OM in the News: U.S. Manufacturing Is Adding Jobs Again, but the Hiring Rules Have Changed

For years, discussions about reshoring manufacturing in the U.S. focused on one goal: bringing jobs back. Today, a different challenge is emerging. Manufacturing jobs are returning, but the skills required to fill them are changing dramatically.

Manufacturing jobs increasingly require advanced training as factories become more automated and complex

U.S. manufacturers have added 72,000 jobs since December, signaling renewed growth in the sector. However, many of these positions look very different from the traditional factory roles that defined manufacturing in previous decades, writes The Wall Street Journal (Oct. 29, 2026). Automation, robotics, advanced sensors, and data-driven operations are reshaping the workforce and raising the bar for entry.

While production occupations such as assembly and machine operation have declined over the past decade, manufacturers have expanded hiring in engineering, management, business operations, and technical support roles. Modern factories need employees who can analyze data, maintain automated systems, troubleshoot sophisticated equipment, and optimize increasingly complex processes.

This evolution presents both opportunities and challenges for operations leaders.

On one hand, technology-enabled manufacturing creates higher-value jobs that often offer better wages, improved working conditions, and stronger productivity gains. Organizations benefit from greater efficiency, higher quality output, and more scalable operations. In many facilities, repetitive manual tasks are being replaced with roles focused on problem-solving, continuous improvement, and technical expertise.

On the other hand, the growing reliance on skilled labor creates a significant talent challenge. The traditional pathway from high school directly into manufacturing is becoming less common as employers seek workers with certifications, associate degrees, engineering backgrounds, or specialized technical training. As demand for these capabilities increases, companies face the risk of workforce shortages that could limit growth and operational performance.

For operations managers, the implications are clear. Workforce strategy can no longer be separated from operational strategy. Investments in automation must be paired with investments in training, upskilling, and partnerships with educational institutions. The most successful manufacturers will not simply deploy advanced technologies. They will build talent pipelines capable of supporting them.

The future of manufacturing is not just about smarter factories. It is about developing a workforce equipped to operate, improve, and innovate within them.

Classroom Discussion Questions

  1. How should manufacturers balance investments in automation with investments in employee training and workforce development?
  2. What responsibility do manufacturers, educational institutions, and government agencies share in addressing the growing skills gap in advanced manufacturing?

OM in the News: Building a Talent Pipeline the Operations Management Way

Manufacturers often describe the workforce challenge as a shortage of welders, machinists, and young people interested in industrial careers. Yet the most important lesson, writes Industry Week (Oct. 23, 2026), is that talent development should be managed the same way operations leaders manage supply chains: through partnerships, diversification, and long-term investment.

Merrick Industries, a Florida-based manufacturer of weighing and feeding equipment, demonstrates how workforce development becomes more effective when viewed as a system rather than a hiring activity. Instead of relying on a single recruiting source, the company works closely with technical colleges, military installations, economic development organizations, manufacturing associations, and its own employees.

From an operations management perspective, this approach mirrors supplier relationship management. Merrick actively participates in advisory committees with local technical colleges, helping educators understand the skills and behaviors the company needs. This feedback loop improves the quality of graduates entering the workforce while creating a more reliable talent source for the manufacturer.

The company also recognizes the value of military veterans. Thousands of service members transition from military careers in Florida each year, bringing discipline, technical expertise, leadership experience, and often valuable security clearances. These attributes reduce onboarding risk and accelerate workforce readiness.

Another notable lesson is the importance of regional collaboration. Economic development organizations can identify common workforce needs across multiple employers, making it easier to justify investments in training programs and new educational equipment. In essence, they serve as coordinators for a regional talent ecosystem.

Perhaps the most overlooked resource is the existing workforce. Merrick encourages employee referrals, supports work-life flexibility, and helps new hires understand long-term benefits such as health insurance and retirement plans. These practices strengthen retention while reinforcing a culture that attracts future employees.

For operations managers, the takeaway is clear: talent pipelines do not emerge by chance. Like resilient supply chains, they are built through collaboration, shared investment, and continuous relationship management. Organizations that actively cultivate multiple talent sources will be far better positioned to meet future workforce demands and sustain operational excellence.

