Tag: AI

Teaching Tip: Teaching OM in an AI Age

We know our students need to think critically in an AI age to be productive and engaged future employees. One solution, writes Faculty Focus (July 9, 2025), to the triple challenge of fostering critical thinking, meaningful learning, and academic integrity is to double down on transparency. We can emphasize the why we want responsible AI use: why we want students to use their own cognitive abilities for some tasks, why using AI could be helpful at times, and why we’ve crafted AI-integrated assignments in the ways we have.

Here are five steps to update assignments in the AI age:

Step 1: Take a critical look at your current syllabus. If AI can easily complete a task (try running your instructions through ChatGPT to find out), maybe it’s no longer a relevant measure of authentic learning. Add new instructional practices (like modelling AI use) and new components of the assignments you update or keep.

Step 2: Consider whether and how students should use AI on the assignments. Students want to know exactly what is appropriate for AI use in your class. A helpful tool for this process is the 5-level AI Assessment Scale (AIAS). The levels range from No AI, AI Planning, AI Collaboration, Full AI, and AI Exploration. Each one identifies and sanctions different ways students can use AI in appropriate and meaningful ways to support their learning.

Step 3: Discuss and model your expectations. Students are not sure what is acceptable in this current moment. What better way to help them feel confident while developing the AI skills they need than modelling what you’re looking for? Take class time or record a video for your online class to teach your students what you expect them to do with AI for each assignment, what not to do, and what you’ll be looking for in their finished product.

Step 4: Ask students to disclose their AI use. One approach is to use the AI Disclosure (AID) framework to document how students used AI, or add an appendix to each assignment, or add comments or footnotes to make transparent what they wrote and what AI wrote. 

Step 5: If you suspect inappropriate use of AI, don’t accuse students of cheating. Instead, have a conversation with them. A primary goal of the AIAS is to facilitate discussions about AI use.

As I pointed out in a recent blog, our author team can help. We have developed AI exercises for each chapter of the new 15th edition.

OM in the News: Amazon Is on the Cusp of Using More Robots Than Humans

The automation of Amazon facilities is approaching a new milestone: There will soon be as many robots as humans. The e-commerce giant, which has spent years automating tasks previously done by humans in its facilities, has deployed more than one million robots in those workplaces, reports The Wall Street Journal (July 1, 2025). That is the most it has ever had and near the count of human workers at the facilities.

Mobile robots reposition package carts

Company warehouses buzz with metallic arms plucking items from shelves and wheeled droids that motor around the floors ferrying the goods for packaging. In other corners, automated systems help sort the items, which other robots assist in packaging for shipment.

One of Amazon’s newer robots, called Vulcan, has a sense of touch that enables it to pick items from numerous shelves. Amazon has taken recent steps to connect its robots to its order-fulfillment processes, so the machines can work in tandem with each other and with humans. Now some 75% of Amazon’s global deliveries are assisted in some way by robotics. The growing automation has helped Amazon improve productivity, while easing pressure on the company to solve problems such as heavy staff turnover at its fulfillment centers.

For some Amazon workers, the increasing automation has meant replacing menial, repetitive work lifting, pulling and sorting with more skilled assignments managing the machines. Amazon has trained more than 700,000 workers across the world for higher-paying jobs in mechatronics and robotics apprenticeships.

The number of packages that Amazon ships itself per employee each year has also steadily increased in the past decade to 3,870 from 175, an indication of the company’s productivity gains.

Amazon is also rolling out artificial intelligence in its warehouses to improve inventory placement, demand forecasting, and the efficiency of its robots. Amazon said it will cut the size of its total workforce in the next several years.

