Category: Chapter 7

Posts relating to Chapter 7

Guest Post: Robots as a Service– Is Warehouse Automation Going Subscription?

 Dr. Jon Jackson, Associate Professor of Operations Management at Providence College, raises an interesting issue regarding warehouse robotics. Jon has created AI classroom exercises for each chapter of our text. They are found on-line in the Instructor’s Resource Manual.

Warehouse automation typically requires a significant upfront investment. But what if companies could rent the robots instead?

A recent Wall Street Journal article (August 17, 2026) highlights the growing use of subscription-based robotics in warehouses. Instead of purchasing robots outright, companies can pay a monthly fee to use them, potentially changing the economics of automation.

North American companies ordered nearly 18,000 warehouse robots worth $1.2 billion in the first half of 2026; both figures are up from 2025. Simultaneously, the average U.S. warehouse wage reached $26.85 per hour, up 41% over the past decade. Even with the higher wages, nearly 392,000 jobs remain open in the transportation, warehousing, and utilities industries. Together, these trends create strong incentives for companies to consider greater investment in warehouse automation.

Traditionally, investing in robots means committing substantial capital to equipment that may become obsolete or may not provide enough value to justify the investment. A subscription model changes that calculation by shifting some of the financial risk from a capital investment to an ongoing operating expense. Companies can avoid much of the upfront capital expense, scale automation up or down as demand changes, and potentially gain access to newer technology as it becomes available.

This flexibility could be particularly valuable in warehouses with seasonal or uncertain demand. Rather than purchasing enough robots to handle peak demand (and leaving them underutilized during slower periods), companies could potentially add robotic capacity when they need it.

There are trade-offs, however. Subscription fees may ultimately cost more than purchasing equipment outright, and companies become dependent on the robotics provider for technology, maintenance, and service.

From an operations perspective, “Robots as a Service” raises an interesting question: Should automation be treated as a capital investment or as a variable operating expense? As robotics technology improves, the answer may increasingly depend on how much flexibility a company values.

Classroom Discussion Questions

  1. When might renting robots be preferable to purchasing them?
  2. How does a subscription model change the risk associated with investing in automation?

Guest Post: Artificial Intelligence and Public Transportation

Prof. Howard Weiss shares his insights with opur readers monthly.

AI is becoming an increasingly important tool in public transportation and traffic management. Traffic cameras have been used since 1956, when they were introduced in London. Modern AI-enabled cameras, however, do more than record images. They can interpret what they see and identify traffic violations and other events in real time.

Cities and transportation agencies worldwide are experimenting with this technology. In Greece, AI cameras identify drivers who fail to wear seat belts, who use cell phones, or who exceed speed limits. In Goa, India, 26 locations have installed cameras to identify similar violations. Mississippi uses AI cameras to alert officers to traffic violations. Houston is purchasing AI cameras to improve traffic flow. In the Dallas–Fort Worth area, AI cameras assist criminal investigations

AI cameras are also being used on buses. In Philadelphia, SEPTA uses AI-powered cameras to identify vehicles illegally parked and blocking buses. Broward and Miami-Dade Counties in Florida use cameras mounted on school buses to identify motorists who fail to stop when buses are loading or unloading students.

Benefits and Costs The primary benefit of AI cameras is improved public safety. Several communities have reported reductions in traffic violations after cameras were installed. AI can also reduce the number of police officers required for traffic enforcement and can speed citation processing. 

Citywide systems can require millions of dollars in capital investment, as well as expenses for software development and preventive and breakdown maintenance, as discussed in Chapter 17 of your Heizer/Render/Munson textbook. Fines may offset some costs.

Privacy is another concern. Although systems identify vehicles rather than drivers, they can collect information about where vehicles travel and when. This raises important questions about data retention, access, and appropriate use.

Implementation and Quality Control The experience of several communities demonstrates the importance of quality control. Miami-Dade and Philadelphia both used warning periods before imposing fines. Miami-Dade later suspended its program because of inaccurate citations and subsequently modified the system to improve consistency and transparency. In Greece, reports found significant discrepancies between AI-generated citations and violations ultimately determined to be valid. Some drivers were cited even though they had stopped because of an ambulance or police vehicle.

Classroom Discussion questions:

  1. What rules should governments establish regarding data collection, storage, and access?
  2. What quality-control procedures should be required before a driver is fined based on an AI-generated violation?

