Chapter 1 – Page 3 – The OM Blog by Heizer, Render, & Munson

OM in the News: Manufacturing Jobs are Looking Very Different

Digital transformation and Industry 4.0 are changing manufacturing, but the fact is that skilled operations talent is increasingly harder to come by. With projections that 2.4 million manufacturing jobs will be unfilled by 2028, the question becomes: What talent and skills do companies need in order to succeed in the factory of the future? Industry Week (Aug. 25, 2021) looks at four manufacturing jobs and how they are expected to evolve.

Production Planners will shift from managing shop floor issues to more proactive roles in which they analyze data insights, manage exceptions and identify opportunities for continuous improvement. They will move from using manual processes for monitoring inventory to using predictive analytics and “digital twins” (virtual representation of a part or a process) to create optimized production schedules and proactively manage inventory issues. And they will need skills in lean and six sigma, data analysis and visualization.

Industrial Engineers will increasingly use digital twins and other cyber-physical systems, in addition to other methods of automation, to create greater connectivity between manufacturing processes and shop floor operations. They will need skills in the areas of design for manufacturability, data science, python and R programing languages, co-bots, IoT sensors, digital twins and wearables.

Machine Operators. Today’s operators tend to specialize in one machine or product line and rely on personal judgment in overseeing machines and processes, leaving room for human error. In the future, operators will use digital twins and AI to proactively identify and solve issues. They will be trained as generalists who can work across machines and product lines.

Quality Analysts. Today’s quality experts are often making changes to standards in reaction to customer complaints, bad yields, or defective products. In the future, they will be able to monitor processes in real time, predict quality issues before they occur, and quickly trace and diagnose any issues through the use of digital twins, advanced analytics and the ability to embed intelligence quality controls. This will require an understanding of big data, data science, and machine learning.

But beyond the clear need for a much higher level of digital acumen, there is also a critical need for human skills that machines cannot replicate such as conceptual thinking, decision-making, problem-solving, and innovation.

Classroom discussion questions:

How many of your students will consider manufacturing jobs? Why?
Explain the concept of a “digital twin.”

Good OM Reading: Jeff Immelt’s “Hot Seat”

Why would Jeff Immelt write a book about his troubled tenure as GE’s CEO from 2001 to 2017? “My tenure had ended badly,” he acknowledges on page 1 of Hot Seat. “My legacy was, at best, controversial.”

Less than a week into Immelt’s tenure, replacing the famous Jack Welch, the 9/11 terrorist attacks shook the nation, and the company, to its core. GE was connected to nearly every part of the tragedy—GE-financed planes powered by GE-manufactured engines had just destroyed real estate that was insured by GE-issued policies. Immelt would lead GE through many more dire moments, from the 2008–09 Global Financial Crisis to the 2011 meltdown of Fukushima’s GE-designed nuclear reactors. He set out to make GE more global, more rooted in technology, and more diverse. But GE struggled. “It became clear right away that my main role would be Person to Blame,” he says.

In Hot Seat, Immelt offers a candid interrogation of his tenure. The most crucial component of leadership, he writes, is the willingness to make decisions. But knowing what to do is a thousand times easier than knowing when to do it. Perseverance, combined with clear communication, can ensure progress, if not perfection, he says.

Immelt rose through the ranks during a largely “tranquil” time when China was a sleeping giant and the U.S. economy expanded at a reliably impressive rate. The world he inherited as CEO, however, was “raucous, volatile and unpredictable,” full of bursting bubbles (dot-com, housing, power), disruptive rivals and increased scrutiny.

Hot Seat provides interesting OM insights and is worth the read. Here is one quote: “It may sound weird to say it, but I am certain that I would never have become GE’s CEO if I hadn’t first learned to fix refrigerators. In 1989, however, when GE moved me to Louisville to become head of customer service for GE Appliances, I wasn’t so sure it was a promotion. GE’s refrigerators had begun failing at an alarming rate. The problem, we soon figured out, was faulty compressors, which have to work harder when the weather gets hot. So the hotter it got, the more our compressors failed. First, we heard from customers in Puerto Rico. Then Florida. After a little investigation, we determined that every single compressor inside 3.3 million refrigerators was going to fail, one by one, in a wave that would roll across the country from the warmest spots to the coolest. We figured refrigerators in Maine would be the last to give out, but their time would come. Each repair would cost us $210—more than half what customers had paid in the first place. It was a disaster.”

Many critics would say Immelt’s entire tenure was a disaster as well.

