Tag: AI

OM Podcast #48: Cold Storage, AI, and the Future of Industrial Facilities

In this episode of the Podcast, Professors Barry Render and Misty Blessley sit down with David Aschenbrand, Executive Managing Director at Newmark, to explore how cold storage and temperature‑controlled facilities are evolving in today’s operations and supply chain environment. Drawing on his background across logistics, transportation, warehousing, and industrial real estate, David explains how cold storage facilities support food, pharmaceutical, and other temperature‑sensitive supply chains, and what clients look for when developing or operating these specialized buildings.

The conversation highlights how facility design decisions—such as location, building footprint, dock configuration, and proximity to ports—can directly affect labor availability, transportation efficiency, and long‑term operational performance. David shares insights on the growing role of automation and AI in industrial facilities, while emphasizing the continued importance of skilled trades and hands‑on roles that support these operations.
The episode concludes with a discussion of what rising labor costs mean for cold storage operators. Together, the hosts and guest offer a practical look at how operations management, facility design, workforce trends, and technology intersect in modern cold chain and warehouse environments.

 

TRANSCRIPT LINK
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Prof. Barry Render
Prof. Misty Blessley
Dave Aschenbrand

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Instructors: assignable auto‑graded exercises using this podcast are available in MyLab OM. To learn more, view our earlier blog post featuring Chuck Munson or contact your Pearson representative: Find your rep

OM in the News: FedEx Is Planning an AI Agent Workforce

FedEx is building out an army of AI agents to work alongside its human workforce, positioning itself to tap the latest wave of technology crashing through corporate America, reports The Wall Street Journal (March 13, 2026).

The shipping giant, which already deploys artificial intelligence in software development and other areas, is now looking to drive AI agents further into operations, including network planning and business processes. By 2028, FedEx expects to have AI integrated into more than half of its core operational workflows.  FedEx is currently focused on setting up the underlying data and management foundation to oversee its AI bots.

Though logistics providers like FedEx are aiming to adopt AI, they’re grappling with challenges like managing numerous, disconnected data sources. “Logistics can be very fragmented—especially if you think of a global organization with their network being everywhere, it makes it difficult to standardize,” said an industry consultant.

As its underlying tech is completed, FedEx expects to roll out AI and AI agents that connect macro and microeconomic trends to better plan its network. In marketing and campaign management, FedEx will create a hierarchy in which there’s a “manager agent,” an “audit agent” and a “worker agent.” The goal of the hierarchy is to ensure that the agents have a trail of accountability for their actions.

At the moment, FedEx’s enterprise data platform, called Atlas, supports more than 200 AI use cases across the supply chain, commercial teams and enterprise functions. It has already turned on AI agents in areas such as software development, where they are developing and testing code. In operations, agents are helping customers clear customs more quickly.

Plans for FedEx’s AI agents also involve getting its humans ready to interact with the technology. the company just launched an AI education program for 300,000 of its employees, as well as a more advanced version for its technology workers. Each employee received a customized training depending on their role. FedEx says it doesn’t plan for those agents to replace its workers.

Classroom discussion questions:

  1. Why is FedEx pushing for more AI agents?
  2. How will agents be used in operations?

OM Podcast #47: Leadership and Continuous Improvement

In our latest podcast, Barry Render interviews John Dyer, a well‑known speaker, consultant, and expert in continuous improvement, and the author of The Façade of Excellence: Defining a New Normal of Leadership. With over 40 years of experience—including roles at GE, Ingersoll Rand, and years of consulting across manufacturing, government, and nonprofit sectors—John brings a depth of practical insight that leaders at every level can learn from.

In this episode, Barry and John discuss:

  • What operational excellence really means beneath the surface
  • Why so many continuous improvement initiatives fail after 12–18 months
  • The psychology behind middle‑management resistance
  • The shift from “manager” to “coach” as the core leadership evolution
  • How empowerment really works
  • How AI will reshape teamwork, decision‑making, and PDCA cycles
  • Real‑world examples of fully empowered, high‑performance teams

This is an outstanding conversation for instructors, operations leaders, and students who want an honest, experience‑grounded perspective on building sustainable cultures of excellence.

