The Role of Artificial Intelligence in the Development of Resilient Supply Chains

Key Chapter Title List

1. The Missing Link Between AI and Supply Chains in the Biden Executive Order
2. Advantages of AI-Driven Supply Chain Planning
3. The Role of AI in Supply Chain Mapping
4. Utilizing AI to Monitor Supply and Demand Changes
5. Using AI to Design Effective Response Plans for Supply Chain Disruptions
6. The Impact of AI on Employment and Public Policy

Document Introduction

The concept of artificial intelligence was first proposed in the 1950s, but it only entered the public consciousness on a wide scale after the ChatGPT frenzy at the end of 2022; the term supply chain management was born in the 1980s, but its importance was not fully recognized until the COVID-19 pandemic caused long-term shortages of various products. Today, an increasing number of enterprises are beginning to use AI to manage global supply chains, giving rise to two core questions: Can AI enhance supply chain resilience? What impact will AI have on employment in the field of supply chain management?

Both the U.S. Biden administration and the European Union have introduced relevant policies. The former focuses on AI governance and enhancing supply chain resilience through executive orders, while the latter reached a provisional agreement on the "EU Artificial Intelligence Act" to regulate high-risk AI systems. However, neither U.S. nor European policies have adequately connected the intrinsic link between AI development and supply chain resilience. The Biden administration secured $52.7 billion in funding through the CHIPS and Science Act to support the construction of resilient supply chains for critical products and established the White House Council on Supply Chain Resilience, yet it has not filled this policy gap.

AI possesses the potential to transform supply chain operations, with its core value manifesting in multiple dimensions. In supply chain planning, AI can process massive amounts of real-time data, improve demand forecasting accuracy, and help enterprises optimize production, inventory, and logistics plans. Early adopters have achieved significant results: a 15% reduction in logistics costs, a 35% improvement in inventory levels, and a 65% increase in service levels. Furthermore, 70% of surveyed enterprise CEOs acknowledge the strong return on investment brought by AI.

Supply chain visibility is the foundation for enhancing resilience, and AI provides crucial support for supply chain mapping. Since only 2% of enterprises can achieve visibility beyond their tier-2 suppliers, supply chains are vulnerable to shocks from natural disasters, geopolitical factors, etc. AI tools can accurately extract data in multiple formats and languages, integrate information such as orders and customs declarations, and construct supply chain tier maps. AI tools from companies like Altana have already implemented this functionality and improved communication efficiency among partners.

At the level of supply and demand monitoring, AI can integrate point-of-sale data, social media information, etc., using real-time dashboards to warn of abnormal demand changes, even capturing early signals of panic buying. Simultaneously, it can analyze traffic data from multiple links such as ports and warehouses to promptly identify supply disruption issues. The U.S. Department of Transportation's Freight Logistics Optimization Works (FLOW) initiative, launched in 2021, is a successful practice in this area.

Building a resilient supply chain requires the ability to quickly detect disruptions, design response plans, and deploy them rapidly. AI helps enterprises formulate optimization plans such as product design adjustments and supplier switching by simulating the impact of different response strategies. It can also proactively refine supply chain policies based on factors like seasonality and macroeconomic trends. It is noteworthy that AI will not reduce employment in the field of supply chain management; instead, it will eliminate clerical and data entry positions, giving rise to new professions such as research scientists and AI ethics analysts. At the same time, human involvement will be required for data filtering, ethical control, and validating the reasonableness of AI suggestions.