The term artificial intelligence (AI) was first introduced in the 1950s, but it wasn’t until the launch of ChatGPT, which amassed over 100 million users within just two months in late 2022, that the public began to take notice. Similarly, the importance of “supply chain management,” a term coined in the 1980s, was largely overlooked until the COVID-19 pandemic led to prolonged shortages of various products, from personal protective equipment to semiconductors. Today, an increasing number of companies are turning to AI to manage their global supply chains. Two questions arise: can AI enhance supply chain resilience? What impact will AI have on employment in supply chain management?
The missing link between AI and supply chain in Biden’s executive orders
The Biden administration has paid considerable attention to both global supply chains and AI. In 2023, President Biden signed two executive orders: one regarding governance and responsibility in AI development and another to improve supply chain resilience. In June 2023, the White House released a progress report to build resilient supply chains for four critical products: semiconductors, large-capacity batteries, critical minerals and materials, and active pharmaceutical ingredients. The development of resilient supply chains is a key component of Bidenomics. For this endeavor, Biden attained $52.7 billion from Congress through the CHIPS and Science Act. Later, in October 2023, the White House released a report summarizing President Biden’s executive order on Safe, Secure, and Trustworthy AI. The order requires developers of powerful AI systems to meet certain safety standards before publicly releasing their solutions. Finally, President Biden announced in November 2023 the establishment of the new White House Council on Supply Chain Resilience to develop new capabilities to monitor existing and emerging risks and to detect and respond to supply chain disruptions in critical sectors with supply chain partners. Although these policies are promising, American policymakers have yet to address the inherent connection between AI development and supply chain resilience.
This trend is not limited to the United States. First proposed in 2021, the EU Parliament reached a provisional agreement with the Council on the EU AI Act in December 2023. The policy provides guidance for high-risk AI systems and breaks down responsibilities throughout the AI supply chain with requirements for importers, distributors, and other supply chain stakeholders.
The benefits of AI-enabled supply chain planning
AI has the potential to revolutionize supply chain operations by improving decision-making and efficiency. According to a 2022 McKinsey survey, respondents reported that the highest cost savings from AI are in supply chain management. Specifically, AI can add value to supply chain planning, including production, inventory management, and product distribution. Companies can also leverage AI-powered tools to process vast amounts of real-time data and improve the accuracy of demand forecasting. With more precise demand forecasts, AI-enabled tools can help firms optimize production and inventory plans across various locations and select the most cost-effective logistics solutions.
Early adopters of AI-enabled supply chain management have reduced logistics costs by 15 percent, improved inventory levels by 35 percent, and enhanced service levels by 65 percent. Adopting AI tools to manage manufacturing operations can be costly, but 70 percent of the respondents from a survey of CEOs of over 150 firms agreed that AI is delivering a “strong ROI.” Despite the potential of AI in supply chains, AI should not decrease employment in supply chain management. Rather, it should create new opportunities to mitigate potential risks associated with adopting new technologies.
The role of AI in mapping supply chains
AI can certainly make internal operations more efficient; this starts with achieving supply chain visibility (i.e., the ability to view and track inventory levels as goods move along the supply chain). Visibility would allow firms to respond to disruptions in real time. A 2021 survey revealed that only 2 percent of companies claimed to have visibility beyond their second-tier suppliers–those who supply materials and parts to their direct suppliers. Without strong visibility, company supply chains are susceptible to disruptions caused by issues such as natural disasters, pandemics, geopolitical issues, trade barriers, and product recalls. Therefore, firms should seek to leverage AI to enhance supply chain visibility.
Mapping the supply chain is a crucial step towards enhancing its resilience, and AI tools can provide substantial assistance in this regard. These tools can gather records like product orders, customs declarations, and freight bookings, which are often represented in various formats and languages. AI algorithms can extract relevant data from both structured and unstructured documents with high precision. AI tools can compile and synthesize this raw data, enabling a firm to map out its different supply chain tiers. For instance, Altana, an AI startup that creates dynamic maps of global supply chains, has developed a generative AI tool that utilizes both public and private data to map a company’s supply chain. This tool is complemented by a large language model (LLM)-informed assistant that responds to employees’ queries posed in plain language. Using document processing systems to capture, analyze, and share documents, such as invoices, bills of lading, and purchase orders, Altana can enhance efficiency and accuracy in logistics and improve communication among supply chain partners.
