Artificial intelligence and machine learning are equipping global supply chains with the tools they need to address heightened risk in the era of COVID-19.

Along with the effects of the pandemic come tougher regulations to meet carbon-emission standards and reduce greenhouse gases. In Europe, new rules cover emissions generated throughout the supply chain, from acquisition of raw materials to delivery of finished goods. Meanwhile, the International Maritime Organization has agreed to a new set of guidelines for reducing the carbon intensity of ships. Taken together, these measures could mark the end of the era of cheap international shipping. 

Companies have long taken the logistics links between distinct parts of their global supply chains for granted. Predictable performance by the major transportation modes meant that they could confidently build geographically distributed supply chains based on the cost advantages of Asian manufacturing. But recent events have challenged the validity of these assumptions. Today, there’s a need to respond rapidly to unanticipated issues with real-time information.

AI and machine learning can help companies accurately forecast demand, improve inventory management and reduce emissions and waste in their supply chains. One maritime transportation provider applied machine learning to existing historical data to create more reliable baseline probability forecasts. It was also able to reduce waste through fewer stockouts and instances of excess inventory. 

Even when it’s available, the information needed to engage in rapid decision-making doesn’t always appear in an easily digestible form. AI and machine learning can identify patterns at the onset of an issue, while helping supply chains to become more flexible in the event of sudden changes in demand. In the process, businesses can greatly improve their supply chain visibility and responsiveness.

AI and machine learning can also play an important role in reducing supply chain costs. According to McKinsey, early adopters reduced inventory expense by 35%, and logistics costs by 15%. AI’s ability to minimize errors and delays further reduces overall expense.

Yet another cause of supply chain waste is damaged, ruined or spoiled inventory, especially in the transportation of fragile materials. AI-powered sensors can track individual shipments, giving managers real-time visibility into the state of inventory, then issuing alerts when conditions become unsafe. Finally, AI-driven systems can autonomously order new materials when supply falls below specified levels. 

The last three decades saw tremendous growth in trade and expansion of global supply chains. Today, however, businesses need to be able to manage a host of new risks in real time, while focusing on resilience and sustainability. Those that successfully navigate these challenges will be the ones that embrace AI and machine learning to stay compliant, reduce costs and enhance decisions through access to real-time data.

Boris Khazin is global head of governance risk and compliance at EPAM Systems, Inc.