OM in the News: Made in America–Again

Looking to 2021 and beyond, there is more reason for hope in U.S. manufacturing than at any time since the 1990s, reports The Wall Street Journal (Dec 17, 2020). Three major themes are gaining traction that will carry manufacturing to new prosperity: a quick recovery from the recession; localization of supply chains (onshoring); and technological advancements that level the playing field between the U.S. and low-cost countries.

U.S. manufacturing lost its lead some time ago. Lack of sustained investment, noncompetitive labor rates and degrading infrastructure opened the door for low-cost countries, notably China, to take the lead as manufacturers shifted production overseas. The end result was an industrial sector that leaked jobs and fell behind in technology. So why a turnaround?

N95 face masks being made at a GM plant in Mich. The auto maker started processing face masks in response to the pandemic.

First, the industry is poised to emerge from the Covid-19 recession much more quickly and robustly than it usually does from downturns. Data are overwhelmingly supportive of an industrial economy on the mend. With low interest rates and rising order books, manufacturers are boosting investments in both factories and new products.

Second, pre-pandemic, there was already a rising concern around supply-chain risks. Companies that a decade ago felt comfortable as suppliers consolidated and centralized—often solely in China—began to lose faith in globalization and made plans to onshore. Firms began to see that shipping intermediary products halfway around the world and often back again was no longer productive. These concerns were elevated to near panic as supply chains shut down in the early days of Covid. High-profile shortages, such as the lack of PPE, served as a broader wake-up call for the localization of supply-chains.

Third, automation and other technologies are almost at the point where the U.S. can produce the same quantity of product with half of the employees that would be needed in a similar factory in China. And advancements are accelerating. With progress in data analytics, low cost cloud computing and AI, the American factory is evolving into a new age.

Classroom discussion questions:

Chapter 1 in your Heizer/Render/Munson OM text describes 3 productivity variables. How does each apply in this article?
What other current problems have encouraged the localization of supply chains?

OM in the News: Eight Drivers for Manufacturing’s Next 50 Years

In the last several decades, we’ve seen major disruptions to the manufacturing environment. We experienced the “China Price,” which prompted offshoring of manufacturing operations, nearly decimating U.S. manufacturing. More recently we’ve seen the trend toward personalized products, resulting in smaller lot sizes, thus straining traditional economies of scale production. And the “Amazon Effect” of rapid turnaround in orders and delivery times of 2 days or less continues to challenge the longer lead times typical in manufacturing.

“What might manufacturing look like in 2030, or 2070?” asks Industry Week (Feb, 10, 2020). In the future we will still have large-volume, low-mix operations that will continue to harvest the advantages of economies of scale production. However, the competitive dynamics of manufacturing will change for a large portion of the traditional manufacturing world. Industry Week sees 8 drivers to the future:

1. Quality will still be Job 1, but how we achieve it will change. With sensors everywhere, critical operational variables will be exposed.

2. Economies of scale will coexist with economies of one production. 3D printing/additive manufacturing technologies will have matured and will be cost competitive.

3. Because of 3D printing, production will be more closely tied to either the location of these raw materials or the location of the customer.

4. Automation will continue to replace repetitive tasks, and the costs of robots and their control systems will decline to a point where even smaller manufacturers can take advantage of them.

5. Products will be made through naturalistic design and their materials will be functionally graded to combine materials in new ways.

6. Humans and digital tools will not only coexist; they will be tightly integrated through AI. Wearables and exoskeleton supports will increase human performance and improve safety.

7. Strategic partners will collaborate to create end-to-end solutions that manufacturers can deploy with limited tweaking.

8. Manufacturing operations will be guided by a unified architecture that links the edge (asset) to the cloud.

Classroom discussion questions:

What changes do you think will take place in manufacturing in the next decade?
Where can 3D printing play a role in change?

OM in the News: Apple Believes in Squeezing Its Supply Chain

To understand Apple’s evolving place in the tech world, consider that one of its most important executives today is a guy whose job is badgering suppliers to get costs down. Tony Blevins, VP-Procurement, will stop at little to get a favorable deal. He has paraded manufacturers past competitors in Apple’s lobby and spurned a UPS contract by sending it back to UPS executives through FedEx. He persuaded subcontractors not to pay a chip maker that Apple was in litigation with, depriving the chip company of $8 billion.

The supply chain was always a critical piece of the Apple formula—alongside, if duller than, its magic designs, writes The Wall Street Journal (Jan. 24, 2020). CEO Tim Cook built the supplier network and instilled rigorous frugality in it as he did so. Today the supply chain looms larger than ever at Apple. Slowing iPhone sales, combined with the increasing cost of new features, make the job of hammering down expenses critical.

