OM in the News – The OM Blog by Heizer, Render, & Munson

OM in the News: GM’s $4.5B Supply Chain Deal to Secure Critical Parts

General Motors’ assembly plant in Fort Wayne

General Motors has entered a $4.5 billion purchasing facility with Procura Auto Parts to safeguard its supply chain against disruptions such as natural disasters, cyberattacks, or sudden demand surges. The automaker is aiming to secure supplies of high-risk components through a financing arrangement of prefunding the purchase of essential parts, reports The Wall Street Journal (Aug. 12, 2026).

Procura’s Role: The London–Frankfurt–New York-based inventory management firm will buy and store critical parts from GM’s suppliers, acting as an intermediary to secure inventory.
Funding: A bank syndicate led by JPMorgan Chase and Banco Santander will finance Procura’s purchases.
GM’s Commitment: GM will issue irrevocable payment undertakings (IPUs) to repay Procura once the parts are used in production

GM has not disclosed the exact components, but past disruptions have involved semiconductor chips, rare earth metals, and wire harnesses.

The move follows years of global automotive supply chain shocks, including the COVID-19 pandemic and semiconductor shortages that halted production at multiple plants. By prepaying suppliers, GM aims to:

Preserve working capital by avoiding short-term cash outflows.
Maintain production continuity during unexpected shortages.
Reduce vulnerability to just-in-time delivery risks.

This complements earlier GM initiatives, such as a long-term chip supply agreement with Micron Technology for memory and storage components. It reflects a broader industry shift toward strategic inventory management and supplier diversification after repeated disruptions.

In summary: GM’s $4.5B deal with Procura is a proactive, multi-year supply chain hedge designed to ensure access to critical parts, protect cash flow, and maintain manufacturing resilience in a volatile global market.

Classroom discussion questions:

1. How does GM’s decision to invest heavily in securing critical components—such as semiconductors—illustrate the growing importance of supply chain resilience in modern operations management, and what risks is the company trying to mitigate?

2. GM’s partnership with suppliers like Procura reflects a shift toward deeper, long‑term collaboration. In what ways can strategic supplier relationships improve operational performance, and what challenges might arise when firms commit to such large, multi‑year agreements?

OM in the News: AI and the Perfect Pringle Potato Chip

For decades, the Pringle has been one of the most recognizable snacks in the world—uniform, crunchy, and engineered for consistency. But behind that iconic saddle‑shaped chip lies a surprisingly complex manufacturing challenge, writes The Wall Street Journal (Aug. 11, 2026). At Pringle’s factory in Poland, engineers have spent the last four years pursuing a bold goal: use AI to make every single Pringle a perfect one.

A sensor analyzes Pringle’s manufacturing line in Kutno, Poland

This ambition led to a deep partnership with Siemens and a $4–5 million investment to build an AI‑powered digital twin (our topic in Module F of the text) of the Pringles production line. Unlike traditional 3‑D models, this digital twin operates in real time, ingesting more than 200 data points every millisecond—from flour particle size to dough humidity to subtle variations in potato batches. These micro‑changes matter. Even potatoes from the same supplier can behave differently depending on the season, affecting texture, shape, and fry behavior.

Historically, dough makers relied on intuition: stretching dough by hand, weighing chips fresh from the fryer, and adjusting machines after spotting issues. Today, AI models simulate outcomes continuously, predicting how each batch will behave and recommending precise tweaks—more oil here, less water there—to keep quality stable. Human operators still make the final call, but the system’s guidance has already delivered measurable results: 10% improvement in chip quality, 13% reduction in waste, and a 40%+ return on investment on the Poland line alone.

The success is prompting expansion. Belgium is next, followed by U.S. production lines in 2027. Siemens sees this as part of a broader shift—digital twins are maturing across industries, from rockets to batteries to microchips. What began as static models are now intelligent optimization engines.

For Pringle, the future is even more ambitious. The company hopes its AI will eventually handle multiple varieties of potatoes, corn, and rice, adapting automatically while still producing the same signature chip. In other words, the perfect Pringle may soon be less a product of uniform ingredients and more a triumph of adaptive, intelligent manufacturing.

Classroom discussion questions:

How might AI‑driven digital twins change the future of food manufacturing, and what new skills will workers need as these systems become more common?
Should companies rely on AI to make real‑time production decisions, or is it important that humans remain the final decision‑makers? Explain your reasoning.

OM in the News: How Ford Blended Veteran Engineering Wisdom with AI to Climb the Quality Rankings

In an industry racing toward electrification, automation, and software‑defined everything, Ford’s latest quality turnaround offers a refreshing reminder: sometimes the most advanced strategy is pairing new tools with old wisdom, writes Design News (July 30, 2026).

After years of slipping in JD Power’s Initial Quality Study, Ford surged to the top of the mass‑market rankings in 2026. The secret wasn’t a flashy new platform or a radical manufacturing overhaul. Instead, Ford made a deliberate investment in something the industry has quietly undervalued—deep, hard‑earned engineering experience.

Ford’s iPhone-based AI inspection system at the Kentucky Truck plant confirms the correct fit of electrical connectors while they are still easily accessible for correction.

The company deployed more than 350 senior engineers, known internally as “gray beards,” to tackle persistent quality issues. Many were coaxed out of retirement, bringing decades of pattern recognition, intuition, and problem‑solving discipline back into the fold. Their mission: diagnose chronic issues, mentor younger teams, and challenge design decisions before they reached production.

