Chapter 7 – The OM Blog by Heizer, Render, & Munson

OM in the News: One Way to Power New AI Data Centers

Where is the energy to power the hundreds of new data centers that are popping up to run artificial intelligence demands coming from? “In the battle for AI dominance, every engine of the economy is getting recruited into the fight—including jet engines'” writes The Wall Street Journal (Feb. 18, 2026).

Jet engines are a natural fit. Power equipment giants GE Vernova, Siemens Energy, and Mitsubishi Heavy Industries already sell power turbines—known as aeroderivatives—that are modeled after these very jet engines. Aircraft engine companies such as GE Aerospace , Howmet Aerospace and Woodward also sell land-based aeroderivative turbines or components.

Yet designing the turbine, which keeps as much of the original jet engine features as possible, is a roughly 18-month undertaking. Instead, it only takes 30 to 45 days to convert a plane’s jet engine to a power-generating turbine. (There are 2 main modifications to convert an aircraft engine to a land-based natural gas turbine. One is replacing the fuel nozzles to utilize natural gas instead of jet fuel. The other is replacing the large fan on the front of the flight engine with a much smaller fan).

Retired aircraft, at an Air Force base near Tucson, Ariz

A company can remanufacture jet-engine parts with a few years of remaining life for use in power turbines, where they can operate for many additional years. Narrow-body jet engines experience higher stress from repeated takeoffs and landings. Power turbines can run as peakers—turning on only when demand surges—or continuously as baseload. Either way, they accumulate less wear and tear.

About 1,600 commercial aircraft engines are retired every year. If a third of those engines get converted into turbines, that would represent about 13 GW of capacity, or more than a quarter of the existing global natural gas turbine capacity.

AI-obsessed tech giants are planning to spend more than $700 billion in capital expenditures this year. The lure of that cash pile will generate a lot of creativity in the power sector.

Classroom discussion questions:

Why is there a need to convert jet engines?
Discuss the growth of data centers and the demands they create. (See our recent post on that topic.)

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:

Is the current workforce capable of managing AI-manufacturing demands?
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:

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

OM in the News: Building a Humanoid Robot

Armies of humanoid robots are poised to march into the world’s factories. But before they’re ready to turn a wrench, they must solve what Elon Musk calls “the hands problem.”

Creating the mechanical equivalent of the human hand is a challenge that has been stumping researchers for years, writes The Wall Street Journal (Oct. 27, 2025) . Replacing muscle and skin with motors and sensors is a critical step in making humanoids a versatile source of labor, potentially unlocking a global market that could reach $5 trillion by 2050.

The robotic hand of the future will need many sensors to emulate a human hand. Holding a pencil, for example, would require sensors along the sides of several fingers.

Tesla’s humanoid robot—called Optimus—is good at walking, but making hands that can match a human’s has been a far tougher job. “In order to have a useful generalized robot, you do need this,” Musk said. “You do need an incredible hand.”

Boston Dynamics has equipped its Atlas humanoids with hands that have only three fingers. They can form a palm that allows the robot to lift boxes or brace itself. One digit also can rotate to serve as a thumb, letting the robot grasp objects. The humanoid can pick up auto parts, pump a dumbbell and pluck a tissue from a box. But a robotic hand must make trade-offs between strength, dexterity, slenderness and ruggedness. Increasing one attribute can diminish another.

Industrial robots have relied on pincerlike hands for decades, and are still the most cost-effective form. MicroFactory (in San Francisco) produces a $5,000 robot that has two arms, one of which typically is equipped with a tool, the other with a 2-digit gripper that holds an object in place. That setup can perform most of the functions needed for electronics assembly, such as soldering, inserting screws or peeling off protective films.

The difficulties of re-creating the human hand lead to questions about why it is being done, given that the real thing already exists in humans. The answer: the shortage of workers for factory and caregiving jobs is driving the need for alternatives.

Classroom discussion questions:

There are almost a half-million open factory jobs in the U.S. Given the tight job market, will your students be willing to take them?
Why are humanoid robots so sought after?