The result is a company less identified with visionary leaders and more of an operations juggernaut with rich profit margins it intends to keep. Blevins enforces manufacturing deadlines that help the company fill orders on time around the world. He manages semiconductor suppliers, making him the bearer of bad news if Apple sets out to replace their chips with an in-house product. Under Blevins, Apple pays Intel $10 per modem chip, roughly 50% less than Samsung pays Qualcomm. Blevins even rotates staff members every few years to keep them from developing supplier relationships that might dilute their focus on saving Apple money.

Apple believes that saving 10% on the cost of parts could boost profits more quickly than selling more computers, exactly the point we make in the text on page 7 in Table 1.1, “Options for Increasing Contribution.”

Classroom discussion questions:

Why is Blevins so important to Apple?
Explain the implications of Table 1.1.

OM in the News: Factories Demand White-Collar Education for Blue-Collar Work

An engineer creates 3-D blueprints to program machines that manufacture customer orders at a parts manufacturer

New manufacturing jobs that require more advanced skills are driving up the education level of factory workers who in past generations could get by without higher education, writes The Wall Street Journal (Dec. 10, 2019). American manufacturers are, for the first time, on track to employ more college graduates than workers with a high-school education or less, part of a shift toward automation that has increased factory output, opened the door to more women and reduced prospects for lower-skilled workers. “You used to do stuff by hand,” said a U. of Chicago prof. “Now, we need workers who can manage the machines.”

U.S. manufacturers have added more than a million jobs since the recession. Over the same time, they employed fewer people with at most a high-school diploma. Employment in manufacturing jobs that require the most complex problem-solving skills, such as industrial engineers, grew 10% between 2012 and 2018; jobs requiring the least declined 3%. (More than 40% of manufacturing workers have a college degree, up from 22% in 1991).

Improvements in manufacturing have made American factories more productive than ever and, despite recent job growth, require 1/3 fewer workers than the nearly 20 million employed in 1979, the industry’s labor peak. The workers that remain do much more cognitively demanding jobs. At Caterpillar, over 80% of job openings require or prefer a college degree. A majority of the company’s production jobs called for a degree or specialized skill.

At Harley-Davidson’s engine plant in Milwaukee, robotic arms now ferry motorcycle pieces, taking over the tough, repetitive work formerly done by employees. The machines have made the workplace safer, mirroring a national trend. In 2018, factory workers were hurt at half the rate as in 2003.

Classroom discussion questions:

How do these changes impact productivity, as discussed in Chapter 1 of your Heizer/Render/Munson text?
How many students in your class are looking for jobs in manufacturing?

OM in the News: Fiat Chrysler to Open New Factory in Detroit

Fiat Chrysler plans to open a new vehicle factory in Detroit, the first new U.S. assembly plant to be built by a major domestic car maker in at least a decade, reports The Wall Street Journal (Dec. 7, 2018). The Italian-American auto maker plans to make an SUV at the new factory as part of its efforts to expand its iconic Jeep brand. The firm will roll out more truck and SUV models as it responds to a sharp consumer shift away from passenger cars.

The move comes as GM has upset lawmakers about its plans to end production at assembly plants in Ohio and Michigan. President Trump and other elected officials also have blasted GM for the cuts, which will result in up to 6,700 factory layoffs and 8,100 salaried workers layoffs.

The factories GM plans to idle are making slow-selling sedan lines. Fiat Chrysler, which eliminated nearly all of its car lines a year ago, has much less empty factory space in the U.S. Many of its truck and SUV plants operate around the clock making popular models like the Jeep Wrangler and Cherokee, and its Ram truck. Fiat Chrysler has continued to max out its existing factory space. The auto maker’s plant utilization—a measure of its output versus its production limit—is 92%. That is far higher than the 81% rate at Ford and 72% at GM.

Assembly plants are typically major employers, hiring several thousand workers on multiple shifts, and can take up to two years to construct and cost about $1 billion.

Classroom discussion questions:

What major trends are taking place in the auto industry?
Why is plant utilization important?

Teaching Tip: Talking to Students About Manufacturing

Our OM students hold many misconceptions about the manufacturing industry. There’s a widespread belief that the U.S. manufacturing industry is in decline, that jobs are going overseas, and that the industry doesn’t provide fulfilling or well-paying careers, particularly for younger workers. But this couldn’t be farther from reality.

