Category: OM in the News

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

OM in the News: Walmart’s Automation Journey Reveals the Real Challenges of Warehouse Transformation

For years, automation has been promoted as the future of warehouse operations. Walmart’s ongoing multibillion-dollar effort to automate its supply chain demonstrates both the promise and the reality of large-scale operational transformation. Walmart relies on over 140,000 warehouse workers to keep goods moving. It isn’t an easy job. A worker might walk 20,000 steps a shift.

The retailer is modernizing a network that supports more than 5,000 U.S. stores while attempting to keep nearly 200 supply chain facilities operating, reports The Wall Street Journal (Oct. 9, 2026). What sounds like a straightforward technology upgrade has turned into a decade-long lesson in OM.

Warehouse processes that seem simple to humans are often extraordinarily difficult to automate. Tasks such as removing products from cartons, sorting individual items, building store-ready pallets, and handling irregularly sized products require flexibility that robots still struggle to replicate consistently.

Walmart has invested heavily in automated storage systems, robotic arms, autonomous mobile robots, and advanced software. These technologies promise faster inventory movement, improved accuracy, lower stockout rates, and more efficient store replenishment. Yet implementation has revealed many operational hurdles. Robots require maintenance, packaging must be redesigned, legacy facilities need retrofitting, and suppliers must sometimes modify products so automated systems can handle them properly.

An interesting operations challenge is that Walmart is redesigning warehouses while they remain in service. Unlike a greenfield facility built specifically for automation, most of Walmart’s distribution centers were constructed decades ago. Integrating new technologies into existing operations adds substantial complexity and risk.

The case also illustrates a broader lesson about process improvement. Automation does not simply replace labor with machines. Instead, it changes the nature of work. While some manual tasks may decline, demand grows for technicians, engineers, and operators who can monitor, troubleshoot, and optimize increasingly sophisticated systems.

Walmart’s experience highlights that automation is a strategic capability, not a quick fix. Competitors such as Amazon have raised industry expectations for speed and efficiency, creating an automation arms race. Companies that pursue automation should be prepared for experimentation, setbacks, and continuous learning.

Walmart’s journey is a reminder that successful automation is not primarily a technology challenge. It is an operations, process design, workforce, and change management challenge.

Classroom Discussion Questions

  1. Walmart chose to automate existing warehouses while keeping them operational rather than building entirely new facilities. What are the advantages and disadvantages of this strategy from an OM perspective?
  2. Automation can improve productivity and accuracy, but it also introduces new costs such as maintenance, energy consumption, and specialized labor. How should managers evaluate whether an automation investment is truly successful?

OM in the News: Ergonomics by Design– How Formula One Uses Scheduling to Outsmart Jet Lag

In operations management, we often talk about efficiency, precision, and human performance. Few industries embody these principles as intensely as Formula One—and nowhere is this more evident than in how teams handle jet lag at the Singapore Grand Prix.

Each year, roughly 3,000 F1 personnel travel halfway around the world for a race that demands peak cognitive and physical performance. Instead of fighting jet lag, they engineer it, writes The Wall Street Journal (Oct. 6, 2026). Their solution is elegantly simple: don’t adjust at all.

Singapore’s night race creates a unique ergonomic opportunity. Because most European races start in the afternoon, teams can maintain their home‑time schedule even while operating in Southeast Asia. Drivers, engineers, and support crews wake up around 2 p.m. local time, eat “breakfast” in the late afternoon, and work deep into the night. They effectively live in a controlled nocturnal bubble where Europe’s clock rules everything.

This approach isn’t just about comfort—it’s about performance. Racing at 200 mph requires sharp reflexes, stable mood, and predictable cognitive rhythms. By keeping circadian patterns intact, teams avoid the biological confusion that normally accompanies long‑haul travel. As one driver put it, the method is “quite pleasant,” and more importantly, it works.

The ergonomic design extends beyond sleep schedules. F1’s governing body restricts car work to afternoons, preventing overexertion and aligning labor demands with the shifted day. Track sessions are scheduled late to match European TV audiences, reinforcing the shared time illusion. Leaders like Audi’s racing director begin their “day” at 3:30 p.m., collaborate with engineers still in Europe’s daylight, and wind down around 3:30 a.m.

