Chapter 1 – The OM Blog by Heizer, Render, & Munson

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:

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

OM in the News: The AI Splurge and Big Tech’s Workforce

Tech companies are rushing to trade their people for more chips. “Some of those companies might come to regret the exchange,” writes The Wall Street Journal (April 27, 2026).

Microsoft (by 7%), Block (parent of Square and Cash App by 40%) and Meta (by 8,000) are just the latest major tech companies trying to scale back their workforces in the name of AI. Layoffs affecting 45,800 tech employees were just announced, making March 2026 the worst month for reported tech-job reductions in at least 2 years.

Companies are straining to portray the cuts as evidence that they are confident in an AI future in which more workers will be replaced by machines. Tech companies are shelling out as much as they can—more than their rivals, they hope—on AI chips and data centers that could put them in the lead in a race they feel they can’t afford to lose. That in turn is heightening competition over who can use AI to help do more with a lot less, freeing up money to spend on expensive chips.

Dressing up layoffs as visionary moves for the age of AI carries certain risks. Rampant layoffs hurt morale and create an exit incentive for other employees, especially talented ones with alternatives. For all of AI’s capabilities, people will be needed to figure out business models, deal with customers and, importantly, make sure AI tools are being deployed and used safely.

The layoffs also lend credence to a growing public perception that AI isn’t a panacea but a job killer. That will feed a backlash that is already constraining AI, as more communities are fighting against the construction of massive data centers.

The reduction in workforces sends two messages. First, it indicates tech companies will stop at nothing to spend on AI, something markets have often cheered. Second, it says tech companies believe they can operate fine with fewer employees, even after a couple of years of cuts that followed a Covid-era hiring spree.

Classroom discussion questions:

What are the tradeoffs in reducing tech headcounts?
What are the implications for our students and recent grads?

OM in the News: Understanding Manufacturing AI Terminology

Walk into almost any operations and supply chain meeting today and you’ll hear it:

“We should use AI for this.”
“Can we plug in an LLM?”
“Let’s add a copilot.”

Thanks to Industry Week (April 13, 2026) here is a guide to the AI vocabulary showing up in manufacturing and supply chain environments.

Machine Learning Machine learning uses historical data to detect patterns, improve predictions and support decisions. In real-world operations, that includes:

Demand forecasting
Inventory optimization
Predictive maintenance
Quality and anomaly detection

LLM (Large Language Model) LLM refers to systems that can read and generate human-like text. It processes and generates language based on patterns learned from large datasets. It shows up:

Summarizing supplier emails or RFQs
Drafting customer responses
Translating ERP data into plain language

LLMs don’t “know” a business unless connected to the firm’s data. Without that context, they can sound confident—but be wrong.

Copilots “Copilot” is one of the most overused—and misunderstood—terms. They are a layer that sits on top of a business system (ERP, CRM, email) to assist users in real time. It is useful for:

Suggesting responses inside email
Helping navigate ERP workflows
Recommending next steps

A copilot doesn’t replace a system—it improves how people interact with it.

Agents Agents move from assisting to acting. They are systems that can take a goal and execute steps to achieve it.

Examples:

Monitoring inventory
Detecting shortages
Reaching out to suppliers
Proposing or initiating reorders

Most agent-based systems are still early. They require strong guardrails and tight integration to work reliably in production environments.

Embeddings (The Quiet Connectors) Embeddings convert a company’s data into a format AI systems can understand and search. That’s what allows AI to:

Reference ERP data
Search internal documents
Provide context-aware responses

For operations students and faculty, the goal is not to become AI experts. It’s to understand the language well enough to ask better questions and identify where these tools can create real advantage.

OM in the News: Six Jobs From the 1950s That Barely Exist Today

The U.S. workforce has transformed dramatically since the 1950s, a decade marked by economic prosperity, suburban expansion, and rapid industrialization, writes History Facts. Some careers that may have seemed stable and essential then, but time, technology, and changing needs have made them and many others all but disappear. Here are 6 jobs that were popular in the 1950s but are now nearly extinct.

Telephone Switchboard Operator. Before direct-dial telephone systems took over, switchboard operators were the backbone of communication, ensuring calls reached the right destination. In the 1950s, the U.S. had about 1,342,000 telephone switchboard operators. It was a demanding job that required quick reflexes. By the 1970s, automated dialing systems phased out the need for human operators.