OM in the News: U.S. Manufacturing Resurgence Will Be Powered by Cobots

Once a luxury reserved for big manufacturers, smaller, smarter, more flexible and less expensive “cobots”—collaborative robots—are bringing automation to every fabricator, no matter the size. The slow, fragile recovery of American goods production wouldn’t be possible without them, writes The Wall Street Journal (Oct. 11-12, 2025).

The number of U.S. companies that make physical things reached a low point in 2014 and has grown since then. Yet they are trapped in a never-ending labor shortage as skilled workers age out, and young people fail to take their place.

China has the greatest number of industrial robots, including these at a factory in Nanjing.

China has become the de facto manufacturer of the world’s goods, owing not only to its enormous population of engineers, technicians and machinists but also its 2-million-plus army of industrial robots. Now the U.S. is attempting to claw back some of those contracts—called “reshoring”—and robots can in some cases quadruple worker output.

The push to bring manufacturing back to the U.S., and the demand for industrial goods to power America’s AI-fueled economy, are driving automation adoption and innovation. “Automation is key to reshoring, plain and simple,” says one CEO.

Cobots have become radically easier to program over the past decade, and now people can use a simple tablet interface to instruct them to perform specific sequences of actions. Programming the older robots common in automotive factories since the 1960s took years of training.

Cobots are part of a broader trend in robotics: Specialized robots that use sensors to safely navigate human environments. They can cope with more variability than previous industrial robots, which had no sensing abilities. This has been essential to the rise of Amazon and its superfast fulfillment, and now it’s coming to manufacturing.

China is indisputably the leader in high-volume manufacturing, and companies that want the biggest volumes of manufactured parts for the lowest possible price continue to send work there. And though many U.S. manufacturers can’t match their Chinese peers in volume, they are competing by using automation to tackle smaller batches of goods under tight deadlines. Manufacturers in the U.S. are now asking how to reshore the making of critical parts.

Classroom discussion questions:

What is a “cobot” and how does it differ from a robot?
Why has China become such a powerful manufacturing hub?

OM Podcast #40: AI in the MBA Classroom and Beyond

We’re excited to share another Heizer/Render/Munson OM Podcast episode! Today, Barry Render sits down with David Rosenthal, a recent MBA graduate from the University of Texas at Austin, to explore how artificial intelligence is transforming business education and early career experiences.

David shares insights into AI in the classroom, AI as a central part of his internship, and his entrepreneurial journey using AI to build his app, Fantasy Fusion Sports.

Heizer Render Munson OM Podcast · OM Podcast #40: AI in the MBA Classroom and Beyond

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

Have you subscribed to this podcast on Apple Podcasts? Just go to your Apple Podcasts app, search “Heizer Render Munson OM Podcast,” and subscribe to get all our podcasts on your mobile device as soon as they come out!

OM in the News: Generative AI in the Factory

Generative AI is a type of artificial intelligence that creates new content and ideas, such as text, images or code, by learning from vast amounts of existing data. Two primary high-impact applications for generative AI have emerged, writes Industry Week (Sept. 11, 2025). The first is its revolutionary role as a new type of user interface. The second is its unprecedented ability to unlock knowledge from the vast sea of unstructured data that permeates every factory.

Perhaps the most immediate and profound impact of generative AI in industry is its function as a “generative user interface” or “Gen UI.” For decades, interacting with complex industrial software and data systems required specialized training. Engineers needed to learn specific query languages to pull data; operators had to navigate complex, menu-driven screens on a human-machine interface; maintenance staff had to know exactly where to find a specific manual in a labyrinthine document management system. The Gen UI changes everything. It provides a conversational, natural language layer that sits between the human user and complex backend systems. It radically lowers the barrier to entry for accessing critical information.

With a Gen UI, an engineer can simply ask, “Show me the pressure and temperature trends for Reactor 4 during the last production run of Product XYZ and flag any anomalies.”

The second game-changing application for GenAI is taming the document tsunami. For many enterprises, as much as 80% of their data is “unstructured”—locked away in formats that are difficult for traditional analytics to parse. Factories run on this data: PDF operating manuals, schematics, environmental compliance reports, maintenance work orders and operator logbooks. For decades, the immense knowledge trapped in these documents has been largely inaccessible at scale.

