factories – The OM Blog by Heizer, Render, & Munson

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:

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

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:

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

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: 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.

OM in the News: A Safe Return to Manufacturing Productivity

COVID-19 is changing everything in manufacturing. Companies face a long journey to the “next normal,” one that will likely have far-reaching financial and operational implications, writes Industry Week (July 14, 2020). Immediate priorities include creating a safe work environment for production employees. Missteps could invoke legal or regulatory actions, something all companies want to avoid. As many manufacturers enter the Recover phase of COVID-19, one that is marked by restarting production at plants in regions that have been impacted differently by virus outbreaks, workforce safety becomes a critical priority. The restart/ramp-up should generate considerations across the work itself, the workforce, and the workplace.

Work: How will new physical distancing constraints and supply/demand variability be incorporated into operations? Are there opportunities to remove humans from processes through automation and/or robotics?

Workforce: How will workers “feel” safe and come back to work willingly? What new policies and procedures are required to protect employees, reduce risk of spread (e.g. personal protective equipment (PPE), break room policies)?

Workplace: What physical/operational changes are necessary to meet health and safety requirements? What technologies and solutions could create a safer work environment in plants and facilities?

A holistic approach toward the recovery phase should include solutions that address all three of these areas. It will likely blend strategy and process changes with advanced technologies, which can hold the key to a robust recovery for manufacturers. Some of the smart factory technologies that many manufacturers have already been piloting, such as data analytics–71% in a recent study, sensors–54% and wearables–29%, could dramatically accelerate the pathway to recovery.

Classroom discussion questions:

What other complications will operations managers face when reopening factories or service facilities?
What role can sensors play?

OM in the News: “Slave Wages” Haunt England’s Online Retailer Boohoo

“I’d rather manufacture in Bangladesh than in Leicester, because they’re far further advanced in terms of labor protection,” says the CEO of the retailer Esprit in Financial Times (July 10, 2020). Adds the former CEO of New Look, “In Leicester . . . it’s slave-like conditions. Everybody knows about it and some firms are clearly ignoring it.” They are referring to the $5 billion fast-fashion business Boohoo, located in Leicester, England. Boohoo is the biggest buyer from garment workshops (over 75%) in a city battling claims over poor working conditions.

“We’re kind of put into a cage and we have to run around like rats,” said the GM at Top Fashion, a local clothing manufacturer. Leicester’s problem of illegal factories has been an open secret for almost a decade, with police this week walking the dilapidated factory corridors looking for evidence of modern slavery.

Over the past 15 years, the revival of Leicester’s textile trade has been the story of Boohoo’s rise. Abandoned by big retailers 3 decades ago, Leicester’s industry splintered into 1,500 mini-factories, typically employing fewer than 10 people. This flotilla of small workshops competed with rivals in Bangladesh and Turkey by offering an ultra-flexible service, handling small orders in quick time. It helps Boohoo test almost 3,000 lines of clothes every week and ramp up production of trends that catch on.

One study found below minimum wage employment to be “endemic”. More than 3/4 of garment workers earned $4.40 an hour–half the minimum wage. So cheap were rates that agents directed work to Leicester that was supposed to be completed in Romania. Some employers preyed on the vulnerability of local workers who are often South Asian immigrants with poor English and few options. And to make matters worse, the pandemic has Leicester under lockdown because of the virus’ spread in the garment industry.

Boohoo’s practice of throwing new clothes out (at low prices) to see what sticks is a great OM story of speed and flexibility, tying to our discussion of achieving competitive advantage in Chapter 2.

Classroom discussion questions:

How is Boohoo able to adjust its offerings so quickly?
What is the ethical dilemma that Boohoo competitors in Leicester face?

OM in the News: Post-Pandemic Supply Chains and Automation

A U.S.-based engineer working from home uses software to examine a manufacturing line in China.

Factories around the world are turning to technology to help them safely open back up after being shut down by the coronavirus pandemic, reports The Wall Street Journal (June 15, 2020). Software, sensors, robotics and A.I. tools that make it easier for workers to keep their distance in factories and let engineers monitor and fix problems remotely have surged in demand. “Covid has really been the catalyst for the adoption of software solutions to automate workflows and make it more efficient when you have less people around doing things,” said one industry expert.