 

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:

  1. Why did AI fail the first time and will it work this time for ordering?
  2. 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:

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

OM in the News: AI-Based Robots

Should companies deploy robots at their plant if they could virtually reprogram themselves to perform new and different tasks, asks Industry Week (May 13, 2026)? We’re nearly at the end of the AI hype cycle, when suggestions for how to leverage the technology become less flashy and more realistic.

Now Siemens has just revealed Eigen, an AI agent that can replace manual coding or programming for programmable logic controllers, distributed control systems, and robotics applications, updating code or instructions to reflect new priorities and goals.

Siemens says that engineering and reconfigurations constitute 70% of the entire lifecycle cost of a robot. If, however, an AI agent like Eigen can shorten the time needed to make these adjustments, it makes the robot more efficient, and small and medium-sized businesses might be better able to afford deploying the technology.

“There’s a kind of new age of automation arising, because with AI assistance to program robots and PLCs, it means you could suddenly automate much smaller lot sizes on a good return of investment,” says the firm’s CEO of its automation division.

Eigen can help manufacturers deal with a lack of coders and programmers. Another Siemens exec adds “We don’t attract the best of the programmers to the manufacturing floor. … So getting programmers to come and code our controllers or robotic systems? That was a scale up bottleneck. Bringing in AI to reprogram things, reprogram the whole process, will be more game changing in the U.S. than in Germany, where I see when people with Master’s degrees on the manufacturing floor, which is not the case in the U.S. Humans must always remain in the loop, however. Agentic AI is like an orchestra and humans the conductors.”

In short, Eigen acts as an AI-agent that handles the tedious, expensive back-end coding of robotics, making automation flexible, cheaper and more accessible to smaller firms.

Classroom discussion questions:

  1. What is Eigen‘s role?
  2. What is the roadblock to more robotic use in small manufacturers?

OM in the News: Drones Gain Manufacturing Altitude

Drones, in the headlines every day from Russia, Iran and Hezbollah, are also real manufacturing tools that can add valuable context and support. “They provide clear value in activities that are traditionally labor-intensive, disruptive or difficult to perform safely,” writes Industry Week (May 7, 2026).

Drones are making inroads in manufacturing in these two areas:

Inventory tracking:  Indoor drones equipped with barcode scanners and computer vision systems can perform inventory scans with minimal disruption. In large warehouses or storage areas, they can significantly reduce cycle-count time and increase count frequency. The real-time data can be integrated into corporate software platforms, reducing excess stock and decreasing production delays due to stockouts.

Inspection: Conventional industrial inspections often require scaffolding, rope access, shutdowns or travel. They expose workers to heights, to confined spaces or to environmental hazards. Drones can transform this activity. High-resolution visual, thermal, ultrasonic and lidar payloads allow inspection of hard-to-reach areas, including storage tanks and silos, pipe racks, roof structures and overhead utilities. For remote manufacturing facilities, a drone mission can eliminate days of logistics and increase safety performance.

The business impact is measurable: reduced downtime, lower mobilization costs, reduced safety risk and faster response to problem detection. In the energy and utilities sector, drone-based inspection has been estimated to reduce inspection costs by 70% and downtime by 90%.

Future innovation: The next stage in drone deployment is autonomous operation. Drones will increasingly operate from fixed docking stations, launching automatically to perform scheduled inspection missions. Data collected during these flights can be transmitted to AI-enabled analytics platforms and integrated into operational systems in near real-time. Coordinated drone swarms may be used to conduct large-scale inspections and surveys across a large infrastructure.

Challenges: Concerns about workforce displacement are common, and the increased adoption of drones is no exception. But drone programs typically augment skilled labor rather than replace it. Technicians can become certified drone pilots, remote inspection specialists, data analysts or AI-assisted defect reviewers.

Modern industrial drones are connected devices and must be treated as operational technology nodes within the broader cybersecurity architecture. A compromised drone platform presents a risk that goes beyond simple device failure. It may expose critical infrastructure data or provide a means to compromise enterprise networks.

Still, a fully scaled drone program that makes good use of its data is a significant strategic asset.

Classroom discussion questions:

  1. What other industries are already commonly using drones?
  2. Discuss their use in modern warfare.

OM in the News: UPS Turns to RFID

United Parcel Service is rolling out technology to more closely track the billions of small packages that move through its U.S. network each year, reports The Wall Street Journal (April 15, 2026).

UPS has invested $100 million to date to set up RFID technology across its network

The company said the change will increase visibility throughout its small-package delivery network, while increasing delivery accuracy and reducing the manual labor needed to scan individual parcels.

