artificial intelligence – The OM Blog by Heizer, Render, & Munson

OM Podcast #35: A Look at Procurement and AI in Global Supply Chains

Hope everyone is having a great end of their semester! In our latest podcast, Barry Render interviews Chris Calabretta, founder of Silk Road Supply Chain Advisors, which specializes in transforming supply chain operations and procurement and purchasing functions. In this podcast Barry and Chris will be discussing how procurement in global supply chains has been totally refocused since Covid, especially in the area of biologics.

Heizer Render Munson OM Podcast · OM Podcast #35: Procurement in Global Supply Chains

Transcript

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

Have you subscribed to this podcast on Apple podcasts? Just go to your Apple podcasts app, search “Heizer Render Munson OM Podcast,” and subscribe to get all our podcasts on your mobile device as soon as they come out!

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 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.

Heizer Render OM Podcast · OM Podcast #31: The Impact of AI

Transcript

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

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

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

Guest Post: How Will Artificial Intelligence Impact ERP Systems?

Katie Decker is Marketing Manager at Account Mate, a California software firm with over 150,000 clients. She regularly shares her ERP expertise with our readers.

The integration of AI into Enterprise Resource Planning (ERP) systems (the topic of Ch. 14 in your Heizer/Render/Munson text) may revolutionize how businesses manage their operations. There is a lot of buzz around how AI will impact all businesses, and ERP software is not exempt. AI can transform ERP systems from mere transactional platforms to intelligent systems capable of predictive analytics, process automation, and enhanced decision-making.

Benefits of AI-Enhanced ERP Systems

Increased Efficiency: Automation of routine tasks and processes reduces manual effort, speeds up operations, and increases overall efficiency.
Cost Savings: AI-driven optimizations lead to cost savings in various areas, including inventory management, supply chain operations, and customer service.
Better Decision-Making: Enhanced analytics and predictive capabilities provide more accurate and timely information, enabling better decision-making.
Improved Customer Satisfaction: AI-powered customer service tools and personalized experiences lead to higher customer satisfaction and loyalty.
Scalability: AI-enhanced ERP systems can scale easily to handle growing data volumes and business complexity, making them suitable for businesses of all sizes.

Challenges and Considerations

Data Quality: The effectiveness of AI depends on the quality of data. Businesses must ensure their data is accurate, clean, and well-organized.
Integration: Integrating AI with existing ERP systems can be complex and may require significant changes to infrastructure and processes.
Change Management: Implementing AI requires changes in workflows and employee roles. Effective change management and training are essential for successful adoption.
Security and Privacy: AI systems handle sensitive data, making robust security measures and compliance with data privacy regulations crucial.

AI is poised to have a profound impact on ERP systems, transforming them into intelligent platforms that can predict, automate, and optimize various business processes. By leveraging AI, businesses can achieve greater efficiency, cost savings, and enhanced decision-making. However, successful implementation requires careful planning, quality data, and a focus on change management.

OM in the News: Using AI to Design New Cars

Researchers have found that machine learning and artificial intelligence (AI) can significantly reduce cost and time in product design (the topic of Chapter 5), not only in the actual generative design of the product, but also in the predictive analysis of whether consumers will be attracted to certain designs.

Toyota is using AI to design better cars faster

“It’s well understood in the automotive industry that aesthetics are critically important to market acceptance. An improved aesthetic design has demonstrated that it can boost sales 30% or more,” says a Yale U. prof (see INFORMS.org Dec. 11. 2023). “That’s why automakers are known to invest over $1 billion in the design of a single model.”

The current auto design process relies on the conventional human development of designs and prototypes, along with in-person testing of possible designs with actual consumers. These consumer evaluations feature what is called the A/B testing of alternative designs in laboratory test markets. The industry calls them “theme clinics,” in which hundreds of targeted consumers are recruited and brought to a central location to evaluate aesthetic designs. Consumers are asked to rate the designs based on established benchmarks, such as scales for “sporty,” “appealing,” “innovative” and “luxurious,” among other characteristics.