The Manufacturing Institute says that nearly 3.5 million manufacturing jobs will need to be filled in the next decade, and 2 million of those jobs will go unfilled (Industry Week, Oct. 23, 2018). The available jobs, even at the lower rungs of manufacturing, pay well, too. According to the Bureau of Labor Statistics, the average annual salary for manufacturing production jobs is $44,595 ($21.44 per hour). In truth, the average pay for the manufacturing industry is comparable with jobs in the technology sector. For example, the average base pay for a manufacturing supervisor is $64,118, for a manufacturing engineer $71,679, and for a director of manufacturing, $146,412. That’s significantly more than what most students expect when they think about compensation in the manufacturing industry.

There’s also a perception that manufacturing jobs are repetitive, monotonous, underpaid, and involve working in decrepit, dirty factories. But the industry has evolved and is more dynamic and complex than it used to be. There’s more technology, more data, more analysis, more creativity, more gamification, more critical thinking, and more problem solving.

Our students often don’t view manufacturing as a desirable career option, and that poses a big problem. The growth of industry depends on worker participation of all demographics. Hopefully, our OM course will show that there are real opportunities for them, from professional growth to dynamic learning environments to competitive compensation. While the service sector remains a big part of the U.S. economy, manufacturing also contributes mightily and isn’t going away anytime in the foreseeable future.

OM in the News: Seven Jobs Robots Will Create

People are needed to oversee the work of machines to make sure they’re doing their jobs properly.

As machines get smarter, will millions of people will be left obsolete and jobless? Yes, jobs will be lost, and many people will be forced to learn new skills. But “AI opens up opportunities for many new jobs to be created—some that we can’t even envision now,” writes The Wall Street Journal (April 30, 2018). McKinsey predicts that artificial intelligence and automation could add 20-50 million jobs globally by 2030. Here is a look at 7 of them:

AI Builders There will be a greater need for people who can develop the underlying systems that make AI work. Other fields will need people with knowledge of how to integrate their work with AI.

Customer-Robot Liaisons Companies that make AI applications use “customer success managers” to help ease clients into working with the systems, answering complaints and making adjustments. This is currently among the most sought-after jobs on ZipRecruiter.

Robot Managers Even though AI can be amazingly smart at some jobs, its judgment can be very limited compared with a person’s. Hence the job someone who oversees the work of machines to make sure they’re doing their jobs properly, and intervenes if the AI asks for help in a tricky situation.

Data Labelers For AI to properly understand the world, it needs humans to explain what things are—meaning, the data that the AI absorbs need to be labeled. That could mean identifying objects in images.

Drone-Performance Artists Drones are starting to work their way into the arts, where they act as dynamic light installations and flying props. And there is a growing need for artists who can customize those drones.

AI Lab Scientists Experts are needed to teach AIs about the life sciences or chemistry so that computers can surface novel ideas. Technicians, who test the results that AI comes up with to see which are valid and which aren’t, are also needed to help make machines smarter.

Safety and Test Drivers Most self-driving vehicles aren’t fully capable of working on their own just yet—and that means opportunities for people who help the vehicles do their jobs safely.

Classroom discussion questions:

1.Which of these jobs will fall under the purview of operations managers?

2. What kind of jobs will be lost in the AI revolution?

OM in the News: Productivity and “Digitization”

Lufthansa tests humanoid robot “Josie Pepper” at airport in Munich last week.

The next wave of productivity growth will be driven by digitization, writes The Wall Street Journal (Feb. 23, 2018). The diffusion of new technologies into everyday use holds promise for bringing back the kinds of annual 2% productivity growth seen in the past, but digitization is still at an early stage in many industries. Looking at the past half-century, the time from commercial availability of new technologies to 90% adoption ranges from about 8 to 28 years.

It’s a matter of some urgency. U.S. worker productivity grew below its long-run average for the 7th straight year in 2017, advancing a meager 1.2% from 2016. In Chapter 1, we note that labor productivity—real economic output divided by the numbers of hours worked—is key for economies to grow, improve living standards and keep inflation in check.

The waning of the 1990s productivity boom and the aftereffects of the financial crisis dragged down productivity growth by 1.9% on average across western countries since the mid-2000s. The retail sector is one of the laggards on digitization, along with agriculture, construction, hospitality, health care, government and education. Industries at the forefront of digitization include technology, media, and professional and financial services.

The retail industry is in the throes of technological disruption, with bricks-and-mortar retailers facing steep competition from e-commerce sales. Yet only 9 cents out of every dollar spent on retail is spent online, suggesting there is still enormous room for digitization.