For the first time, organizations can ask complex questions across their entire document library: “Analyze all maintenance comments from the last five years for our compressor fleet and identify the most common precursor to failure.”

Generative AI is not a magic bullet, but it is a profoundly valuable addition to the Industrial AI toolbox. Its true power today is unlocked when we see it for what it is: a revolutionary interface that makes other systems easier to use.

Classroom discussion questions:

What is the difference between Gen AI and Gen UI?
Give an example of how Gen UI can be used in a factory making a product with which you are familiar.

Guest Post: What a Chinese Drone Ban Means for U.S. Farming

Dr. Misty Blessley is a professor at Temple U. She shares her insights monthly.

DJI, a Chinese company and the world’s largest manufacturer of commercial and industrial drones, faces scrutiny in the U.S. over alleged cybersecurity risks. It is now close to being banned here.

One U. S. business that sells spray-drone kits reported that its challenges began last year when importing DJI drones became significantly more difficult. This uncertainty has caused concern across industries that rely on these tools, from public safety and construction to supply chain logistics. For American agriculture specifically, a ban could cut off access to vital equipment, leaving fields unmonitored, untreated, and risking harvest losses.

DJI drones are favored by farmers as they save weeks of labor by spraying seeds, fertilizer and fungicide from the sky.

Agriculture has adopted drones more rapidly than almost any other sector. Monitoring drones help detect disease and water stress early, while spray drones enable precise application of fertilizer and pesticides during narrow weather windows. They have become crucial for reducing input costs (China is accused of subsidizing their drone industry, which might explain some of the cost differences), protecting yields, and facilitating smooth food movement through supply chains. If imports are halted, many farmers could miss critical windows, leading to lower yields and creating issues along the supply chain, from processors to consumers.

Mitigation Strategies
To prepare, farming businesses should apply lessons learned from managing recent supply chain disruptions:
Diversify suppliers – Start testing U. S. or non-Chinese alternatives, even if they are currently less cost-effective. Early adoption minimizes dependence.

Stock critical parts – As restrictions tighten, building an inventory now provides a safety buffer.

Use mixed fleets – Combine current drones with alternative technologies like ground sprayers to prevent single points of failure.

Plan operational slack – Stagger schedules or adjust operations to account for potential delays.

Collaborate and advocate – Engage with farm bureaus and trade associations to push for phased implementation, subsidies, or funding for domestic options.

Classroom discussion questions:
1• What are the challenges and drawbacks of each mitigation strategy?
2• Considering that the Chinese drone ban is likely, how should user decision-making be updated? (Refer to Module A Decision-Making Tools and consider these facts:◦ Drones can cut labor costs by up to 90% and reduce chemical use by 20–30%. ◦ A high-end U.S.-made drone can cost nearly $30,000, compared to a similar DJI unit costing $6,500).

OM in the News: The AI-Driven Assembly Line

More than a century ago, Ford’s moving assembly line reorchestrated work. Instead of a master mechanic walking to each car to perform complex tasks, the car moved to workers, who each executed a single repeatable action. As a result, work became easier, costs fell and return on investment skyrocketed.

Here are two areas in which this might unfold:

• Onboarding. In a large enterprise, bringing a new hire up to speed requires 10 to 20 hours of effort. Forms, policy explanations and training sessions add up quickly. In an AI-first model, an onboarding orchestrator bot coordinates with AI agents that handle compliance (background checks, payroll setup, training) and equipment (granting access to systems and tools). A virtual assistant answers questions from the new hire.

• Software delivery. Today, new information-technology features—such as launching a new process control program—are specified by operations managers, designed by user-experience teams, coded by developers and tested before use. This cycle can take 80 to 85 business days, with error rates around 10% to 15%.

In an AI-first setting, a digital IT orchestrator bot coordinates AI agents that do everything from product specifications and design to coding and testing, delivering the first version. People then review the newly created features and test their AI-generated code and design. Instead of arriving in 3 months, features are ready in weeks. Time to delivery falls by 60% to 70%, while code quality improves through continuous AI-driven testing.

The common threads of AI-native work are clear: Orchestration moves from humans to AI. Specialized AI bots handle repeatable tasks. Human experts intervene when judgment, negotiation or oversight is required. Results come faster, with lower unit costs and better customer experience.