Manufacturers are focusing on using software to dynamically change assembly lines. And they are using A.I. to remotely do quality inspections in real-time. For U.S. electronics manufacturers, mistakes, defects and wasted time add up to 25% of costs and often require engineers from the U.S. to visit factories in China to fix problems. A.I. systems can scan images of every product produced on an assembly line to identify anomalies and defects. Engineers can then analyze and fix them remotely.

One Calif. food manufacturer remained open during the pandemic by using enterprise resource planning (ERP) software to remotely manage its manufacturing, supply chain and finances, letting 30% of its employees work from home. Meanwhile, technology is helping manufacturers deal with disruption to global supply chains stemming from factory shutdowns. Clear Metal, in San Francisco, has proprietary data from sources such as satellite data, shipping ports and trucking companies, along with A.I. that can predict problems in supply chains and help companies change shipping methods or suppliers in real-time.

And of course, supply-chain problems caused by factories closing in China have caused companies to look to move manufacturing closer to home. The only way to do that is automation, with factories closer to customers. Previously, automation was only used by large factories with budgets of millions of dollars with long production cycles. But automated assembly lines are now available for use in smaller spaces than large factories, with one machine doing the work of 3 people at a fraction of the cost.

Classroom discussion questions:

How can technology help improve OM?
Why is automation important in reshoring?

OM in the News: Data Analytics for Factories

A Norsk Hydro aluminium plant in Norway. The company’s CIO, called the availability of data during the pandemic “a clear game-changer.”

Manufacturers will be spending far more on data management and analytics tools in the aftermath of the coronavirus outbreak, and will be using those tools for deeper insight into operations, sales and supply chain disruptions, reports The Wall Street Journal (June 3, 2020).

Data—produced by shop-floor scanners and other hardware tools—can now be used to more accurately measure and improve the performance of production-line machinery. Such benefits are expected to spur annual spending by global manufacturers on data management and analytics to nearly $20 billion by 2026, up from $5 billion this year.

Advanced data tools will give factories a clearer view of operations and equipment performance, allowing them to speed up production, reduce waste, improve their product quality and avoid downtime by more quickly identifying maintenance issues, among other things. Factories will also be able to identify and extract relevant data sets to feed into artificial intelligence software designed to predict production and supply chain problems. “It’s a case of going from reactive analytics, reporting on what happened, to proactively analyzing what might happen and the suggested actions to take,” said one industry expert.

The pandemic has made manufacturers aware of the need for more sophisticated ways to monitor operations, especially when plants are accessible to only a handful of workers. “We’re working with clients on taking unprecedented amounts of data and deriving insights that can shift decision-making,” said the CIO of NTT Data Services, referring to streams coming from shop-floor sensors, machinery, supply-chain fleets and other systems. Manufacturers are using that data to get a better view of equipment performance and maintenance needs, quality control and workplace safety.

Classroom discussion questions:

What is the difference between descriptive, predictive, and prescriptive analytics (see Module G in your Heizer/Render/Munson OM text)?
Which of these methods is discussed in this article? Why?

OM in the News: Retooling China

It isn’t clear how long it will take for the rest of China to follow Dongguan’s example.

Factories in the southern Chinese city of Dongguan once employed what one employee called a “magnificent sea of people.” But rising labor costs and a new generation of Chinese with little interest in toiling in factories forced a new tack, reports The New York Times (July 5, 2018). Now the sea of people is being replaced by a whirring array of boxy machines, each performing work that used to take 15 people. The factory changes suggests that Beijing’s vision of Made in China 2025 — the ambitious state-driven plan to retool China’s industries to compete in areas like automation, microchips and self-driving cars —is coming from the bottom up: from the businesses and cities across China that know they must modernize or perish. Dongguan long relied on making and exporting shoes, toys and electronic parts to the U.S. and Europe.

The average worker’s income rose fourfold over the past decade. Fewer young people wanted to work on dull and stressful assembly lines, preferring service jobs — like waiting tables and delivering e-commerce packages — that let them interact with people or move around. Some factories moved to lower-cost countries or shut down for good. Dongguan’s companies had to do something. They committed to modernizing.

