Barry Render

Guest Post: Defense Supply Chains at the Heart of National Security are Under the Microscope

Prof. Misty Blessley, Associate Professor of Supply Chain Management at West Virginia University, brings up a timely topic.

A major shift is underway in U.S. defense procurement policy, according to Reuters.com (July 20, 2026). President Trump recently signed an executive order making it harder for defense contractors to obtain waivers that allow them to purchase critical minerals and materials from China and other prohibited foreign suppliers. Contractors must now demonstrate that they searched for alternative sources, disclose where materials originate, and provide a plan to reduce dependence on those suppliers.

This policy aligns closely with the goals of the Buy American Act of 1933, which requires federal agencies to give preference to domestically produced goods when purchasing supplies. The law was designed to support American manufacturing, strengthen the nation’s industrial base, and reduce dependence on foreign suppliers. While waivers have long been available when products were unavailable domestically or significantly more expensive, the new executive order signals a tougher approach to those exceptions in defense contracting.

Why have defense contractors relied on foreign suppliers? In many cases, the answer involves availability. Critical minerals and specialized components are often sourced through global supply chains, with some materials heavily available from prohibited foreign suppliers. Cost can also play a role. Foreign suppliers may offer lower prices, allowing contractors to control expenses.

At the heart is the urgent need to keep weapons flowing to U.S. forces and allies. According to Peter Navarro, White House senior counselor for trade and manufacturing, “This is not paperwork. It is battlefield preparation.”

From a supply chain perspective, the most important aspect of the executive order is its focus on visibility and resilience. The Pentagon has been directed to map lower-tier suppliers and identify vulnerabilities hidden deep within defense supply networks, to give a clearer view whether foreign-controlled suppliers could threaten weapons production during a conflict. Contractors must also evaluate suppliers for foreign ownership, financial stability, and manufacturing risks.

The U.S. government is not leaving contractors high and dry. Recent investments in companies such as MP Materials, a domestic producer of rare earth materials, demonstrate efforts to make buying American easier.

Classroom Discussion Questions:

What opportunities do you see resulting from this executive order?
Do the benefits of increased supply chain security outweigh the likely increase in costs? Why or why not?

OM in the News: A False Positive on Taylor Farms Lettuce

The U.S. Food and Drug Administration just announced that lettuce from a key supplier linked to the ongoing cyclosporiasis outbreak has not tested positive for the parasite, reversing an earlier finding by the agency. The FDA previously identified contamination in a sample of lettuce from Taylor Farms de Mexico, but after retesting, said the finding was inaccurate.

“Due to the complexity in detection of Cyclospora, FDA laboratory experts re-reviewed the sample results and have concluded that the finding does not represent true amplification and should be considered a false positive,” the agency said.

Taylor Farms, meanwhile, has voluntarily recalled all iceberg lettuce grown in central Mexico and will no longer source from the region for the remainder of the growing season. The recalled items included private-label shredded lettuce and iceberg salad packs sold at Walmart, but no Taylor Farms-branded products were involved, according to the company.

Classroom discussion questions:

Supplement 6 in your Heizer/Render/Munson text is on the topic of Statistical Process Control. What tools could be used to manage this issue?
What other famous outbreaks have been caused by lettuce?

Guest Post: Packaging Food– Production, Inventory, Training

Prof. Howard Weiss raises an interesting issue that can save consumers and companies money.

Most discussions of OM focus on creating products and services. However, Chapter 5 of your Heizer/Render/Munson textbook reminds us that product design also includes developing packaging to prevent damage and ensure safety. One packaging issue that is important is the date labels found on food products.

A report on food waste found that more than 50 different date labels are used on packaged foods sold in the U.S. The types of labels can be classified as

Label Type	Example
Quality	Best By
Safety-Oriented	Discard After
Production and Packaging	Pack Date
Storage and Handling	Freeze By
Freshness-Focused	Fresh Until
Retail and Inventory	Last Date of Sale
Specialty Product	Harvest Date

The use of these labels is largely unregulated. With the exception of infant formula, federal law does not require manufacturers to place date labels on food products. Further, manufacturers are generally free to choose whichever terminology they prefer. This lack of consistency often leads consumers to discard perfectly safe food, increasing household costs and contributing to unnecessary food waste.