Classroom discussion questions:

Why is productivity an important OM issue?
What is meant by digitization, and how can it impact productivity?

OM in the News: Machines are Making Your Sushi, and That’s Good

In the belly of a machine about the size of an office printer, a plastic roller presses sticky rice onto a bed of seaweed. The oxygen-to-grain ratio has been precisely calibrated with the aid of X-ray tests. A razor slices the sheet into a flawless rectangle, which plunks down onto a steel tray ready to be stuffed with ruby-red tuna or smoked eel.

The $14,000 robot can help a food prep worker churn out 200 sushi rolls an hour—up from the 50 or so a chef could make by hand, according to its maker, Autec USA. Autec says orders have quadrupled over 5 years amid rising sushi consumption and a growing chef shortage. Among its customers is Whole Foods.

Service industries have lagged other sectors in spending on labor-saving equipment during this economic expansion, because as long as workers were plentiful and wages stagnant, it made more sense to hire than to invest in automation. Services make up a growing share of the U.S. economy—64% of gross output last year, up from 40% in the 1950s—but their share of capital expenditures has been relatively flat over time.

“Now, many businesses are going beyond replacing old equipment,” writes Businessweek (Dec.25, 2017). They’re also pouring money into new technology, along with buildings and production equipment. For the first time since 2000, service sector investment in intellectual-property products (think software and R&D) has surpassed 4% of GDP.

Autec expects demand for its robots to stay strong. Its most popular machines are an example of the kind of automation that can make life easier for workers—rolling out rice over sheets of nori is one of the most difficult, and tedious, parts of sushi making—rather than make them obsolete. Customization—inserting different kinds of filling—still requires humans.

Classroom discussion questions:
1. Why did service sector investment lag behind the industrial sector?

2. Why is efficiency improvement important in the service sector?

Video Tip: How Baseballs Are Made

Despite its uncomplicated appearance, the baseball is in fact a precision-made object, and one that has often been the subject of heated controversy throughout its history.

An official Major League baseball consists of a round cushioned cork center called a “pill,” wrapped tightly in windings of wool and polyester/cotton yarn, and covered by stitched cowhide. Approximately 600,000 baseballs are used by all Major League teams combined during the course of a season. The average baseball remains in play for only 5-7 pitches in a Major League game. Each ball must weigh between 5 and 5.25 ounces and measure between 9 and 9.25 inches in circumference to conform to Major League standards. Your students will enjoy this 5 minute video showing the manufacturing process.

Such uniformity was nonexistent in the early years of baseball’s history, when balls were either homemade or produced on a custom-order basis as a sideline by cobblers, tanners and other small business owners. In 1872, the modern standard for the baseball’s weight and size was established. The production of balls became more consistent during the remainder of the decade, thanks largely to the demands made on manufacturers by the newly formed National League, the first professional baseball league.

At the turn of the century, the baseball had a round rubber core. This gave way in 1910 to the livelier cork-centered ball, which was itself replaced two decades later by the even more resilient cushioned cork model. The baseball has undergone only one significant change since that time, when a shortage in the supply of horses in 1974 prompted a switch from horsehide to cowhide covers.

OM in the News: The U.S. Productivity Picture–Good or Bad?

“Perhaps 2018 will be the year productivity finally begins to pick up,” writes The Wall Street Journal (Dec.12, 2017). Technologies such as speech recognition, online chatbots and machine learning are being quickly adopted, capital spending is up, and tight labor markets give companies an incentive to find better ways of working. But productivity defies forecasters, who have wrongly predicted an uptick in productivity for over a decade. The real story is how little anyone really understands about what moves productivity, even though as we write in Chapter 1: “Only through increases in productivity can the standard of living improve.”

The basics are in Equation (1-1): Labor productivity is real economic output divided by the numbers of hours worked. How many gingerbread lattes can each Starbucks barista churn out per hour? Give them a better machine or better training and the productivity rises. Economists say it is years of weak corporate investment, a dire education system, an aging workforce, and a shift from high-productivity manufacturing to low-productivity service sector that have made productivity worse.

The first half of the 1990s had a “productivity paradox” of technological change being highly visible, but not showing up in the economic data. Just as with the past decade’s development of smartphones, apps, financial technology and machine learning, it took time for laptops and PCs to increase output. It happened suddenly, with productivity leaping 2.5% in 1996 and growing that fast on average over the next decade.