“What this does is it offers our customers real-time, near real-time, visibility of where their packages are at within our network,” said a UPS exec.

The capability is a step beyond the shipment-tracking information widely used today, which relies on workers scanning bar codes as packages enter and leave warehouses or vehicles. That tracking point typically lags behind a package’s current location, leaving gaps in visibility where packages may be misplaced or lost.

UPS is now embedding RFID tags into shipping labels and has installed RFID sensors on all its U.S. delivery trucks, at its more than 5,500 retail stores and in its final-mile delivery centers.

The technology allows UPS to automatically sense and track when a package crosses a threshold into or out of a building or vehicle. That will give customers a more up-to-date, accurate picture of where packages are, though it does not include real-time location tracking.

The company in part uses the technology to identify what it calls misloads, where packages are loaded onto the incorrect delivery truck. The RFID tag on a given package sets off a sensor as it’s loaded into a delivery truck and makes a noise indicating if the package is on the wrong vehicle.

UPS said misloads have dropped near 70% since it started using the technology in 2024, and that the RFID technology will eliminate about 20 million manual scans per day.

The high cost of individual tracking devices and the complexity of small-package delivery networks have limited tracking technology to more industrial applications as well as shipping high-value goods such as healthcare products, electronics and luxury items. UPS said the cost of RFID tags has come down to a few cents each, allowing the company to deploy the technology at scale.

Classroom discussion questions:

  1. What are the advantages and disadvantages of RFID?
  2. Why are misloads to be avoided?

Good OM Reading: The Algorithm– How Tesla Drives Innovation

Elon Musk calls it “the algorithm,” a distillation of lessons learned while relentlessly increasing production capacity at Tesla’s Nevada and Fremont factories.  And anyone can tap into the powerful management techniques behind Elon Musk’s success. At least that’s the thesis of a new book by former Tesla President Jon McNeill.
“The Algorithm” argues there are five steps that explain how Musk wants his teams at the electric-car company and rocket-maker SpaceX to operate.  “Much of the genius in Musk’s companies come from the legions of smart people empowered by the Algorithm,” McNeill writes. “They’re chasing stretch goals with free license to question everything and innovate boldly.”

 

The 5-Step Operational Algorithm is a structured approach to decision-making, innovation, and efficiency used at Tesla, SpaceX, and other Musk firms. It consists of these 5 sequential steps: 

  1. Question Every Requirement– Identify the origin of each requirement and challenge its necessity, regardless the rank of the person making the recommendation. The goal is to make requirements less “dumb” and ensure they serve the final objective.

  2. Delete Any Part or Process You Can– Remove unnecessary steps or components. Musk emphasizes that if you donot occasionally cut back at least 10%, you likely haven’t deleted enough. 

  3. Simplify and Optimize– Focus on improving only what remains after deletion. Avoid optimizing  processes that shouldn’t exist. 

  4. Accelerate Cycle Time– Speed up processes only after simplification and optimization, ensuring efficiency without reinforcing unnecessary steps. 

  5. Automate Last– Implement automation only after all prior steps are completed to avoid automating inefficiencies.

 

 

OM in the News: AI’s Big Manufacturing Productivity Gains

The efficiency and productivity improvements AI can deliver through automation and digitalization will help bridge manufacturing’s workforce gap, writes Industry Week (March 13, 2026).

Similar to the PC revolution decades ago, all signs point to AI following suit with enhanced productivity and profitability. Productivity soared when PCs became interconnected across organizations. Manufacturing will see the same breakthrough with “embedded AI”—to help ease workforce bottlenecks with specific solutions. On the shop floor, for example, predictive-maintenance AI (see Chapter 17) can analyze sensor data to forecast equipment failures and avoid labor-sapping downtime.

AI vision systems (Chapter 7) can catch defects on production lines at a pace beyond human capabilities and without the repetition-induced fatigue and employee turnover. Collaborative robots (cobots) and automated mobile robots transport material and can assist with assembly and repetitive operations. AI’s coding capabilities extend to numerical control and other industrial equipment, speeding up setup time and productivity in hard-to-fill technical positions.

The interaction of embedded AI, agent-based AI, and machine learning across different areas of an organization holds the greatest promise in solving long-term labor shortages. AI can already let a customer snap a photo of a damaged part and identify it for replacement. Its real power will manifest when AI can also determine the part’s inventory status and locations, establish shipping terms and timing, add the part to the procurement queue to replenish once it’s sold, alert engineering that a design change for a chronic defect may be in order, and propose alternative designs.