 

TRANSCRIPT LINK
A Word document of this podcast will download by clicking the word Transcript above.

John Dyer
Prof. Barry Render

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Just open your Apple Podcasts app, search “Heizer Render Munson OM Podcast,” and subscribe to get our newest episodes as soon as they’re released!

Instructors: assignable auto‑graded exercises using this podcast are available in MyLab OM. To learn more, view our earlier blog post featuring Chuck Munson or contact your Pearson representative: Find your rep

 

OM in the News: The Robotics Supply Chain

The next 20 years are not just about making robots better, but also about how they will be used in all sorts of industries, from small tests to big factories. The real challenge is having specialized engineering skills, great manufacturing, and dominating software,  reports Industry Week (March 11, 2026). 

There are 6 key areas that make all the difference in this industry.  Here is a breakdown of the cost of the parts that go into a robot:

1. Actuators & Gearboxes (35-40%): The physical muscle.

2. Robot Structure / Manipulators (15-20%): The physical frame and integration.

3. Sensors & Perception (10-15%): The eyes and ears.

4. AI Compute / Control (10-15%): The operational brain.

5. Battery / Power Systems (10-15%): The energy storage for mobile units.

6. Precision Motion Components (5-10%): The components required for fine movements.

This list shows that a robotics breakthrough isn’t just software advances; it depends on physical components and the supply chains that produce them. But there are 3 chokepoints (bottlenecks).

 #1: Precision Reducers, controlled by Japan. Robots can’t move with a lot of power and precision without special parts (harmonic and cycloidal reducers). Two companies in Japan make 70% of these parts used all over the world. Spending more money won’t allow other companies to make these parts, because they need special knowledge about metals and years of experience making precise parts.

 #2: AI Compute (The Intelligence Standard), controlled by the  U.S. Today’s robots, especially those that use reinforcement learning, need powerful computers to work properly. NVIDIA’s CUDA system has become the leading platform used by robots that learn and think. Making a better chip is not enough if you can’t replace the software that all robotics engineers already use.

#3: Battery Supply Chain, controlled by China.  Robots are changing from big, stationary machines to mobile ones. This means batteries are now a crucial part of making them work. One company in China, CATL, controls 1/3 of the world’s battery market. China has a very strong grip on this supply chain.

The global map of robotics is specialized. There is a multi-polar supply chain that is difficult to disrupt:

USA: “The “Brain.” (software, autonomy, AI compute).

Japan: The “Hardware King.” (motors, gearboxes, precision engineering).

Germany: The “Precision Engineer.” ( mechanical systems, high-end production).

China: The “Scale & Power.” (manufacturing speed, massive infrastructure, battery supremacy).

Taiwan: The “Linear Specialist.” ( The linear guides and ball screws essential for motion).

Classroom discussion questions:

  1. Why must operations managers understand these costs and bottlenecks?
  2. What are the supply chain implications?

Good OM Reading: Supply Chains as a Source of Competitive Differentiation

A new report from the Kearney consulting group (Feb. 4, 2026), called The Top Five Supply Chain Bets for 2026, concludes that as customers punish inconsistency faster than ever, companies that can deliver reliability will expand market share. Kearney offers this analysis:

This forces a shift from one supply chain to a portfolio of capabilities designed around distinct value propositions including speed, reliability, customization, cost-to-serve, and compliance. Where commercial commitments are made in isolation from operations, the consequences surface later through margin erosion, excess inventory, and lost customers.

Supply chain becomes the operating core of the customer promise, and leadership must be explicit about where it will overperform and equally clear about where performance ambition can be more modest by design.

Leading organizations are becoming more deliberate about how they serve each channel, market, and customer, including the trade-offs required and their operational implications. Align those choices with differentiated supply chain capabilities for each segment and translate them into targets for the core KPIs (service, cost, cash, risk). Finally, leverage the integrated planning and execution process to deliver consistently against those objectives.

