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Supply Chain
AI Is Helping Brands Address Forced Labor in Supply Chains

As companies have faced reputational risk due to the uncovering of labor abuses from suppliers that many did not even realize were supplying them, there’s been a push to better manage and ensure supply-chain transparency — with technology playing a key role.

Since its release earlier this year, Chat GPT-4 and other artificial intelligence (AI) tools from Google, Microsoft and other tech companies have led to a plethora of speculation about how entire industries could be transformed.

But can AI also help companies manage their supply chains — and, more specifically, eliminate the risk of forced labor and other human rights violations among their suppliers? Justin Dillon, founder of supply-chain management software FRDM, certainly thinks so — he’s seen massive potential since the company integrated AI earlier this year.

“AI is allowing FRDM to fill data gaps in supply chains and mining large data sets for risk intelligence,” Dillon told Sustainable Brands®. “AI is helping us quickly identify key attributes of these sub-suppliers needed to provide accurate risk analysis.”

FRDM is, in fact, one of several supply-chain tracing tools — along with Altana AI, (en)visible, Provenance and Sourcemap — that are incorporating AI to better provide brands with insights into their supply chains. All were formed with the goal of using technology to increase visibility into global supply chains, and empower brands to reduce or eliminate social and environmental risks.

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“One of the key advantages of AI is its ability to provide unparalleled visibility into complex supply chains,” Jay Risser, an AI educator and expert, explained in a blog post. “AI-driven supply chain visibility enables businesses to make informed decisions and build partnerships with suppliers committed to ethical practices.”

It’s a big challenge. Since globalization took off, supply chains have gotten incredibly complex. A simple product such as a lamp or even a jacket can now contain dozens of different components, sourced from different countries. While this has made a plethora of consumer goods available at accessible costs to millions around the world, it’s also led to many unintended problems; and labor rights and environmental harms have been discovered in the supply chains of many of the world’s biggest brands.

As companies began to face reputational risk due to civil society groups and journalists uncovering human rights or labor abuses from suppliers that many did not even realize were supplying them, there’s been a push to better manage and ensure supply-chain transparency, with technology playing a key role.

One of the most unique technology providers in this space is Altana AI — which, unlike FRDM or Provenance, was founded specifically to take advantage of the unique capabilities of AI.

“We're the world's only dynamic, intelligent map of the global supply chain,” Altana AI CEO Evan Smith told SB. “We're using artificial intelligence both to build that map and then we use AI to situate our customers inside of that map and show them what their supply chain connections are beyond their direct relationships.”

This approach would, according to Smith, not have been possible even with the most advanced machine learning technology from a few years ago. And it’s already paid off, with one client using it to meet the strict requirements of the Uyghur Forced Labor Prevention Act (UFLPA), which forbids companies from importing any products from the Xinjiang region of China.

“We connected our platform to the data of one of the biggest brands in the world and found that roughly a third of their supply chain had exposure to Xinjiang,” Smith said. “What they've been doing since then is work with their most important garment manufacturers through our platform to engage the upstream supplier network.” The impact? That brand has seen fewer shipments blocked by US Customs and Border Protection due to forced labor links.

Today, the UFLPA is just one of many new reporting requirements for companies. Others include modern slavery acts in the UK and California, human-rights due-diligence regulations in Germany, and many voluntary ESG reporting requirements.

“Transparency in supply chains is quickly becoming a factor in how companies do business with each other, and how investors fund,” Dillon says.

Of course, AI is merely a tool; and brands must be aware of its limitations and that it still requires a lot of work to provide it with the data it needs to make accurate assessments of forced-labor risk — AI can’t replace bad management practices.

“We meet companies who expect our tech to find receipts or invoices from sub-suppliers deep in a supply chain,” Dillon says. “While AI may map supplier relationships, it will not be able to pull a paper receipt out of the desk drawer of a sub-supplier.”

Moreover, AI can’t make up for the fact that, in many places, data about worker conditions is lacking. Adding to the challenge is deliberate attempts to hide forced labor — of particular concern in China, where traditional tools such as social audits are no longer possible; and the government is increasingly hiding even basic data.

“There are definitely more and more attempts to obfuscate and avoid enforcement,” Smith asserts. “AI is not a silver bullet; it's not an all-seeing eye.”

One area that concerns Smith is forced labor transfers of Uyghurs — in which Uyghur workers are sent to factories around China, including outside of Xinjiang. This was exposed in a 2020 report from the Australian Strategic Policy Institute; but real-time data is lacking.

“There's nothing in our technology that can identify the actual movement of Uyghur people,” he explained. “Unfortunately, I think this is going to be a cat-and-mouse game for a long time.”

Still, Smith, Dillon and others see a real role for AI in pushing us towards a more sustainable and ethical business future. While AI won’t make supply chains transparent or sustainable on its own, it can be a valuable tool for dedicated brands and enable real, meaningful action at scale.