Here is a  current example involving AI across systems: the big  semiconductor company AMD is using generative AI to track down the root cause of delivery delays, simplifying complex supply chain interactions to transform a complex, specialist-dependent, labor-intensive manual process into faster issue resolution and better decision-making. The system cuts the time needed for what was a 14-step process taking 20-30 minutes by 90%, saving more than 3,100 staff hours a year.

Also coming soon to these intelligent product recommendation engines is an ability to parse what can be 50-page tender documents to extract multiple configurable products for sales quotes. That not only saves time, but also enables junior staff to handle work that has previously required experienced hands.

Classroom discussion questions:

  1. What can AI do to improve a procurement system?
  2. What does “embedded AI” mean?

OM in the News: FedEx Is Planning an AI Agent Workforce

FedEx is building out an army of AI agents to work alongside its human workforce, positioning itself to tap the latest wave of technology crashing through corporate America, reports The Wall Street Journal (March 13, 2026).

The shipping giant, which already deploys artificial intelligence in software development and other areas, is now looking to drive AI agents further into operations, including network planning and business processes. By 2028, FedEx expects to have AI integrated into more than half of its core operational workflows.  FedEx is currently focused on setting up the underlying data and management foundation to oversee its AI bots.

Though logistics providers like FedEx are aiming to adopt AI, they’re grappling with challenges like managing numerous, disconnected data sources. “Logistics can be very fragmented—especially if you think of a global organization with their network being everywhere, it makes it difficult to standardize,” said an industry consultant.

As its underlying tech is completed, FedEx expects to roll out AI and AI agents that connect macro and microeconomic trends to better plan its network. In marketing and campaign management, FedEx will create a hierarchy in which there’s a “manager agent,” an “audit agent” and a “worker agent.” The goal of the hierarchy is to ensure that the agents have a trail of accountability for their actions.

At the moment, FedEx’s enterprise data platform, called Atlas, supports more than 200 AI use cases across the supply chain, commercial teams and enterprise functions. It has already turned on AI agents in areas such as software development, where they are developing and testing code. In operations, agents are helping customers clear customs more quickly.

Plans for FedEx’s AI agents also involve getting its humans ready to interact with the technology. the company just launched an AI education program for 300,000 of its employees, as well as a more advanced version for its technology workers. Each employee received a customized training depending on their role. FedEx says it doesn’t plan for those agents to replace its workers.

Classroom discussion questions:

  1. Why is FedEx pushing for more AI agents?
  2. How will agents be used in operations?

OM in the News: The Robotics Supply Chain

The next 20 years are not just about making robots better, but also about how they will be used in all sorts of industries, from small tests to big factories. The real challenge is having specialized engineering skills, great manufacturing, and dominating software,  reports Industry Week (March 11, 2026). 

There are 6 key areas that make all the difference in this industry.  Here is a breakdown of the cost of the parts that go into a robot:

1. Actuators & Gearboxes (35-40%): The physical muscle.

2. Robot Structure / Manipulators (15-20%): The physical frame and integration.

3. Sensors & Perception (10-15%): The eyes and ears.

4. AI Compute / Control (10-15%): The operational brain.

5. Battery / Power Systems (10-15%): The energy storage for mobile units.

6. Precision Motion Components (5-10%): The components required for fine movements.

This list shows that a robotics breakthrough isn’t just software advances; it depends on physical components and the supply chains that produce them. But there are 3 chokepoints (bottlenecks).

 #1: Precision Reducers, controlled by Japan. Robots can’t move with a lot of power and precision without special parts (harmonic and cycloidal reducers). Two companies in Japan make 70% of these parts used all over the world. Spending more money won’t allow other companies to make these parts, because they need special knowledge about metals and years of experience making precise parts.

 #2: AI Compute (The Intelligence Standard), controlled by the  U.S. Today’s robots, especially those that use reinforcement learning, need powerful computers to work properly. NVIDIA’s CUDA system has become the leading platform used by robots that learn and think. Making a better chip is not enough if you can’t replace the software that all robotics engineers already use.

#3: Battery Supply Chain, controlled by China.  Robots are changing from big, stationary machines to mobile ones. This means batteries are now a crucial part of making them work. One company in China, CATL, controls 1/3 of the world’s battery market. China has a very strong grip on this supply chain.

The global map of robotics is specialized. There is a multi-polar supply chain that is difficult to disrupt:

USA: “The “Brain.” (software, autonomy, AI compute).