Another area of concern is AI as it moves along the continuum from experimentation to earnings impact. Kearney offers the following analysis:

In 2026, many pilots will fail to progress beyond experimentation. The root causes are predictable: unclear value cases, poor data quality, fragmented technology stacks, and pilots that were never designed to scale.

AI in supply chains needs to be treated as an industrial capability, with clear ownership, governance, monitoring, and integration into day-to-day processes. Organizations that remain in experimentation are accumulating prototypes and skepticism, while those that focus are translating AI into measurable improvements in cost, cash, service, and risk.

Leading organizations are managing AI use cases as a portfolio, with explicit scale and stop gates. A small number of use cases that materially affect service, cost, cash, or risk are being industrialized, while others are time-boxed with clear exit criteria. Investment is concentrating on priorities with the highest enterprise impact, including decision speed, resilience, and sharpening competitive supply chain advantage.

Classroom discussion questions:

  1. How might AI be used in supply chain management?
  2. Why does Kearney think supply chains are becoming the source of competitive differentiation?

OM in the News: AI Makes a Fundamental Shift in Manufacturing

For two decades, manufacturing has been defined by a relentless pursuit of optimization. We automated assembly lines (Ch. 9), digitized records and built predictive maintenance models (Ch. 17), all in the service of marginal gains in efficiency.

While this approach yielded significant returns, we have reached the ceiling of what traditional, rule-based automation can achieve. “In 2026, the industry is undergoing a fundamental shift toward a model of agentic enablement,” says the head of Google Cloud, Praveen Rao in Industry Week (Jan.22, 2026).

Rao says this isn’t just a technical upgrade; it is a new industrial model where AI moves from a tool that recommends to an agent that achieves. It also isn’t about reducing headcount, but about transforming operators into “super-users”: who are empowered by AI to solve more complex problems and drive higher value. He makes 3 predictions:

The Agentic Supply Chain: Beyond Prediction to Execution Historically, manufacturers have been forced to lock up vast amounts of capital in finished goods, essentially “betting” on demand forecasts. Agentic AI changes this math by allowing production to align dynamically with real-time customer intent. Traditional predictive models could warn of a supplier disruption, but it still required a human to spend days rerouting logistics. In 2026, AI agents will close this loop. Systems will be empowered to detect a tier-2 supplier failure in the middle of the night.

The Rise of the Technocrat Entering the era of the Technocrat, the factory worker of the future will no longer be measured by analog tools of the past—the hammers and the screwdrivers, but by mastery of generative AI for rapid troubleshooting and agentic AI for process orchestration.

Hyper-Personalized Intelligence on the Shop Floor  The AI agents of the future will possess “long-term memory,” understanding the specific context and historical preferences of every shop-floor operator. This is agentic AI acting as a personalized performance coach, delivering the right insight to the right person at the exact moment of need.

Rao concludes: “The transition from fragmented automation to integrated, agentic systems is the new industrial paradigm. The companies that fail to adopt an agent-first mindset will not just fall behind; they will find themselves competing against living factories that can think, adapt, and execute 24/7 without friction.”

Classroom discussion questions:

  1. Explain what an AI agent does.
  2. Give an example of personalized intelligence on the shop floor.

OM Podcast #44: Inside the Cold Storage Industry with Dr. Anna Johnson

Happy New Year!  In our first episode of 2026, Professors Barry Render and Misty Blessley sit down with Dr. Anna Johnson, Vice President of Marketing and Commercial Strategy at U.S. Cold Storage, to explore the fascinating world of temperature-controlled logistics.

Dr. Johnson explains how third-party logistics providers keep America’s food supply safe and efficient, why 98% of U.S. food storage is outsourced, and how sustainability initiatives like anaerobic digestion are reducing food waste.

Prof . Misty Blessley
Prof. Barry Render

The conversation also dives into industry trends—from the surge in capacity during COVID to the current state of the market—and highlights how AI, robotics, and digital twins are transforming operations, and creating new roles for skilled workers in this evolving sector.

