Tag: AI

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

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

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

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

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

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

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

Classroom discussion questions:

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

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

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

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

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

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

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

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

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

Classroom discussion questions:

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

OM in the News: Humanoid Robots Finally Get Real Jobs

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

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

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

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

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

Classroom discussion questions:

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

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

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

 

 

Transcript

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

 

Charlie Render

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Prof. Barry Render
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our podcasts on your mobile device as soon as they come out!

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

OM in the News: AI and Warehouse Supply Chain Disruptions

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

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

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

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

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

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

Classroom discussion questions:

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

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

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

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

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

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

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

Classroom discussion questions:

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

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

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

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

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

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

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

Classroom discussion questions:

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

Good OM Reading: Top 10 Supply Chain Trends for 2025

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

Here are the top ten trends:

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

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

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

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

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

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

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

Classroom discussion questions:

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

OM in the News: Navigating Supply Chain Disruptions

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

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

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

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

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

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

Classroom discussion questions:

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

OM in the News: Amazon’s New Robotic Warehouse and Humans

Amazon just opened its most-automated warehouse yet. But underneath the robotics and artificial-intelligence technology at the site, the facility will still rely on thousands of employees, writes The Wall Street Journal (Dec. 7, 2024). The 3 million-square-foot building in Shreveport, La., is Amazon’s first warehouse to use automation and AI at every step of the fulfillment process and be able to handle one million orders a day.

Amazon’s Sparrow device uses suction cups to lift items and artificial intelligence software to identify objects by color, shape and size

The facility shows how companies are spreading automation through their distribution centers to get online orders to consumers at an ever faster pace. The sprawling site also demonstrates the challenges Amazon and other companies face as they seek to turn over some of the most physically demanding and repetitive warehouse tasks to robots.

Amazon hired more than 1,400 people at the Shreveport distribution center and plans to eventually employ 2,500 workers picking orders, loading and unloading trucks and managing the robotics systems. The idea is to speed up operations, save on labor costs and make warehouses safer for the workers that remain. Amazon has been the subject of government scrutiny over the treatment of the workers at its facilities. The warehousing sector had one of the highest rates of injuries and illnesses in the U.S., with 4.7 cases recorded per 100 workers compared with the national average of 2.4 cases per 100 workers.

Some traditional warehouse roles have proved too difficult for Amazon to fully automate, however, partly because the company sells more than 400 million widely varied products that range in size, weight and fragility, from dog toys to toaster ovens. Humans can easily look into a storage container packed full of goods, identify a particular item and know how to pick it up and handle it, whether it is a bottle of shampoo or a sweater.

 “The tactile grasp that the human hand has, and the situational awareness and the perception of the human brain, is unmatched,” said Amazon’s chief technologist. Instead, robots at the facility carry storage containers full of merchandise to human employees who look inside and pick out the item a customer ordered, then place that item into a tote box that goes onto a conveyor belt and is taken to be packaged.

Classroom discussion questions:

  1. What are the advantages and disadvantages of automating warehouses?
  2. Why do warehouses have high injury rates?

OM in the News: Junk Is Needed for the New Electric Era

Circuit boards from thousands of different products arrive at Glencore’s Rhode Island facility, where the company determines the copper content and the value of the waste.

One of the world’s largest miners is digging into America’s junk drawers, old phones and landfills. The quarry: bits of copper to meet the needs of the energy transition and data boom.  Shredded cellphones, obsolete computer cables and chewed-up cars are heaped 30 feet high outside Glencore’s 97-year-old copper smelter deep in Canada’s boreal forest. There, the scrap is melted with copper concentrate from mines to produce fresh slabs of metal.

Shifting from fossil fuels to more renewable electricity promises to remake commodity markets, writes The Wall Street Journal (Nov. 21, 2024). If America requires less crude oil and coal, it will in turn need copper for everything electric. “In the next 25 years we will consume more copper than humanity has consumed until now,” says Glencore’s  recycling head.

Data centers being built to facilitate AI and store smartphone videos are full of copper. So are the phones. Even if rich mine deposits are found, it takes decades to bring them online. That prevents miners from responding quickly to new demand, which leaves scrap to balance the market. Copper never goes away and is infinitely recyclable.

Miles worth are strung through homes and cars and along rights of way, carrying electricity and drinking water. But a lot sits in junk yards and landfills. When prices rise, there is more incentive to get it. Copper prices are currently among the highest ever. Nearly half of demand will be met with recycled copper by 2050, up from about a third today.

Germany’s Wieland began construction in 2022 on a $100 million recycling facility in Shelbyville, Ky. Another German firm, Aurubis, is building an $800 million recycling facility in Augusta, Ga. Glencore recently bought a failed electronics recycling facility in Arkansas and will use it, too, to gather scrap. Glencore found that the concentration of copper in landfilled auto fluff can be more than twice that found in geologic mines.

