Editor's Note: In this excerpt adapted from his book, Reinventing the Supply Chain: A 21st-Century Covenant with America, University of Denver supply chain professor Jack Buffington reimagines how U.S. supply chains could be structured. He argues that U.S. supply chains are currently optimized to meet private market objectives and rarely “consider the resiliency needed to ensure the public good in a time of crisis.”
He proposes that public sector investment could be used to develop community-based supply chains that would “advocate for their citizens through innovation and enterprise.” These community-based supply chains, or enterprise zones, would use advanced digital technology to manufacture and sell products first locally and then globally. Buffington argues that these enterprise zones are not meant to displace large companies such as Amazon and Walmart, rather they are to act as their future competitors.
Since the beginning of the 21st century, one of China’s goals has been to create a supply chain strategy that balances itself through “dual circulation,” which means keeping its economy open to the world when it is in China’s best interest to do so, but then pulling back from globalization when its necessary to stabilize its domestic markets. The concept is an intentionally vague term and does not seem to be clearly articulated in any detail in official Chinese government economic plans, but it has been a foundational strategy of the nation since it entered the World Trade Organization (WTO) at the beginning of the 21st century. It is sometimes described as “capitalism with Chinese characteristics.” …
To compete with this 21st-century model of globalization, the United States needs to develop its own version of dual circulation with American characteristics by creating a community-based supply chain system. … This would involve the creation of an enterprise innovation model that takes advantage of the strengths of the American culture and of a reindustrialization strategy to develop an American Silk Road of sorts. This model would consist of a networked collection of micro-manufacturing hubs at the community level using broadband infrastructure, advanced manufacturing techniques, and a modernized approach to American education. This model would leverage both America’s innovation engine and emerging 21st-century technologies.
This community-based supply chain systems would focus internally first and then project outward to the world, from localization to globalization, to create a “glocal” model. Compared to China’s approach of dual circulation, which controls the balancing of supply and demand through a centralized governmental system in which all roads lead back to Beijing, this American system would work through individualized producer and consumer channels. … The goal would be to reform markets through people, process, and technology, not bureaucracy and policy tactics.
America’s model should be for the public sector to incentivize nodal self-reliance, allowing the individual to express himself or herself through the market. This is a paradigm shift from large-scale global multinational corporations achieving economies of scale, often enabled by large institutions.
Through public investment in community-based infrastructures such as 3D printing and logistics centers, the United States could put communities that have been excluded from economic development for over half a century back on the map. … An entire glocalized supply chain could be constructed both by and for the community and networked across the planet. This networked supply chain could connect to the next town over, to an Asian corporation, to a seaport, or to any municipality worldwide. Such a model offers unlimited possibilities, all within the community’s own control rather than dependent on big government or business.
Think of this community-based system as one that mirrors the internet, a decentralized array of clusters, constantly changing, always connected. Its logistics are virtual algorithms rather than physical routes and destinations. … This model starts virtual and then is physical. Fulfillment in this new model is done virtually as much as possible before transitioning to traditional logistical forms such as warehousing, distribution, transportation, and retail stores.
Take, for example, West Baltimore, a crumbling community plagued by drug dealing and limited to remedial employment opportunities, such as retail food service at minimum wage. If West Baltimore had access to an upgraded broadband infrastructure, an open-source blockchain system to enable transactions, and an advanced manufacturing center to promote production, its schools could teach its students to act as nodes within a community-based supply chain system. A virtual logistics system would allow them to create a network across other communities through the internet. The planning, sourcing, and distribution of materials and services could be transacted through the blockchain. These materials and services could then be manufactured into products and distributed and retailed within a peer-to-peer model. Physical supply chains have flown over and around communities like West Baltimore; digital systems can reintroduce a communal and global approach or glocal virtual logistics.
A proposed glocal model is antithetical to what logistics professionals have been taught for decades: that optimization is a physical point A to B process primarily focused on cheap labor markets and technology. In this new model, let’s call it Logistics 2.0, supply chains will become more virtual than physical. Rather than optimizing from point A to point B to enable cheaper prices for consumers and producers, a digital supply chain system can eliminate these spatial challenges by eliminating these physical limitations and redefining who is a customer and who is a producer. Consumers and producers can be anyone, living anywhere, so as long as they are networked to do so. …
A national platform to kick off a community-based supply chain network would commence through incentives and subsidies for a public broadband infrastructure and public education reform. With this platform in place, a digital infrastructure would network each community in the nation and around the world, similar to how a national railroad system helped to create the physical network of the U.S. supply chain over a century ago. Then each state and local municipality could determine its separate economic development plan, perhaps through the seeding of business case funding for local communities to begin justifying their models. For example, the U.S. government could fund the infrastructure and educational strategy, and Maryland could fund the feasibility study and proof of concept of a community-based supply chain based in the city of Baltimore. If the feasibility study is justified, the federal and state governments could then offer further incentives or subsidies for local communities to purchase additional equipment for the community system, such as 3D printers, information technology equipment, and so on.