Dr. Anna Johnson

 

Read the full transcript

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OM in the News: AI Is Mining Our Trash for Treasure

Here’s a job the computers can take without much complaint: sorting recyclables. For humans, it is a foul, laborious job that entails standing over a conveyor belt, plucking beer cans and detergent bottles from a stream of refuse. The job pays little and is hard to fill.

At one recycling facility near Hartford, machines are taking over the dirtiest jobs, reports The Wall Street Journal (Jan. 8, 2026). A few workers remain on the line, mostly to watch for hazardous items. Otherwise, the system of conveyors, magnets, optical sorters and pneumatic blocks runs largely unmanned. The technology allows them to sort up to 60 tons an hour of curbside recycling into precisely sorted bales of paper, plastic, aluminum cans and other materials. The material is sold to mills, manufacturers and remelt facilities, which pay more for cleaner bales.

AI is used to instantly spot recyclables and send instructions to machinery down the line at to remove them.

Watching over it all are computers that analyze material as it passes by at 7 mph. The devices use AI to identify recyclables, flag food-grade material, gauge items’ mass, assess market value and calculate points at which a robotic claw might best clasp each piece.

 The U.S. 50% aluminum tariff has lifted demand for scrap metal, while pulp mill closures have left box makers more reliant than ever on old corrugated containers. And consumer goods companies want to reclaim their bottles as states adopt extended producer responsibility laws aimed at reducing plastic pollution.

Part of the problem: Americans’ poor recycling habits are an obstacle to profit. A lot of beer cans and delivery boxes never even make it to sorting centers. A study in Virginia’s waste stream showed that 28% was recyclable, yet the system was stuck at a recycling rate of about 7% no matter how much it spent trying to teach people how and what to recycle.

The big breakthrough in recycling technology has been combining vision recognition systems with pneumatic blocks. Using puffs of air to separate items has proved much faster and more accurate than robotic pickers, which are limited to about 40 items a minute, compared with thousands for pneumatic system.

Classroom discussion questions:

  1.  Why has recycling been so inefficient?
  2. Should job loss through automation be a concern?

OM in the News: PepsiCo Turns To Digital Twins To Rethink Plants

We posted recently about the joint nuclear fusion digital twin work of Siemens and NVIDIA. Today’s news is that PepsiCo is working with the same two firms  to change how it designs, tests, and expands its plants and warehouses using AI and digital twins. “Physical industries are entering the age of AI. For companies with real-world assets, digital twins are the foundation of their AI journey,” said NVIDIA’s CEO.

By modeling factories and distribution centers digitally before making physical changes, PepsiCo hopes to cut down on costly mistakes while improving speed and capacity.

With AI-driven digital twins, teams can simulate plant layouts, equipment movement, and supply chain operations in detail, reports SupplyChain (Jan. 7, 2026). Instead of expanding facilities the old way, which can be slow and expensive, they can test changes virtually and see what works before spending money on physical upgrades.

“The scale and complexity of PepsiCo’s business is massive—and we are embedding AI throughout our operations to better meet the increasing demands of our consumers and customers,” said PepsiCo’s CEO. The digital models recreate machines, conveyors, pallet routes, and even worker movement, helping teams spot problems early and test different setups in weeks instead of months.

By finding bottlenecks and unused capacity in a virtual setting, teams increased throughput by 20%. The same approach has also shortened design cycles and helped cut capital spending by 10-15%. Testing ideas digitally first, teams can plan ahead, compare options, and move faster without the usual surprises that come with physical expansion.

Classroom discussion questions:

  1.  How is PepsiCo employing digital twins?
  2. How do AI and digital twins work together?

OM in the News: 3 Core Skills for the AI Manufacturing Workforce

 Companies invest heavily in workforce development—global corporate training represents over a $350 billion market—but few can answer the fundamental question: Does our workforce actually possess the capabilities required for AI-era manufacturing? The problem, writes IndustryWeek (Dec. 16, 2025), is that firms are training for yesterday’s skills while tomorrow’s requirements remain undefined.