Light exposure—one of the strongest regulators of the human internal clock—is carefully controlled. Teams rely on blackout curtains and avoid sunlight to prevent unwanted circadian shifts. In essence, they create a fully engineered environment where human performance is protected through scheduling discipline.

This year brought a twist: a nearby Malaysian race forced teams briefly onto a daytime schedule before immediately switching back to nights in Singapore. Even F1’s best planners couldn’t fully solve that disruption.

Still, the core lesson stands: ergonomics isn’t just equipment—it’s scheduling. And when designed intentionally, it can turn even jet lag into a competitive advantage.

Classroom discussion questions:

  1. How can organizations use scheduling and environmental controls (like light exposure or mandated work hours) to improve employee performance during travel or shift work?
  2. What challenges might everyday businesses face when trying to adopt Formula One–style “time zone anchoring,” and how could they overcome them?

OM in the News: Smarter Hospital Alarms Show How Better Processes Save Lives

Hospitals are some of the most complex operations environments in the world, writes The Wall Street Journal (Sept. 29, 2026). Thousands of interdependent processes—clinical workflows, monitoring systems, documentation, staffing, and technology—must function with precision. When any part of that system becomes noisy, inefficient, or overloaded, the consequences can be serious. A striking example comes from an unlikely OM case study: hospital alarm fatigue.

In modern care units, monitors and pumps generate constant beeps signaling everything from minor fluctuations to life‑threatening deterioration. For patients, the noise disrupts sleep and recovery. For nurses, the sheer volume of alarms creates cognitive overload, making it harder to distinguish true danger from routine variation. In OM terms, this is a quality‑control failure caused by excessive false positives, poor signal design, and misaligned workflow triggers.

Researchers are now applying OM thinking to redesign the alarm ecosystem.

Reducing Non‑Actionable Variation Stanford Medicine introduced time delays and conditional triggers to filter out brief, self‑correcting changes in vital signs. This is classic process‑control logic: eliminate noise so workers can focus on meaningful signals. The results were dramatic—alarm volume dropped by up to 82% without compromising safety. Nurses reported better responsiveness, demonstrating how reducing process clutter improves human performance.

Human‑Centered Design for Quality Another team is redesigning alarms using principles from music cognition. Instead of harsh electronic tones, alarms use recognizable “auditory icons” and musical cues to convey urgency and type of problem. This is OM’s human‑factors engineering at work: improving quality by making information more intuitive, reducing worker stress, and speeding decision‑making.

Predictive Systems to Prevent Failure The most powerful OM innovation may be the machine‑learning tool Concern, which analyzes patterns in nursing documentation to detect deterioration before vital signs trigger alarms. It provides a quiet, color‑coded risk indicator rather than another disruptive alert. In a 60,000‑patient trial, units using Concern saw lower mortality and shorter stays. This is proactive capacity and risk management, shifting from reaction to prevention.

The OM Lesson Alarm fatigue isn’t just a clinical issue—it’s an operations problem. Smarter triggers, better signal design, and predictive analytics show how OM principles can transform safety, reduce waste, and improve outcomes in high‑stakes environments.

Classroom Discussion Questions

  1. How should hospitals balance the need for constant patient monitoring with the risks of alarm fatigue?
  2. What lessons from hospital alarm redesign—such as smarter triggers, better sound design, or early‑warning systems—could be applied to other industries that rely heavily on alerts?

OM in the News: AI, Data Centers, Climate Control, and the Future of OM

As companies gathered for Climate Week, a tension was impossible to ignore: the same firms showcasing emissions‑reduction progress were simultaneously accelerating one of the most energy‑intensive buildouts in modern operations—AI data centers. These facilities have become a lightning rod for communities and regulators, especially as their power and cooling demands surge, reports The Wall Street Journal (Sept. 21, 2026).

For operations managers, the implications are profound. A standard AI‑ready data center consumes as much electricity as 100,000 households, and the largest facilities under construction will use 20 times more, driven primarily by cooling loads required to keep high‑density compute equipment stable. This is no longer an IT issue—it is an operations ecosystem challenge involving energy procurement, water usage, thermal engineering, community relations, and regulatory compliance.