Milkman. Having fresh milk delivered to your doorstep was once a common part of American life. The local milkman made rounds, leaving glass bottles on doorsteps and retrieving empty ones. This service was necessary before the widespread adoption of home refrigeration. By 2005, this number had dwindled from over 50% of homes receiving delivery to just 0.4%.

Elevator Operator. In the mid-20th century, elevator operators were essential for manually controlling elevators in department stores, office buildings, and hotels. At its peak, the profession employed more than 90,000 workers. Only a few historic buildings still employ operators today, for nostalgia.

Typist. Secretarial jobs became essential during the Industrial Revolution, as businesses generated more paperwork than ever before. By 1950, secretarial work had become the most common occupation for women, with 1.7 million employed. While secretarial roles still exist today, the number of workers specifically categorized as “word processors and typists” has declined to 37,200.

Motion Picture Projectionist. Projectionists played a vital role in the moviegoing experience in the 1950s, operating and maintaining film projectors in theaters. By 2013, 92% of movie theaters had made the switch to digital projection. In 1950, 26,000 people were employed as projectionists. By 2023, that number had fallen to 2,610.

Gas Station Attendant. Full-service gas stations were the standard in the 1950s, with attendants pumping gas, checking oil levels, cleaning windshields, and inspecting tire pressure. The 1973 oil crisis, which led to soaring gas prices, accelerated the transition to self-service as both businesses and consumers sought cost-saving measures. (Today, New Jersey is the only state that prohibits drivers from pumping their own gas).

Classroom discussion questions:

What jobs that exist today do you think will be extinct in 20 years?
What new jobs have been created in this past 1/2 century?

Guest Post: Martin Guitars and Operations

Prof. Howard Weiss, retired from Temple U., illustrates his wide range of interests.

Martin is a guitar manufacturer that began operations in 1833. Martin specializes in acoustic guitars which account for about half as many guitars as electric guitars in the global guitar market. It is one of the most popular brands along with Fender, Gibson, Yamaha, Ibanez and Taylor.

Location: Martin began its operation in Manhattan. In 1839 Martin opened a plant in Nazareth PA, 90 miles due west of its NYC plant. In 1989 Martin opened a plant in Sonora, Mexico in order to make guitars that were more affordable. It is worth noting that two of Martin’s competitors, Fender and Taylor guitars also have plants in Mexico. These guitars are commonly referred to as MIM (Made in Mexico). See Ch.8.

Capacity: Martin has made over 3 million guitars since its inception, including one million since 2016. It currently produces a total of 500 guitars per day, 6 days per week, at the two plants. (See Supp. 7)

Forecasting: Clearly demand has been increasing. Martin’s forecasting needs to consider historical and causal analysis (see Ch. 4) since certain events can spike or drop the sales. For example, sales increased more than usual during the folk music craze and also when MTV was running its Unplugged series (featuring acoustic guitars). At first, COVID caused a decline in sales due to cancelled concerts and closed stores. But then there was an increase in demand, especially for beginner guitars since people were looking for activities while at home and could order guitars online.

Supply Chain: The supply chain (Ch. 11) begins in the forest and at the lumber facilities both in the U.S. and India.

Layout: Martin uses process layout–see Ch.7. Most of the work is done by hand but there are robots in the factory.

Safety: With all of the woodwork that is being performed the major safety concern is that of sawdust.

Quality Control: The incoming wood is inspected by humans because machines cannot pick up defects in the wood. Each guitar is checked for tone. The guitar gets put in a case, but then sits for 4 days and then undergoes rigorous testing to make certain the guitar parts, e.g. neck, bridge, tuning pegs, still work. (See Ch. 6).

Classroom Discussion Questions

How could Martin use the Quality Control techniques discussed in Ch. 6 of your text book?
What are some possible reasons Martin relocated from Manhattan to Nazareth, PA?

OM in the News: AI Makes a Fundamental Shift in Manufacturing

For two decades, manufacturing has been defined by a relentless pursuit of optimization. We automated assembly lines (Ch. 9), digitized records and built predictive maintenance models (Ch. 17), all in the service of marginal gains in efficiency.