Classroom discussion questions:

  1. Research Amazon’s history of using robotics.
  2. What are the advantages of introducing more robots?

Teaching Tip: The 15th Edition Ties AI Into Your OM Class

Prof. Jon Jackson

Our new 15th edition, just released, brings the topic of artificial intelligence into the course with AI in Action boxes and new material throughout the text. But we have gone a step further through our Instructor’s Resource Manual, a fantastic teaching tool. If you are new at teaching the course, you will find this 400 page guide an invaluable resource. Each chapter now contains an AI in the Classroom section, created by Prof. Jon Jackson at Providence College, providing 15-20 minute exercises. Here is a sampling from 3 early chapters:

Chapter 1
All firms, regardless of industry, can use productivity measures to track process performance. This exercise is designed to explore relevant productivity measures for different industries with the help of an AI-powered chatbot. Student groups can explore the following types of
facilities/firms (each group will pick one):
 Manufacturing facility  Market research firm
 Warehouse facility  Accounting firm
 Retail store  Financial services firm
 Restaurant  HR department
Students can use the following AI prompt structure: ROLE: I am a manager in a [insert facility/firm here]. GOAL: I want to measure productivity (an output divided by an input). REQUEST: generate 5 measures of productivity.
In groups, students can compare AI responses, evaluate the validity of the productivity measures (connecting to Chapter 1 definitions), and identify the best productivity measures to implement.

Chapter 3
A work breakdown structure (WBS) can provide a hierarchical description of a project into more and more detailed components. This activity is designed to practice this process for fictional projects around campus with the help of an AI-powered chatbot. Student groups can explore the following projects (each group will pick one):
 College graduation party  Student art exhibition
 Charity 5K event  Entrepreneur shark tank
 Intramural sports tournament
Students can use the following AI prompt structure: ROLE: I am the project manager for an upcoming [insert event here]. GOAL: I want to create a work breakdown structure to break the project into more manageable components. REQUEST: generate a work breakdown structure with 4 main tasks, each with 2 subtasks. For each subtask, also provide a short description and an estimated duration to complete the subtask.
In groups, students can compare AI responses, identify if any main tasks are missing (or unnecessarily included), and evaluate the accuracy of duration estimates.

Chapter 4
AI-powered chatbots can be helpful to enhance our understanding of confusing topics, but it isn’t guaranteed to provide accurate information. This in-class activity (15-20 minutes) is designed to get us in the habit of being critical of AI output, and if necessary, re-prompting the AI-powered chatbot to give a better answer. Student groups will try to answer the following questions with the help of the AI-powered chatbot:
 When does a 2-period weighted moving average equal the Naïve approach?
 When does the exponential smoothing method equal the Naïve approach?
 When is it best to use MAD vs. MAPE?
In groups, students can critically assess the accuracy of the AI responses (referencing material in Chapter 4) and identify more effective ways to prompt AI-powered chatbots.

For a desk copy of the 15th edition, please click on this link.

OM in the News: A.I. and Computer Programming Productivity

Since at least the industrial revolution, workers have worried that machines would replace them, writes The New York Times (June 8, 2025). But when technology transformed auto-making, meatpacking and even secretarial work, the response typically wasn’t to slash jobs and reduce the number of workers. It was to break them into simpler tasks to be performed over and over at a rapid clip. Small shops of skilled mechanics gave way to hundreds of workers spread across an assembly line. The personal secretary gave way to pools of typists and data-entry clerks.

Workers complained of speed-up, work intensification, and work degradation. Now this appears to be happening with A.I. in one of the fields where it has been most widely adopted: coding.

As A.I. spreads through the labor force, many white-collar workers have expressed concern that it would lead to mass unemployment. But the more immediate downside for software engineers appears to be a change in the quality of their work. It is becoming more routine, less thoughtful and, crucially, much faster pace.

Like assembly lines of old that we discuss in Chapter 1, A.I. can increase productivity. Microsoft found that programmers’ use of an A.I. coding assistant called Copilot, which proposes snippets of code that they can accept or reject, increased output more than 25%. Amazon’s CEO wrote that generative A.I. was yielding big returns for companies that use it for “productivity and cost avoidance.”
Shopify, a company that helps entrepreneurs build e-commerce websites, announced that “A.I. usage is now a baseline expectation” and that the company would “add A.I. usage questions” to performance reviews.

The shift has not been all negative for workers. At Amazon and other companies,  A.I. can relieve employees of tedious tasks and enable them to perform more interesting work. Amazon says it saved “the equivalent of 4,500 developer-years” by using A.I. to do the thankless work of upgrading old software. Many Amazon engineers use an A.I. assistant that suggests lines of code. But the company has more recently rolled out A.I. tools that can generate large portions of a program on its own. One engineer called the tools “scarily good.”

