Chapter 7 – The OM Blog by Heizer, Render, & Munson

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:

What are the advantages and disadvantages of RFID?
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:

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.
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.
Simplify and Optimize– Focus on improving only what remains after deletion. Avoid optimizing processes that shouldn’t exist.
Accelerate Cycle Time– Speed up processes only after simplification and optimization, ensuring efficiency without reinforcing unnecessary steps.
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:

What can AI do to improve a procurement system?
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:

Why is FedEx pushing for more AI agents?
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:

Why must operations managers understand these costs and bottlenecks?
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:

Why is there a need to convert jet engines?
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:

Is the current workforce capable of managing AI-manufacturing demands?
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:

Why the push for robotics and AI in China?
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:

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?
Why are humanoid robots so sought after?

OM in the News: U.S. Manufacturing Resurgence Will Be Powered by Cobots

Once a luxury reserved for big manufacturers, smaller, smarter, more flexible and less expensive “cobots”—collaborative robots—are bringing automation to every fabricator, no matter the size. The slow, fragile recovery of American goods production wouldn’t be possible without them, writes The Wall Street Journal (Oct. 11-12, 2025).

The number of U.S. companies that make physical things reached a low point in 2014 and has grown since then. Yet they are trapped in a never-ending labor shortage as skilled workers age out, and young people fail to take their place.

China has the greatest number of industrial robots, including these at a factory in Nanjing.

China has become the de facto manufacturer of the world’s goods, owing not only to its enormous population of engineers, technicians and machinists but also its 2-million-plus army of industrial robots. Now the U.S. is attempting to claw back some of those contracts—called “reshoring”—and robots can in some cases quadruple worker output.

The push to bring manufacturing back to the U.S., and the demand for industrial goods to power America’s AI-fueled economy, are driving automation adoption and innovation. “Automation is key to reshoring, plain and simple,” says one CEO.

Cobots have become radically easier to program over the past decade, and now people can use a simple tablet interface to instruct them to perform specific sequences of actions. Programming the older robots common in automotive factories since the 1960s took years of training.

Cobots are part of a broader trend in robotics: Specialized robots that use sensors to safely navigate human environments. They can cope with more variability than previous industrial robots, which had no sensing abilities. This has been essential to the rise of Amazon and its superfast fulfillment, and now it’s coming to manufacturing.

China is indisputably the leader in high-volume manufacturing, and companies that want the biggest volumes of manufactured parts for the lowest possible price continue to send work there. And though many U.S. manufacturers can’t match their Chinese peers in volume, they are competing by using automation to tackle smaller batches of goods under tight deadlines. Manufacturers in the U.S. are now asking how to reshore the making of critical parts.

Classroom discussion questions:

What is a “cobot” and how does it differ from a robot?
Why has China become such a powerful manufacturing hub?

OM Podcast #40: AI in the MBA Classroom and Beyond

We’re excited to share another Heizer/Render/Munson OM Podcast episode! Today, Barry Render sits down with David Rosenthal, a recent MBA graduate from the University of Texas at Austin, to explore how artificial intelligence is transforming business education and early career experiences.

David shares insights into AI in the classroom, AI as a central part of his internship, and his entrepreneurial journey using AI to build his app, Fantasy Fusion Sports.

Heizer Render Munson OM Podcast · OM Podcast #40: AI in the MBA Classroom and Beyond

TRANSCRIPT
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: Generative AI in the Factory

Generative AI is a type of artificial intelligence that creates new content and ideas, such as text, images or code, by learning from vast amounts of existing data. Two primary high-impact applications for generative AI have emerged, writes Industry Week (Sept. 11, 2025). The first is its revolutionary role as a new type of user interface. The second is its unprecedented ability to unlock knowledge from the vast sea of unstructured data that permeates every factory.