Mentech, a telecom equipment supplier there, once had hundreds of workers winding, packaging and testing magnetic wires, all by hand. Today, the company is desperate for workers. On the side of one factory building it lists the on-the-job benefits it offers: monthly wages with overtime of up to about $1,100, air-conditioned dormitories, and free Wi-Fi.

Today, a factory floor that once needed over 300 workers now needs 100. More than half of the factory has been automated. The workers clustered around the machines will probably be replaced by machines themselves in a year or two. “The biggest trend in manufacturing is that automation is irreversible,” says a Chinese industry expert.

Classroom discussion questions:

In what ways has Chinese manufacturing paralleled the history of manufacturing in the U.S?
Why are Chinese firms having trouble staffing their factories?

OM in the News: Small Factories Emerge as a Weapon in U.S. Cities

James Branch works as a skilled machine operator at Marlin, which makes specialized baskets.

The New York Times (Oct. 30, 2016), tells the story of the unlikely survival of Baltimore’s Marlin Steel, a rare breed: the urban industrial manufacturer. Marlin, a 50-year old company that makes steel baskets, is a thriving factory in a place that factories have fled — first to the South, and later to Asia.

How did Marlin survive? Over the course of a decade, it invested in robots that churned out baskets 100 times as fast as human beings. Marlin trained its workers to operate the robots, which cost several $100,000 each, and hired engineers to help design ever-more-sophisticated products to win customers and stay ahead of overseas rivals. Automation did not mean the elimination of jobs– in fact, it saved the company– by producing many more baskets, with only a few more workers, each paid well over $50,000.

Factories will never employ the masses of Americans they once did. Automation and foreign competition will not abate. Over the last 20 years, industrial employment has dropped by 1/3. Only 12.3 million Americans work in the sector today, millions fewer than in leisure and hospitality. But small manufacturers like Marlin are vital if the U.S. is to build a society that offers greater opportunities for everyone.

Today, smaller plants are particularly important to job creation in factory work. As megafactories are the exception, small manufacturing is holding its own. Out of 252,000 manufacturing companies in the U.S., only 3,700 had more than 500 workers. The vast majority employ fewer than 20.

While they may not rival the scale of 1950s assembly lines, these smaller craft-type producers hold out hope for cities, particularly as some companies look to move jobs back from overseas to be closer to customers and more nimble to supply customized, small-batch orders. And, these jobs pay more. Manufacturing workers typically earn over $26 an hour.

Classroom discussion questions:
1. What was Marlin’s OM strategy?

2. Why will millions of manufacturing jobs never return?

OM in the News: Levi Strauss’s Push for More Ethical Factories

In an attempt to bolster its ethical credentials and meet the demands of increasingly fussy millennial consumers, Levi Strauss is offering a new financial incentive to suppliers as far away as Bangladesh and China to meet environmental, labor and safety standards. The jeans maker is providing lower-cost working capital to those of its 550 suppliers who do best on those measures. The project sprang out of the 2013 Rana Plaza factory collapse in Bangladesh, which left more than 1,100 dead and prompted new scrutiny of international fashion brands’ supply chains.

“The move reflects two important trends in globalization,” writes The Financial Times (Nov. 4, 2014). As consumers fret about the conditions under which their clothes are made, fashion brands are facing greater pressure to ensure their suppliers in places like Bangladesh, Cambodia and Vietnam abide by higher standards. In some cases that issue, together with rising wages and costs in China and other production centers, is leading to brands “reshoring” production closer to home. But the combination of those pressures and the way global supply chains are becoming ever more intricate is also leading multinational companies to build tighter bonds with suppliers and to use new tools to manage them.

Levi Strauss’s VP of sustainability said the company now relies on “fewer, more capable” vendors and that its relationships go back an average of 10 years with top contractors. The firm claims to require its suppliers to abide by some of the strictest labor standards in the garment industry and employs full-time inspectors to visit factories around the world. It also is rare among fashion brands in publishing a full list of the factories and suppliers it uses around the world. It has, however, had dark chapters in its past. In the early 1990s Levi Strauss was accused of using Chinese prison labor to make clothes. It withdrew production from China on human rights grounds for five years, becoming an example of the potential pitfalls of doing business in China.

Classroom discussion questions:

1. Why is Levi Strauss making this move?

2. What are the advantages of having fewer vendors?