Classroom discussion questions:

  1. How can A.I. transform factory jobs?
  2. Professors’ jobs?

 

 

OM in the News: Locating an AI Data Center Means Huge Power Needs

Meta Platforms just scooped up 2,700 acres of Louisiana farmland for what would be its largest-ever data center, built over flat rice fields in one of the poorest corners of the state.  At 4 million square feet, or 70 football fields, Meta’s data center will cost $10 billion and sit on more acreage than L.S.U. in Baton Rouge, which has more than 34,000 students. CEO Zuckerberg says the site will be used to train future versions of Meta’s open source AI models and be “so large it would cover a significant part of Manhattan.”

Building advanced artificial-intelligence systems will take city-sized amounts of power, which has turbocharged electricity demand projections for the first time this century, reports The Wall Street Journal (March 31, 2025). 

operations management and artificial intelligence and AI and location
Construction at the site of Meta’s new data center in Holly Ridge, La

Tech companies are pressing into unexpected parts of the country, far from traditional data-center markets such as Northern Virginia. They are hunting for huge swaths of flat land with access to natural gas and transmission lines, landing them on the doorstep of oil-and-gas country. To meet the voracious power needs of the project and other growth, Entergy Power intends to spend $3.2 billion to build three natural gas-fired power plants, tapping the state’s vast gas reserves.

In tiny Holly Ridge, La., hundreds of pieces of construction equipment are rolling past, with 5,000 construction workers on the way. Meta will bring money, jobs and local tax revenue. But the project also threatens to burden electricity customers across much of Louisiana with higher costs if demand from the tech giant eventually dries up.

L.S.U. estimates Meta could use 15% of Louisiana’s current electricity generation. That is worrisome to other utility customers largely because of the mismatch between the 40- 50 year lifespan of gas-fired power plants and Entergy’s 15-year deal with Meta.

Meta’s permanent jobs—around 500—are fewer than the thousands that might have accompanied an auto factory. For a region with a median household income of $53,000, the impact will be meaningful, though. Average salaries at Meta are projected at $82,000.

As we discuss in Chapter 8, Location Strategies, states often must offer financial incentives to draw major new employers. To woo Meta, Louisiana approved a sales-tax exemption for data-center equipment and helped procure more land from local farmers.

Classroom discussion questions:

  1. Are the incentives offered Meta unusual or risky?
  2. Why are data centers and their current technologies controversial?

OM in the News: AI Will Soon Take Your Order at Taco Bell

operations management blog artificial intelligenceYum Brands, owner of Taco Bell, Pizza Hut, and KFC is partnering with Nvidia to build a range of new AI-driven services in its restaurants. The first—AI-powered voice-ordering at the drive-through lane and on the phone—was built using tools from Nvidia, and will begin rolling out at 500 Yum Brand restaurants this year.

The ultimate goal is to move all orders through digital channels instead of human order-takers, an effort Yum says will boost sales. Yum will also use AI to enhance a number of the company’s internal operations.

Other planned changes include the use of computer vision to spot fumbled orders and AI that filters internet chatter on the restaurants for useful feedback for their managers. “Yum and other quick-service-restaurant chains, like McDonald’s, have been leaning into more digital experiments for efficiency gains and improved customer satisfaction as inflation squeezes low-income diners,” writes The Wall Street Journal (March 19, 2025). 

Voice-ordering has been a priority for Yum for some time as it works to receive 100% of its orders through digital channels rather than through humans. Currently it is above 50% including orders that come through its app or online, up from 19% in 2019. Consumers end up spending more when they buy via digital channels because the restaurant can upsell, personalize and entice eaters through notifications.

Yum is currently evaluating whether existing CCTV cameras can provide images sharp enough for computer vision to determine whether the food received is what was ordered or whether it’s missing any ingredients. “Order accuracy is a big problem that a lot of quick-service restaurant companies face,” says the Chief Technology Officer.

In-restaurant workers won’t disappear. Instead, they will focus more on customer service, for instance, helping people with orders.