So a big problem for forecasters is that technological change comes in unpredictable waves. In the long run productivity is all about innovation. But productivity did leap 3% in the 3rd quarter of this year, and while quarterly data are volatile, it is plausible that a productivity pickup is coming soon. A lesson many economists take from the past 10 years is that productivity has permanently slowed. Perhaps a better lesson is just that it is hard to forecast.

Classroom discussion questions:
1. Why is the productivity rate important to ordinary people around the world?

2. Why is productivity important to operations managers?

Good OM Reading: Faster, Higher, Farther–The Volkswagen Scandal

Two years ago, Volkswagen proudly reached its goal of surpassing Toyota as the world’s largest automaker. But in Fall 2015, the EPA disclosed that VW had installed software in 11 million cars that deceived emissions-testing mechanisms. By early 2017, VW had settled with U.S. regulators and car owners for $22 billion, with additional lawsuits still looming. In Faster, Higher, Farther, New York Times reporter Jack Ewing details the conspiracy. He describes VW’s rise from “the people’s car” during the Nazi era to one of Germany’s most prestigious and important global brands, touted for being “green.” The first half of the book is the story of VW, the legendary creation of the Beetle by the Nazis, and the car’s role as a counterculture icon during the 1960s.

Ewing then portrays VW chairman Ferdinand Piëch and CEO Martin Winterkorn. The author argues that the corporate culture they fostered drove employees, working feverishly in pursuit of impossible sales targets, to illegal methods. Within a year of taking over, Winterkorn had announced a plan for VW to attain “world domination.” His diesel fuel and a “clean diesel” marketing campaign became vital components of this strategy. Although diesel leads to fuel efficiency, it also leads to high toxic emissions. Unable to build cars that could meet emissions standards honestly, engineers were left with no choice but to cheat. VW then compounded the fraud by spending millions marketing this clean diesel.

In 2013, the lie was first exposed by a handful of student researchers on a shoestring budget at West Virginia University who tested the fuel emissions of a diesel Passat, a diesel Jetta and a diesel BMW. The vehicles passed EPA standards when tested in a controlled lab-setting. But when the cars were tested in a non-lab setting, the Passat and Jetta exhibited nitrogen oxide emissions that were off the charts. As we know, this led eventually to the guilty plea to criminal charges in a landmark Department of Justice case.

In dealing with ethics of OM, here is a global company whose deceit half destroyed it–and the story is not finished.

Guest Post: Productivity, Forecasting and Excel with Real Data

Our Guest Post today comes from Howard Weiss, who is Professor of Operations Management at Temple University. Howard has developed both POM for Windows and Excel OM for our text.

I often search the web for real data that I can use for forecasting, and just came across data from Lowes 10 – Year Financial Information report that can be used for both productivity and forecasting.

The report has 5 sections, with 2 that are of major interest to OM. The 1st is titled “Stores and People” and lists the productivity inputs and outputs of: (1)Number of stores; (2)Selling square feet; (3)Number of employees; (4)Total customer transactions; (5)Average ticket.

The next section includes the net sales. I have the students perform several exercises using these data. Here are 5 years of past data.

The Exercises

Exercise 1 – Data integrity: For each of the years the net sales should be equal to the anticipated net sales (my definition) given by the average ticket multiplied by the number of transactions. Of course, the anticipated and reported net sales are not exactly equal. I ask the students to compute the percentage difference between the reported net sales and the anticipated net sales and also to determine the MAPE differences.

Exercise 2 – Productivity: For each year, there are 3 productivity measures that can be computed comparing net sales to number of stores, selling square feet and number of employees. Unfortunately, there are no multipliers available to compute the multifactor productivity measures for the 10 years.

Exercise 3 – Productivity change: For all years except the first, I ask the students to compute the productivity change for each of the 3 productivity measures. There is one small issue the students need to recognize. The data is given as most recent first.

Exercise 4 –Graph in Excel: I ask the students to graph the 3 sets of productivity measures. If the students create scatter graphs using the dates in row 3 and the productivity measures that they create in a row below the data then the graph will be fine. If the students create a line graph using only the computed productivity measures then the graph will run backwards. That is, the time axis will be backwards. This is important for the final exercise.

Exercise 5 – Regression/Trend Line – I ask the students to draw a regression/trend line for each of the three measures. I have shown my students that right-clicking on the graph is the easiest way to create the line. I also ask the students to find the three average productivity changes based on the slope of the line in each of the three graphs.

The students very much appreciate applying Productivity to real data, using data that has more than 2 periods and having the opportunity to work in Excel, especially with the graphing capability and regression capability within the graph option.