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 related to governance and accountability in AI development and another to improve supply chain resilience. In June 2023, the White House released a progress report on building resilient supply chains for four key products: semiconductors, large-capacity batteries, critical minerals and materials, and active pharmaceutical ingredients. The development of flexible supply chains is a key component of Bidenomics. For this effort, Biden secured $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 Safe, Secure, and Trusted AI Executive Order. The order requires developers of powerful AI systems to meet certain security standards before releasing their solutions publicly. Finally, President Biden announced in November 2023 the creation of a new White House Council on Supply Chain Resilience to monitor current and emerging risks and work with supply chain partners to identify supply chain disruptions in key areas. And new capabilities must be developed to respond to them. While these policies are promising, U.S. policymakers have yet to address the inherent link between AI development and supply chain resilience.
This trend is not limited to America. 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 the requirements for importers, distributors, and responsibilities throughout the AI supply chain. and other supply chain stakeholders.
Benefits of AI-powered 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 greatest cost savings from AI are in supply chain management. In particular, 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 forecasts. With more accurate demand forecasting, AI-powered tools can help firms optimize production and inventory plans across locations and select the most cost-effective logistics solutions.
Early adopters of AI-powered supply chain management have reduced logistics costs by 15 percent, improved inventory levels by 35 percent, and increased service levels by 65 percent. Adopting AI tools to manage manufacturing operations can be expensive, but 70 percent of respondents to a survey of CEOs from more than 150 firms agreed that AI is delivering “strong ROI.” Despite the potential of AI in supply chains, AI should not diminish employment in supply chain management. Rather, it should create new opportunities to reduce the 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 (ie, the ability to see and track inventory levels as goods move along the supply chain). Visibility will allow firms to respond to disruptions in real time. A 2021 survey revealed that only 2 percent of companies claimed to have more visibility than their second-tier suppliers – those who provide materials and parts to their direct suppliers. Without strong visibility, a company’s supply chains are susceptible to disruptions caused by issues such as natural disasters, pandemics, geopolitical issues, trade disruptions and product recalls. Therefore, firms should leverage AI to increase supply chain visibility.
Mapping the supply chain is an important step towards increasing its resilience, and AI tools can provide considerable assistance in this regard. These tools can aggregate records such as product orders, customs declarations, and freight bookings, often displayed in different formats and languages. AI algorithms can extract relevant data from both structured and unstructured documents with high accuracy. AI tools can compile and synthesize this raw data, helping a firm map its various supply chain tiers. For example, Altana, an AI startup that creates dynamic maps of global supply chains, has developed a creative AI tool that uses both public and private data to map a company’s supply chain. The tool is complemented by a large language model (LLM)-aware assistant that answers employee questions in plain language. By using a document processing system to capture, analyze, and share documents such as invoices, bills of lading, and purchase orders, Altana can increase efficiency and accuracy in logistics and between supply chain partners. Can improve communication.
Using AI to detect changes in supply and demand
AI can also help firms predict market demand and customer sentiment. Using scanner data collected at point-of-sale locations along with extensive data from customer reviews and blog posts on social media, AI-based tools, such as Google’s Video AI, collect and analyze text, images and videos. can do Google Video AI can then create a real-time, end-to-end supply chain dashboard that can generate alerts for unusual changes in demand due to competition or product issues. AI can also detect early signs of panic buying using big data sources. The Google Video AI dashboard can then identify the root causes of such abnormalities.
In addition to detecting changes in demand in real time, AI tools can compile and analyze data on traffic conditions at various supply chain tiers such as ports and warehouses. These tools can detect supply disruptions due to supply and worker shortages, factory shutdowns and shipping delays, among other issues. For example, when West Coast ports experienced unusual delays in September 2021, the US Department of Transportation developed a national transportation supply chain dashboard that tracked the movement of goods from ports to retail stores. tracked three key indicators: the number of imported containers, the level of US retail inventory, and the availability of consumer goods on the shelves. Tracking these indicators in real time provides the ability to detect and react to abnormal patterns.