While this approach yielded significant returns, we have reached the ceiling of what traditional, rule-based automation can achieve. “In 2026, the industry is undergoing a fundamental shift toward a model of agentic enablement,” says the head of Google Cloud, Praveen Rao in Industry Week (Jan.22, 2026).

Rao says this isn’t just a technical upgrade; it is a new industrial model where AI moves from a tool that recommends to an agent that achieves. It also isn’t about reducing headcount, but about transforming operators into “super-users”: who are empowered by AI to solve more complex problems and drive higher value. He makes 3 predictions:

The Agentic Supply Chain: Beyond Prediction to Execution Historically, manufacturers have been forced to lock up vast amounts of capital in finished goods, essentially “betting” on demand forecasts. Agentic AI changes this math by allowing production to align dynamically with real-time customer intent. Traditional predictive models could warn of a supplier disruption, but it still required a human to spend days rerouting logistics. In 2026, AI agents will close this loop. Systems will be empowered to detect a tier-2 supplier failure in the middle of the night.

The Rise of the Technocrat Entering the era of the Technocrat, the factory worker of the future will no longer be measured by analog tools of the past—the hammers and the screwdrivers, but by mastery of generative AI for rapid troubleshooting and agentic AI for process orchestration.

Hyper-Personalized Intelligence on the Shop Floor The AI agents of the future will possess “long-term memory,” understanding the specific context and historical preferences of every shop-floor operator. This is agentic AI acting as a personalized performance coach, delivering the right insight to the right person at the exact moment of need.

Rao concludes: “The transition from fragmented automation to integrated, agentic systems is the new industrial paradigm. The companies that fail to adopt an agent-first mindset will not just fall behind; they will find themselves competing against living factories that can think, adapt, and execute 24/7 without friction.”

Classroom discussion questions:

Explain what an AI agent does.
Give an example of personalized intelligence on the shop floor.

OM in the News: The AI Revolution and Productivity

Most speculation about AI has focused on its potential to kill jobs and on the policies that government might implement to control AI. But it’s important to remember that our only window into the future is the past. From the colossal changes wrought by the Industrial Revolution to the Digital Revolution of the last quarter-century, improvements in technology have created an array of jobs that far exceeded—in quantity and quality—the ones eliminated, elevating standards of living.

In America growth during the Industrial Revolution was of biblical proportions, writes The Wall Street Journal (Nov. 2, 2025). From 1870 to 1900 real gross domestic product tripled, the population and labor force roughly doubled, and output in manufacturing grew sixfold. Per capita income rose by 110% between 1865 and 1910, while real wages of manufacturing workers increased an estimated 173%. Life expectancy rose by a quarter as inflation-adjusted costs of food, clothing and shelter dropped by roughly 50%.

During the Digital Revolution of the last quarter-century, U.S. GDP rose by 66%. Data show the extraordinary capacity of the American economy to absorb new technology. Since 2000 on average 5 million Americans have either been laid off or quit their job every month, but the economy has created 5.1 million better-paying jobs a month. This creative destruction isn’t new. In 1810, 81% of Americans worked in agriculture; today only 1.2% do. In 1953, 32% of Americans worked in factories. As real industrial production quadrupled, the share of the labor force in manufacturing declined to 7.8% in 2025.

Almost every discussion of AI calls for extensive economic assistance for those who are displaced. Yet this impedes workers’ transition to new jobs.

American exceptionalism—in which Americans are more productive and have higher living standards—depends in part on our extraordinary ability to adjust to change. Europe makes it hard to lay people off, which constrains the ability to create jobs. In China, most industrial subsidies go to noncompetitive industries, not to the potential winners of the future.

By letting the market system develop and absorb AI technology, we can achieve a second economic miracle, which will enrich America and the world.

Classroom discussion questions:

Will AI create more jobs than it destroys?
Will overall productivity measures improve in this AI revolution?

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.

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

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:

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

Guest Post: Fashion Influencers and Revamping Costly Product Returns

Temple U. Professor Misty Blessley raises an interesting inventory issue–returns.