Classroom discussion questions:

OM in the News: AI in Retail Operations–Technology Is Easy, Change Is Hard

Retailers continue to invest heavily in AI, but the biggest challenge is not the technology itself, reports Raconteur (Oct. 19, 2026). It is integrating AI into daily operations and helping employees adapt their workflows to use it effectively.

A recent example comes from Everseen, whose computer vision platform helps retailers identify checkout errors at self-service stations. The company’s rollout with Morrisons, one of the UK’s largest supermarket chains with about 500 stores and 10 million weekly customers, highlights an important reality of operational transformation: AI success depends as much on people and processes as it does on algorithms.

Self-checkout systems place unexpected demands on customers, asking them to perform tasks once handled by trained cashiers. AI can help reduce errors by identifying missed scans and prompting corrective actions. While this can improve loss prevention, deploying such technology requires coordination among software providers, hardware suppliers, installers, and store operations teams.

The larger operational lesson is that AI introduces new workflows that employees must learn and manage. Store associates need training not only on how the technology works but also on how to respond when AI identifies an issue. This makes AI deployment a change management initiative rather than a simple technology project.

Retail operations have also become increasingly complex. Associates are now expected to support online order fulfillment, curbside pickup, inventory management, and in-store customer service, often with little increase in staffing levels. AI offers an opportunity to simplify these responsibilities by surfacing actionable insights instead of requiring employees to search for problems manually.

For example, computer vision and inventory monitoring systems can alert staff when shelves need replenishment, helping prevent stockouts and lost sales. The value is not in generating more data but in enabling faster and better decisions. Operational excellence comes when AI transforms information into action.

The key takeaway is that AI should be viewed as an operational capability, not just a technology investment. Organizations that pair AI implementation with workforce training, process redesign, and clear operational objectives will be better positioned to realize measurable business value.

Classroom Discussion Questions

  1. Why is change management often a greater challenge than the AI technology itself during operational transformations?
  2. How can retailers ensure that AI improves employee productivity and customer experience rather than adding complexity to frontline operations?

Guest Post: Uber Freight’s Transformation from Freight Broker to Supply Chain Partner

Dr. Jon Jackson is Professor of Operations Management and Sr. Associate Dean at Providence College

Recent developments at Uber Freight offer a compelling example of how logistics providers are evolving from transportation brokers into strategic supply chain partners. According to a recent Wall Street Journal report (Oct. 14, 2026), the company is undergoing a turnaround under the leadership of Rebecca Tinucci, a former Tesla executive known for scaling complex operational networks. Her challenge is to strengthen Uber Freight’s positioning in transportation management and supply chain technology.

Evidence that this strategy may be gaining momentum emerged in a recent partnership announcement involving Oxea, a global chemical manufacturer (Supply Chain Dive, Oct. 1, 2026). Oxea selected Uber Freight to manage its logistics operations across North America and Europe. Rather than simply arranging freight movements, Uber Freight will oversee transportation across truckload, rail, and ocean modes, allowing the company to manage the network as an integrated system rather than a collection of regional operations.

The partnership is particularly noteworthy because chemical supply chains present significant operational challenges. Chemical shipments often require specialized equipment, extensive documentation, regulatory compliance, and close coordination among manufacturing plants, carriers, and customers. By managing these activities as a single network, Uber Freight aims to provide greater visibility across the supply chain, enabling disruptions to be identified earlier and addressed more quickly.

The broader story here is the growing importance of data and technology in logistics. Uber Freight’s transportation management system provides shipment visibility, carrier performance dashboards, and integration with enterprise resource planning (ERP) systems. The company also leverages an AI platform built on insights from more than 20 million delivered shipments to help customers analyze network performance and identify potential disruptions earlier. For companies such as Oxea, these capabilities offer the opportunity to improve service reliability, coordinate transportation across multiple modes, and respond more effectively to uncertainty.

 Classroom Discussion Questions

  1. How does Uber Freight’s evolution from freight broker to supply chain partner reflect broader trends in digital transformation and supply chain integration?
  2. What operational advantages and risks might arise when a company such as Oxea relies on a single provider to manage transportation across multiple countries and transportation modes?

OM Podcast #55: Supply Chain Operations at Coca-Cola

In our latest podcast, Professors Barry Render and Misty Blesley interview Brandi Short, President of ABARTA Coca-Cola Beverages. ABARTA is the 10th largest bottler across the Coca-Cola system in North America, and its exclusive distributor throughout Pittsburgh, Erie, Johnstown, Altoona, Harrisburg, Lancaster, Reading, Mt. Pocono, the Lehigh Valley and parts of West Virginia, Ohio and Warren County NJ.