Manufacturing faces a dual disruption. AI, robotics and automation are reshaping production at unprecedented speed, while skilled labor shortages intensify when experienced workers retire, taking decades of knowledge with them. Most training programs rarely assess whether workers developed the fundamental capabilities needed to work effectively in AI-augmented environments.

There are the 3 Core Skills needed:

1. Human+ capability This isn’t about workers learning to code or becoming data scientists. Human+ is the ability to work effectively alongside AI and automation—knowing when to trust algorithmic recommendations, when to override them based on judgment and how to optimize human-machine collaboration for maximum productivity. Manufacturers invest millions in AI-powered quality control systems, predictive maintenance platforms, and autonomous production scheduling—then struggle to achieve projected ROI because their workforce lacks the core skills to extract value from these technologies.

2. Agentic AI orchestration As AI-era manufacturing evolves from simple automation to autonomous agents that manage complex workflows, workers need the capability to orchestrate multiple AI systems effectively. Agentic AI orchestration is the ability to coordinate these systems so they don’t work at cross-purposes. It means understanding how to deploy AI agents for quality control, predictive maintenance, supply chain optimization and production scheduling—and managing the interactions between these systems when they conflict or produce unexpected results.

3. Interoperability catalysis Modern manufacturing runs on complex networks: older machines next to new robots, ERP systems talking to manufacturing systems, logistics platforms feeding production plans, and partner data coming in from suppliers. Interoperability catalysis is the ability to make all of that actually work together:

  • Legacy and modern systems (the 40-year-old CNC and the AI-powered vision system)
  • Digital and physical environments (ERP and planning data vs. shop-floor reality)

The Path Forward: Manufacturing’s competitive advantage in the AI era won’t come from having the most advanced technology. It will come from having a workforce capable of extracting maximum value from that technology. These 3 core skills represent the foundation. Manufacturers who systematically assess and develop these capabilities will thrive as AI reshapes production.

Classroom discussion questions:

  1. Is the current workforce capable of managing AI-manufacturing demands?
  2. Are business students interested and willing to take these jobs?

OM in the News: Robots Are Remaking Chinese Industry

Sam Altman wants AI to cure cancer. Elon Musk says AI robots will eliminate poverty. China is focused on something more prosaic: making better washing machines. While China’s long-term AI goals are no less ambitious than ours, its near-term priority is to shore up its role as the world’s factory floor for decades to come, reports The Wall Street Journal (Nov. 25, 2025).

Midea, an appliance maker, deploys robots to work under an AI ‘factory brain’ that acts as a central nervous system for its plant in Jingzhou.

The Chinese push is fueled by billions of dollars in government and private development– transforming every step of making and exporting goods. A clothing designer reports slashing the time it takes to make a sample by more than 70% with AI. Washing machines in China’s hinterland are being churned out under the command of an AI “factory brain.”

Port shipping containers whiz about on self-driving trucks with virtually no workers in sight, while the port’s scheduling is run by AI.

Chinese executives liken the future of factories to living organisms that can increasingly think and act for themselves, moving beyond the preprogrammed tasks at traditionally-automated factories. This could further enable the spread of “dark factories,” with operations so automated that work happens around the clock with the lights dimmed.

The advances can’t come quickly enough for China as its population is shrinking, young people are avoiding factory jobs, and pushback against Chinese exports has intensified.

AI offers a lifeline to head off those risks, by helping China make and ship more stuff faster, cheaper and with fewer workers. China wants to deploy what is available today quicker than the U.S. can, locking in any advantages. It installed 295,000 industrial robots last year, 9 times as many as the U.S. and more than the rest of the world combined. Its stock of operational robots surpassed 2 million in 2024

Today, China’s average factory wages are far higher than in countries such as India. Many young Chinese are unwilling to work in factories.  The shortage of skilled labor in key manufacturing sectors could reach 30 million this year. Since most Chinese are optimistic about AI, this allows the government to deploy the technology quickly. About 83% of Chinese believe AI-powered products and services are more beneficial than harmful, double the level in the U.S.