Japan: The “Hardware King.” (motors, gearboxes, precision engineering).

Germany: The “Precision Engineer.” ( mechanical systems, high-end production).

China: The “Scale & Power.” (manufacturing speed, massive infrastructure, battery supremacy).

Taiwan: The “Linear Specialist.” ( The linear guides and ball screws essential for motion).

Classroom discussion questions:

  1. Why must operations managers understand these costs and bottlenecks?
  2. What are the supply chain implications?

OM in the News: One Way to Power New AI Data Centers

Where is the energy to power the hundreds of new data centers that are popping up to run artificial intelligence demands coming from? “In the battle for AI dominance, every engine of the economy is getting recruited into the fight—including jet engines'” writes The Wall Street Journal (Feb. 18, 2026). 

Jet engines are a natural fit. Power equipment giants GE Vernova, Siemens Energy, and Mitsubishi Heavy Industries  already sell power turbines—known as aeroderivatives—that are modeled after these very jet engines. Aircraft engine companies such as GE Aerospace , Howmet Aerospace and Woodward also sell land-based aeroderivative turbines or components.

Yet designing the turbine, which keeps as much of the original jet engine features as possible, is a roughly 18-month undertaking.  Instead, it only takes 30 to 45 days to convert a plane’s jet engine to a power-generating turbine. (There are 2 main modifications to convert an aircraft engine to a land-based natural gas turbine. One is replacing the fuel nozzles to utilize natural gas instead of jet fuel. The other is replacing the large fan on the front of the flight engine with a much smaller fan).

Retired aircraft, at an Air Force base near Tucson, Ariz

A company can remanufacture jet-engine parts with a few years of remaining life for use in power turbines, where they can operate for many additional years. Narrow-body jet engines experience higher stress from repeated takeoffs and landings. Power turbines can run as peakers—turning on only when demand surges—or continuously as baseload. Either way, they accumulate less wear and tear.

About 1,600 commercial aircraft engines are retired every year. If a third of those engines get converted into turbines, that would represent about 13 GW of capacity, or more than a quarter of the existing global natural gas turbine capacity.

AI-obsessed tech giants are planning to spend more than $700 billion in capital expenditures this year. The lure of that cash pile will generate a lot of creativity in the power sector.

Classroom discussion questions:

  1. Why is there a need to convert jet engines?
  2. Discuss the growth of data centers and the demands they create. (See our recent post on that topic.)

OM in the News: 3 Core Skills for the AI Manufacturing Workforce

 Companies invest heavily in workforce development—global corporate training represents over a $350 billion market—but few can answer the fundamental question: Does our workforce actually possess the capabilities required for AI-era manufacturing? The problem, writes IndustryWeek (Dec. 16, 2025), is that firms are training for yesterday’s skills while tomorrow’s requirements remain undefined.

Manufacturing faces a dual disruption. AI, robotics and automation are reshaping production at unprecedented speed, while skilled labor shortages intensify when experienced workers retire, taking decades of knowledge with them. Most training programs rarely assess whether workers developed the fundamental capabilities needed to work effectively in AI-augmented environments.

There are the 3 Core Skills needed:

1. Human+ capability This isn’t about workers learning to code or becoming data scientists. Human+ is the ability to work effectively alongside AI and automation—knowing when to trust algorithmic recommendations, when to override them based on judgment and how to optimize human-machine collaboration for maximum productivity. Manufacturers invest millions in AI-powered quality control systems, predictive maintenance platforms, and autonomous production scheduling—then struggle to achieve projected ROI because their workforce lacks the core skills to extract value from these technologies.

2. Agentic AI orchestration As AI-era manufacturing evolves from simple automation to autonomous agents that manage complex workflows, workers need the capability to orchestrate multiple AI systems effectively. Agentic AI orchestration is the ability to coordinate these systems so they don’t work at cross-purposes. It means understanding how to deploy AI agents for quality control, predictive maintenance, supply chain optimization and production scheduling—and managing the interactions between these systems when they conflict or produce unexpected results.

3. Interoperability catalysis Modern manufacturing runs on complex networks: older machines next to new robots, ERP systems talking to manufacturing systems, logistics platforms feeding production plans, and partner data coming in from suppliers. Interoperability catalysis is the ability to make all of that actually work together:

  • Legacy and modern systems (the 40-year-old CNC and the AI-powered vision system)
  • Digital and physical environments (ERP and planning data vs. shop-floor reality)

The Path Forward: Manufacturing’s competitive advantage in the AI era won’t come from having the most advanced technology. It will come from having a workforce capable of extracting maximum value from that technology. These 3 core skills represent the foundation. Manufacturers who systematically assess and develop these capabilities will thrive as AI reshapes production.

