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Unlocking Transparency in the Supply Chain with trusted AI
AI-powered insights for supply chains can help organizations unify fragmented data, uncover hidden connections, and anticipate disruptions.
Enterprises are challenged by supply chains that are layered, opaque and contain complex relationships that traditional tools cannot decipher. Today’s supply chains face immense disruption due to political instability, climate events, and increasing tariffs, among other factors. Organizations are forced to react rapidly diversifying and onboarding more suppliers, which often leads to more intermediaries, less transparency, and often more integrity issues.
Without a clear view of the entire supply chain ecosystem, companies risk regulatory, reputational, and resiliency challenges. For example:
How can organizations like yours protect against regulatory, reputational risks such as foreign interference or sanctions evasion if they only have a 1-dimensional, narrow view of their Supply Chain eco-system?
Without the full transparency of an organization's multi-tier supply chain, how can you assess the resilience of that supply chain to the impact of world events?
AI’s role in enhancing transparency and resilience
AI, which has been around for decades, is now seen as a powerful tool in solving complex supply chain issues. Up until very recently this was all about machine learning (ML) models that use data from the past to predict risks and identify opportunities, such as potential fraud, geopolitical threats, and raw material shortages.
Generative AI and large language models, like ChatGPT, are increasingly being used by organizations across multiple different use cases to enhance transparency in the supply chain. These models can make sense of historical data, market conditions, weather patterns, and even geopolitical events, all of which play crucial roles in identifying supply chain risks.
The constant stream of innovative and powerful use cases is exciting, however there are challenges including:
Machine Learning and Generative AI technology is only as powerful as the quality and availability of the data that powers it.
AI monitoring and tools are often deployed in silos, with some delivering an “inside out” view of risk or an “outside in” view, and missing crucial context.
Key techniques to prepare data for AI
For AI to be effective, it’s essential to have quality, contextual data, ensuring that data is properly prepared to drive AI models. One of the key techniques in data preparation is entity resolution, which involves consolidating fragmented data about people, businesses, or products into a unified view. This enables organizations to understand the full scope of their supply chain ecosystem.
Once data is unified, advanced techniques like graph analytics can provide deeper insights into the relationships and behaviors of entities within the supply chain. This helps to uncover hidden connections, such as companies that may be obfuscating their identities or engaging in complex, high-risk behavior.
Bringing data together for better decision-making
The process starts with obtaining a unified, contextual view of the supply chain ecosystem. By combining both internal and external data points (like contract data, corporate registries, shipping data and news), businesses can monitor and identify potential risks in real-time. Early warning signals, such as geopolitical events or natural disasters, can be tracked and modeled to anticipate how they might impact the supply chain.
Once data is contextualized, AI can help organizations decide and act. Machine learning models can automatically detect risks and opportunities, while generative AI can simulate world events, propose actions, and offer solutions.
Real-world examples and case studies
A practical example of AI in action comes from the fight against corruption. In one European case, a health contract valued at hundreds of millions of dollars had been awarded to a supplier whose products were ultimately destroyed for not meeting requirements. By unifying and contextualizing internal data, offshore leaks, and news sources, AI helped uncover hidden connections between the supplier and shell companies, and flagged potential corruption risks.
In another case related to supply chain resilience, AI helped provide transparency into a critical contract for a project in Asia. By unifying shipping data, news and corporate registry, the AI system identified a second-tier manufacturer in Thailand, highlighting potential vulnerabilities in the supply chain.
Future-proofing supply chains with AI and contextual data
AI has the potential to revolutionize the way organizations manage their supply chains by enhancing transparency and resilience. With the right data, AI models can predict and mitigate risks, identify hidden connections, and provide real-time insights into the state of the supply chain. As businesses continue to face ever-evolving challenges, embracing AI-driven solutions will be key to maintaining integrity and resilience in today’s complex and disruptive environment.