Through this model, local entrepreneurs—or nodes—are funded in an innovation-based approach, but one with lower barriers to entry than exist today (such as limits on significant capital funding requirements that make entrepreneurship a high-risk, entry-restricted endeavor). Building this 21st-century model of innovation, entrepreneurship, and supply chain through a community-based network model will take time and will require commitment. It will require iteration as the public and private sectors, as well as the nodes and existing businesses, learn how to compete against existing large multinational corporations. This is the real competitive advantage of this system compared to China’s state-owned enterprises.
Benefits for Amazon's customers--who include marketplace retailers and logistics services customers, as well as companies who use its Amazon Web Services (AWS) platform and the e-commerce shoppers who buy goods on the website--will include generative AI (Gen AI) solutions that offer real-world value, the company said.
The launch is based on “Amazon Nova,” the company’s new generation of foundation models, the company said in a blog post. Data scientists use foundation models (FMs) to develop machine learning (ML) platforms more quickly than starting from scratch, allowing them to create artificial intelligence applications capable of performing a wide variety of general tasks, since they were trained on a broad spectrum of generalized data, Amazon says.
The new models are integrated with Amazon Bedrock, a managed service that makes FMs from AI companies and Amazon available for use through a single API. Using Amazon Bedrock, customers can experiment with and evaluate Amazon Nova models, as well as other FMs, to determine the best model for an application.
Calling the launch “the next step in our AI journey,” the company says Amazon Nova has the ability to process text, image, and video as prompts, so customers can use Amazon Nova-powered generative AI applications to understand videos, charts, and documents, or to generate videos and other multimedia content.
“Inside Amazon, we have about 1,000 Gen AI applications in motion, and we’ve had a bird’s-eye view of what application builders are still grappling with,” Rohit Prasad, SVP of Amazon Artificial General Intelligence, said in a release. “Our new Amazon Nova models are intended to help with these challenges for internal and external builders, and provide compelling intelligence and content generation while also delivering meaningful progress on latency, cost-effectiveness, customization, information grounding, and agentic capabilities.”
The new Amazon Nova models available in Amazon Bedrock include:
Amazon Nova Micro, a text-only model that delivers the lowest latency responses at very low cost.
Amazon Nova Lite, a very low-cost multimodal model that is lightning fast for processing image, video, and text inputs.
Amazon Nova Pro, a highly capable multimodal model with the best combination of accuracy, speed, and cost for a wide range of tasks.
Amazon Nova Premier, the most capable of Amazon’s multimodal models for complex reasoning tasks and for use as the best teacher for distilling custom models
Amazon Nova Canvas, a state-of-the-art image generation model.
Amazon Nova Reel, a state-of-the-art video generation model that can transform a single image input into a brief video with the prompt: dolly forward.
Economic activity in the logistics industry expanded in November, continuing a steady growth pattern that began earlier this year and signaling a return to seasonality after several years of fluctuating conditions, according to the latest Logistics Managers’ Index report (LMI), released today.
The November LMI registered 58.4, down slightly from October’s reading of 58.9, which was the highest level in two years. The LMI is a monthly gauge of business conditions across warehousing and logistics markets; a reading above 50 indicates growth and a reading below 50 indicates contraction.
“The overall index has been very consistent in the past three months, with readings of 58.6, 58.9, and 58.4,” LMI analyst Zac Rogers, associate professor of supply chain management at Colorado State University, wrote in the November LMI report. “This plateau is slightly higher than a similar plateau of consistency earlier in the year when May to August saw four readings between 55.3 and 56.4. Seasonally speaking, it is consistent that this later year run of readings would be the highest all year.”
Separately, Rogers said the end-of-year growth reflects the return to a healthy holiday peak, which started when inventory levels expanded in late summer and early fall as retailers began stocking up to meet consumer demand. Pandemic-driven shifts in consumer buying behavior, inflation, and economic uncertainty contributed to volatile peak season conditions over the past four years, with the LMI swinging from record-high growth in late 2020 and 2021 to slower growth in 2022 and contraction in 2023.
“The LMI contracted at this time a year ago, so basically [there was] no peak season,” Rogers said, citing inflation as a drag on demand. “To have a normal November … [really] for the first time in five years, justifies what we’ve seen all these companies doing—building up inventory in a sustainable, seasonal way.
“Based on what we’re seeing, a lot of supply chains called it right and were ready for healthy holiday season, so far.”
The LMI has remained in the mid to high 50s range since January—with the exception of April, when the index dipped to 52.9—signaling strong and consistent demand for warehousing and transportation services.
The LMI is a monthly survey of logistics managers from across the country. It tracks industry growth overall and across eight areas: inventory levels and costs; warehousing capacity, utilization, and prices; and transportation capacity, utilization, and prices. The report is released monthly by researchers from Arizona State University, Colorado State University, Rochester Institute of Technology, Rutgers University, and the University of Nevada, Reno, in conjunction with the Council of Supply Chain Management Professionals (CSCMP).