Classroom discussion questions:

  1. Is the current workforce capable of managing AI-manufacturing demands?
  2. Are business students interested and willing to take these jobs?

OM in the News: Robots Are Remaking Chinese Industry

Sam Altman wants AI to cure cancer. Elon Musk says AI robots will eliminate poverty. China is focused on something more prosaic: making better washing machines. While China’s long-term AI goals are no less ambitious than ours, its near-term priority is to shore up its role as the world’s factory floor for decades to come, reports The Wall Street Journal (Nov. 25, 2025).

Midea, an appliance maker, deploys robots to work under an AI ‘factory brain’ that acts as a central nervous system for its plant in Jingzhou.

The Chinese push is fueled by billions of dollars in government and private development– transforming every step of making and exporting goods. A clothing designer reports slashing the time it takes to make a sample by more than 70% with AI. Washing machines in China’s hinterland are being churned out under the command of an AI “factory brain.”

Port shipping containers whiz about on self-driving trucks with virtually no workers in sight, while the port’s scheduling is run by AI.

Chinese executives liken the future of factories to living organisms that can increasingly think and act for themselves, moving beyond the preprogrammed tasks at traditionally-automated factories. This could further enable the spread of “dark factories,” with operations so automated that work happens around the clock with the lights dimmed.

The advances can’t come quickly enough for China as its population is shrinking, young people are avoiding factory jobs, and pushback against Chinese exports has intensified.

AI offers a lifeline to head off those risks, by helping China make and ship more stuff faster, cheaper and with fewer workers. China wants to deploy what is available today quicker than the U.S. can, locking in any advantages. It installed 295,000 industrial robots last year, 9 times as many as the U.S. and more than the rest of the world combined. Its stock of operational robots surpassed 2 million in 2024

Today, China’s average factory wages are far higher than in countries such as India. Many young Chinese are unwilling to work in factories.  The shortage of skilled labor in key manufacturing sectors could reach 30 million this year. Since most Chinese are optimistic about AI, this allows the government to deploy the technology quickly. About 83% of Chinese believe AI-powered products and services are more beneficial than harmful, double the level in the U.S.

Classroom discussion questions:

  1. Why the push for robotics and AI in China?
  2. What can the U.S. and Europe do to remain competitive?

OM in the News: Building a Humanoid Robot

Armies of humanoid robots are poised to march into the world’s factories. But before they’re ready to turn a wrench, they must solve what Elon Musk calls “the hands problem.”

Creating the mechanical equivalent of the human hand is a challenge that has been stumping researchers for years, writes The Wall Street Journal (Oct. 27, 2025) . Replacing muscle and skin with motors and sensors is a critical step in making humanoids a versatile source of labor, potentially unlocking a global market that could reach $5 trillion by 2050.

The robotic hand of the future will need many sensors to emulate a human hand. Holding a pencil, for example, would require sensors along the sides of several fingers.

Tesla’s humanoid robot—called Optimus—is good at walking, but making hands that can match a human’s has been a far tougher job. “In order to have a useful generalized robot, you do need this,” Musk said. “You do need an incredible hand.”

Boston Dynamics has equipped its Atlas humanoids with hands that have only three fingers. They can form a palm that allows the robot to lift boxes or brace itself. One digit also can rotate to serve as a thumb, letting the robot grasp objects. The humanoid can pick up auto parts, pump a dumbbell and pluck a tissue from a box. But a robotic hand must make trade-offs between strength, dexterity, slenderness and ruggedness. Increasing one attribute can diminish another.

Industrial robots have relied on pincerlike hands for decades, and are still the most cost-effective form. MicroFactory (in San Francisco) produces a $5,000 robot that has two arms, one of which typically is equipped with a tool, the other with a 2-digit gripper that holds an object in place. That setup can perform most of the functions needed for electronics assembly, such as soldering, inserting screws or peeling off protective films.

The difficulties of re-creating the human hand lead to questions about why it is being done, given that the real thing already exists in humans.  The answer: the shortage of workers for factory and caregiving jobs is driving the need for alternatives.

Classroom discussion questions:

  1. There are almost a half-million open factory jobs in the U.S. Given the tight job market, will your students be willing to take them?
  2. Why are humanoid robots so sought after?