Henry Ford pioneered a new way of doing business in the 1900s. We have a similar opportunity in 2025. While Ford’s assembly line turned employees into specialists, putting AI at the center of business turns us all into generalists, allowing us to be creative, enlist problem-solving skills and handle ambiguous work.

Classroom discussion questions:

How else might AI be used in factories?
What is an AI “agent”?

Guest Post: Merging OM Tradition with Digital Innovation

Dr J. Prince Vijai is Assistant Professor of Operations Management at IBS Hyderabad, in India.

The transition from traditional OM to digital operations is not a replacement but an evolution. Digital tools enhance the classical OM framework by adding intelligence, speed and adaptability.

1. Process Optimization and Automation In classical OM, process optimization involved detailed mapping and iterative improvements. With digital operations, AI can now identify inefficiencies, simulate improvements and automate decision-making without human intervention. Siemens has integrated sensors, cloud platforms and AI to create a digital thread across product design, manufacturing and logistics resulting in a 20% reduction in production time and a 30% reduction in energy consumption.

2. Inventory and Supply Chain Management Traditional inventory models rely on forecasts and safety stock assumptions. Digital operations use real-time data from IoT sensors and machine learning to predict demand, monitor inventory levels and automate replenishment. For instance, Walmart uses AI and IoT to streamline its vast supply chain, reducing stockouts and improving shelf availability.

3. Forecasting and Scheduling Operations managers have long used statistical tools for forecasting. Digital operations use advanced analytics and machine learning to provide more accurate, dynamic forecasts. Real-time analytics enables organizations to quickly adapt to market changes, weather disruptions or supply chain breakdowns.

4. Quality Management Traditional quality management emphasizes inspection and control charts. Digital quality management integrates data from machines, sensors and customer feedback for continuous, real-time quality assurance. Predictive maintenance, enabled by digital twins and IoT, reduces downtime and improves asset reliability. For example, GE developed digital twins to monitor the performance of jet engines in real time, enabling predictive maintenance and reducing unexpected failures.

The shift to digital operations is not without challenges. Employees accustomed to traditional processes may resist adopting new technologies. Data from different departments or legacy systems can be siloed, limiting visibility and coordination. Implementing AI, IoT and automation involves significant expenses. And digital operations increase exposure to cyber risks.

Future trends include:

Hyperautomation that combines AI and machine learning to automate increasingly complex tasks.
Cognitive operations that use AI not just to automate but to learn and adapt continuously.
Edge computing that enables data processing closer to the source (e.g., in factories or stores) for faster insights.
Green operations that leverage digital tools to track carbon footprints and support sustainable practices.

Embracing the synergy between OM and digital operations is a strategic imperative for long-term success.

OM in the News: America’s Newest Auto Plant Is Full of Robots

Hyundai uses robotic dogs in assembly lines

At Hyundai Motor Group’s ultramodern new auto plant, robots perform a stunning array of tasks. They move materials, attach doors and do almost all of the welding. Dog-like robots, their snouts laden with cameras, prance across the floor to inspect partially built Ioniq electric vehicles.

The factory, which opened near Savannah, Ga., late last year, deploys 750 robots, not counting the hundreds of autonomous guided vehicles that glide across the floor. About 1,450 people work alongside them. That roughly 2-to-1 ratio of humans to robots compares with the U.S. auto-industry average of 7-to-1, writes The Wall Street Journal (Aug. 25, 2025).

The human workforce is sparse in much of Hyundai’s plant. Metal arms move slabs of steel through presses that stamp them into components of the frame. An array of robots weld those parts together without a person in sight.

Human beings are still in the driver’s seat for some jobs. But it isn’t until the frames emerge from the paint shop that people take over. Hundreds are stationed along two assembly lines where seats, dashboards and other components are added. They spot burrs that must be smoothed and bits of trim that need replacing. They snap fabric door panels into place with grommets, push electrical connectors together until they click and duck into places robots can’t reach to bolt down seats and attach shock absorbers.