Specifically, 48% of respondents identified rising tariffs and trade barriers as their top concern, followed by supply chain disruptions at 45% and geopolitical instability at 41%. Moreover, tariffs and trade barriers ranked as the priority issue regardless of company size, as respondents at companies with less than 250 employees, 251-500, 501-1,000, 1,001-50,000 and 50,000+ employees all cited it as the most significant issue they are currently facing.
“Evolving tariffs and trade policies are one of a number of complex issues requiring organizations to build more resilience into their supply chains through compliance, technology and strategic planning,” Jackson Wood, Director, Industry Strategy at Descartes, said in a release. “With the potential for the incoming U.S. administration to impose new and additional tariffs on a wide variety of goods and countries of origin, U.S. importers may need to significantly re-engineer their sourcing strategies to mitigate potentially higher costs.”
Grocers and retailers are struggling to get their systems back online just before the winter holiday peak, following a software hack that hit the supply chain software provider Blue Yonder this week.
The ransomware attack is snarling inventory distribution patterns because of its impact on systems such as the employee scheduling system for coffee stalwart Starbucks, according to a published report. Scottsdale, Arizona-based Blue Yonder provides a wide range of supply chain software, including warehouse management system (WMS), transportation management system (TMS), order management and commerce, network and control tower, returns management, and others.
Blue Yonder today acknowledged the disruptions, saying they were the result of a ransomware incident affecting its managed services hosted environment. The company has established a dedicated cybersecurity incident update webpage to communicate its recovery progress, but it had not been updated for nearly two days as of Tuesday afternoon. “Since learning of the incident, the Blue Yonder team has been working diligently together with external cybersecurity firms to make progress in their recovery process. We have implemented several defensive and forensic protocols,” a Blue Yonder spokesperson said in an email.
The timing of the attack suggests that hackers may have targeted Blue Yonder in a calculated attack based on the upcoming Thanksgiving break, since many U.S. organizations downsize their security staffing on holidays and weekends, according to a statement from Dan Lattimer, VP of Semperis, a New Jersey-based computer and network security firm.
“While details on the specifics of the Blue Yonder attack are scant, it is yet another reminder how damaging supply chain disruptions become when suppliers are taken offline. Kudos to Blue Yonder for dealing with this cyberattack head on but we still don’t know how far reaching the business disruptions will be in the UK, U.S. and other countries,” Lattimer said. “Now is time for organizations to fight back against threat actors. Deciding whether or not to pay a ransom is a personal decision that each company has to make, but paying emboldens threat actors and throws more fuel onto an already burning inferno. Simply, it doesn’t pay-to-pay,” he said.
The incident closely followed an unrelated cybersecurity issue at the grocery giant Ahold Delhaize, which has been recovering from impacts to the Stop & Shop chain that it across the U.S. Northeast region. In a statement apologizing to customers for the inconvenience of the cybersecurity issue, Netherlands-based Ahold Delhaize said its top priority is the security of its customers, associates and partners, and that the company’s internal IT security staff was working with external cybersecurity experts and law enforcement to speed recovery. “Our teams are taking steps to assess and mitigate the issue. This includes taking some systems offline to help protect them. This issue and subsequent mitigating actions have affected certain Ahold Delhaize USA brands and services including a number of pharmacies and certain e-commerce operations,” the company said.
Editor's note:This article was revised on November 27 to indicate that the cybersecurity issue at Ahold Delhaize was unrelated to the Blue Yonder hack.
The new funding brings Amazon's total investment in Anthropic to $8 billion, while maintaining the e-commerce giant’s position as a minority investor, according to Anthropic. The partnership was launched in 2023, when Amazon invested its first $4 billion round in the firm.
Anthropic’s “Claude” family of AI assistant models is available on AWS’s Amazon Bedrock, which is a cloud-based managed service that lets companies build specialized generative AI applications by choosing from an array of foundation models (FMs) developed by AI providers like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon itself.
According to Amazon, tens of thousands of customers, from startups to enterprises and government institutions, are currently running their generative AI workloads using Anthropic’s models in the AWS cloud. Those GenAI tools are powering tasks such as customer service chatbots, coding assistants, translation applications, drug discovery, engineering design, and complex business processes.
"The response from AWS customers who are developing generative AI applications powered by Anthropic in Amazon Bedrock has been remarkable," Matt Garman, AWS CEO, said in a release. "By continuing to deploy Anthropic models in Amazon Bedrock and collaborating with Anthropic on the development of our custom Trainium chips, we’ll keep pushing the boundaries of what customers can achieve with generative AI technologies. We’ve been impressed by Anthropic’s pace of innovation and commitment to responsible development of generative AI, and look forward to deepening our collaboration."