Perhaps the most immediate and profound impact of generative AI in industry is its function as a “generative user interface” or “Gen UI.” For decades, interacting with complex industrial software and data systems required specialized training. Engineers needed to learn specific query languages to pull data; operators had to navigate complex, menu-driven screens on a human-machine interface; maintenance staff had to know exactly where to find a specific manual in a labyrinthine document management system. The Gen UI changes everything. It provides a conversational, natural language layer that sits between the human user and complex backend systems. It radically lowers the barrier to entry for accessing critical information.

With a Gen UI, an engineer can simply ask, “Show me the pressure and temperature trends for Reactor 4 during the last production run of Product XYZ and flag any anomalies.”

The second game-changing application for GenAI is taming the document tsunami. For many enterprises, as much as 80% of their data is “unstructured”—locked away in formats that are difficult for traditional analytics to parse. Factories run on this data: PDF operating manuals, schematics, environmental compliance reports, maintenance work orders and operator logbooks. For decades, the immense knowledge trapped in these documents has been largely inaccessible at scale.

For the first time, organizations can ask complex questions across their entire document library: “Analyze all maintenance comments from the last five years for our compressor fleet and identify the most common precursor to failure.”

Generative AI is not a magic bullet, but it is a profoundly valuable addition to the Industrial AI toolbox. Its true power today is unlocked when we see it for what it is: a revolutionary interface that makes other systems easier to use.

Classroom discussion questions:

What is the difference between Gen AI and Gen UI?
Give an example of how Gen UI can be used in a factory making a product with which you are familiar.

Guest Post: What a Chinese Drone Ban Means for U.S. Farming

Dr. Misty Blessley is a professor at Temple U. She shares her insights monthly.

DJI, a Chinese company and the world’s largest manufacturer of commercial and industrial drones, faces scrutiny in the U.S. over alleged cybersecurity risks. It is now close to being banned here.

One U. S. business that sells spray-drone kits reported that its challenges began last year when importing DJI drones became significantly more difficult. This uncertainty has caused concern across industries that rely on these tools, from public safety and construction to supply chain logistics. For American agriculture specifically, a ban could cut off access to vital equipment, leaving fields unmonitored, untreated, and risking harvest losses.

DJI drones are favored by farmers as they save weeks of labor by spraying seeds, fertilizer and fungicide from the sky.

Agriculture has adopted drones more rapidly than almost any other sector. Monitoring drones help detect disease and water stress early, while spray drones enable precise application of fertilizer and pesticides during narrow weather windows. They have become crucial for reducing input costs (China is accused of subsidizing their drone industry, which might explain some of the cost differences), protecting yields, and facilitating smooth food movement through supply chains. If imports are halted, many farmers could miss critical windows, leading to lower yields and creating issues along the supply chain, from processors to consumers.

Mitigation Strategies
To prepare, farming businesses should apply lessons learned from managing recent supply chain disruptions:
Diversify suppliers – Start testing U. S. or non-Chinese alternatives, even if they are currently less cost-effective. Early adoption minimizes dependence.

Stock critical parts – As restrictions tighten, building an inventory now provides a safety buffer.

Use mixed fleets – Combine current drones with alternative technologies like ground sprayers to prevent single points of failure.

Plan operational slack – Stagger schedules or adjust operations to account for potential delays.

Collaborate and advocate – Engage with farm bureaus and trade associations to push for phased implementation, subsidies, or funding for domestic options.

Classroom discussion questions:
1• What are the challenges and drawbacks of each mitigation strategy?
2• Considering that the Chinese drone ban is likely, how should user decision-making be updated? (Refer to Module A Decision-Making Tools and consider these facts:◦ Drones can cut labor costs by up to 90% and reduce chemical use by 20–30%. ◦ A high-end U.S.-made drone can cost nearly $30,000, compared to a similar DJI unit costing $6,500).

OM in the News: The AI-Driven Assembly Line

More than a century ago, Ford’s moving assembly line reorchestrated work. Instead of a master mechanic walking to each car to perform complex tasks, the car moved to workers, who each executed a single repeatable action. As a result, work became easier, costs fell and return on investment skyrocketed.