Classroom discussion questions:

  1. Why the push for robotics and AI in China?
  2. What can the U.S. and Europe do to remain competitive?

OM in the News: AI and the Learning Curve

Scale drives efficiency—for almost a century, industrial planners have relied on this simple principle. In 1936 aeronautical engineer Theodore Wright discovered that costs fell in a predictable way every time production doubled. The more you produce, the cheaper things become, in part because the learning cost per unit declines. This is the topic of Module E in your text.

Artificial intelligence has accelerated this principle, writes The Wall Street Journal (Oct. 23, 2025). It is rewriting Wright’s Law, which assumes that experience follows production: You make mistakes, learn from them and improve. AI makes it possible for experience to come before production. Simulation can happen millions of times before a single box is shipped. Experience scales almost instantly at no real cost. The learning curve doesn’t only steepen. It collapses.

That means knowledge that once took decades of human trial and error can emerge in weeks, days, even hours. In a supply chain, this is a profound shift. Decisions about capacity, warehouse space, routing, technology adoption and risk management can be modeled, tested and optimized in advance. The costs of imprecise planning shrink dramatically.

AI is breaking Wright’s Law because the learning cycle is no longer physical but computational. Models can test, fail and improve millions of times faster than any team of human engineers. Experience can be generated in advance, and at negligible cost.

The implications for logistics are extraordinary. AI agents will negotiate, reroute and optimize flows of goods in real time. Traditional ownership models, fleets, warehouses and even labor could be replaced by dynamic orchestration of perfectly used assets.

This new golden age of logistics will unveil solutions to problems we may not even know exist. Wright’s Law still matters, but perhaps AI has broken it.  The challenge will be not building the tools but surviving the pace of their consequences.

Classroom discussion questions:

  1. Why can AI have this impact on learning curves?
  2. Besides logistics, which is mentioned in this article, can AI impact operations?

OM in the News: The AI’s Industry 100-Hour Workweeks

The explosive growth of artificial intelligence has forced leading tech companies to rethink their human resource strategies and job design, reports The Wall Street Journal (Oct. 23, 2025). As the demand for rapid innovation intensifies, organizations like Google, Microsoft, Meta, and Anthropic are relying on small, highly skilled teams to push the boundaries of AI development. These teams often work 80 to 100 hours per week, far exceeding the traditional schedules we discuss in Chapter 10, as they race to keep up with the pace of technological change.

Several researchers compared the circumstances to war. “We’re basically trying to speedrun 20 years of scientific progress in two years,” said one Anthropic scientist. “Extraordinary advances in AI systems are happening every few months. It’s the most interesting scientific question in the world right now.”

This environment has led to a redefinition of job roles and expectations. Rather than adhering to standard 9-to-5 or even the demanding “9-9-6” (9 a.m. to 9 p.m., six days a week) schedules, some AI workers describe “0-0-2” routines—working around the clock with minimal breaks. The pressure is especially acute for those directly involved in developing new AI models, where the unpredictability of research outcomes and the speed of breakthroughs require constant adaptability.

To support these extreme demands, companies are adapting their HR strategies. Some provide weekend meals and ensure continuous staffing, while others appoint rotating “captains” to monitor model outputs and oversee product development. These measures aim to sustain productivity and manage burnout, acknowledging that the traditional boundaries between work and personal life have blurred for many in the field.

Job design in this context emphasizes autonomy, intrinsic motivation, and a sense of mission. Many top AI researchers are driven not just by compensation but by the excitement of discovery and the belief that their work is shaping a pivotal moment in history. This self-motivation reduces the need for formalized overtime requirements, as employees willingly invest extra hours to stay ahead in the competitive landscape.

But this also raises concerns about sustainability and well-being. While some workers have become wealthy from their efforts, most have little time to enjoy their success or maintain relationships outside of work. The model raises questions about long-term retention and the potential need for more balanced, human-centered HR strategies as AI becomes further integrated into mainstream business operations.

