In May 2023, the World Health Organization (WHO) declared an end to the COVID-19 pandemic. Despite that announcement, fallout from the pandemic continues to reverberate through global supply chains, exposing their opacity and fragility and catalyzing their transformation. Geopolitical issues, such as Russia’s invasion of Ukraine and rising tensions between the United States and China, have shaped supply-chain transformation, resulting in what I call a “supply-chain iron curtain” that is poised to complicate international trade.
But that does not mean the end of globalization. Rather, it reflects growing regionalization of global supply chains. Companies are deviating from a traditional, cost-driven approach to supply-chain management by making strategic and operational decisions increasingly in light of geopolitical considerations. A strategy known as “friend-shoring” has gained traction among many political and business leaders. Companies use that approach to develop supply-chain resilience by sourcing goods from ideologically compatible countries and regions. Alternative trade strategies include reshoring, which brings manufacturing back from other countries, and nearshoring, which involves sourcing from nearby countries — e.g., Canada and Mexico from the perspective of companies based in the United States.
Artificial intelligence (AI) plays an important role in initiating global supply-chain strategies. Although AI is commonly associated with data analytics, it also has a remarkable ability to handle large volumes of unstructured data in real-time. Instances of unstructured data, such as social-media posts, emails, audiovisual files, and news reports, pose difficulties for traditional data collection and interpretation. Organizations miss out on valuable insights that can improve their supply-chain resilience and efficiency when they ignore such information. This is where AI models come into play.
AI-Infused Supply Chains
Modern AI models are based on deep neural networks that are adept at detecting patterns in vast amounts of unstructured data. By analyzing disparate data sources — e.g., social-media posts indicating unexpected spikes in demand for specific goods and news reports covering local political unrest and natural disasters — AI models can predict potential supply-chain disruptions. As the models adapt and learn, their predictions gain accuracy, enabling businesses to respond quickly and effectively.
In recent years, environmental, social, and governance (ESG) issues have come to the forefront of business operations because regulators, investors, researchers, and customers increasingly demand quantitative measures of sustainability performance from companies. However, ESG measures are meaningless unless they extend beyond a company’s boundaries to include performance across a supply chain. AI can help monitor a company’s carbon footprint throughout its entire supply chain and help ensure labor compliance throughout. AI also can evaluate potential supply partners based on their ESG ratings.
How to interact with an AI-infused supply-chain landscape is an increasingly important consideration. The pandemic catalyzed AI use in many ways by pushing businesses to their breaking points and forcing them to adapt. However, the use of AI in global supply remains in its infancy. Accelerating adoption may require a new catalyst, such as another major disruption or increased awareness of AI’s potential to improve supply-chain resilience and ESG compliance. Growing interest in generative AI (e.g., DALL.E-2 and ChatGPT) also could catalyze further adoption.
Companies need to realize that potential uses for AI go beyond data analysis. AI can help to usher in a new era of resilient, ethical, and sustainable supply-chain management. Integrating AI into supply-chain management is about more than just business survival. It ensures long-term competitiveness for companies in a world where ESG issues and geopolitical instability are at the forefront of both strategic and operational decision-making. Companies that leverage AI will be prepared for the next phase of global supply-chain transitions, gaining a competitive advantage in a world permanently changed by the COVID-19 pandemic.
Tinglong Dai is a professor of operations management and business analytics at Johns Hopkins University.
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