Artificial intelligence and machine learning are providing global supply chains with the tools they need to address the heightened risks of the COVID-19 era.
With the impact of the pandemic, regulations are tightening to meet carbon emission standards and reduce greenhouse gases. In Europe, new regulations target emissions generated throughout the supply chain, from the acquisition of raw materials to the delivery of finished goods. Meanwhile, the International Maritime Organization has agreed to new guidelines to reduce the carbon intensity of ships. Taken together, these measures could mark the end of the era of cheap international shipping.
Businesses have long taken for granted logistical links between different parts of the global supply chain. Predictable performance from key transportation modes meant we could confidently build geographically distributed supply chains based on the cost advantages of Asian manufacturing. However, recent events have cast doubt on the validity of these assumptions. Today, we need to take advantage of real-time information to quickly respond to unexpected problems.
AI and machine learning can help companies accurately forecast demand, improve inventory management, and reduce emissions and waste in their supply chains. A shipping provider applied machine learning to existing historical data to create more reliable baseline probability forecasts. It also reduced waste by reducing out-of-stocks and excess inventory.
Even when available, the information you need to make quick decisions is not always presented in an easy-to-understand format. AI and machine learning can help identify patterns in the early stages of a problem while also helping supply chains become more flexible in the event of sudden shifts in demand. In the process, companies can significantly improve the visibility and responsiveness of their supply chains.
AI and machine learning also play a key role in reducing supply chain costs. According to McKinsey, early adopters cut inventory costs by 35% and logistics costs by 15%. AI’s ability to minimize errors and delays further reduces overall costs.
Yet another source of supply chain waste is inventory damage, breakage, or breakage in the transportation of particularly fragile materials. AI-powered sensors track individual shipments, giving managers real-time visibility into inventory status and alerting them when conditions become dangerous. Finally, the AI-driven system can autonomously order new material if supply falls below a specified level.
Over the last 30 years, trade has grown significantly, expanding global supply chains. Today, however, businesses need to be able to manage a multitude of emerging risks in real time while focusing on resilience and sustainability. Companies that successfully navigate these challenges will be those that employ AI and machine learning to stay compliant, reduce costs, and enhance decision-making through access to real-time data.
Boris Khazin is Global Head of Governance Risk and Compliance at EPAM Systems, Inc.