Recognizing this problem, California became the first state to standardize food date labeling with a law that just went into effect and applies to all food products sold within the state. Products sold outside the state are exempt. The law is aimed at reducing food waste and lowering greenhouse gas emissions.

The expected benefits are substantial. Standardized labels should reduce consumer confusion, preventing the premature disposal of safe food and eliminating an estimated 70,000 tons of food waste annually in California. Consumers and retailers should also realize significant economic savings through reduced waste, while fewer discarded food products will lower methane emissions from landfills and benefit the environment.

These regulations have important implications for manufacturers, processors, and retailers. Companies selling products in California must comply with the new requirements by requiring packaging redesigns that use only the standardized “Best If Used By” and “Use By” labels and prohibiting “sell by” dates. Employees will require training and must also be able to explain the new system accurately to customers. Finally, inventory management systems and stock rotation procedures must be updated to ensure products are handled according to the new labeling standards.

Classroom Discussion Questions

How can you tell if food is safe after its “Use or freeze by” date?
Does your state have laws regarding packaging labels?

OM in the News: A New Foe Has Emerged for Data Centers–Farmers

America’s farmers and cattle ranchers are raising red flags about the potential drain on resources that the data-center construction boom poses to rural regions of the farm economy. The agriculture industry is warning that the AI-focused facilities are gobbling up farmland acreage, electricity and water needed to raise livestock and grow crops.

The Wall Street Journal (July 11, 2026) reports that tech companies are investing unprecedented sums of money to finance a construction boom across the U.S. of huge data centers to fuel America’s AI ambitions, largely in rural areas. Data center projects have been touted as a new source of growth for small towns and flyover country.

There are about 5,000 finished or under-construction data centers across the U.S. Farmland is an attractive target for technology companies. Data centers need large amounts of flat land and access to water and energy, the same as farmers do. Tech companies have faced backlash from locals concerned about power usage and the strain on their local grids. Lawmakers in about two dozen states are considering banning or restricting their development.

Farmers fear new data centers, some of which consume power equivalent to that of a midsize city, will drive up their utility bills when America’s farmers are already struggling with higher costs.

But data centers aren’t the only reason farmland is declining in the U.S. Expanding residential development, farmer consolidation and ranch land being converted to hunting grounds in western states have been happening for years. Farming acreage declined by an area about the size of Maine between 2017 and 2022.

Two sidebars on the growth of data centers:

A group of 96 families in Salem Township, Pa., sold 1,700 acres of land to data-center developer QTS for $586 million.
Meta has expanded its Northeast Louisiana data-center project to 5 gigawatts of capacity, raising the cost to more than $50 billion!

Classroom discussion questions:

The Data Center Coalition, a trade group for technology companies constructing the facilities, said : “When it comes to losing farmland, nobody is forcing farmers to sell.” Comment on this industry response.
Why are famers opposed to rural data centers? Are these fears justified?

Guest Post: The Gas Station Life Cycle

Prof. Howard Weiss, retired from Temple U., shares his thoughts with our readers monthly.

The history of gasoline service stations provides an excellent illustration of life cycles as discussed in Chapter 5 of your Heizer/Render/Munson textbook

Before There Were Gas Stations The first practical gasoline-powered auto was developed in 1886 by Carl Benz. Prior to that, some inventors experimented with steam-powered vehicles, while others relied on kerosene because it was inexpensive, widely available, and burned efficiently. Early motorists purchased gasoline in bulk containers from pharmacies, blacksmith shops, or general stores and then poured the fuel into their vehicles using a funnel. Although this process seems inconvenient today, it was sufficient when automobiles were still a novelty.

Life Cycle Stage 1: Introduction

In 1905 the Automobile Gasoline Company opened the first gas station, in St. Louis. The station consisted of little more than a curbside pump with a hose, allowing motorists to fuel their vehicles directly instead of using portable containers. Gasoline sold for 25 cents a gallon. In 1913, the first drive-in service station opened in Pittsburgh, and also expanded the customer experience by offering free air, water, crankcase service, tire installation, and road maps.