But Ford didn’t stop at human expertise. The automaker layered in targeted AI tools designed to amplify—not replace—engineers’ capabilities. Assembly teams now use iPhone‑based inspection systems to detect misaligned connectors or missing fasteners. Machine‑learning models sift through hundreds of thousands of transmission test traces daily, flagging anomalies that would be invisible to the human eye. Generative AI supports design reviews, helping engineers explore optimized geometries and identify potential failure points earlier in development.

This hybrid approach—experienced engineers empowered by precise AI—has reshaped Ford’s culture. Instead of hiding flaws, teams now celebrate finding them early. Quality issues aren’t embarrassments; they’re opportunities to strengthen the product before customers ever touch it.

Ford’s rise in the rankings isn’t just a win for the brand. It’s a case study for the entire manufacturing world: the future isn’t human versus AI. It’s human with AI, working side by side to build better products than either could alone.

Classroom discussion questions:

How does Ford’s use of “gray beard” engineers challenge common assumptions about innovation and workforce development in high‑tech industries?
Ford now “celebrates finding failures” as part of its quality culture. What are the advantages and potential risks of adopting this mindset in engineering organizations?

OM in the News: Beyond Outsourcing–Why Best-Shore Is Reshaping the Future of OM

For more than three decades, outsourcing followed a simple logic, moving work to lower-cost locations to reduce expenses and gain efficiency, writes Material Handling & Logistics (July 24, 2026). That model delivered real benefits, but the business environment has changed. Organizations now face pressure to innovate faster, strengthen resilience, protect worker safety, and meet increasingly complex regulations.

Instead of focusing solely on low-cost offshore outsourcing, organizations are adopting to a best-shore strategy, an approach that evaluates the nature of the work and places it in the location that delivers the greatest overall business value. The best-shore model recognizes that cost matters, but so do collaboration, speed, resilience, compliance, talent availability, and operational continuity.

Supply chain disruption, cybersecurity threats, geopolitical uncertainty, workforce shortages, regulatory change, and rising customer expectations have created new challenges that traditional outsourcing models were not designed to address. The lowest-cost location is now not always the lowest-cost solution.

A manufacturing plant cannot afford prolonged downtime because a critical engineering issue must wait until another region wakes up. A logistics operation cannot delay responding to a cybersecurity incident due to communication barriers.

Under the best-shore approach, organizations leverage a combination of offshore, nearshore, and onshore resources, assigning responsibilities based on business requirements rather than geography alone. Best-shore models incorporate three complementary delivery layers.

Offshore: Scale and Cost Efficiency Offshore delivery remains an essential component of global operations. Countries with large technical talent pools offer access to skilled professionals at competitive costs. Offshore teams often provide the scale necessary to support large transformation programs, application development initiatives, engineering projects, testing activities, data management, and managed services.

Nearshore: Speed and Collaboration Nearshore delivery helps bridge the gap between cost efficiency and operational responsiveness by locating teams in similar time zones and cultural environments. Teams can collaborate during the same business day, participate in real-time meetings, respond quickly to changing priorities, and engage more directly with business stakeholders.

Onshore: Governance and Business Alignment Certain activities benefit from being located close to the business itself. Executive engagement, regulatory compliance, stakeholder management, strategic planning, safety oversight, and customer relationships often require local presence and a deep understanding of business context.

Recent years have demonstrated how vulnerable organizations can become when they depend too heavily on a single geography, supplier, or operating model. A diversified best-shore model helps reduce concentration risk.

Classroom discussion questions:

Summarize the differences between best-shoring and off-shoring.
Provide an example of a firm that has best-shored.

OM in the News: The Short Life Cycle of the A-380 Jumbo Jet

For most of the aviation industry, the Airbus A380 is remembered as an engineering marvel that never made commercial sense. Airbus expected to sell more than 1,200 aircraft when it launched the program in 2000 but ultimately delivered just 251 before ending production in 2021, reports Arabian Gulf Business Insight (July 13, 2026). This was a direct result of a lack of orders, resulting in production capacity outpacing demand

A scrapped ex-Emirates Airbus A380 will be used for hard-to-replace parts now that production ceased

Airbus launched the A380 for a future dominated by congested mega-hubs, while airlines increasingly favored smaller, more efficient long-haul aircraft such as the Boeing 787 and Airbus A350. Those jets made it easier to fly nonstop between more city pairs, leaving the A380 exposed to high operating costs, limited route flexibility, and dependence on consistently full flights.

For airline planners, a carrier did not need 500 or more passengers to make a route viable. A 787 or A350 could carry 250 to 330 passengers over long distances while using two engines instead of four. That made it easier to match capacity to demand, add seasonal routes, increase frequency, or serve thinner markets. More frequent flights on smaller aircraft also appealed to business travelers, who often value schedule choice more than the prestige of flying on the largest aircraft.

The A380 was not discontinued because it was a technical failure. It was discontinued because its economics became difficult for most airlines to make work consistently. A fully loaded A380 could move a huge number of passengers efficiently between major hubs, but the aircraft’s advantage depended heavily on high seat occupancy, strong premium demand, and routes large enough to support hundreds of seats year-round.

Four engines were one of the biggest cost disadvantages. By the time the A380 entered service, long-range twin-engine aircraft were becoming increasingly capable, and the 787 and A350 could fly many long routes with far less fuel burn and lower maintenance complexity. The A380 required expensive maintenance and specialized operational support. Its size meant larger landing gear, more tires, more brakes, bigger systems, and extensive cabin areas to inspect and maintain. Even routine cleaning, catering, and boarding involved more time and labor.

In the end, its operational advantages became harder to unlock.

Classroom discussion questions:

What airplane had a shorter life cycle?
What plane has a longer life cycle?

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?

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?

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 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?

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?