Prof. Barry Render
Prof. Misty Blessley
Prof. Misty Blessley
Brandi Shortt

 

 

 

 

They discuss how the Coca-Cola bottling system operates and explore Brandi’s experiences with supply chain operations.  The conversation covers AI, managing supply challenges during COVID, warehouse automation, and balancing supply chain risk.

TRANSCRIPT LINK

A Word document of this podcast will download by clicking the word Transcript above.

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: AI’s Real Role in Manufacturing– Empowering the Workforce

For years, headlines have suggested that artificial intelligence will replace manufacturing workers. The reality on factory floors tells a very different story, writes Industry Week (Oct. 13, 2026).

Manufacturers today face a growing workforce shortage, not a workforce surplus. Experienced machinists, maintenance technicians, and production leaders are retiring faster than they can be replaced, taking decades of knowledge with them. At the same time, demand for skilled labor continues to outpace supply, creating a gap that threatens operational performance, growth, and competitiveness.

The OM challenge is clear: firms cannot simply hire their way out of this problem. The solution lies in helping existing teams become more productive, effective, and informed. This is where AI can deliver real value.

The most successful organizations are not using AI as a standalone technology project. They are embedding it into day-to-day operations to eliminate repetitive work, accelerate decision-making, and allow employees to focus on solving problems rather than gathering data. Companies are now using AI to automate order processing, streamline purchasing workflows, and reduce administrative burdens that consume valuable employee time.

What separates high-performing manufacturers is not simply automation. It is the ability to move quickly from information to action. Modern operations require systems that can identify changes, provide insights, and support rapid decisions in an increasingly complex environment of supply chain disruptions, market volatility, and workforce constraints.

For OM teams, AI should be viewed as a workforce amplifier. By reducing manual tasks, improving visibility, and supporting frontline decision-making, AI helps organizations maximize the capabilities of their current workforce while improving retention and operational resilience.

The future of manufacturing will not be defined by AI replacing people. It will be defined by organizations that successfully combine human expertise with AI-powered tools to achieve higher productivity, stronger execution, and sustainable growth.

  1. How can manufacturing leaders implement AI in ways that enhance employee productivity and job satisfaction rather than creating concerns about job displacement?
  2. What operational processes in a manufacturing organization would benefit most from AI-driven decision support, and what challenges might arise during implementation?

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 Podcast #54: Defense Strategy, Military Logistics, and Strategic Stockpiles

Col. Jay Reckard

In our latest podcast episode, Barry and Misty interview Colonel Jay Reckard, who retired from the U.S. Army after 28 years of service and now works as a defense strategist through his consulting practice. During his military career, Jay served in a variety of leadership roles, culminating as director of the U.S. Army Chemical Materials Activity.

In this podcast, Barry, Misty, and Jay discuss the critical role of logistics and supply chains in military operations. Topics include strategic stockpiles, chemical and biological defense, 3D printing in deployed environments, munitions readiness, contested logistics, and the supply chain implications of global events such as activity in the Strait of Hormuz and the South China Sea.

Prof. Barry Render
Prof. Misty Blessley

As we prepared for this episode, Jay took a moment to reflect on his friend and fellow soldier, the late Retired Colonel Jeffrey J. Britton (1969–2026). He shared how Jeff’s leadership in military logistics made it possible for others, including Jay, to accomplish their missions. We were touched by Jay’s tribute and join him in remembering Jeff’s dedicated service and lasting impact.

“Jeff Britton was the finest logistician I ever served with. He was intelligent, capable, and a man of deep character. His strengths were many and his weaknesses few, and he cared for his soldiers more than himself. Jeff’s steadfast leadership of the 193d BSB solved every logistics challenge facing the 4th MEB. Above all, he loved his family. I will always be in his debt and wish him lasting peace.”    

 

TRANSCRIPT LINK

A Word document of this podcast will download by clicking the word Transcript above.

Have you subscribed to this podcast on Apple Podcasts? Just open 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 are released.

Did you know every podcast is available to assign from within Pearson’s MyLab product for the Heizer, Render Munson Operations and Supply Chain title?  You can find them under the Podcast chapter.

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?