The factory was designed so that robots do tasks that are dangerous, repetitive or physically demanding. People are left to troubleshoot, monitor quality and bring craftsmanship to the manufacturing process. “We’re not trying to minimize human involvement—we’re trying to maximize human potential,” said the CEO.

“But the minute humans become more expensive, more recalcitrant, the more automation you’re going to get,” said an industry expert. The auto industry today is heavily robotized, particularly in Hyundai’s home country of South Korea. The country has one of the world’s lowest birthrates, helping to drive its adoption of the machines.

A complete robot takeover is decades away. Robots still struggle to handle fabric and other limp materials, and performing the most complex jobs will take technological breakthroughs that aren’t yet on the radar.

Classroom discussion questions:

Will robots ever take over 100% in auto manufacturing?
What are the OM implications of the 2 to 1 ratio?

Guest Post: Self-Service and Mass Transit Difficulties

Professor Howard Weiss, developer of our POM and Excel OM software, shares his thought with our readers monthly.

In a blog last year, the difficulties with self-service at Walmart and other retailers were discussed. Mass transit also has self-service problems, but they differ from those at retailers. Fare evasion has emerged as a significant fiscal challenge for mass transit agencies as they incur large losses such as:

MTA (New York City) – $690 million
MBTA (Boston) – $644 million
TfL (London, England) – $175 million
SEPTA (Philadelphia area) – $20 million
BART (San Francisco) – $20 million
West Chester, NY – $12 million
MCTS (Milwaukee) – $ 4 million

Milwaukee estimates that one bus route has a 33% fare evasion route and would like to reduce it to 15%. TfL claims a 3.5% rate of fare evasion with a target of 1.5%

Self-service fare collection was developed in Europe in the 1960s by transit agencies facing labor shortages and the need to reduce costs. Originally, subway passengers went through a turnstile serviced by someone who collected the fare. In most modern systems turnstiles are unstaffed, and many riders have been jumping half-height turnstiles or sneaking in behind another passenger. On buses, some riders enter through the rear exit or emergency doors.

In response to these challenges, transportation authorities are implementing next generation fare evasion gates, typically with full height glass, that make it harder to evade the fare. Another approach is to have more rigorous fare enforcement by hiring more police. Camera technology now includes using facial recognition to identify repeat offenders. Several transportation authorities are turning to educating riders about the harm of fare-jumping and the penalties for fare-jumping. This includes SEPTA’s posted $300 fine for failing to pay for a ride. These changes will take time and investment to be fully implemented.

When riders are caught evading fares, some systems enforce zero-tolerance policies, issuing citations for every offense, while others adopt a graduated response that begins with a warning. In certain cases, enforcement officers are given discretion to determine the appropriate response.

In some countries, such as Germany, there are no gates or turnstiles, but train passengers are regularly inspected to see that they have a paid ticket and, if not, will be fined. Alternatively, some countries and cities have implemented zero-fare systems, saving money on fare collection and obviously eliminating fare-evasion.

Classroom Discussion Questions:

Why does self-service, such as that described in your textbook about Alaska Airlines, work well for airlines but not for mass transit?
What is the major advantage of the German, no turnstile method?

OM in the News: Walmart’s AI “Super Agents”

Walmart has developed an AI strategy in the creation of four “super agents” reports The Wall Street Journal (July 24, 2025). Agents refer to artificial intelligence tools that can independently take some action on behalf of a user. One is for customers, one is for employees, one is for engineers, and one is for sellers and suppliers. The super agent for each group will tap the capabilities of a number of behind-the-scenes agents, all in a single unified experience.

“Artificial intelligence is already changing how we work,” said Walmart’s CEO. “Learning and applying what we learn, as we build new tools, is the responsibility and an opportunity for all of us to improve experiences for our customers, members and fellow associates.”

The firm believes it is critical to stay ahead of the technology curve in an area like retail, where the top 10 retailers can change dramatically decade to decade. Its hope is that AI agents will help deliver top-line growth, as they give customers more personalized and enticing shopping experiences, as well as bottom-line savings, where they can help manage supply chains and inventory more efficiently.

Walmart’s situation is unique, with most companies still figuring out how to deploy even one AI-powered agent that can perform a task autonomously or in coordination with humans.