Life Cycle Stage 2: Growth

During the growth stage, automobile ownership expanded rapidly, and so did the number of gasoline outlets. By the 1920s, major oil companies had standardized station designs and logos, creating recognizable national brands. Self-service fueling was introduced in the 1940s and became commonplace during the 1970s, reducing labor costs while increasing customer convenience. Today, many filling stations are combined with convenience stores such as Wawa, where merchandise sales often generate higher profits than gasoline itself.

Life Cycle Stages 3 & 4: Maturity and Decline

The number of gasoline outlets peaked at around 285,000 in 1975 and then entered a steady decline. Small independent stations found it increasingly difficult to compete with larger, multi-pump convenience stores that benefited from economies of scale and higher-margin retail sales. The number has been fairly constant at about 145,000 since 2000.

The growth of EV charging stations, beginning around 2007, interestingly, has not yet produced a corresponding decline in the number of gas stations. The future of gas stations will partially depend on the pace of EV adoption, but its history remains an excellent example that not all life cycles have the same shape.

Classroom Discussion Questions;

What information would you want in order to forecast the number of gasoline outlets that will exist in 2040.
Could linear regression be used to forecast the number of electric charging stations in 2040?

OM in the News: AI Data Centers, Water, and Sustainability

Large water tanks at the Meta data-center campus in Arizona

Amazon, Google, and Microsoft are among the tech companies spending an estimated $1 trillion on AI infrastructure this year and last. In some regions, they are using far more water than they report, depending on how data centers are powered. And their water consumption is projected to grow rapidly in coming years. Water demands of the biggest infrastructure buildout in U.S. history could lead to regional fights over who gets an increasingly scarce resource, writes The Wall Street Journal (July 3, 2026).

Google’s just-released 2025 sustainability report is an instructive example. The company said it consumed 10.9 billion gallons of water—a 34% increase from 2024—almost all for data-center cooling. Meta’s water use was 19 billion gallons in 2024. Meta has a plan to “become water positive in 2030,” in part through water-restoration projects. Microsoft has announced data centers that will have zero water use, as well as a commitment to “community-first AI infrastructure.” This includes a pledge to “replenish more water than we use.”

Cheap land and cheap power have put data centers in the high water-stress areas such as Arizona. About 2/3 of new data-center construction in the U.S. is in water-stressed areas. Phoenix is an example: the total water demands of data centers there amount to 3% of the city’s annual water use. By 2031, they could be in excess of 20%, a number approaching total water used by residents to maintain all of Phoenix’s lawns and landscaping.

Recently, Nvidia said it had solved the data-center water issue, showing off a closed-loop cooling system that doesn’t require additional water once filled. Unfortunately, most existing data centers use evaporative cooling systems that are energy-efficient but water-hungry, and retrofitting those could be prohibitively expensive.

In these boom times, it’s clear why AI data centers are in the spotlight. And the lack of transparency and widespread use of non-disclosure agreements by many data-center builders has only drawn more suspicion and distrust. These are among the reasons that $170 billion of AI data-center capacity has been blocked, stalled or canceled since 2024.

Classroom discussion questions:

What solutions are being considered to deal with electricity and water demands?
Are data centers overbuilding?

OM Podcast #51: AI in Healthcare and the Future of Patient Care

Happy summer to everyone! In our latest podcast, Professors Barry Render and Misty Blessley interview Dr. Satinder Singh, Medical Director at Lehigh Valley Health Network, Jefferson Health.

Dr. Singh shares insights from his journey into medicine and leadership, and discusses how artificial intelligence is transforming clinical practice. From improving physician efficiency to strengthening the doctor–patient relationship, this episode explores how AI is already being used in healthcare—and where it’s headed next.

A key focus of the conversation is how tools like ambient AI can reduce administrative burden, allowing physicians to spend more time at the bedside. The discussion also highlights important considerations such as accuracy, oversight, and the responsible use of AI in medical decision-making.