Classroom discussion questions:

  1. Your comments on the 100 hour workweek?
  2. Is this a valid human resource strategy?

OM in the News: Amazon’s AI-Robotics Warehouse Revolution

 

Amazon is rapidly transforming its e-commerce fulfillment operations through a bold integration of artificial intelligence (AI) and robotics, reports The Wall Street Journal (Oct. 23, 2025). The company’s vision is clear: make human workers more efficient while automating repetitive, menial tasks. At the heart of this shift is Amazon’s Shreveport, Louisiana facility, which now boasts ten times as many robots as a typical warehouse. This leap in automation enables packages to move through the system 25% faster, with anticipated cost savings passed on to customers.

Safety and efficiency are top priorities. Robots now handle tasks such as sorting packages, transporting carts, and retrieving out-of-reach items. Amazon is also investing in its workforce, offering apprenticeships to train employees in managing these advanced systems. The company’s latest innovations include Blue Jay, a robot arm designed for sorting in tight spaces, and Eluna, an AI agent that helps managers optimize staffing and avoid bottlenecks. Blue Jay’s rapid development—just over a year, compared to 3 years for previous models—was made possible by generative AI, which allowed for virtual prototyping.

The company aims to deploy Blue Jay robots in urban, space-constrained warehouses, enabling same-day delivery networks that are both faster and more cost-effective.

These advances could save Amazon billions annually. By the end of next year, nearly 40 fulfillment centers will be equipped with robots, with an estimated to $4 billion in yearly cost reductions. This automation trend is expected to reduce the need for both warehouse and white-collar workers. In fact, the average number of workers per facility dropped to around 670 in 2024, the lowest in 16 years.

Amazon is testing augmented-reality glasses 

Amazon’s automation push extends beyond warehouses. Augmented-reality glasses are being tested for delivery drivers, helping them identify packages and navigate routes more efficiently.

Amazon’s journey began with its $775 million acquisition of Kiva Systems in 2012. Today, three-quarters of its deliveries involve some form of robotic assistance. The company’s latest announcements—Blue Jay, Eluna, and AR glasses—signal a new era where AI and robotics are supercharging logistics, reshaping the future of retail fulfillment.

Classroom discussion questions:

  1. How does Eluna work?
  2. Why is Amazon trying to eliminate warehouse jobs?

OM in the News: China’s Rare-Earth Escalation Threatens the Global Economy

China’s newest restrictions on rare-earth materials would mark a nearly unprecedented export control that stands to disrupt the global economy and threaten the supply chain for semiconductors, writes The Wall Street Journal (Oct. 10, 2025). Chips are the lifeblood of the economy, powering phones, computers and data centers needed to train artificial-intelligence models. The rule also would affect cars, solar panels and the equipment for making chips and other products, limiting the ability of other countries to support their own industries. China produces roughly 90% of the world’s rare-earth materials.

A rare-earths production site in China

Global companies that sell goods with certain rare-earth materials sourced from China accounting for 0.1% or more of the product’s value would need permission from Beijing, under the new rule. Tech companies will probably find it extremely difficult to show that their chips, the equipment needed to make them and other components fall below the 0.1% threshold.

“These rare-earth minerals and the ability to refine them are just the basis of modern civilization,” said  one industry expert. “It’s an economic equivalent of nuclear war—an intent to destroy the American AI industry,” added a second. The U.S. and other countries are pouring hundreds of billions of dollars into data centers, making AI a key economic engine. China gaining control of the technology would potentially let it catch up in the AI race and upend the world order.

The semiconductor supply chain is vulnerable to actions like China’s because large chip plants require big capital investments from an ecosystem of companies providing specialized equipment, intricate technical processes and final packaging. Companies in the U.S., Taiwan, Japan and the Netherlands all collaborate with one another.

The Trump and Biden administrations have offered subsidies and other policies to aid the process, but domestic capacity generally remains in its infancy. Some analysts said the new rules will fuel new urgency for big tech companies to invest more in these areas.

Classroom discussion questions:

  1. Why are rare earths so important?
  2. Why doesn’t the U.S. produce and process the minerals needed?