How supply chain AI is used across industries

Using Artificial Intelligence in supply chain management may feel trendy, but leveraging this advanced technology is more than just keeping up with the supply chain Joneses. AI can easily streamline your supply chain processes, highlighting potential disruptions, monitoring established situations, and suggesting mitigation options.   

For the doubters out there, let’s face it: unwieldy and complex global supply chains aren’t likely to become more straightforward and simpler with time, even if companies attempt to nearshore their suppliers as much as possible. Instead, AI can take the complicated nature of these supply chains and help you map and visualize each element, collating data to give you detailed information on a supplier’s history, importance within your supply chain, risk factors, and more. 

AI is already transforming supply chain management across industries, doing more than just collating data. It helps customers uncover hidden relationships and anomalies within their data, offering insights that enhance their understanding of the supply ecosystem. By simplifying processes and analyzing data, AI enables supply chain experts to make proactive, educated decisions. Here are examples of how companies leverage AI-powered tools for supply chain success. 

Case study: Using AI for multi-tier mapping  

A large, global CPG company wanted to understand the extent of their supply chain network and inherent risk, especially when it came to climate change. Additionally, the company wanted better information and warnings in relation to suppliers with potential compliance and reputational risks.   

Using Everstream’s industry knowledge graph, powered by AI, the company was able to fully visualize their network, including sub-tier suppliers. The graph was created by applying AI to data sources relating to the world’s global supply chain, including import, export, and shipment records. While this would have been manual, error-prone and time-consuming for a supply chain management expert, the AI was able to fuse the data with other sources, producing a network that was easy to understand and that provided actionable intelligence. Finally, human experts from Everstream and the company were able to verify and assess the AI’s output, ensuring accuracy and completeness. 

Figure 1: Everstream Analytics engine of AI and ML to generate actionable intelligence   

Case study: Using AI to predict the impact of a supply chain disruption  

A chemical company needed greater accuracy in their weather forecasting capabilities, as climate events generally had a significant impact on their supply chain operations. Since their climate data had never been as accurate as it could be, they weren’t able to make proactive decisions, or even react quickly enough to prevent disruptions. 

AI-powered climate models allowed them to understand the risks associated with emerging weather events, even unpredictable ones. Using up-to-date meteorological data, combined with specific information about the chemical company’s supply chain, the AI model was able to provide earlier alerts to the potential risks of an abnormal weather pattern. In turn, the supply chain management team were able to turn these alerts into proactive decisions, saving the company time and money, and reducing the overall disruption. 

Case study: Using AI to fully understand supply chain risks  

A pharmaceutical manufacturer wanted a better understanding of their supply chain risks, while also minimizing the time and money associated with due diligence and risk scoring. Their current process was not only incomplete and inconsistent, but also extremely manual and time-consuming.  

By feeding all of the due diligence information on their suppliers into an AI tool, Everstream were able to create a simple, data-driven, and uniform risk scoring process for this company. The AI was then able to continuously monitor for risks using a variety of data streams and contextual information, updating the score and alerting the company when necessary. The supply chain management team was able to adjust the model, ensuring accuracy and utility. 

Case study: Using AI to prevent forced labor in supply chains  

An automotive original equipment manufacturer (OEM) needed to stay compliant with the US’ Uyghur Forced Labor Protection Act (UFLPA). Therefore, they needed to be aware of any potential forced labor risks within their supply chain.   

Everstream provided them with a UFLPA Risk solution, which gave the company insights into a UFLPA Supplier Watch List. AI was then able to comb through forced labor risk data to highlight any hidden risky suppliers, including aliases, subsidiaries, and other connections to noncompliant suppliers. Human oversight was then used to verify the AI’s findings, and then alerted the company to the forced labor risk. Ultimately, this allowed the company to maintain and prove their compliance to the UFLPA, and take quick action when a noncompliant supplier was found.  

Next Steps  

These are just four examples of how AI can revolutionize your supply chain management process. AI is a great tool, and when combined with human experts, can provide extremely useful analysis, insight, alerts, and suggestions for supply chain success.  

If your company wants to take advantage of AI benefits within your supply chain, start by looking at your current processes. AI is great at collating great amounts of data, categorizing and analyzing it, so that your supply chain management team can focus on making effective decisions. Look for the places in your supply chain management process that are currently data-rich and time-consuming. Or, look for situations that change quickly, where data needs to be analyzed immediately to provide your team with actionable insights. These are just two simple examples where an AI-powered tool could help your company streamline your processes.  

Remember, AI is ultimately a tool, and works best when paired with human involvement and oversight. AI results should be verified and subject to your company’s subject matter experts, and algorithms and models should be regularly adjusted accordingly.   

The AI revolution isn’t coming to replace supply chain managers, but it will make them more effective. By using AI to crunch large amounts of data quickly, provide helpful analysis, and offer informed suggestions, supply chain management teams can focus on making the best decision possible in the face of emerging and inherent risks. AI can help your company weather all kinds of disruptions, so it’s worth taking the time to find the right tools for your supply chain.  

Don’t be a company that waits too long to take the leap – implement AI tools today to ensure tomorrow’s supply chain success.