Using AI to detect changes in demand and supply
AI can also help firms gauge market demand and customer sentiment. Utilizing scanner data collected at point-of-sale locations, along with vast data from customer reviews and blog posts on social media, AI-based tools, such as Google’s Video AI can gather and analyze text, images, and videos. Google Video AI can then develop a real-time, end-to-end supply chain dashboard that can generate alerts for abnormal demand changes due to competition or product issues. The AI can even detect early signs of panic buying using large data sources. The Google Video AI dashboard can then pinpoint the underlying causes for such abnormalities.
In addition to real-time detection of demand changes, AI tools can compile and analyze data on traffic conditions at different supply chain tiers such as ports and warehouses. These tools can detect supply disruptions caused by supply and worker shortages, factory shutdowns, and shipping delays, among other issues. For instance, when ports on the West Coast faced unprecedented delays in September 2021, the US Department of Transportation developed a national transportation supply chain dashboard that tracked three key indicators of goods moving from ports to retail stores: the number of imported containers, US retail inventory levels, and the on-shelf availability of consumer goods. Tracking these indicators in real time offered the ability to detect and react to anomalous patterns as they occurred.
Using AI to design effective responses to supply chain disruption
A supply chain becomes more resilient when it can quickly detect and respond to disruptions, thereby minimizing the impact. According to the supply chain risk management literature, three capabilities are necessary to build resilience: (1) detecting a disruption quickly, (2) designing an effective solution in response to the disruption, and (3) deploying the solution swiftly. Traditionally, firms have ensured supply chain resilience by developing advanced systems to enhance detection, setting up proactive contingency plans, and conducting stress tests for rapid deployment. However, AI and Industry 4.0 technologies, such as sensors, blockchains, and data analytics, can amplify these resilience capabilities manyfold.
With the ability to detect abnormal changes in supply and demand, AI tools can help companies evaluate and compare the effectiveness of different response strategies by conducting simulations. These simulations assess the impact of each possible response on demand and supply as well as the recovery time from disruptions. By analyzing the simulated results and examining the effects of different responses on various supply chain partners, a firm can swiftly develop a well-informed strategy in response to a sudden change. Response strategies may involve modifications to product design, adjusting prices, and switching upstream suppliers. In terms of potential use cases, AI could help a government agency design a supply chain for medical countermeasures to defend against bio-attacks. Retail companies could use AI to simulate and predict the impact of implementing rationing policies in retail stores. More broadly, the goal is to evaluate alternative scenarios to ensure resilience against potential unforeseen disturbances and understand how mitigation strategies affect each part of the supply chain.
AI can help firms respond to crises, but importantly, it can also help companies strengthen supply chains before they are strained. AI can recommend changes to a company’s supply chain policies based on a multitude of factors, such as seasonality and macroeconomic trends. For instance, AI can identify the best supply chain configuration, the optimal number of suppliers (and their locations), and the most favorable terms of the supply chain contracts.
AI implications for employment and public policy
While AI holds immense potential for developing resilient supply chains, the Biden administration can coordinate with the European Union to mitigate various risks arising from AI-enabled global supply chains. Similar to how Biden has pursued responsible AI development in the United States, he should work with European regulators to ensure that the data with which LLMs are trained is sourced ethically and avoids copyright violations. Responsible AI development is key to the stability of supply chains, as AI becomes increasingly engrained in supply chain management.
Even with the help of American and European regulators, firms must manage certain risks through human involvement. AI-enabled supply chains will transform the role of supply chain professionals, eliminating jobs in clerical and data entry, but they will also create new jobs. The data used to train AI and AI-produced insights can be biased. Hence, humans must identify the most relevant data on which to train LLMs and ensure adherence to ethical guidelines. AI also does not always comprehend the contexts and nuances of global supply chains; humans must interpret and examine the appropriateness of AI-generated recommendations. Thus, new personnel, including research scientists, chatbot developers, AI ethics, and bias analysts, are necessary to develop resilient supply chains. Additionally, to manage increasingly complex supply chain operations amidst exceedingly complex geopolitical issues, the role of supply chain managers has never been more critical.
AI promises to disrupt industries, including justice, retail, marketing, transportation, media, and biosciences. Indeed, the World Economic Forum commented that the future of work is changing with machines and AI likely to take on an increasing share of work. The interplay between AI and supply chain management, two critical sectors, is more important than ever to create economic stability and resiliency. However, the future is not as pessimistic as Elon Musk’s claim that AI will take most jobs away–at least not in supply chain management for the foreseeable future.
Maxime C. Cohen is the Scale AI Chair Professor of Retail and Operations Management at McGill University and a Visiting Professor at Yale School of Management.
Christopher S. Tang is a UCLA distinguished professor and the Edward W Carter Chair in business administration.
To read the full article published by Georgetown Journal of International Affairs, click here.