Prof. Misty Blessley

Finally, Dr. Singh shares his perspective as an educator, emphasizing the importance of building strong clinical fundamentals before using AI as a tool to enhance performance.

Heizer Render Munson OM Podcast · OM Podcast #51: AI in Healthcare and the Future of Patient Care

LINK TO PODCAST TRANSCRIPT

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: Giving M&M’s a MAHA Makeover

As we note in Chapter 5, Design of Goods and Services, the dynamic market places changing demands on products. Such is the case for M&M. Mars, the manufacturer of M&Ms, under pressure from the “Make America Healthy Again” (MAHA) campaign, is striving to make them without artificial dyes.

So M&M’s will mark a milestone in their 85-year history in August, debuting a version temporarily missing two hallmarks colors: brown and blue. Mars plans to color the remaining foods with dyes derived from natural sources.

The task is proving much harder and more expensive than it sounds, writes The Wall Street Journal (June 23, 2026). Blue is proving difficult to re-create affordably, and at scale. To complete the challenge, the company couldn’t reliably churn out brown M&M’s, which, it turns out, include a fair bit of blue. Some 100 employees are working on its natural-color efforts. A quarter of them are dedicated solely to finding a proper blue!

Companies have scoured the globe in search of raw materials to create a natural blue food color that can withstand changes to heat, light and pH. They have experimented with juice from a fruit found in Central and South America, petals from the butterfly pea flower in Southeast Asia, and spirulina (which works best, but leaves a sticky film akin to dental plaque inside the factory pipes).

All this means that Mars must upgrade more than 300 machines across its plants to handle spirulina. The company is installing new mixing tanks, paddles and motors. Moreover, new cleaning equipment will also be required to blast machines on the M&M’s lines for longer stretches, with hotter water and more force.

The company pledged a decade ago to cut artificial dyes from all of its human food. But it changed course for candy, saying many consumers weren’t worried about the dyes in those products.

This M&M story illustrates the pervasiveness of issues with new or altered designs. As is often the case at M&M and elsewhere, cross functional teams are needed for experimenting, international suppliers must be developed, capital investment for new equipment must be devoted, and new maintenance procedures to keep equipment working must be created.

Classroom discussion questions:

Why is the move to healthier candy so difficult?
Why not just drop the brown and blue M&M’s?

OM in the News: Chick-fil-A Dethroned as Quality Fast Food Chain Leader

America’s favorite fast-food chicken chain has been knocked from the top spot in a closely watched customer satisfaction ranking, reports FoxNews.com (June 16, 2026). Chick-fil-A, which topped the annual American Customer Satisfaction Index (ACSI) in 2025, fell to second place this year.

Jersey Mike’s claimed first place, with an ACSI score of 84 out of 100, edging Chick-fil-A’s 83. The shift marks the first time in over a decade that a new chain has led the ACSI’s quick-service restaurant (QSR) category. ASCI credited Jersey Mike’s with maintaining high customer satisfaction while rapidly expanding its restaurant footprint, writing :”Jersey Mike’s success is consistent with their business performance, including rapid unit growth, strong customer demand, and a model designed around throughput and off-premise convenience from high digital pickup usage.”

Jersey Mike’s, founded in 1956, has more than 4,000 locations across the U.S. and Canada, and is best known for its made-to-order subs and “Mike’s Way” sandwiches. A narrow menu adds to its success in the ratings.

Following Chick-fil-A were Jimmy John’s and Panda Express, which tied for third place with scores of 81. KFC, Papa Johns and Pizza Hut followed with scores of 80 — while Domino’s, Raising Cane’s, Starbucks and Subway each earned ratings of 79. Burger King, Culver’s, Dunkin’, Little Caesars and Panera Bread rounded out the next tier with scores of 78. Ranking near the bottom were Dairy Queen and McDonald’s, which tied for last place with scores of 72.

The ACSI study was based on 16,464 surveys.

Classroom discussion questions:

Research the ACSI study approach and comment.
Why is McDonald’s tied for last place?