Using AI to design effective responses to supply chain disruptions
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 essential to building resilience: (1) detecting a disruption early, (2) devising an effective solution in response to the disruption, and (3) deploying the solution quickly. to do Traditionally, firms have ensured supply chain resiliency by developing advanced systems to increase detection, quickly setting up contingency plans, and conducting stress tests for rapid deployment. However, AI and Industry 4.0 technologies, such as sensors, blockchains, and data analytics, can multiply these flexibility capabilities.
With the ability to detect unusual changes in supply and demand, AI tools can help companies assess and compare the effectiveness of different response strategies. These simulations examine the impact of each possible response on supply and demand, as well as the recovery time from disruptions. By analyzing simulation results and evaluating the impact of different responses on different supply chain partners, a firm can quickly develop an informed strategy in response to a sudden change. Response strategies may include modifying product designs, adjusting prices, and changing upstream suppliers. In terms of potential use cases, AI could help a government agency design a supply chain for medical countermeasures to defend against a bioattack. Retail companies can 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 unexpected disruptions and to 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 come under pressure. AI can recommend changes to a company’s supply chain policies based on a variety of factors, such as seasonal and economic trends. For example, AI can identify the optimal supply chain configuration, the optimal number of suppliers (and their locations) and the most favorable terms of supply chain contracts.
Implications of AI for employment and public policy
While AI has enormous potential to develop resilient supply chains, the Biden administration could coordinate with the European Union to mitigate various risks posed by AI-enabled global supply chains. Just as 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 ethically sourced. is done and avoids copyright infringements. Responsible AI development is key to supply chain sustainability, as AI is increasingly embedded in supply chain management.
Even with the help of US and European regulators, firms must manage certain risks through human involvement. AI-powered supply chains will change the role of supply chain professionals, eliminating jobs in clerical and data entry, but they will also create new jobs. Data used to train AI and AI-generated insights can be biased. Therefore, humans must identify the most relevant data on which to train the LLM and ensure adherence to ethical guidelines. AI also doesn’t always understand the context and nuances of global supply chains. Humans should properly interpret and evaluate AI-generated recommendations. Thus, new personnel, including research scientists, chatbot developers, AI ethicists, and bias analysts, are essential to developing resilient supply chains. Additionally, in order to manage increasingly complex supply chain operations amid highly complex geopolitical issues, the role of supply chain managers has never been more important.
AI promises to disrupt industries, including justice, retail, marketing, transportation, media, and biosciences. In fact, the World Economic Forum commented that the future of work is changing with machines and that AI is likely to take over an increasing share of work. The interaction between AI and supply chain management, two key areas, is more important than ever to drive economic stability and resilience. However, the future is not as bleak as Elon Musk claims that AI will take over most jobs – at least not in supply chain management for the foreseeable future.
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Maxim C. Cohen Scale AI Chair Professor of Retail and Operations Management at McGill University and Visiting Professor at the Yale School of Management. He is also Chief AI Officer at ELNA Medical and Scientific Advisor in AI at IVADO Labs. He is actively advising corporations, retailers, and startups on topics related to pricing, retail, and data science. He has collaborated with many companies, including Google, Wiz, Oracle Retail, IBM Research, Via, Spotify, Aldo Group, Circle K, Loblaws, Canadian Tire, L’Oréal, and Staples, and several startups. Serves on the Advisory Board of His research and teaching have garnered 30+ awards, including Poets&Quants Best 40-Under-40 MBA Professors, RETHINK Retail’s Top Retail Influencers, MSOM Young Scholar Prize, and Best OM Paper in Management Science.
Christopher S. Tang is the Edward W. Carter Distinguished Professor and Chair in Business Administration at UCLA. He is senior associate dean for global initiatives and faculty director of the Center for Global Management at the UCLA Anderson School of Management. He served as Dean of the National University of Singapore Business School and Senior Advisor to the President of the National University of Singapore. In addition to his extensive academic writing and consulting in global supply chain management, he speaks regularly on NPR, ABC, BBC, CBS, Fox News, NBC, and Voice of America. He has also published more than 100 articles in The Wall Street Journal, Barron’s, Bloomberg Law, Los Angeles Times, Chicago Tribune, San Francisco Chronicle, China Daily, South China Morning Post, Le Monde and The Street Times.
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