Category: OM in the News

Bringing your the lastest happenings in operations managment straight from the popular business press

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: Digital Twins and Nuclear Fusion

Digital twins, which we cover in Module F (Simulations and Digital Twins), is a big topic at Nvidia and Siemens as they work together to make nuclear fusion a commercial reality. In that chapter (see p. 847), we define a digital twin as:  “an electronic virtual replica of an operation that allows organizations to mimic how a product, process, or system will perform.”

Workers at Commonwealth Fusion Systems’ campus in Devens, Mass

Fusion engineers at the Nvidia/Siemens venture, called Commonwealth Fusion Systems (CFS), will use its digital twin to run simulations, ultimately to hasten the goal of producing fusion energy at a commercial scale. CFS “will be able to compress years of manual experimentation into weeks” with the AI assistance, said its CEO.

Nuclear fission, which splits atoms to produce energy, is already in use in power plants, reports The Wall Street Journal (Jan. 7, 2026).  But many companies see fusion, the energy process that powers the sun by joining atoms together, as a longer-term bet because it can provide much more energy in a cleaner process. Nuclear energy appeals to tech giants because it releases minimal carbon emissions while providing round-the-clock power—particularly as they look to fuel their AI ambitions.

CFS said it was working with Google on an AI project, and explained that that effort has created something like a co-pilot for its fusion machine, while the digital twin plan “is the virtual airplane.” Google also recently signed a power purchase agreement with CFS to secure energy from what could be the first grid-scale fusion plant.

“The race is on for AI. Everyone is trying to get to the next frontier,” said Nvidia’s CEO.

Classroom discussion questions:

  1. Provide other examples of how digital twins can be used.
  2. Why is this fusion project so important as an OM tool?

Our Top 10 Posts in 2025

Happy and Healthy New Years from our team of coauthors–Jay, Barry  and Chuck. As we close out 2025, we wanted to share the ten most highly read posts this year.

  1. The Supply Chain of the Future— a story of the “connector states” in Asia, led by Vietnam and Cambodia
  2. Top Five Global Supply Chain Risks–which include climate change, tariffs, cybercrime, rare minerals, and forced labor
  3. How China’s BYD is Squeezing Suppliers in the EV Price War–The Chinese automaker follows the word neijuan, which refers to a situation in which people work hard and compete fiercely without anyone getting ahead.
  4. Why Is It So Difficult for Robots to Make Your Nike Sneakers?— Nike has poured millions into an ambitious effort to partly automate what has always been a highly labor-intensive industry.
  5. The U.S. Made T-Shirt–Walmart has pledged to buy more items that were made, grown or assembled in the U.S.
  6. Supply Chains and Tariffs–Some manufacturers have reconfigured supply chains by reshoring portions of  production, by nearshoring—leveraging the USMCA free trade agreement (see Ch.2) to source more from Mexico and Canada—and by growing trade with countries such as India and Vietnam, which offer cost advantages.
  7. Holy Guacamole!–Few companies can match Chipotle Mexican Grill’s avocado appetite.
  8.  U.S. Energy Independence and Manufacturing–Over the past two decades the U.S. has evolved from a degree of foreign-energy dependency that threatened its economy and national security to the premier energy producer in the world.
  9. McDonald’s Gives Its Restaurants an AI Makeover–The fast-food giant’s new initiative uses artificial intelligence to target order accuracy and help restaurants detect equipment issues before they fail
  10. Starbucks Uses New Technology to Fill Orders Faster–Starbucks says new technology is helping fix one of its customers’ biggest gripes: waiting too long for their coffee

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: Salvaging Critical Minerals From Old Laptops and Phones Isn’t So Easy

While electronic waste (e-waste) seems almost infinite, from fried computers to dormant BlackBerry phones, securing discarded tech for metals recycling can be quite tricky.

Electronic waste is dropped on to a conveyor belt during a process to harvest rare earth and other metals in France.

Recycled lithium, copper and other critical minerals can find new life in everything from electric vehicles to battery storage. The push to recycle metals in the U.S. comes amid intensifying efforts to compete with China, which dominates the critical minerals market, reports The Wall Street Journal (Dec. 1, 2025).