OM in the News: Agentic AI Revolutionizes the Factory

It’s 4 a.m. at a large automotive parts plant. The night-shift supervisor freezes as the dashboard flashes an alert: a critical spindle is vibrating out of tolerance. In the old world, he’d wait for maintenance to evaluate and decide. But today, an AI agent has already paused the line, checked service records and called the right technician—before he even takes a step toward the control room.

That’s the new reality for many manufacturers facing a stubborn obstacle: the ever-widening gap between data and decisive action. Now, a new class of digital entities is changing that equation. AI agents powered by decision intelligence are beginning to sense, reason and act across the manufacturing ecosystem, cutting decision latency from minutes to milliseconds.

Think of AI agents as the digital nervous system of a modern factory. They continuously sense what’s happening across machines, people and systems, then respond intelligently without losing context. Across the manufacturing stack, they’re quietly reshaping work for every role:

On the shop floor: Agents merge operations and information technology (IT) data to give operators real-time context. They can recommend optimal machine parameters, trigger tool-change schedules, balance workloads across lines or alert technicians before deviations escalate. Maintenance teams can use agents to predict component wear and plan interventions that don’t interrupt production– a topic in Chapter 17.

In production and quality operations: Agents help supervisors and quality staff detect process drift early. They analyze sensor data, images and process variables, suggesting immediate corrections or automated parameter tuning. In continuous manufacturing, this can mean fewer rejects and less rework, which we discuss in Chapter 6.

In ERP and planning: Agents connect production, procurement and finance systems. A planning agent (see Chapter 14) can run simulations of “what if” scenarios, what happens if a supplier shipment is delayed or if energy costs spike and recommend production adjustments.

Across the supply chain: Agents can constantly monitor inventory, supplier performance and logistics signals. When a potential shortage or delay is detected, they are able to trigger contingency workflows such as redistributing available stock, recommending alternate suppliers or rescheduling deliveries–see Chapters 11 and 12.

To sum it up: “Tomorrow’s factories won’t just inform — they’ll decide,” writes Industry Week (June 12, 2026).

Classroom discussion questions:

Summarize what AI agents can do in a factory setting.
How does agentic AI have the potential to change the manufacturing operation?

OM in the News: Patriot Missiles and Second Tier Suppliers

The newest Patriot surface-to-air missiles can be fired in seconds, but take more than two years to build and cost around $4 million each. Despite that math, the U.S. and its allies can’t get enough of them, reports The Wall Street Journal (June 10, 2026).

Pentagon officials just reached an agreement with Lockheed Martin to more than triple production of the latest Patriot, the PAC-3, to 2,000 a year. But the weapons maker isn’t expecting to hit that target until the end of 2030. Why is that?

Lockheed is facing a litany of challenges to hit its target. It counts more than 400 companies that provide parts for its missile. More than 80% are at the second tier—the PAC-3 suppliers’ suppliers. But these firms provide components to more than one missile program. That makes it harder to increase production of one type of missile without disrupting the supply chain for another in-demand weapon.

And some missile circuitry is considered commercially obsolete, forcing the U.S. to rely on expensive equipment from foreign suppliers. The “seeker” in the missile’s nose—a vital part that allows the interceptor to lock onto incoming missiles and aircraft—comes from a single Boeing factory.

Boeing said the company has sped up “seeker” production by adding robotic equipment and finding new suppliers to provide parts like circuit cards. L3Harris plans to boost its rocket-motor production capacity as it brings more manufacturers into its supply chain. “You need the whole ecosystem to line up,” L3Harris’ CEO. “If we quadruple a missile, we’ve got to quadruple the cases. We’ve got to quadruple the igniters, valves, the throttles.”

For decades, the military favored lean supply chains and peacetime efficiency. That approach saved money, but under the pressure of conflict its weakness is revealed. Depending on one qualified source for a key missile component is not the answer. Selective redundancy and second-sources for critical components may be.

Classroom discussion questions:

Why are tier 2 suppliers a problem in many supply chains?
Summarize all the issues slowing the production of Patriot missles.