Auto makers invest more than $100,000 per theme clinic for one new vehicle design. Because there are multiple aesthetic designs per vehicle, and more than 100 vehicles in its product line, General Motors alone, for example, spends tens of millions of dollars just on theme clinics.

Researchers found ways to augment the traditional product development process with machine learning tools that address both the generation of the design itself, and the testing of possible consumer acceptance or rejection of the design. They developed a generative model that creates new product designs and allows designers a tool to morph potential designs more efficiently and effectively. Their predictive model helps identify those designs with high aesthetic scores. They created their models using data from an auto firm, using images of 203 SUVs that were evaluated by targeted consumers, and 180,000 high-quality unrated images.

With advancements in machine learning algorithms and computer vision technology, AI is also capable of predicting safety risks on roads by analyzing data from sensors attached to vehicles.

Classroom discussion questions:

What does A/B testing mean?
Why is AI a valuable tool in design of cars–and other consumer products?

Guest Post: AI Scores Big on Operations Exam, How About Your Students?

Our Guest Post today comes from Dr. Misty Blessley, who is Associate Professor of Statistics, Operations, and Data Science at Temple University

Astonishment best describes my initial reaction to hearing about ChatGPT, but this was mostly due to the reaction of all those people who asked me what I thought about ChatGPT in the month of December, 2022. “What do you think about ChatGPT?!!!”, I was asked, and this was not by people in our academic circle. Fresh off of the Annual DSI Conference and Thanksgiving, I was waist deep in all things December (i.e., wrapping up the semester, holidays, family, and etc.), but I could not shake the astonishment in everyone’s voices. Curious, I looked into what OpenAI, a San Francisco based firm with connections to Elon Musk and Microsoft Corporation, who launched the ChatGPT chatbot on November 30, 2022, had generally accomplished for the masses.

This month, ChatGPT hit close to home when I learned that it passed Wharton Professor Christian Terwiesch’s final exam in his MBA level operations management class. To be clear, “An AI bot passed this Wharton professor’s exam,” writes the Philadelphia Inquirer (January 25, 2023). As mirrored by an OM colleague, my astonishment turned to excitement and terror. Digging into Terwiesch’s article (cited below), ChatGPT “has shown a remarkable ability to automate some of the skills of highly compensated knowledge workers”. However, it has also failed to handle some complex problems, made mistakes in simple math and benefitted by having a human to prompt after a failure.

Open AI states that: “Our mission is to ensure that artificial general intelligence benefits all of humanity.” This chatbot can be used in many academic disciplines. A Wharton faculty member in innovation and entrepreneurship requires its use (see NPR, January 26, 2023). Let those of us in operations use it as a teaching tool, such that OpenAI’s mission is accomplished.

Here is the link to read the full article: Christian Terwiesch, “Would Chat GPT3 Get a Wharton MBA? A Prediction Based on Its Performance in the Operations Management Course”, Mack Institute for Innovation Management at the Wharton School, University of Pennsylvania, 2023

Classroom discussion questions:
1. How can students use ChatGPT to score big on operations exams taken on their own?
2. Given that ChatGPT benefits by having a human to provide hints after failing to solve a problem, what nuances do humans bring to the table that may be difficult to incorporate into a chatbot?

OM in the News: AI and Amazon’s Army of Workers

Amazon uses software to manage in a way that’s unlike almost any other company, reports The Wall Street Journal (Feb.6-7, 2021). Whether they’re driving a delivery van or picking items from shelves, Amazon’s employees are monitored, evaluated, rewarded and even flagged for reprimand or coaching by software.

Amazon is expanding automated capabilities, including fleets of robots in warehouses.

Executives at the company are emphatic about their desire to preserve the health of employees, and give them opportunities to grow and develop, but the way Amazon manages both employees and seller-partners with algorithms is often at odds with those values.

Throughout the supply chain of Amazon’s e-commerce operation, humans are onboarded rapidly into jobs that require almost no training. This is possible because of how directed and constrained by algorithms and automation these roles have become. In some fulfillment centers, employees who pick items for orders from robot shelves are surveilled by AI-enabled cameras. A cloud-connected scanning gun monitors the rate at which they pick items, the number and duration of their breaks and whether they’re grabbing the right items and putting them in the right places. Managers step in only if software reports a problem, such as a worker falling behind.