Classroom discussion questions:

  1. How else can AI be used in this industry?
  2. What are some disadvantages of depending on AI?

OM in the News: McDonald’s Gives Its Restaurants an AI Makeover

McDonald’s is giving its 43,000 restaurants a technology makeover, starting with internet-connected kitchen equipment, artificial intelligence-enabled drive-throughs and AI-powered tools for managers, reports The Wall Street Journal (March 5, 2025).

McDonald’s is introducing new technology in part to drive better experiences for its crews. “Our restaurants, frankly, can be very stressful,” said the CIO.

The goal? To drive better experiences for its customers and workers who today contend with issues ranging from broken machines to wrong orders. To accomplish that, McDonald’s tapped Google Cloud to bring more computing power to each of its restaurants—giving them the ability to process and analyze data on-site. The setup, known as edge computing, can be a faster, cheaper option than sending data to the cloud, especially in more far-flung locations with less reliable cloud connections.

McDonald’s is also exploring the use of computer vision (see Chapter 7), the form of AI behind facial recognition, in store-mounted cameras to determine whether orders are accurate before they’re handed to customers.

Additionally, the ability to tap edge computing will power voice AI at the drive-through. Edge computing will also help its restaurant managers oversee their in-store operations by creating a “generative AI virtual manager,” which handles administrative tasks such as shift scheduling on managers’ behalf.

AI will be able to help McDonald’s tailor its promotions and offers by using customer data such as prior purchasing history, and even linking it with weather data. “A customer who we know loves our sweet treats could get an offer through the app for a McFlurry on a hot summer day,” said the firm’s CIO.

Despite its first-mover advantage, McDonald’s will still face challenges including cost and the difficulty of rolling out the same technology across franchises and corporate-owned locations. But, compared with some of its quick-service restaurant peers, McDonald’s has been relatively aggressive at investing in new digital technologies. That, combined with the vast amount of data it has collected on its customers, gives the fast-food giant a leg up on figuring out how to improve customer loyalty.

Classroom discussion questions:

  1. What is “edge computing” and why is it a powerful tool for OM?
  2. Summarize the technology makeover being undertaken. Why is the firm going down this expensive path?

OM in the News: Humanoid Robots Finally Get Real Jobs

Science fiction has long been full of robots that look, move and even think like we do. In the real world humanoid forms have, until very recently, been a nonstarter. Hard to build, expensive, slow and lumbering, they have never made sense compared with the countless other varieties of purpose-built—and vastly more affordable—robots that have multiplied rapidly in the past decade.

That’s changing. As global demand for new kinds of robots has shot up, mass manufacturing and falling costs for components are making them cheaper to produce, writes The Wall Street Journal (Feb. 27, 2025). Just as important, new kinds of AI—some close kin to the kind that has upended the priorities of tech companies and governments since the debut of ChatGPT—are animating robot bodies in ways that simply weren’t possible even a few years ago.

More than a dozen startups worldwide are now offering humanoid robots. All have grand projections of a science-fiction future of limitless human assistance from our mechanical serfs; several already have their bots undergoing testing in real-world factories and warehouses.

A key advantage that makers tout: Unlike most current automation, humanoid robots can do more than one thing. “Humanoid robots are the first category of robots that can be doing completely different tasks based on the needs of the business or the time of the shift,” says one industry exec.

Some believe that just as chatbots are soon to attain a level of ability that could allow them to perform tasks with little or no human supervision, robots will be next. Already, the latest wave of artificial-intelligence tech—both hardware and software—is enabling robots, and in particular humanoid robots, to behave in ways that were beyond the state of the art.

Classroom discussion questions:

  1. Why is the field of  humanoid robotics advancing quickly now?
  2. How are robots typically used in manufacturing?

OM Podcast #31: The Impact of AI on Jobs and on the Environment

In our latest podcast, Barry Render interviews Charlie Render, President of Render Analytics, which helps businesses of all kinds implement AI.  Charlie is also the creator of the popular job-search engine, Apply Genie (ApplyGenie.ai). In this episode, Barry and Charlie discuss the impact of AI on the environment and on jobs.

 

 

Transcript

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

 

Charlie Render

Have you subscribed to this podcast on Apple podcasts? Just go to your Apple podcasts app, search “Heizer Render OM Podcast,” and subscribe to get all

Prof. Barry Render
Prof. Barry Render

our podcasts on your mobile device as soon as they come out!