Classroom discussion questions:

  1. Why the demand for copper?
  2. What makes copper recycling attractive?

OM in the News: Reducing Manufacturing Waste

With inflation keeping the cost of raw materials high, it has become more important than ever for manufacturing companies to reduce waste as much as possible. Not only is this strategy good for the environment, and the company’s bottom line, but it can also boost employee well-being and morale, writes Industry Week (Nov. 12, 2024). Here are five approaches:

  1. Use Less Material. One obvious solution is to cut down on the amount of materials used. To help identify where waste is coming from – whether it is using more energy or thread than needed to produce a shirt, or printing reports that could easily be shared digitally – a thorough examination of a company’s practices is the first step. Recycling should be prioritized, including printer cartridges, old computers, monitors and batteries from small devices. Recycle containers should be near every workstation.
  2. Save Time. Time management can help cut down on waste substantially by reallocating unnecessary work to more important tasks that will help boost profit. Real-time tracking using radio-frequency identification (RFID) uses radio waves to follow a product from the beginning of manufacturing all the way through to shipping. This helps identify how to potentially streamline and speed up the production process.
  3. Embrace Artificial Intelligence. AI has the potential to discover new areas for improvement that humans may not be able to identify on their own. For example, it could be used to analyze the motion of workers and products throughout the manufacturing process. Cameras can be placed throughout a factory to capture the necessary information for the AI system to review and analyze.
  4. Optimize Workflow. Another type of waste that is important to a manufacturing company’s success is excess movement. When an employee is able to produce more without having to work as hard physically, there is less wear and tear on their bodies. This results in less injuries and sick time needing to be taken, happier employees and ultimately an increase in worker productivity.
  5. Utilize Talent. Initiating training programs to educate employees on best practices can also reduce waste. When employees perform work that unnecessarily squanders both materials and time, they need to be taught there is a better and often easier way to complete those jobs.

Classroom discussion questions:

  1. In addition to these 5 ideas in Industry Week, provide several others to reduce waste.
  2. How else can AI be used in a manufacturing process?

OM in the News: Manufacturing and Early AI Adoption

Manufacturers are betting artificial intelligence (AI) can help address pressing challenges, from supply chain volatility to the shortage of skilled workers. Three-quarters of  manufacturing executives say that adopting emerging technologies such as AI is their top priority in engineering and R&D, says Industry Week (Oct. 4, 2024)

AI is a broad term that encompasses basic data analytics (Module G in our text), machine learning, deep learning, and generative AI. Adopters are using AI to solve key problems in procurement, assembly, maintenance, quality control, and warehouse logistics. Some are deploying generative AI to synthesize huge volumes of unstructured data. Others are experimenting with AI service bots that partner with field technicians, for instance, to recognize more quickly when maintenance is required and to improve the quality of that work.

AI can also report supply chain bottlenecks in real time and predict potential disruptions in advance. In manufacturing it can include: minimizing assembly defects and improving quality control; boosting productivity; and streamlining warehouse management.

For example, one manufacturer adopted AI-based video processing to track manual assembly activities and automate quality checks of those activities,. This reduced failures in the assembly process by 70%, while also cutting down efforts for quality checks by 50%.

Another firm adopted an AI-powered industrial copilot that converts natural language into code and translates old programming languages into natural language, completing both tasks faster and better than human developers. Engineers using this AI solution were 5% more productive.

AI can also help ensure that warehouses operate at top efficiency, carrying items that meet demand and minimizing extra inventory. One company adopted an AI-based inventory management system that helped it minimize overstock while still fulfilling all orders. AI also provides more flexible job production planning so that companies can allocate specific assembly activities to the most relevant assembly expert at a given time to maximize productivity.

As a growing number of companies experiment with and deploy new AI solutions, they are raising the industry bar for productivity and performance. The article suggests that  companies that defer investing will need to run twice as fast to keep pace.

Classroom discussion questions:

  1. Summarize the AI advantages noted in the Industry Week article.
  2. Provide additional examples of potential AI use in manufacturing. In services.

Guest Post: Using AI to Decrease Food Waste and Combat Food Insecurity

Temple University Professor Misty Blessley raises an interesting issue in her Guest Post today.

The Supplemental Nutrition Assistance Program (SNAP), overseen by the U.S. Department of Agriculture (USDA), plays a crucial role in combating food insecurity across the U.S. SNAP offers monthly benefits through an Electronic Benefit Transfer card, enabling food- insecure individuals and families to purchase food at authorized retailers.

Food insecurity, defined as a lack of consistent access to adequate and safe nutrition, affects about 13% of the U.S. population. Delaware recently became the first state to pilot an AI-powered app aimed at linking surplus food with SNAP demand. With over 30% of food wasted during production and distribution, food insecurity is increasingly viewed as a supply chain challenge.

This new AI-powered app is instrumental in combatting food insecurity by addressing the potential for waste along the supply chain.

How it works: The app, called the Smart Shopper app, allows producers and retailers to offer SNAP-approved items at discounted prices in locations where surplus inventories and unmet SNAP demand overlap. SNAP recipients can download digital coupons to purchase food that would otherwise be wasted. Developed by the creators of Priceline, the app operates similarly by offering discounted goods, much like Priceline offers unused hotel rooms and airplane seats. One key advantage is its ability to predict food surpluses at their source, rather than merely reacting to food approaching its expiration date.

The benefit: The app extends the value of SNAP benefits, helping recipients make fewer compromises when deciding which foods to purchase. It also creates a win-win situation.
Delaware noted that “when we help our most vulnerable buy locally grown products, they receive the most nutritious, freshest food Delaware has to offer, and we support small farms, boosting and growing the local economy.” Additionally, the app addresses a $250 billion food waste issue at the retail level. The app is expected to become available nationwide.

Classroom discussion questions:
1. Refer to Introduction to Big Data and Business Analytics in Module G of your Heizer/Render/Munson text. In what ways does the Smart Shopper app move decision-makers from information to optimization?
2. Why is inventory record accuracy important to the proper functioning of the Smart Shopper app?
3. How are agricultural products the same as/different than hotel rooms and airplane seats?