Climate control is emerging as the defining constraint. Gas‑turbine backup systems raise air‑quality concerns. Water‑intensive cooling technologies trigger scrutiny in drought‑sensitive regions. States like Virginia are already issuing new standards for water use and energy efficiency, signaling a wave of regulatory pressure that will reshape site selection and facility design.

At the same time, national security priorities are pushing for rapid AI expansion, creating a dual mandate: scale fast, but scale responsibly. Companies are responding with renewable power purchase agreements, advanced cooling systems, and more transparent community engagement. Yet firms still struggle to communicate how their operational plans address environmental risks—especially to residents living near proposed sites.

For OM leaders, the strategic question is shifting from How do we build more compute? to How do we build sustainable, resilient, community‑aligned compute capacity? This requires integrating energy modeling, climate‑risk forecasting, water‑management strategies, and cross‑functional planning across engineering, facilities, and public affairs.

AI may ultimately help solve climate challenges—from grid optimization to disaster prediction—but only if its physical footprint becomes operationally sustainable. The next frontier in operations management is mastering the climate‑control and infrastructure demands of AI at scale.

Classroom discussion questions:

  1. How should operations managers balance the explosive growth of AI data‑center demand with rising environmental and community constraints?
  2. What innovations in cooling, energy procurement, and facility design could meaningfully reduce the climate impact of AI‑driven data centers?
  3. How does the community concern with the infrastructure demands of data centers relate to the “Factors That Affect Location Decisions” in Chapter 8, Location Strategies?

OM in the News: The Two‑Cent Failure That Shut Down a Factory

In operations management, small and overlooked details can have outsized consequences. A well‑known example comes from a manufacturing plant where a single 2‑cent bolt caused nearly a month of downtime. The incident illustrates that the failures that matter most are not always the ones that occur most frequently, writes Industry Week (Sept. 22, 2026).

Predictive maintenance systems (see Ch. 17 in your Heizer/Render/Munson text) are designed to monitor components that commonly wear out. Sensors track performance, data models identify warning signs, and technicians intervene before breakdowns occur. When focused on familiar failure patterns, these systems perform effectively and prevent costly interruptions.

However, predictive systems only detect what they are built to observe. In this case, the bolt that failed was not instrumented. It was considered too inexpensive, too simple, and too unlikely to break. Because it was not monitored, the system had no way to provide an early warning. When the bolt snapped, the machine stopped immediately.

The true cost of the failure was not the price of the bolt but the time required to recover. The part was specialized and not kept in inventory. It had to be ordered from a specific supplier, and until it arrived, the production line remained offline. The result was weeks of lost output—far more expensive than any of the common failures the system was designed to predict.

The OM lesson is clear: rare failures can be more damaging than frequent ones if the organization is unprepared to respond quickly. Effective maintenance planning requires looking beyond the usual wear‑and‑tear components and identifying parts that, if they failed unexpectedly, would be difficult or slow to replace.

The most costly outage often comes from the part no one thought to monitor. Recognizing this blind spot—and planning for it—is essential for building resilient operations.

Classroom Discussion Questions

  1. How should organizations balance monitoring common failures with preparing for rare but high‑impact failures?
  2. What criteria—not just failure frequency—should be used to decide which machine components deserve monitoring or backup inventory?

OM in the News: Why America Needs Manufacturing Capability—Not Just More Factories

For decades, operations leaders have been trained to hunt for bottlenecks inside the plant: slow lines, underutilized machines, rework loops, labor gaps. But today’s manufacturing constraints increasingly sit outside the four walls, write Profs. Kenneth O’Connor and Gerry Goldstein in Industry Week (Sept. 18, 2026). As the U.S. confronts rising demand in defense, aerospace, semiconductors, robotics, and advanced materials, one truth is becoming unavoidable: building more factories is not the same as building more manufacturing capability.

Capability comes from ecosystems—dense, coordinated networks of suppliers, logistics partners, research institutions, workforce pipelines, and public‑sector stakeholders. A factory produces products. An ecosystem produces scalable, resilient capacity.

Recent defense production challenges illustrate this clearly. After the U.S. sought to ramp 155mm artillery output from 14,000 to 100,000 rounds per month, progress stalled not because of floor space, but because of ecosystem gaps: propellants, explosives, metal parts, specialized equipment, qualified technicians, and capital investment. These constraints lived between organizations, not inside a single plant.