Instructors, assignable auto-graded exercises using this podcast are available in MyLab OM. See our earlier blog post with a recording of author and user Chuck Munson to learn how to find these, or contact your Pearson rep to learn more! https://www.pearson.com/en-us/help-and-support/contact-us/find-a-rep.html

OM in the News: AI and Warehouse Supply Chain Disruptions

The ability to react quickly to supply chain disruptions is critical, and companies are under increasing pressure to predict and prevent them before they occur. Instead of managing reactively, firms are turning to artificial intelligence (AI) and predictive analytics to revolutionize operations, writes Material Handling & Logistics (Feb. 13, 2025). AI provides the tools and insights to anticipate disruptions and optimize processes in real-time.

By analyzing vast amounts of operational data, AI can identify patterns and trends that may indicate potential bottlenecks. This allows companies to foresee bottleneck issues such as labor shortages, equipment breakdowns, or delayed shipments before they occur, giving them time to adjust and implement preventive strategies.

At the heart of this proactive approach is predictive analytics, our topic in Module G. Predictive analytics uses historical data, machine learning algorithms and statistical models to forecast future events and behaviors. For example, if a shortage is predicted, the system can recommend adjusting staffing levels or reallocating resources to avoid delays. Similarly, predictive analytics can predict when certain equipment may require maintenance or inventory levels are likely to drop below critical thresholds, allowing a business to take preventive actions and avoid disruptions.

Bottlenecks are among the most significant threats to warehouse efficiency. These disruptions can lead to delays, increased costs and missed deadlines, impacting customer satisfaction and profitability. Predictive analytics allows businesses to foresee bottlenecks before they become critical. For example, suppose analytics indicate that a certain shipping lane will be delayed due to increased demand or reduced capacity. In that case, a warehouse can reroute goods to avoid congestion.

To summarize, there are four  key advantages of using AI in warehouse operations: (1) Improved Resource Allocation, (2) Increased Labor Efficiency, (3) Reduced Downtime and Delays, and  (4) Enhanced Decision-Making.

With real-time data and forward-looking forecasts, operations managers can make better, more informed decisions about handling day-to-day operations and long-term strategies. This leads to better outcomes and improved performance across the entire supply chain.

Classroom discussion questions:

  1. How can AI be used to improve warehouse operations?
  2. What is the difference between descriptive analytics and predictive analytics? (See Module G of your Heizer/Render/Munson text)

OM in the News:  Additive Manufacturing–Faster and More Versatile

Additive manufacturing (AM) is changing every year, writes Industry Week (Jan. 24, 2025). Design and manufacturing will take advantage of not only new 3D printers in 2025, but also new materials and software that all together drive new ways to use additive.

Speed and its benefits: 3D printers are getting a lot faster. The latest iterations can be up to 5 times faster than their predecessor printers. When you can make a prototype five times faster than before, you don’t just do the old processes faster; you use new processes – like agile process (see Chapter 3), for example. Hardware can become more like software, with multiple iterations a day not out of the question.

Mass customization: AI and machine learning will play a larger role in enabling truly personalized products at scale, optimizing designs based on customer preferences and real-time feedback. This will lead to faster and more cost-effective production of personalized items, from medical implants to consumer goods, allowing even small-scale manufacturers to compete with traditional mass production.

Distributed manufacturing: Additive will enable production to move closer to the end customer, reducing lead times and environmental impact. Digital databases will replace physical inventory, and manufacturing will happen on demand.

Hybrid processes: The integration of additive with traditional manufacturing techniques can integrate data feedback loops for enhanced precision in multi-material and complex geometry production. This hybrid approach will be used to create high-performance, lightweight parts for industries like aerospace and automotive, expanding the use of additive manufacturing for critical, high-precision applications.

Classroom discussion questions:

  1. How would you define additive manufacturing?
  2. Provide an example of a product made with this technique. (Hint: see Chapter 5 of your Heizer/Render/Munson text)

OM in the News: AI Is Becoming Every Product Designer’s Companion

Artificial intelligence tools like ChatGPT and Microsoft Copilot have already proven their value in many product designers’ daily design work, asking questions about design decisions and giving advice on design and CAD strategies. (See Chapter 5 in your Heizer/Render/Munson OM text). But AI is moving beyond being a tool to becoming an active collaborator, reports Industry Week (Jan. 24, 2025). There are a huge number of exciting AI applications in design:

Generative design as a standard: AI-driven generative design tools are becoming more popular, producing optimized solutions that human designers might never consider. These tools will seamlessly handle constraints like material properties, manufacturability and sustainability.