Fashion influencers and their followers are contributing to the increase in rising product returns. According to the National Retail Federation, returns accounted for 17% of retailers’ total 2024 sales. Online purchases have a 26% return rate compared to in-store purchases (10%). Many online shoppers intentionally buy items they plan to return. Statista reports that clothing (24%), shoes (16%), and accessories (12%) are the most returned products – the exact product footprint of fashion retailers. Several recent articles shed light on the influencer effect and tips for revamping costly product returns in retail fashion.

Fashion influencers have popularized trends that promote returns behavior:

Hauls: Influencers showcase purchased fashion items, then decide whether to keep or return them based on follower feedback.
Wardrobing: Influencers buy items for temporary use such as content creation and return them afterward.
Bracketing: Influencers buy multiple sizes or colors of a product to find the perfect fit, with the intention of returning the rest. About 58% of consumers buy multiple sizes for this reason, with 75% of returns attributed to fit.
Influencing: 56% of followers make purchases recommended by an influencer, many of which are later returned.

Returns come with significant costs, including shipping, restocking, reselling at a discount, and administrative expenses. Retailers are adopting strategies to curb or better manage returns:

Charging return fees: Brands like Zara and H&M now charge for returns.
Clarifying return policies: Shortened return windows, stricter conditions for full refunds, and more items marked as final serve to narrow return opportunities.
Improving sizing tools: Enhanced size charts, virtual reality fitting tools, and online fitting rooms help shoppers make better choices.
Implementing logistics systems: Retailers are investing in digital tools to streamline and manage returns more efficiently.

As discussed in Chapter 1 of your Heizer/Render/Munson textbook, best practice can be achieved when operations and supply chain management, marketing, and finance work together.

Classroom discussion questions:

After 89% of retailers adjusted their policies to deter returns, 59% saw return rates increase. What factors could explain why these policies fail to get the desired result?
The SCOR Model, discussed in Chapter 11, outlines attributes for processes like source, make, and deliver. How are the attributes for returns like or different from these processes?

OM in the News: U.S. Energy Independence and Manufacturing

Let’s look today at how over the past two decades the U.S. has evolved from a degree of foreign-energy dependency that threatened its economy and national security to the premier energy producer in the world. This is an especially useful fact for U.S.-based manufacturers, who consume 1/3 of the country’s available energy resources each year.

Since the start of this country’s industrial revolution in the mid-19th century, Americans took their vast energy resources for granted. That sense of security ended with the OPEC oil embargo of the 1970s. In subsequent decades, independence on overseas energy supplies rose. By 2006, U.S. energy consumption outpaced production by 29 quadrillion BTUs, and imports outpaced exports by 30 quadrillion BTUs.

That all changed with the “shale gale” of the early 2000s, writes Industry Week (Jan. 9, 2025). The shale gale was tied to the hydraulic fracking revolution, made possible by new horizontal drilling and mapping technologies. By 2016, more than half of all American oil output resulted from fracking; by 2018 the U.S. became the world’s top crude oil producer; and by 2019 it was a net total energy exporter.

Last year the U.S. produced 13.4 million barrels per day (b/d) of crude oil, twice as much as a decade ago. This easily tops Saudi Arabia’s 10.8 million b/d and Russia’s 10.7 million b/d. In other words, the U.S. (and its manufacturing base) is no longer beholden to OPEC or a geopolitical adversary for energy resources.

The U.S. is also the world’s largest producer of natural gas, surpassing Russia in 2011. In fact, the U.S. produces almost all the natural gas it consumes and is the globe’s largest exporter of LNG. About 40% of the nation’s electricity needs are met through natural gas power plants, twice as much as through coal-fired power plants. Cheap natural gas has been a tremendous boon to the American economy over the past two decades.

And despite the environmental backlash to nuclear energy over the past 40 years, the U.S. still generates the most nuclear power worldwide, producing 780,000 gigawatt hours (GWh) annually, compared to runner-up China with 400,000 GWh.

Finally, there’s the generation of renewable energy, comprising hydro, wind, biomass, solar, and geothermal sources. While China is the world leader in that energy category, generating 31% of global renewable electricity, the U.S. is runner-up with 11% of world production. Solar and wind energy are expected to lead the growth in U.S. power generation through 2026.

Classroom discussion questions:

Why is this an OM issue?
Which energy sources do you think will dominate in 5 years?