The four super agents are at different stages of development. The customer-facing super agent, Sparky, is already live. Marty, the supplier-facing super agent, launches in Fall and will include functions like checking the analytics on purchases and suggesting and putting into motion advertising campaigns. The employee and engineering super agents are expected in the next year.

Classroom discussion questions:

Explain what an AI “agent” does.
Why does the firm want to be a leader in AI technology and how is it implementing this goal?

OM in the News: Amazon Is on the Cusp of Using More Robots Than Humans

The automation of Amazon facilities is approaching a new milestone: There will soon be as many robots as humans. The e-commerce giant, which has spent years automating tasks previously done by humans in its facilities, has deployed more than one million robots in those workplaces, reports The Wall Street Journal (July 1, 2025). That is the most it has ever had and near the count of human workers at the facilities.

Company warehouses buzz with metallic arms plucking items from shelves and wheeled droids that motor around the floors ferrying the goods for packaging. In other corners, automated systems help sort the items, which other robots assist in packaging for shipment.

One of Amazon’s newer robots, called Vulcan, has a sense of touch that enables it to pick items from numerous shelves. Amazon has taken recent steps to connect its robots to its order-fulfillment processes, so the machines can work in tandem with each other and with humans. Now some 75% of Amazon’s global deliveries are assisted in some way by robotics. The growing automation has helped Amazon improve productivity, while easing pressure on the company to solve problems such as heavy staff turnover at its fulfillment centers.

For some Amazon workers, the increasing automation has meant replacing menial, repetitive work lifting, pulling and sorting with more skilled assignments managing the machines. Amazon has trained more than 700,000 workers across the world for higher-paying jobs in mechatronics and robotics apprenticeships.

The number of packages that Amazon ships itself per employee each year has also steadily increased in the past decade to 3,870 from 175, an indication of the company’s productivity gains.

Amazon is also rolling out artificial intelligence in its warehouses to improve inventory placement, demand forecasting, and the efficiency of its robots. Amazon said it will cut the size of its total workforce in the next several years.

Classroom discussion questions:

Research Amazon’s history of using robotics.
What are the advantages of introducing more robots?

OM in the News: The Holy Grail of Automation–Now a Robot Can Unload a Truck

The robots are coming for the last human warehouse jobs. Loading and unloading a truck is backbreaking, mind-numbing work that retailers and parcel carriers have tried to solve for years. Workers may not stay long in these jobs. Summers and winters are particularly grueling for anyone stuck in a metal trailer, slinging heavy boxes. Injuries are common.

Automating this process has long been the holy grail of warehouse logistics, writes The Wall Street Journal (June 24, 2025). When loaded, packages must be fitted together to fill the available space and be sorted by weight—with the heaviest items on the bottom—so they don’t topple or break. Unloading them is challenging, too, because the unloader must move in and out of a trailer, ferrying packages of different sizes and weights.

On a typical warehouse floor today, every task might be heavily automated—except for workers loading and unloading the trucks. People who have worked these jobs say they have to stand for extended periods, hefting boxes as heavy as 70 pounds. New advances in robotics are changing that.

Improved sensors and algorithms, advancements in AI and faster image-processing technology are making these robots proficient players in tasks that are like a game of 3-D Tetris. This shifts the burden of physical lifting from humans to robots. Proponents of the technology say it will mean more efficiently packed pallets, fewer damaged items and a lot of time saved. The machine-learning algorithm isn’t aware of what’s inside each package, but does know the item’s weight, approximate center of gravity and how fragile it is.

DHL’s Stretch robot can unload 580 cases an hour, twice the rate of a human unloader.

The robot will use the information it has on an item to choose the optimal pallet and determine where the it should be placed to minimize damage, but still allow the highest possible number of boxes to be packed in the given space.

DHL just signed an agreement with Boston Dynamics for 1,000 of these robots. United Parcel Service is also increasing automation at its facilities, including for loading and unloading trailers—a move that will help the company cut costs. FedEx has been testing and refining the truck-loading process in one of its facilities since 2023. Walmart also has introduced robots that can unload a truck.

Classroom discussion questions:

Why is this an important OM advancement?
How else are robots now being used in warehouses?