AI-Powered decision support: Product designers are increasingly relying on AI to analyze massive datasets, predict performance outcomes and recommend design improvements. AI-powered digital engineering tools that accelerate physics simulation through “simulation surrogates” are helping designers make faster, smarter decisions throughout the product lifecycle. AI models trained on numerical analysis can run simulations up to 1,000 times faster.

Human-AI collaboration: The best results come from teams that embrace a symbiotic relationship between AI and human creativity. AI can help human designers answer questions by doing the legwork and analysis on documents and information.  AI will provide expert advice on how to use other computing tools like computer-aided design (CAD), product data management (PDM), and simulation, much like expert human designers provide advice to their colleagues. The latest conceptual industrial design tools take simple sketches and generate rendered design concepts based on text prompts, such as “orange and black controller, Nintendo-like.” Meanwhile, AI assistants for computer-aided manufacturing (CAM) software–see Chapter 7– can quickly produce machining strategies, reducing time spent on CAM programming and making manufacturing processes more efficient.

Customer-centric design: AI is also playing a significant role in aligning products with customer preferences. The latest AI product development solutions mine product reviews and other customer feedback data to synthesize design directions tailored to consumer tastes or specific attributes like performance. This insight can help teams create products that better meet market demands.

Classroom discussion questions:

  1. How is AI revolutionizing product design?
  2. Are there disadvantages in becoming AI-dependent?

Good OM Reading: Top 10 Supply Chain Trends for 2025

In its new report, Top 10 Supply Chain Trends, the Association for Supply Chain Management states the supply chain landscape will continue to evolve at an unprecedented pace. To be competitive, companies will consider technological advancements and innovation, geopolitical shifts, and evolving consumer expectations.

Here are the top ten trends:

  1. Artificial Intelligence. AI will be employed for better decision-making, optimized transportation routes, prediction of demand fluctuations and automated quality control inspections. Smart robots work alongside humans to perform packaging and assembly, while automation tools such as computer vision systems identify product defects.
  2. Global Trade Dynamics and Geopolitical Policies. Supply chain organizations will prioritize diversification and contingency planning to address challenges related to global trade dynamics and geopolitics. These companies will spread supply sources across multiple regions and develop backup plans.
  3. Big Data and Advanced Analytics. Tapping into vast amounts of supply chain data, businesses will improve inventory management, supply chain visibility, forecasting of demand and production, transportation and logistics processes, and decision-making. Big data and analytics will also enable better predictive maintenance, digital twin modeling and AI-powered insights.
  4. Cybersecurity. Supply chains will prioritize cybersecurity to protect sensitive data and critical operations.
  5. Agility and Resilience. Organizations will prioritize agility and resilience to adapt to rapidly changing market conditions by implementing flexible manufacturing systems and advanced technologies including robotics and AI. Real-time visibility, diversified supplier bases and robust contingency plans will further enhance their resilience.
  6. Visibility and Traceability. By implementing real-time tracking systems, tapping into IOT-enabled devices and leveraging blockchain technology, companies will better monitor the movement of goods, identify potential disruptions and improve supply chain efficiency.
  7. Digital Integration and Connectivity. To improve efficiency, transparency and resilience, supply chains will implement the latest technologies — particularly AI, robotics and automation, cloud computing, and the IOT, making it possible to streamline operations and  reduce costs.
  8. Strategic Sourcing and Supplier Management. Advanced analytics and AI-powered tools will help identify and assess potential risks, such as geopolitical events and natural disasters. By tracking and analyzing key metrics, organizations will be able to select suppliers that align with sustainability goals.
  9. Workforce Evolution. By upskilling and reskilling employees, businesses will ensure their workforces are equipped to handle the demands of an increasingly automated and digital supply chain.
  10. Risk Management. By mapping networks, evaluating suppliers, forecasting demand and simulating scenarios, organizations will be able to handle potential disruptions.