This pattern repeats across high‑value sectors. A manufacturer may have demand but lack second‑tier suppliers. It may have equipment but not technicians. It may have prototypes but no test stands. It may have engineering talent but no digital‑thread integration across partners. These are ecosystem bottlenecks—and they determine whether plant capacity becomes usable capability.

Regional strength matters even more. Regions with robust suppliers, applied research institutions, logistics networks, and workforce pipelines outperform those with isolated factories. National initiatives show how shared capabilities and coordinated development can lift entire ecosystems, especially small and mid‑sized manufacturers whose hidden expertise often enables surge capacity.

Operations managers need to ask whether suppliers can scale, where external bottlenecks sit, how long workforce development lead times truly are, how connected their production network is, and whether their ecosystem could respond if demand doubled tomorrow.

Lean operations and efficiency still matter—but the next competitive frontier is adaptive capacity: the ability to shift suppliers, redesign products, qualify new processes, train workers, reroute logistics, and integrate new technologies under pressure. That capability cannot be built in a crisis. It must be cultivated through relationships, coordination, and shared investment long before demand spikes.

Classroom Discussion Questions

  1. Where do the most critical bottlenecks exist in today’s manufacturing ecosystems—inside factories or across supplier, workforce, and logistics networks?
  2. How should regions and firms build “adaptive capacity” rather than simply expanding physical capacity?

OM in the News: AI Moves Into Physical Operations — Transforming Inventory, Quality, and Maintenance

AI is rapidly shifting from digital decision‑support to hands‑on operational control. As AI systems move into the physical world, they are reshaping core OM functions—especially inventory management, quality assurance, and maintenance. For operations managers, this marks a transition from AI as an analytical tool to AI as an active operational partner, writes The Orlando Sentinel (Sept. 20, 2026).

AI and Inventory Management Inventory systems (see Chapter 12 in your Heizer/Render/Munson text) are becoming increasingly autonomous. Instead of relying on periodic counts or manual updates, AI‑enabled sensors and vision systems now track materials in real time—monitoring stock levels, identifying misplacements, and even predicting shortages before they occur. Machine‑learning models can dynamically adjust reorder points, safety stock, and lot sizes based on demand variability, supplier reliability, and production schedules. The result is tighter control, lower carrying costs, and fewer stockouts.

AI‑Driven Quality Assurance Quality management (see Chapter 6) is undergoing a similar transformation. High‑resolution cameras, acoustic sensors, and thermal imaging feed continuous data streams into AI models capable of detecting defects far earlier—and more accurately—than human inspectors. Instead of sampling, AI enables 100% inspection at full production speed. These systems not only flag defects but also trace root causes by linking anomalies to machine settings, operator actions, or upstream materials. Quality becomes proactive rather than reactive.

Predictive and Autonomous Maintenance Maintenance (the topic of chapter 17) is perhaps the area where AI’s physical presence is most visible. Predictive maintenance models analyze vibration, temperature, and performance data to forecast failures before they occur. More advanced systems automatically adjust machine parameters to prevent breakdowns, optimize lubrication cycles, or balance loads across equipment. This reduces downtime, extends asset life, and stabilizes capacity planning.

The OM Challenge Ahead As AI becomes embedded in physical operations, managers must rethink process design, workforce skills, and risk management. Inventory, quality, and maintenance systems will increasingly operate autonomously—but they still require human oversight, ethical safeguards, and strategic alignment. The future OM leader must be fluent in both operational fundamentals and AI‑driven decision‑making.

Classroom Discussion Questions:

  1. How will AI‑enabled real‑time inventory, quality, and maintenance systems change traditional OM decision frameworks?
  2. What new risks arise when AI autonomously adjusts physical processes, and how should managers design controls to mitigate them?

OM in the News: How Manufacturers Are Dealing with the Skilled Labor Shortage

An aging workforce and the reshoring drive are contributing to the skilled labor shortage, reports Industry Week (Sept. 11, 2026). Skilled trades are defined as “the 135 hands-on, in-person trade occupations that don’t require a 4-year degree but that do require one or more of the following: apprenticeship training, certificate completion, education up to and including an associate’s degree, or other on the job training and experience.”