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:

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

OM in the News: The Port Strike and Automation

An economically devastating port strike was averted last week after a 3-day work stoppage. Dockworkers secured a 62% pay raise, but the central dispute was never solely about wages, even though the tentative deal that averted the strike will result in dockworkers at the NY-NJ port earning more than $500,000 a year on average.

Port automation could ease supply chains

The ocean carriers, which pay the bills at U.S. ports, can perhaps afford that level of increase. What they can’t afford, and the U.S. economy can’t either, is a ban on the future use of automated cargo-handling technology at ports along the East and Gulf coasts. “Absolute, airtight language that there will be no automation or semi-automation” remains a key demand of the Longshoremen’s union that still must be negotiated ahead of a new Jan. deadline.

Why is this such a crucial issue, and why do port managers adamantly oppose the demand? The key reason is the need to create future port capacity. Since the U.S. is building new port facilities at a snail’s pace, the only way to expand capacity is by handling more cargo more quickly through existing facilities. The only way to do that is with automated cargo handling.

The lack of automation in the U.S.—only 3 port facilities are fully automated, all on the West Coast—exposes ports as an Achilles’ heel of U.S. trade competitiveness. High costs and inefficiency have long been the status quo. Not one U.S. port ranks in the top 50 globally in productivity, reports The Wall Street Journal (Oct. 8, 2024). Charleston is the highest at No. 53. The consequence of low port productivity is that “instead of facilitating trade, the port increases the cost of imports and exports, reduces competitiveness, and inhibits economic growth,” says the World Bank.

The purpose of automation isn’t to lower costs by replacing workers with machines but to increase it within existing port footprints to accommodate growth. The fully automated Long Beach Container Terminal can handle 12,000 to 15,000 20-foot equivalent units per acre per year versus 1/2 that at a nonautomated terminal.

Classroom discussion questions:

What are the top-ranked ports in the world and how does the U.S. differ from them?
What can the U.S. do to be more competitive?

Guest Post: From Anxiety to Curiosity–The Power of Mathematical Puzzles in Your OM Class

Prof. Andrew Stapleton teaches OM at U. Wisconsin-LaCrosse

Many of us have experienced the anxiety some of our students feel whenever we teach OM techniques. I have found a very effective manner to alleviate it by beginning my lectures with Math Magic.

First, start off the semester with the Phone Number. Tell your students to: (1) Grab a calculator; (2) Key in the first three digits of their phone number (NOT the area code); (3) Multiply by 80; (4) Add 1; (5) Multiply by 250; (6) Add the last four digits of the phone number; (7) Add the last four digits of their phone number again; (8) Subtract 250; (9) Divide by 2. Recognize the number?
Here is why it works:
X = first three digits of your phone number
Y = last four digits of your phone number = [250(80x+1) + (2y-250)]/2 = [20000x + 250 +2y -250]/2 = [20000x + 2y]/2 = 1000x + y = your phone number (this trick doesn’t work if the first digit of the last four is a zero).

Hers is another one: The Rope Around the World. Imagine an un-stretchable rope wrapped completely around the Earth at the equator. Imagine the Earth is as smooth as a cue ball. Here is the question: If you lift that rope exactly one foot above the earth’s surface (ignoring gravity), going all the way around the planet, how much extra rope will you need? The answer is amazing. Students may think they need to Google the diameter of the Earth to figure this one out. Surprisingly, you don’t need to know the Earth’s diameter or radius. You only need to know the formula for the circumference of a circle, i.e., Circumference = 2πr, where the value of π is approximately 3.14 and r stands for the radius.

Answer: You realize you can plug in that extra foot into the circumference formula. When the rope was wrapped around the Earth at the surface, you just have 2πr. When you add in the extra foot, it extends the radius of the Earth by one foot, so you now have 2π(r+1). If you want to find out the difference between the lengths of the two ropes, you subtract the shorter rope on the Earth’s surface from the longer rope suspended one foot above the Earth. 2π(r+1) – 2πr or 2πr + 2π – 2πr = 2π. The two circumferences in the equation cancel out, which leaves just the 2π. Really? It’s true! The rope that is suspended a foot higher all the way around our planet only needs to be 2π or 6.28 feet longer than the rope lying flat on the Earth’s surface.

Challenges like these take help take students’ minds off anxiety they may have felt when we go over a new OM model, making them more receptive to learning a new technique.