“It’s like urban mining,”  said one industry CEO, explaining the benefits of reusing metals from old electronics and scrap waste instead of procuring it directly from the earth. “Rather than going into the mines, we go into our communities,” he said.

Collecting e-waste can be tricky because there isn’t a strong infrastructure to retrieve devices directly from homes, scrapyards, manufacturers or collection sites, and some consumers have privacy concerns when handing over old hardware that could hold personal information.

Meanwhile, large quantities of e-waste are being shipped abroad. About 2,000 shipping containers of electronic waste are sent each month from the U.S. to countries in Asia, particularly Malaysia. But the need to increase the domestic supply of critical minerals has become more urgent, as is evident in the U.S.’s near-total reliance on Chinese imports for lithium-ion batteries.

Shipping e-waste abroad rather than recycling it in the U.S. is “a tragic lose, lose, lose proposition,” said a second industry expert. “The country misses out on the value from the critical metals going to waste, as well as recycling jobs for local workers.”

Most lithium-ion batteries on the market are likely to be hazardous when they are disposed of because they could catch fire or explode if not handled carefully. The environmental footprint of lithium-ion battery recycling emits less than half the greenhouse gases of conventional mining and refinement of metals, and uses about one-fourth of the water and energy of mining.

The global consumption of lithium was estimated to be 220,000 metric tons in 2024—a 29% jump from 2023. But tech recycling in the U.S. has a long way to go. E-waste recycling collection, from relying on municipal return sites to retailer take-back programs, is irregular and fragmented, so recyclers often cannot rely on steady, predictable volumes.

Classroom discussion questions:

  1. Why doesn’t the U.S. recycle all its e-waste?
  2. Could AI help in recycling? (See Supp. 5 of your Heizer/Render/Munson text).

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: Taco Bell Uses OM to Know What You Want to Eat at 2 a.m.

Taco Bell is considered the GOAT of new product ideas, testing hundreds to develop viral hits like Doritos Locos Tacos and Baja Blast, writes The Wall Street Journal (Nov.22-23, 2025). Its product development team (see Figure 5.3 in your Heizer/Render/Munson text) systematically generates, tests, and implements new menu items, focusing on limited-time offerings (LTOs) that keep the brand relevant and appealing. And as we point out in Figure 5.1, the higher the percentage of sales from new products, the more successful the firm.

Inside Taco Bell’s test kitchen

Here is how it works at Taco Bell’s New Product Development Team:

  1.  New products are engineered to use ingredients and equipment already available in restaurants, minimizing the need for additional resources and reducing training time for staff. This approach ensures that operational changes are minimal and that new items can be introduced without disrupting established workflows.

2. Using rapid testing, hundreds of concepts are evaluated annually, but only those that meet operational standards and customer demand advance to market testing. This ensures that only efficient, scalable items reach the menu.

3.  Most new items are offered for 4-6 weeks, allowing for frequent menu refreshes without overburdening operations or inventory management. This strategy keeps the menu dynamic and enables the company to respond quickly to changing consumer preferences.

4. LTOs are crafted for quick and consistent assembly, supporting high throughput and maintaining quality across all locations. Operational teams are trained to execute new items efficiently, ensuring that service standards are upheld even during periods of high demand.

5. New menu innovations are developed with profitability in mind, leveraging cost-effective ingredients. This focus supports growth and helps Taco Bell remain competitive in its sector.

6. The company tracks same-store sales and operational metrics, enabling it to identify opportunities for improvement and maintain operational excellence.

7.  The product development team operates in a collaborative environment. Leadership encourages experimentation.

8. Operational decisions are informed by market research and consumer feedback, ensuring that new products align with customer preferences and operational capabilities.

Taco Bell’s operational strategy enables efficient product innovation, rapid deployment, and consistent execution, supporting both profitability and sustained market leadership in the fast-food industry.