Amazon objected to the characterization that anyone in its facilities is “managed by algorithm,” because all associates have a human manager who is responsible for them and who coaches them. “Our front-line workers are the heart and soul of Amazon,” said an exec. “Only a small percentage of associates are fired or leave the company because of performance issues.”

Whether all this AI, software and automation will be used to ease the burden of its employees, or to force them to work harder to keep up, is a choice all companies face in the age of digitization, and none more so than Amazon.

Classroom discussion questions:

What are the advantages and disadvantages of being an Amazon warehouse worker?
How does Amazon’s approach differ from the four labor standards methods discussed in Chapter 10 of your Heizer/Render/Munson text? (See pages 420-429)

OM in the News: The Intelligent Tire

Goodyear’s intelligent tire uses a sensor, machine-learning algorithms and cloud computing.

The tire, once the most basic of automobile parts, is getting a tech upgrade. Goodyear Tire & Rubber is developing a so-called intelligent tire outfitted with a sensor and proprietary machine-learning algorithms.

The hope is that the tires will help self-driving cars brake at a shorter distance and communicate with autonomous driving systems, reports The Wall Street Journal (March 20, 2020). “We see the tire playing a more important role than ever,” said Goodyear’s CEO. “With the onset of autonomous vehicles, the role of the tire in the performance and safety of the vehicle would increase if we can make that tire intelligent.” (Researchers estimate that 10% to 30% of all vehicles will be fully self driving by 2030).

Goodyear already sells tires that can measure temperature and pressure. The company’s “intelligent” tires have a more advanced sensor to track dozens more measurements such as tire wear, inflation and road-surface conditions. The data is tracked continuously, sent to the cloud and analyzed in real time. The goal is for a self-driving vehicle to adjust and respond to the measurements instantaneously.

For example, if the tire senses that the car is driving over a slick road in cold temperatures, the vehicle will be able to automatically slow down and avoid sudden steering movements, while factoring in the tire’s tread and wear. Experiments showed that self-driving vehicles using Goodyear’s intelligent tires can shorten the stopping distance lost by wear-and-tear on a tire by about 30%. Goodyear’s new technology is expected to be used by consumers by 2021.

Classroom discussion questions:

Referring to Chapter 2 in your Heizer/Render/Munson OM text, how does Goodyear plan to achieve competitive advantage?
What external factors might slow the introduction and success of this new tire?

OM in the News: Artificial Intelligence in the Next Decade

As a new decade approaches and firms move from artificial intelligence (AI) experimentation to implementation, new issues arise. How companies understand and apply this technology will play a pivotal role in how they accelerate efficiencies and growth in the next few years. Analytics News (Dec. 17, 2019) provides these 5 predictions for AI, machine learning and data analytics for 2020:

1.The move to “Transformation-as-a-Service.” Many large corporations realize they need to transform AI and machine learning operations and processes, but they can’t achieve this with speed and meaningful impact. The answer is Transformation-as-a-Service.

2. Customer experience is the main battle ground in digital. There will be two types of companies – those who do customer experience (CX) well and those who go out of business: the True North for digital transformation in 2020.

3. Human in the loop – the increasing value of judgment and reskilling. Humans will play a critical role in the last mile of AI and data analytics. While machines predict and analyze, humans are needed for their judgment, empathy and creative problem-solving. In 2020, the value of data decreasing while the value of human judgment increases.

4. The ethical governance of data, AI and digital. The rise of digital ethics officers, who will be responsible for implementing ethical frameworks to make decisions. This includes security, bias, intended use and built-in governance.

5. Increased modularity in the form of accelerators. Implementing AI is not enough; companies must expedite AI adoption through pretrained experts, or “accelerators.” How accelerators democratize AI will have huge implications given the prediction that by 2025 organizations that are AI leaders will be 10 times more efficient and hold twice the market share.