OM in the News: McDonald’s Tries AI a Second Time

McDonald’s plans to change its drive-thrus in a major way, reports MSN.com (June 5, 2026). Nearly two years after it discontinued its AI ordering system, it says it plans to roll out a new AI-based operating system at all restaurants. A few years ago, McDonald’s joined the likes of Wendy’s and Taco Bell in testing AI-powered ordering systems at its drive-thrus. The ordering chatbots were made in partnership with IBM and released at more than 100 McDonald’s restaurants.

Unfortunately, the system, which was meant to make ordering easier, did anything but, with customers reporting that the automated ordering system added extra items to their tab. McDonald’s ended its partnership with IBM in 2024, but announced that it was exploring “voice ordering solutions more broadly.” Now, the fast food chain has found a new partner in Google to bring its Arch IQ and drive-thru assistant, Archy, back to life.

The brand announced its latest business strategy, called McDonald’s Next. The growth strategy plans to reimagine McDonald’s “menu, consumer, restaurant, and people,” while “imagining” new business and AI.

Stores are currently testing its Arch IQ drive-thru AI system, which can take orders in both English and Spanish with ease. “Archy will not only assist drive-thru orders but act as a master brain to help managers run a better restaurant. It’s like a personal assistant that alerts you to potential bottlenecks or issues,” the firm writes.

“We are currently in five test stores, having processed over 1 million transactions with about 90% of orders completed without human escalations. Impressive for a new test,” said one McDonald’s owner.

Classroom discussion questions:

Why did AI fail the first time and will it work this time for ordering?
How can AI help managers “run a better restaurant?”

OM in the News: Pepsi Trucks Go Driverless

A 26,000-pound box truck loaded with Doritos and Frito-Lay chips rolls out of a distribution center, bound for a Walmart store about 4 miles away. It looks like any other truck, but there is no one at the wheel, writes The Wall Street Journal (June 8, 2026).

This is one of the 35 driverless trucks PepsiCo is running on Arizona roads, marking it as the first major U.S. consumer-goods company with real-life, large-scale use of autonomous trucks on public roads.

Pepsi’s operation, using trucks outfitted with multiple cameras, sensors and computers, is on par with the technical hurdles being cleared by driverless passenger taxis from Waymo and Tesla. The firm operated with a safety driver in each truck for a few years, and started driverless runs in 2025. The trucks have had no accidents on public roads so far.

In instances where the trucks are making deliveries to stores, Pepsi employees are there to meet the trucks and unload them. Many delivery drivers have always been sales representatives too, and not having to travel with the truck allows them more time to interact with store owners to pitch them on the latest promotions.

The driverless trucks are more reliable than human drivers. The on-time arrival performance from driverless trucks reached 99%. Humans can call in sick or hit up against service limits that cap how many hours a day they can be behind the wheel. And the number of truck drivers has been constrained in recent months by new federal rules enforcing English-language proficiency..

The trucks perform best when they shuttle back and forth in repetitive trips—for instance, a 14-mile trip between a Gatorade bottling plant and storage facility. That route has fewer variables compared with routes that have more pickups and deliveries.

Pepsi employs thousands of drivers in the U.S., some represented by unions that have strongly opposed the rollout of autonomous trucks. The company anticipates retraining and redeploying some drivers to other types of work, including managing the new equipment, synchronizing the movement of people who go to the stores, or handling the unloading themselves. But ultimately, the company expects to hire fewer drivers.

Classroom discussion questions:

What are the advantages and disadvantages of these Pepsi trucks?
How will this impact the demand for truck drivers in the next 5-10 years?

Guest Post: How AI Has Transformed Semiconductor Scheduling at NVIDIA and TSMC

Professor Misty Blessley raises an interesting AI issue-chip plant scheduling.

Semiconductors have been called the brains of the modern world. Switching between conducting and blocking electricity, they are essentially “on/off” switches that control the flow of power, and this unique characteristic makes them the building blocks of modern technology. They are behind computing, communications, and energy innovation, and our information dependent society is always hungry for more. Leading chip makers, NVIDIA and TSMC, both use an AI to advance semiconductor design, scheduling, and manufacturing, report ElectronicsUSA (June 4, 2026) and NVIDIA News (May 31, 2026).