Classroom discussion questions:

  1. What items at McDonald’s and Starbucks are LTOs?
  2. Why is Figure 5.1 so important?

OM in the News: Europe’s Move Towards Rare-Earths

Europe is trying to get itself on the global rare-earths map. Estonia, once a textiles hub for the Russian Empire, is now host to Europe’s biggest production plant for the kinds of rare-earth magnets needed in electric cars and wind turbines. It is part of Europe’s push to secure a foothold in a global supply chain dominated at every step by China, reports The Wall Street Journal (Nov. 16, 2025). Financed in part by the EU, the factory is expected to begin deliveries to companies in 2026.

Production of rare-earth magnets is expected to increase at the factory in Estonia, but it still isn’t expected to meet Europe’s projected demand.

The problem: Even at the new factory’s initial planned capacity of 2,000 tons of permanent magnet material, the plant will produce a fraction of what European manufacturers need. There are plans to scale up production to 5,000 tons, but that is still a long way from being enough to break Europe’s dependence on China. Total European demand is forecast to reach about 45,000 tons by 2030.  (Companies in the U.S. are planning to build more than 40,000 tons of capacity by 2030).

After China imposed new export restrictions for rare earths this year, the U.S. stepped up subsidies and other measures to support the industry, spurring a race to build out American mining, processing and manufacturing capacity. Rare earths are also essential to manufacturing many defense systems. European auto suppliers were already eager to diversify their permanent magnet sources before China’s move.

Rare-earth magnets are widely used in products such as electric cars and wind turbines

Europe prospered over recent decades in a global trading system that allowed it to import cheap gas from Russia and rare earths from China, powering its industrial base. But Russia’s invasion of Ukraine and China’s move to restrict rare-earth exports showed how dependent the continent had become on those countries. Europe has some rare-earth processing and recycling facilities but no active rare-earth mining. For now, EU producers are relying on customers being willing to pay a premium to avoid dealing with China’s restrictions.

Classroom discussion questions:

  1. Why does China exert such power over the rare earth supply chain and why is that supply chain so important?
  2. What else can the U.S. and EU do?

OM in the News: Aggregate Planning Using Seasonal Workers

Every year, hundreds of thousands of U.S. workers take on seasonal jobs during the holidays. This is one of the most popular aggregate planning strategies that firms use to deal with capacity, as is noted in Chapter 13 of your Heizer/Render/Munson text.

Holiday shoppers crowded a Kohl’s store in Wisconsin last year.

The retail and transportation-and-warehousing sectors typically rush to hire as they staff up for the holidays and let those workers go once the season is over. In the final three months of last year, the two sectors added 912,000 jobs. They then shed 858,000 jobs over January and February.

While the jobs are temporary, they provide an important source of income for low-wage workers, many of whom move from job to job over the rest of the year, writes The Wall Street Journal (Nov. 12, 2025).

This year, some of the companies that do the most holiday hiring have broken from their usual practice of advertising how many holiday workers they plan to hire. United Parcel Service, for example, said last year that it would hire 125,000 workers in its “holiday hiring spree.” This year, it hasn’t made an announcement. Also holding off: Macy’s, which said in 2024 that it would hire more than 31,500 seasonal workers, and Target, which said last year that it would add 100,000 seasonal jobs. (Both UPS and Target have laid off regular employees this year.)

Through October this year large companies have announced plans to hire 372,520 seasonal workers. That compares with 660,150 at the same point last year.

 But Portugalia Marketplace in Fall River, Mass., which nets about 30% of its annual business in November and December, calls for “all hands on deck”—as it wrote in a recent seasonal hiring posting for its warehouse that fills online orders. The grocer’s staff typically grows from 50 people to 60 near year-end as families shop for holiday groceries and the store hosts more events to attract visitors.

Classroom discussion questions:

  1. What other capacity options do firms have?
  2. Why is seasonal hiring slowing this year? Is it an AI issue?

OM in the News: The Electric F-150’s Short Life Cycle

The first Boeing 737 jet rolled off the assembly line 58 years ago, on April 9, 1967.  That is a long life cycle, given it has still not reached the “decline” phase in Figure 2.5 in your text. But the life cycle certainly looks a lot shorter for electric pickup trucks.