Classroom discussion questions:

Why is AI an important operations tool?
What is the role of the data analyst?

OM in the News: Will A.I. Take Over Project Management?

Managing a project well takes more than just making a great plan in advance and sticking to it. Interdependencies within a project and external changes make outcomes unpredictable. Estimates and many forecasts are at best intuition; at worst, guesses and handwaving. By 2030, 80% of the work of today’s project management (PM) discipline will be eliminated as artificial intelligence (AI) takes on traditional PM functions such as data collection, tracking and reporting, according to a new report by Gartner, Inc. “AI is going to revolutionize how program and portfolio management leaders leverage technology to support their business goals,” says Gartner’s VP.

Providers in today’s project software market, such as Microsoft Project, Primavera, and Trello, are behind in enabling a fully digital project management. The market will focus first on providing incremental user experience benefits to individual PM professionals, and later will help them to become better planners and managers. Gartner thinks that by 2023, technology providers focused on AI, virtual reality (VR) and digital platforms will disrupt the PM market. Generally, the goal is to avoid getting to the end of a project and being surprised.

Data collection, analysis and reporting are a large proportion of the PM discipline. AI will improve the outcomes of these tasks, including the ability to analyze data faster than humans and using those results to improve overall performance. “Using conversational AI and chatbots, PM leaders can begin to use their voices to query a PM software system and issue commands, rather than using their keyboard and mouse,” says the Gardner VP. “As AI begins to take root in the PM software market, those managers that choose to embrace the technology will see a reduction in the occurrence of unforeseen project issues and risks associated with human error.”

Classroom discussion questions:

Will project managers become obsolete?
How will AI software change the field of PM?

OM in the News: Using AI to Keep Trucks on the Road

“In the trucking industry, few things will sour a manager’s mood like a mechanical failure disabling an 18-wheel rig in the middle of a big delivery,” writes The Wall Street Journal (March 12, 2019). But if mechanics can predict when a pump or cable or other component is about to fail, they can avoid having a truck stuck on the side of the road.

NFI Industries Inc., a $2 billion N.J.-based company, is using artificial intelligence to anticipate when the truck components in its 2,200 tractors and 9,700 trailers need adjusting or replacing. By predicting maintenance and reducing malfunctions, NFI expects to reduce truck maintenance and repair costs by 7%, or $1.5-$2 million a year.

NFI’s data is taken from truck sensors, odometers, speedometers, repair logs, temperature logs and other sources. The information collected includes truck ages, route distances, payload weights, weather conditions, driving conditions, and even the braking and accelerating styles of individual drivers. That data is analyzed by Noodle.ai, a San Francisco startup that pushes the information through a supercomputer nicknamed The Beast. Noodle.ai’s machine learning technology synthesizes the disparate bits of data to determine when a $100,000 rig needs an oil change, a filter replacement, a brake adjustment or a new set of tires.

As a result, NFI is jettisoning a sacrosanct industry ritual: regular truck maintenance and mandatory oil changes every 30,000 miles. Instead, the company is switching to less frequent tuneups, as prescribed by AI, that are based on a truck’s age, wear, driving conditions and a host of other factors. NFI’s trucks break down about twice a year, on average. The company expects predictive maintenance to reduce those mishaps to 1.5 breakdowns a year per truck. Among the surprising insights AI has produced: NFI had been procuring a truck model from a manufacturer that offered a $10,000 purchase incentive per truck. But over a lifespan of five to six years, that truck model was costing NFI about $25,000 more in maintenance and repair than other trucks.

Classroom discussion questions:

What is the difference between predictive and preventive maintenance?
What is the role of AI at NFI?

OM in the News: The Hidden Automation Agenda

The Milwaukee offices of the Taiwanese electronics maker Foxconn, which plans to replace 80% of the company’s workers with robots in 5-10 years

In public, many CEOs wring their hands over the negative consequences that A.I. and automation could have for workers. They talk about the need to provide a safety net for people who lose their jobs as a result of automation. But in reality, writes The New York Times (Jan. 26, 2019), many are racing to automate their own work forces to stay ahead of the competition, with little regard for the impact on workers.

