficult to find, Envisage adopted a broader systems approach, collecting additional data from a wider range of sources and analyzing it to identify common characteristics of companies with forced labor cases in their supply chains.

Developers of FLARE worked with Envisage to understand how the various data sets were found, chosen, and determined as potentially useful, based on the limited amount of information in the sector. This insight informed GFEMS approach to gathering and structuring data and ultimately helped train the FLARE model to better assess labor conditions across company profiles.

To learn more about Envisage’s data collection approach, click here.

The Way Forward

It is not just rising consumer demand for ethically sourced goods that is pressuring companies to take action. In recent years, there has been increasing regulatory pressure on companies to monitor and prevent forced labor in their supply chains. Penalties for goods produced under forced labor working conditions can be harsh and are becoming a very real possibility for companies who fail to conduct due diligence. At a recent GFEMS-hosted event, an executive from a rubber glove company that had been sanctioned with a withhold release order said it had cost their business $40 million, six months of imports, and that it had been the worst thing that had ever happened to him personally.

According to one company executive, a withhold release order cost his business $40 million, six months of imports, and was the worst thing that had ever happened to him.

As pressure from both consumers and regulators continues to mount, businesses must be proactive in their response. FLARE can be a valuable tool for companies to monitor and assess risk of forced labor throughout their supply chains. As an open-source proof of concept, its best path forward is integration into existing supply chain management or broader ESG platforms. For more information on integrating FLARE as part of your supply chain monitoring or social compliance efforts, please contact Mihir Prakash at mihir.prakash@gfems.org

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