Also, as companies begin to reshore, demand for more skilled trades workers will continue to rise. According to the 2026 USA Reshoring Survey Report, 36% of manufacturers say they have reshored or are actively executing additional reshoring.

With this additional manufacturing work coming to the U.S., manufacturers are looking for ways to support the growing need for skilled workers. Many say hiring technicians like welders, maintenance and repair techs, and machinists is at a very difficult level.

To fill these roles, 61% of firms are using trade or vocational schools, 58% are using internal upskilling or reskilling programs, 51% are using community college partnerships, 46% are using 3rd party staffing, and 43% are using apprenticeships.

AI can lower barriers to entry as embedded expertise reduces dependence on prior experience, a helpful tool as more workers begin to retire. Manufacturers have an opportunity they need to take advantage of to support the development of skilled trade workers to fill the rapidly expanding gap, ensuring the U.S. can sustain the resurgence of domestic production.

Classroom discussion questions:

1. How should manufacturers balance long‑term investments in automation with short‑term strategies like wage increases or training programs when responding to persistent skilled labor shortages?

2. What operational risks arise when firms rely heavily on overtime, temporary workers, or outsourcing to fill skilled labor gaps?

OM in the News: Seaweed’s Rise in a Sustainable Supply Chain

For centuries, coastal communities have tapped marine environments for food, trade, and early industrial materials.  Ancient East Asian cultures harvested kelp as elite food, tax payment and even currency. In 1658, a Japanese innkeeper accidentally discovered agar by leaving seaweed jelly out in the freezing night air, revolutionizing food and medicine production.

Today, that same resourcefulness is powering a new wave of innovation as seaweed transitions from a traditional commodity into a high‑value industrial input. For OM, this shift represents both an emerging opportunity and a complex execution challenge.

Seaweed’s natural advantages make it uniquely suited for sustainable supply chains, reports ASCM Insights (Sept. 10, 2026). It requires no freshwater, no arable land, and grows rapidly—an appealing profile as organizations face tightening resource constraints and rising sustainability mandates. Researchers and manufacturers are now converting marine algae into bioplastics, fertilizers, renewable fuels, livestock feed, and even biochar, which can lock in up to 80% of the plant’s carbon. These applications don’t just support environmental goals; they create new pathways for operational resilience and carbon‑credit strategies.

Innovation is accelerating across sectors. UK researchers have developed biodegradable alginate‑based plastic films that can reduce single‑use packaging waste. In the U.S., aquaculture projects are transforming native sugar kelp into natural oils for personal care products, bio lubricants, and plastics—supported by federal investment aimed at strengthening domestic supply chains and reducing reliance on imported palm and coconut oils. These breakthroughs demonstrate how seaweed can serve as both a sustainable raw material and a strategic lever for reshoring critical inputs.

But scaling seaweed‑based materials is far from simple. High processing costs, fragmented midstream capacity, and regulatory tensions between multinational processors and local harvesters create operational friction.

Mastering this transition requires rigorous planning and inventory control.  Operations managers must navigate evolving material flows, optimize production networks, and turn early‑stage innovations—like seaweed‑derived inputs—into long‑term competitive advantage.

Seaweed’s industrial future is taking shape and organizations that build the operational expertise will lead as marine‑based materials scale.

Classroom discussion questions:
  1. Seaweed-based materials promise sustainability benefits, but there are steep processing costs. How should operations managers evaluate whether an emerging raw material is truly scalable?
  2.  How can supply chain leaders balance efficiency, regulatory compliance, and community impact when integrating new natural resources into global production networks?

OM in the News: Reshoring is Up, But Problems Persist

Manufacturing companies increased their interest in reshoring operations this year, writes Material Handling & Logistics (Sept. 3, 2026). The 2026 USA Reshoring Survey Report found that 36% of manufacturers had reshored or were actively engaged in additional reshoring– an increase from 29% in 2025.  Projects are still in the works as well, with 32% of manufacturers saying they were currently quoting reshoring projects–double the 16% reported in 2025.

Why Reshore? The top reason might not be a surprise — tariffs. Here is a breakdown of survey results.

