Tag: AI

OM in the News: Using AI to Inspect Fruit at Albertsons Supermarkets

Albertsons Companies just announced the launch of Intelligent Quality Control, an artificial intelligence-powered tool designed to improve the assessment process and consistency of fresh produce in stores, reports Fresh Portal (May 20, 2026).

The solution, developed by the company in collaboration with Google Cloud, uses computer vision and the  Gemini platform to support quality inspectors in distribution centers.

The system can analyze uploaded images of fresh produce and automatically assess visual characteristics against the retailer’s internal quality standards. The system then provides a rating of the product as well as recommendations to support decision-making.

Albertsons developed this tool to support its quality inspectors and increase consistency in evaluations, which is fundamental for fresh products.

The technology is currently in use to inspect strawberries and red and green grapes. The next step will be to expand the solution to the rest of the berry category and subsequently to more fresh products throughout the U.S.

Among the benefits observed, Albertsons emphasized greater uniformity in assessments across inspectors and shifts, a faster inspection process, and enhanced data collection for continuous analysis and improvement.

The new tool is part of Albertsons’ digital transformation strategy, which in recent years has incorporated AI and data science into both internal operations and consumer-facing platforms.

Classroom discussion questions:

  1. How else could vision systems and AI be used in the grocery industry?
  2. What are the advantages to Albertson in using AI?

OM in the News: Drones Gain Manufacturing Altitude

Drones, in the headlines every day from Russia, Iran and Hezbollah, are also real manufacturing tools that can add valuable context and support. “They provide clear value in activities that are traditionally labor-intensive, disruptive or difficult to perform safely,” writes Industry Week (May 7, 2026).

Drones are making inroads in manufacturing in these two areas:

Inventory tracking:  Indoor drones equipped with barcode scanners and computer vision systems can perform inventory scans with minimal disruption. In large warehouses or storage areas, they can significantly reduce cycle-count time and increase count frequency. The real-time data can be integrated into corporate software platforms, reducing excess stock and decreasing production delays due to stockouts.

Inspection: Conventional industrial inspections often require scaffolding, rope access, shutdowns or travel. They expose workers to heights, to confined spaces or to environmental hazards. Drones can transform this activity. High-resolution visual, thermal, ultrasonic and lidar payloads allow inspection of hard-to-reach areas, including storage tanks and silos, pipe racks, roof structures and overhead utilities. For remote manufacturing facilities, a drone mission can eliminate days of logistics and increase safety performance.

The business impact is measurable: reduced downtime, lower mobilization costs, reduced safety risk and faster response to problem detection. In the energy and utilities sector, drone-based inspection has been estimated to reduce inspection costs by 70% and downtime by 90%.

Future innovation: The next stage in drone deployment is autonomous operation. Drones will increasingly operate from fixed docking stations, launching automatically to perform scheduled inspection missions. Data collected during these flights can be transmitted to AI-enabled analytics platforms and integrated into operational systems in near real-time. Coordinated drone swarms may be used to conduct large-scale inspections and surveys across a large infrastructure.

Challenges: Concerns about workforce displacement are common, and the increased adoption of drones is no exception. But drone programs typically augment skilled labor rather than replace it. Technicians can become certified drone pilots, remote inspection specialists, data analysts or AI-assisted defect reviewers.

Modern industrial drones are connected devices and must be treated as operational technology nodes within the broader cybersecurity architecture. A compromised drone platform presents a risk that goes beyond simple device failure. It may expose critical infrastructure data or provide a means to compromise enterprise networks.

Still, a fully scaled drone program that makes good use of its data is a significant strategic asset.

Classroom discussion questions:

  1. What other industries are already commonly using drones?
  2. Discuss their use in modern warfare.

OM in the News: The AI Splurge and Big Tech’s Workforce

Tech companies are rushing to trade their people for more chips. “Some of those companies might come to regret the exchange,” writes The Wall Street Journal (April 27, 2026).

Microsoft (by 7%), Block (parent of Square and Cash App by 40%) and Meta (by 8,000) are just the latest major tech companies trying to scale back their workforces in the name of AI. Layoffs affecting 45,800 tech employees were just announced, making March 2026 the worst month for reported tech-job reductions in at least 2 years.

Companies are straining to portray the cuts as evidence that they are confident in an AI future in which more workers will be replaced by machines.  Tech companies are shelling out as much as they can—more than their rivals, they hope—on AI chips and data centers that could put them in the lead in a race they feel they can’t afford to lose. That in turn is heightening competition over who can use AI to help do more with a lot less, freeing up money to spend on expensive chips.

Dressing up layoffs as visionary moves for the age of AI carries certain risks. Rampant layoffs hurt morale and create an exit incentive for other employees, especially talented ones with alternatives. For all of AI’s capabilities, people will be needed to figure out business models, deal with customers and, importantly, make sure AI tools are being deployed and used safely.

The layoffs also lend credence to a growing public perception that AI isn’t a panacea but a job killer. That will feed a backlash that is already constraining AI, as more communities are fighting against the construction of massive data centers.

The reduction in workforces sends two messages. First, it indicates tech companies will stop at nothing to spend on AI, something markets have often cheered. Second, it says tech companies believe they can operate fine with fewer employees, even after a couple of years of cuts that followed a Covid-era hiring spree.

Classroom discussion questions:

  1. What are the tradeoffs in reducing tech headcounts?
  2. What are the implications for our students and recent grads?

 

 

OM in the News: Understanding Manufacturing AI Terminology

Walk into almost any operations and supply chain  meeting today and you’ll hear it:

“We should use AI for this.”
“Can we plug in an LLM?”
“Let’s add a copilot.”

Thanks to Industry Week (April  13, 2026) here is a guide to the AI vocabulary showing up in manufacturing and supply chain environments.

Machine Learning Machine learning uses historical data to detect patterns, improve predictions and support decisions. In real-world operations, that includes:

  • Demand forecasting
  • Inventory optimization
  • Predictive maintenance
  • Quality and anomaly detection

LLM (Large Language Model) LLM refers to systems that can read and generate human-like text. It processes and generates language based on patterns learned from large datasets. It shows up:

  • Summarizing supplier emails or RFQs
  • Drafting customer responses
  • Translating ERP data into plain language

LLMs don’t “know” a business unless connected to the firm’s data. Without that context, they can sound confident—but be wrong.

Copilots “Copilot” is one of the most overused—and misunderstood—terms. They are a layer that sits on top of a business system (ERP, CRM, email) to assist users in real time. It is useful for:

  • Suggesting responses inside email
  • Helping navigate ERP workflows
  • Recommending next steps

A copilot doesn’t replace a system—it improves how people interact with it.

Agents Agents move from assisting to acting. They are systems that can take a goal and execute steps to achieve it.

Examples:

  • Monitoring inventory
  • Detecting shortages
  • Reaching out to suppliers
  • Proposing or initiating reorders

Most agent-based systems are still early. They require strong guardrails and tight integration to work reliably in production environments.

Embeddings (The Quiet Connectors) Embeddings convert a company’s data into a format AI systems can understand and search. That’s what allows AI to:

  • Reference ERP data
  • Search internal documents
  • Provide context-aware responses

For operations students and faculty, the goal is not to become AI experts. It’s to understand the language well enough to ask better questions and identify where these tools can create real advantage.

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

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

Have you subscribed to this podcast on Apple Podcasts?
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?