Ford is planning to scrap the electric version of its F-150 pickup, according to The Wall Street Journal (Nov. 7, 2025) which would make the money-losing truck America’s first major EV casualty. “The demand is just not there” for the F-150 Lightning and other electric trucks, said one dealer. Stellantis earlier this year called off plans to make an electric version of its Ram pickup. GM plans to  discontinue some electric trucks and sales of Tesla’s Cybertruck tanked this year. The trucks seemed a good bet amid booming EV demand and clean-air mandates that required automakers to sell fewer gas-guzzlers.

Ford halts production of the F-150 Lightning

The Lightning fell far short of expectations as American truck buyers skipped the electric version of the top-selling truck. Overall EV sales are plummeting in the absence of government subsidies.  Ford dealers sold 66,000 gas-powered F-Series pickups, and just 1,500 Lightnings, the fewest of any model. (Ford has racked up $13 billion in EV losses since 2023).

 When Ford launched the Lightning 5 years ago it promised a pickup as fast as a sports car and as affordable as a conventional truck. It would drive hundreds of miles on a single charge, and carry enough voltage to power a home for days. “It’s like a smartphone that can tow 10,000 pounds,” said  the CEO at the launch.

But truck buyers worried the pickups would run out of juice in the middle of a job or a long haul as their range is dramatically reduced when towing big loads or operating in cold weather.

GM has also lost billions on electric trucks after rolling out a string of them, including an electric version of the popular Chevrolet Silverado. GM has three electric pickups, and it sold about 1,800 of them last month.

Ford built up the capacity to make as many as 150,000 Lightnings a year. But the EVs cost billions to develop and manufacture, and are only profitable if they sell in large enough volumes, which they did not.

Classroom discussion questions:

  1. Where do you think all EVs are on the life cycle curve?
  2. Why did so many auto manufacturers misread the demand for electric pickups?

OM in the News: The AI Revolution and Productivity

Most speculation about AI has focused on its potential to kill jobs and on the policies that government might implement to control AI. But it’s important to remember that our only window into the future is the past. From the colossal changes wrought by the Industrial Revolution to the Digital Revolution of the last quarter-century, improvements in technology have created an array of jobs that far exceeded—in quantity and quality—the ones eliminated, elevating standards of living.

In America growth during the Industrial Revolution was of biblical proportions, writes The Wall Street Journal (Nov. 2, 2025). From 1870 to 1900 real gross domestic product tripled, the population and labor force roughly doubled, and output in manufacturing grew sixfold. Per capita income rose by 110% between 1865 and 1910, while real wages of manufacturing workers increased an estimated 173%. Life expectancy rose by a quarter as inflation-adjusted costs of food, clothing and shelter dropped by roughly 50%.

During the Digital Revolution of the last quarter-century, U.S. GDP rose by 66%. Data show the extraordinary capacity of the American economy to absorb new technology. Since 2000 on average 5 million Americans have either been laid off or quit their job every month, but the economy has created 5.1 million better-paying jobs a month. This creative destruction isn’t new. In 1810, 81% of Americans worked in agriculture; today only 1.2% do. In 1953, 32% of Americans worked in factories. As real industrial production quadrupled, the share of the labor force in manufacturing declined to 7.8% in 2025.

Almost every discussion of AI calls for extensive economic assistance for those who are displaced. Yet this impedes workers’ transition to new jobs.

American exceptionalism—in which Americans are more productive and have higher living standards—depends in part on our extraordinary ability to adjust to change. Europe makes it hard to lay people off, which constrains the ability to create jobs. In China, most industrial subsidies go to noncompetitive industries, not to the potential winners of the future.

By letting the market system develop and absorb AI technology, we can achieve a second economic miracle, which will enrich America and the world.

Classroom discussion questions:

  1. Will AI create more jobs than it destroys?
  2. Will overall productivity measures improve in this AI revolution?

 

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:

  1. 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?
  2. Why are humanoid robots so sought after?

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?