  • Tariffs — 65%
  • Geopolitical Risk — 60%
  • Delivery time/proximity to customers — 50%
  • Image/Brand of Made-in-USA — 40%
  • Quality/Rework/Warranty — 25%
  • Having manufacturing near engineering –15%
  • Tax Reduction (from One Big Beautiful Bill) — 10%

Benefits of reshoring. Respondents saw these benefits as a result of their reshoring efforts.

  • Improved speed to market — 70%
  • Better fulfillment/on-time delivery — 65%
  • Logistics savings (freight/transit) — 60%
  • Inventory optimization/lower carrying costs — 40%
  • Enhanced pricing power/brand value –20%

Challenges to Reshoring

However, there are challenges to reshoring, and those have changed over time.  Currently, the top challenge, at 35%, is the availability of labor and overhead costs. Other issues include:

  • Domestic component gaps — 35%
  • Supply chain transition friction –20%
  • Labor availability challenges — 15%
  • Regulatory /compliance burdens –15%

While the Reshoring report is optimistic about reshoring as a way to reduce imports, a recent survey, The Kearney 2026 Reshoring Index, found a less positive view. They said that despite changes in US trade and tariff policies and significant changes in geopolitical realities, as of the end of 2025, America remains even more reliant on imports while its manufacturing capacity remains years away from projected goals.

The report found that despite US manufacturing investments tripling over the past four years, there’s only been 1.5% growth in capacity so far. Resolving the labor issues and other barriers to reshoring, at the end of the day, requires further supply chain adjustment.

Classroom discussion questions:

  1. Why might companies be motivated to reshore even when the outcomes are less positive than expected? What does this tension reveal about strategic decision‑making in operations management?
  2. How do capacity limitations, labor shortages, and domestic component gaps shape the feasibility of large‑scale reshoring? What operational strategies could help firms overcome these constraints?

OM in the News: How Digital Twins Are Transforming Production Planning at Brose

Manufacturing has always required a delicate balance: maximizing throughput, minimizing inventory, adapting to demand shifts, and maintaining quality—all while avoiding costly disruptions. As factories become more automated and interconnected, that balance has grown harder to maintain. A single change in one department can ripple across an entire value stream, creating bottlenecks that traditional planning tools often fail to anticipate.

Brose, a global automotive supplier known for seat structures, door systems, and electric drives, is confronting this challenge head‑on by expanding its use of digital twin technology, reports Industry Week (Aug. 17, 2026). Brose models complete production systems—not just isolated workstations—to understand how materials, resources, and constraints interact under real operating conditions.

Brose’s Plant Simulation software can identify problems early on and avoid future downstream costs

This shift marks a departure from calculations that struggle to capture real‑world variability. Equipment breaks unexpectedly. Operators work at different speeds. Material arrives early, late, or out of sequence. Brose’s digital twin allows engineers to observe these dynamics virtually before making physical changes on the shop floor.

One example involves a palletized manufacturing line where an automated station gradually slowed below its planned cycle time. While the issue was visible on the floor, its broader impact was not. By incorporating process times, operator activities, and material flow relationships into the simulation, Brose identified how the slowdown constrained downstream operations and tested multiple improvement strategies virtually. The result: more confident decision‑making and significantly lower implementation risk.

Brose has also extended simulation into optimization problems, such as determining efficient material drop‑off routes. Instead of manually comparing a handful of options, engineers use algorithms to evaluate thousands of routing combinations against performance objectives—surfacing solutions that would be impractical to discover manually.

Brose’s experience reflects a broader trend: digital twins (the topic of Module F in your Heizer/Render/Munson text) are becoming essential tools for continuous improvement and production planning. As product lifecycles shorten, the ability to experiment virtually before committing resources may become one of the most valuable capabilities on the factory floor.

Classroom Discussion Questions

  1. How does modeling entire value streams—rather than individual processes—change the way engineers identify and solve production problems?
  2. What types of operational decisions benefit most from virtual experimentation before physical implementation?

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:

  1. 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?
  2. 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

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:

  1. How does Ford’s use of “gray beard” engineers challenge common assumptions about innovation and workforce development in high‑tech industries?
  2. 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:

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