All over the world, executives are spending billions of dollars to transform their businesses into lean, digitized, highly automated operations. They see A.I. as a golden ticket to savings, perhaps by letting them whittle departments with thousands of workers down to just a few dozen. A 2017 survey by Deloitte found that 53% of companies had already started to use machines to perform tasks previously done by humans. The figure is expected to climb to 72% next year. Investment bank UBS projects that the A.I. industry could be worth $180 billion by next year.

The author of “AI Superpowers” predicts that A.I. will eliminate 40% of the world’s jobs within 15 years. He said that CEOs were under enormous pressure from shareholders and boards to maximize short-term profits, and that the rapid shift toward automation was the inevitable result. But other experts have predicted that A.I. will create more new jobs than it destroys, and that job losses caused by automation will probably not be catastrophic.

The CEO of the Chinese e-commerce firm JD.com said last year that “I hope my company would be 100% automation someday.” The World Economic Forum estimates that of the 1.37 million workers who are projected to be fully displaced by automation in the next decade, only 1 in 4 can be profitably reskilled by private-sector programs. “The choice isn’t between automation and non-automation,” said the director of M.I.T.’s Initiative on the Digital Economy. “It’s between whether you use the technology in a way that creates shared prosperity, or more concentration of wealth.”

Classroom discussion questions:

What can A.I. do to help OM functions?
Will A.I. replace as many jobs as some predict?

OM in the News: How AI Powers Amazon’s 1-Hour Deliveries

Amazon boxes are scanned on conveyor belts. AI systems keep track of all items in the warehouses, which can be as vast as 1 million square feet.

By the time someone clicks “buy” on Amazon, its Supply Chain Optimization Technologies team has probably expected it. The team forecasts demand for everything sold by Amazon worldwide and underlies the entire Amazon retail operation. Launching their fastest service, Prime Now, Amazon now delivers household basics within hours, thanks to artificial intelligence.

With AI, computers analyze reams of data, making decisions and performing tasks that typically require human intelligence. AI is key to Amazon’s retail forecasting, writes Supply & Demand Chain Executive (Nov. 28, 2018). It is also a key to how Amazon speeds up deliveries: The team predicts exactly where items should be stocked so that they are as close as possible to the people who will buy them, an essential process with the race for same-day and even same-hour delivery. Few other retailers have ventured into these speeds, because they’re very expensive. AI is woven through every part of an Amazon purchase, from the website to the warehouses to the actual delivery. The firm calls it the “first mile,” “middle mile” and “last mile.”

In 2013, Amazon got a patent for “anticipatory shipping.” The idea was to get an order as close as possible to the customer’s address before the customer actually click “buy.” Since then, Amazon has built a massive warehousing footprint around the country, with smaller warehouses closer to city centers where Prime Now promotes super-fast delivery.

Amazon is also now rolling out new efficiency-boosting technology that eliminates the need for the handheld scanners we show in the Chapter 12 Global Profile. The new system retrofits workers’ stations with advanced cameras that can automatically scan items that workers hold in their hands. This kind of innovation is a controversial, where retail store layoffs are rampant, just as automation is reshaping the workforce.

Classroom discussion questions:

What are the operations issues discussed in this article?
How is AI used at Amazon?

Good OM Reading: AI + Blockchain

This new book (www.mkpress.com) alerts readers to the impending collision of the two largest foundational technologies for the coming decades: Artificial Intelligence and Blockchain.

AI has had a long history of hype and excitement about how we can externalize our human skills. Blockchain is the newer technology that is motivated largely by a change in control of cryptocurrencies and inventory. AI, claim the authors, seeks to displace individuals while blockchain seeks to displace a controlling team of individuals. AI will continue to disrupt business in many ways, leading to job loss and rendering irrelevant many human cognitive skills. Blockchain too is challenging and will continue to change the position of trust of central authorities, whether in the government or in big business.