OM in the News: AI and the U.S. Productivity Boost

Worker productivity is regarded as one of the most important drivers of long-term economic performance. As we point out in Chapter 1 of our text, it is essentially just the total output of the economy divided by the number of hours worked, aided by investments in technology and capital. When productivity is booming, it allows the economy to expand faster without triggering inflation, writes The Wall Street Journal (Dec. 26, 2024). That has positive knock-on effects on all kinds of things, including the fiscal health of the federal government.

Total nonfarm business sector labor productivity increased 2.0% from a year earlier in the third quarter—the fifth straight quarter of growth at or above 2%. That is significant as the average rate of growth for the five years before the pandemic was 1.6%.

Jeff Schulze, at ClearBridge Investments, argues this productivity jump is thanks to some unique features of the postpandemic labor market. People have switched jobs, locations and even industries at a high rate, meaning workers are now better matched to their roles. “When you look on the horizon with all this investment in AI, it’s not hard to get too excited about a productivity boom that will move us up to 2.5% or even 3%,” he states.

To see what a big difference faster productivity could make if it is sustained, consider U.S. long-term debt projections based on estimates of total factor productivity (which takes into account the productivity of both labor and capital). The government sees federal debt held by the public rising from 99% of gross domestic product in 2024 to 116% in 2034. This assumes total factor productivity growth of just 1.1% per year. Raising this estimated productivity growth by half a percentage point would mean the debt-to-GDP ratio reaches a more manageable 108% of GDP by 2034.

Optimists argue that the U.S. could do much better. Yardeni Research is an advocate of a “roaring ’20s” scenario, which sees rapid growth this decade, driven in part by an AI productivity boom. Yardeni believes this could reach 3.5% in the second half of this decade. These past booms each had their own drivers: The interstate highway buildout and rapid suburbanization of the 1950s, mainframe computers and jet engines in the 1960s and, of course, PCs and the internet in the 1990s.

Classroom discussion questions:

  1. What are the 3 main drivers of productivity, according to the text?
  2. Why does productivity impact the national debt?

OM in the News: Navigating Supply Chain Disruptions

“Historically, supply chain teams react to crises only after they have already begun,” writes Material Handling & Logistics (Dec. 19, 2024). A crisis starts and the team goes into fire-fighting mode. After the situation is remedied, teams return to business as usual, only to await the next crisis. Balancing strategic imperatives with solving these short-term crises is the key to effective supply management.

For years, supply chain professionals have been forced to play defense, constantly reacting to minimize disruptions as they arise. This approach not only diminishes employee productivity by forcing them to constantly switch between projects and contexts, but it also undermines the perception of the function’s strategic importance.

The Panama Canal is no stranger to challenges and complexities brought about by natural disasters, geopolitical tensions, or technical failures

But new technology is transforming the way supply chains are managed. Instead of addressing problems as they arise, procurement professionals can identify opportunities for strategic value early and often, developing proactive response plans for dealing with predictable disruption events. While the specific timing and severity of disruption events like hurricanes, port closures, labor strikes, or country shutdowns are difficult or perhaps even impossible to predict, there are a finite number of event types each year that can disrupt supply chains, and thus a finite number of response plans that can assure resilient continuity of supply.

With the advent of new predictive procurement tools like those we discuss in Module G (Applying Analytics to Big Data), supply planners and purchasing teams now have the capacity to reduce the chaos of unexpected disruptions.  AI-driven tools are now helping to streamline and automate labor-intensive tasks, allowing procurement teams to quickly identify alternative suppliers and manage spot-market opportunities when unexpected challenges arise. By analyzing data trends, such as historical supplier performance metrics and environmental factors, these predictive procurement systems enable businesses to make more informed decisions proactively.

Identifying alternative sources of supply within a company’s existing supplier base is key, since qualifying new suppliers can be time-consuming, and expanding the total number of suppliers may introduce unnecessary complexity. Also,  securing carriers with secondary capacity is equally important, as logistical challenges often arise when transport routes are disrupted.

Classroom discussion questions:

  1. What tools do AI provide supply chain planners?
  2. What canal issues have companies faced the past two years, and how have they dealt with them?