Here are two areas in which this might unfold:

• Onboarding. In a large enterprise, bringing a new hire up to speed requires 10 to 20 hours of effort. Forms, policy explanations and training sessions add up quickly. In an AI-first model, an onboarding orchestrator bot coordinates with AI agents that handle compliance (background checks, payroll setup, training) and equipment (granting access to systems and tools). A virtual assistant answers questions from the new hire.

• Software delivery. Today, new information-technology features—such as launching a new process control program—are specified by operations managers, designed by user-experience teams, coded by developers and tested before use. This cycle can take 80 to 85 business days, with error rates around 10% to 15%.

In an AI-first setting, a digital IT orchestrator bot coordinates AI agents that do everything from product specifications and design to coding and testing, delivering the first version. People then review the newly created features and test their AI-generated code and design. Instead of arriving in 3 months, features are ready in weeks. Time to delivery falls by 60% to 70%, while code quality improves through continuous AI-driven testing.

The common threads of AI-native work are clear: Orchestration moves from humans to AI. Specialized AI bots handle repeatable tasks. Human experts intervene when judgment, negotiation or oversight is required. Results come faster, with lower unit costs and better customer experience.

Henry Ford pioneered a new way of doing business in the 1900s. We have a similar opportunity in 2025. While Ford’s assembly line turned employees into specialists, putting AI at the center of business turns us all into generalists, allowing us to be creative, enlist problem-solving skills and handle ambiguous work.

Classroom discussion questions:

How else might AI be used in factories?
What is an AI “agent”?

Guest Post: Merging OM Tradition with Digital Innovation

Dr J. Prince Vijai is Assistant Professor of Operations Management at IBS Hyderabad, in India.

The transition from traditional OM to digital operations is not a replacement but an evolution. Digital tools enhance the classical OM framework by adding intelligence, speed and adaptability.

1. Process Optimization and Automation In classical OM, process optimization involved detailed mapping and iterative improvements. With digital operations, AI can now identify inefficiencies, simulate improvements and automate decision-making without human intervention. Siemens has integrated sensors, cloud platforms and AI to create a digital thread across product design, manufacturing and logistics resulting in a 20% reduction in production time and a 30% reduction in energy consumption.

2. Inventory and Supply Chain Management Traditional inventory models rely on forecasts and safety stock assumptions. Digital operations use real-time data from IoT sensors and machine learning to predict demand, monitor inventory levels and automate replenishment. For instance, Walmart uses AI and IoT to streamline its vast supply chain, reducing stockouts and improving shelf availability.

3. Forecasting and Scheduling Operations managers have long used statistical tools for forecasting. Digital operations use advanced analytics and machine learning to provide more accurate, dynamic forecasts. Real-time analytics enables organizations to quickly adapt to market changes, weather disruptions or supply chain breakdowns.

4. Quality Management Traditional quality management emphasizes inspection and control charts. Digital quality management integrates data from machines, sensors and customer feedback for continuous, real-time quality assurance. Predictive maintenance, enabled by digital twins and IoT, reduces downtime and improves asset reliability. For example, GE developed digital twins to monitor the performance of jet engines in real time, enabling predictive maintenance and reducing unexpected failures.

The shift to digital operations is not without challenges. Employees accustomed to traditional processes may resist adopting new technologies. Data from different departments or legacy systems can be siloed, limiting visibility and coordination. Implementing AI, IoT and automation involves significant expenses. And digital operations increase exposure to cyber risks.

Future trends include:

Hyperautomation that combines AI and machine learning to automate increasingly complex tasks.
Cognitive operations that use AI not just to automate but to learn and adapt continuously.
Edge computing that enables data processing closer to the source (e.g., in factories or stores) for faster insights.
Green operations that leverage digital tools to track carbon footprints and support sustainable practices.

Embracing the synergy between OM and digital operations is a strategic imperative for long-term success.