Both these technologies, however, have enormous potential to make positive changes in the world of operations management. AI, rightly engineered and deployed, has the potential to become humanity’s servant, freeing up humans. Blockchain, responsibly governed and deployed, has the potential to democratize society, by eliminating friction in the world’s transactions and eliminating middlemen, and by facilitating a more equitably distributed internet of value. The most efficient and effective ways for this to happen is through a partnership between these two powerful technologies, where blockchain delivers trusted and immutable information for AI, and AI delivers cognition and automation to business processes.

OM in the News: Is Fanuc the Most Important Manufacturing Company in the World?

Businessweek thinks there is one clear winner in the manufacturing world: the $50 Japanese billion company that controls most of the world’s market for factory automation and industrial robotics. “In fact,” writes Businessweek (Oct. 23, 2017), “Fanuc might just be the single most important manufacturing company in the world right now, because everything Fanuc does is designed to make it part of what every other manufacturing company is doing”.

At Fanuc’s Mt. Fuji plant, hundreds of bright yellow Fanuc robots are working around the clock to build other Fanuc robots. Some robots will be shipped elsewhere in Japan, where strict immigration policies and a declining birthrate have left manufacturers of all sizes more dependent on factory automation. But most are bound for China.

Automation has been rising in China over the past decade, partly because, as wages and living standards have risen, workers have proved less willing to perform dangerous, monotonous tasks, and partly because Chinese manufacturers are seeking the same efficiencies as their overseas counterparts. More and more, it’s Fanuc’s industrial robots that assemble and paint automobiles in China, construct complex motors, and make injection-molded parts and electrical components.

And as China goes, so goes the rest of the industrial world. Multinationals that are reshoring operations from Asia to N. America and Europe are doing so in part because automation promises sophisticated production methods and labor savings; they are spending more than ever on industrial robots– 32% more than a year earlier, with many of them are ending up in Midwestern steel and auto manufacturing centers. Orders from the U.S., though, are dwarfed by those from China—some 90,000 units, 1/3 of the world’s total industrial robot orders last year. (Researchers estimate that each new industrial robot displaces 5 human workers).

The key to Fanuc’s success may lie in AI. In the past, the selection of a single part from a bin full of similar parts required skilled programmers to “teach” the robots how to perform the task. Now, Fanuc’s robots are teaching themselves. “After 1,000 attempts, the robot has a success rate of 60%,” the company said. “After 5,000 attempts it can already pick up 90% of all parts—without a single line of program code having to be written.”

Classroom discussion questions:

What is the role of artificial intelligence in robotics?
What will be the impact of robotics on U.S. manufacturing?

OM in the News: AI and Human Resource Strategy

The growing use of AI in the workplace raises many ethical questions.

“Artificial intelligence (AI) is changing the way managers do their job–from those who get hired to how they are evaluated to who gets promoted,” writes The Wall Street Journal (March 13, 2017). Here are 4 examples:

Companies use AI to help them find the best candidates for jobs. Such software often spots the most promising resumes among what may be an unmanageable deluge, or it widens the net so employers can find a more diverse pool of candidates. SAP’s Resume Matcher software reads Wikipedia entries to understand job descriptions, related skills and so on. Then it correlates what it learned with resumes along with notes on whether a given applicant was shortlisted, interviewed, hired and the like.

Once managers have hired ideal candidates, AI can help keep them productive by tracking how they handle various aspects of their jobs—starting with how they use their computers all day. Veriato makes software that logs virtually everything done on a computer—web browsing, email, chat, keystrokes, document and app use—and takes periodic screenshots.

Companies can also track employees’ whereabouts in the office. Bluvision makes radio badges that track movement of people in a building, and display it in an app and send an alert if a badge wearer violates a company policy—say, when a person without proper credentials enters a sensitive area. The system can also be used to track time employees spend at their desks, in the cafeteria or in a restroom.

AI can also help managers peer into personal aspects of job performance that used to be left up to observations—for instance, attitudes toward the job. Veriato analyzes email and other messages, looking at words and phrases employees use. Then it scores those expressions for positive or negative sentiment. The system can set a sentiment baseline over time.

Classroom discussion questions:

Discuss the ethical issues here.
How else might AI help a company’s human resource strategy?