TSMC ( Taiwan Semiconductor Manufacturing Company), the world’s largest semiconductor foundry, makes the most advanced chips on the planet. NVIDIA is a global leader in accelerated computing and AI. For decades, TSMC has manufactured NVIDIA’s chips, and this partnership has come full circle. TSMC uses NVIDIA’s AI technologies inside its fabrication plants (“fabs”), which in the semiconductor industry refers to highly specialized facilities where silicon wafers are processed into microchips.

NVIDIA’s AI models are now embedded directly into TSMC’s manufacturing workflow, transforming production scheduling. A single wafer, a subcomponent of a semiconductor, may require hundreds of tools and thousands of tightly sequenced steps. TSMC can now evaluate millions of scheduling combinations in seconds. This optimizes job sequencing, and instantly rebalancing schedules when tools go down or urgent orders arrive. The result is smoother production flows, less idle time, and higher overall fab productivity. AI comes as close as ever to “running the plant.”

AI driven scheduling is also advancing through emerging capabilities such as predictive dispatching, where models forecast bottlenecks hours ahead and reroute wafers to prevent delays. Another is crossfab load balancing, which evaluates capacity across multiple TSMC sites and shifts work to maximize throughput. Both approaches are expected to reduce fab cycle time 5–10%.

NVIDIA’s AI scheduling helped TSMC cut critical chip production workloads 20-50%. This addresses society’s insatiable demand for semiconductors. It also fuels the next wave of breakthroughs in computing, communications, and energy innovation, the domains powered by these tiny on/off switches.

Classroom discussion questions:

What are the strategic and operational risks if a company relies too heavily on AI to automatically reroute work without human oversight?
Discuss how AI’s ability to evaluate millions of combinations in seconds changes a manager’s approach to bottleneck scheduling compared to manual or sequential sequencing rules (like SPT or FCFS).

OM in the News: The GLP-1 Effect–How Weight-Loss Drugs are Disrupting Inventory Management

The explosive popularity of GLP-1 weight-loss medications like Ozempic and Zepbound is changing more than just waistlines—it is fundamentally disrupting retail supply chains. The Wall Street Journal (June 8, 2026) reports highlights a massive surge in apparel returns, with some retailers experiencing a 50% spike. As consumers rapidly shed pounds, they are ordering multiple sizes of the same garment and sending back the ones that no longer fit.

Jeans, bras and athleisure wear are often the first items replaced as people drop weight.

This phenomenon serves as a real-world case study in the hidden costs of holding inventory and the critical importance of inventory accuracy, topics central to Chapter 12 of your Heizer/Render/Munson text.

The Hidden Cost of Retailing Reverse Logistics. Inventory doesn’t just sit safely on a shelf; it incurs holding costs, including warehousing, labor, and depreciation. When a customer returns an item, those costs multiply. Returned clothing often arrives out of season, forcing retailers to liquidate it at a steep markdown. For a $1 billion retailer, a mere 5- 10% increase in returns can slash gross margins by $20 million.

Mismatched Supply and Demand. Further, GLP-1 users are shifting the entire demand curve. Returns are spiking heavily in medium, large, and extra-large sizes, while demand for smaller sizes is skyrocketing. We write in the text that “inventory exists to decouple various parts of the production and distribution process. However, when demand shifts this rapidly, traditional forecasting models break down, leading to severe stockouts in smaller sizes and costly overages in larger ones.

To combat this, brands like June Adel are dynamically altering their ordering cycles, shifting safety stock levels to favor smaller dimensions, and utilizing stricter ABC analysis to track high-risk, high-return items.

Ultimately, the GLP-1 boom proves that inventory management is never static. To survive, retailers must tighten their supply chains, leverage sharper data, and treat reverse logistics not as an afterthought, but as a core operational variable.

Classroom Discussion Questions

How can retailers use safety stock and reorder point (ROP) calculations to mitigate the unpredictability of rapid consumer weight loss and high return rates?
Considering the high costs associated with reverse logistics, should retailers implement stricter ordering limits or higher restocking fees to optimize their total inventory holding costs, even if it risks lowering customer satisfaction?