Tag: digital twins

OM in the News: AI and the Perfect Pringle Potato Chip

For decades, the Pringle has been one of the most recognizable snacks in the world—uniform, crunchy, and engineered for consistency. But behind that iconic saddle‑shaped chip lies a surprisingly complex manufacturing challenge, writes The Wall Street Journal (Aug. 11, 2026). At Pringle’s factory in Poland, engineers have spent the last four years pursuing a bold goal: use AI to make every single Pringle a perfect one.

A sensor analyzes Pringle’s manufacturing line in Kutno, Poland

This ambition led to a deep partnership with Siemens and a $4–5 million investment to build an AI‑powered digital twin (our topic in Module F of the text) of the Pringles production line. Unlike traditional 3‑D models, this digital twin operates in real time, ingesting more than 200 data points every millisecond—from flour particle size to dough humidity to subtle variations in potato batches. These micro‑changes matter. Even potatoes from the same supplier can behave differently depending on the season, affecting texture, shape, and fry behavior.

Historically, dough makers relied on intuition: stretching dough by hand, weighing chips fresh from the fryer, and adjusting machines after spotting issues. Today, AI models simulate outcomes continuously, predicting how each batch will behave and recommending precise tweaks—more oil here, less water there—to keep quality stable. Human operators still make the final call, but the system’s guidance has already delivered measurable results: 10% improvement in chip quality, 13% reduction in waste, and a 40%+ return on investment on the Poland line alone.

The success is prompting expansion. Belgium is next, followed by U.S. production lines in 2027. Siemens sees this as part of a broader shift—digital twins are maturing across industries, from rockets to batteries to microchips. What began as static models are now intelligent optimization engines.

For Pringle, the future is even more ambitious. The company hopes its AI will eventually handle multiple varieties of potatoes, corn, and rice, adapting automatically while still producing the same signature chip. In other words, the perfect Pringle may soon be less a product of uniform ingredients and more a triumph of adaptive, intelligent manufacturing.

Classroom discussion questions:

  1. How might AI‑driven digital twins change the future of food manufacturing, and what new skills will workers need as these systems become more common?
  2. Should companies rely on AI to make real‑time production decisions, or is it important that humans remain the final decision‑makers? Explain your reasoning.

OM Podcast #44: Inside the Cold Storage Industry with Dr. Anna Johnson

Happy New Year!  In our first episode of 2026, Professors Barry Render and Misty Blessley sit down with Dr. Anna Johnson, Vice President of Marketing and Commercial Strategy at U.S. Cold Storage, to explore the fascinating world of temperature-controlled logistics.

Dr. Johnson explains how third-party logistics providers keep America’s food supply safe and efficient, why 98% of U.S. food storage is outsourced, and how sustainability initiatives like anaerobic digestion are reducing food waste.

Prof . Misty Blessley
Prof. Barry Render

The conversation also dives into industry trends—from the surge in capacity during COVID to the current state of the market—and highlights how AI, robotics, and digital twins are transforming operations, and creating new roles for skilled workers in this evolving sector.

Dr. Anna Johnson

 

Read the full transcript

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OM in the News: PepsiCo Turns To Digital Twins To Rethink Plants

We posted recently about the joint nuclear fusion digital twin work of Siemens and NVIDIA. Today’s news is that PepsiCo is working with the same two firms  to change how it designs, tests, and expands its plants and warehouses using AI and digital twins. “Physical industries are entering the age of AI. For companies with real-world assets, digital twins are the foundation of their AI journey,” said NVIDIA’s CEO.

By modeling factories and distribution centers digitally before making physical changes, PepsiCo hopes to cut down on costly mistakes while improving speed and capacity.

With AI-driven digital twins, teams can simulate plant layouts, equipment movement, and supply chain operations in detail, reports SupplyChain (Jan. 7, 2026). Instead of expanding facilities the old way, which can be slow and expensive, they can test changes virtually and see what works before spending money on physical upgrades.

“The scale and complexity of PepsiCo’s business is massive—and we are embedding AI throughout our operations to better meet the increasing demands of our consumers and customers,” said PepsiCo’s CEO. The digital models recreate machines, conveyors, pallet routes, and even worker movement, helping teams spot problems early and test different setups in weeks instead of months.

By finding bottlenecks and unused capacity in a virtual setting, teams increased throughput by 20%. The same approach has also shortened design cycles and helped cut capital spending by 10-15%. Testing ideas digitally first, teams can plan ahead, compare options, and move faster without the usual surprises that come with physical expansion.

Classroom discussion questions:

  1.  How is PepsiCo employing digital twins?
  2. How do AI and digital twins work together?

OM in the News: Digital Twins and Nuclear Fusion

Digital twins, which we cover in Module F (Simulations and Digital Twins), is a big topic at Nvidia and Siemens as they work together to make nuclear fusion a commercial reality. In that chapter (see p. 847), we define a digital twin as:  “an electronic virtual replica of an operation that allows organizations to mimic how a product, process, or system will perform.”

Workers at Commonwealth Fusion Systems’ campus in Devens, Mass

Fusion engineers at the Nvidia/Siemens venture, called Commonwealth Fusion Systems (CFS), will use its digital twin to run simulations, ultimately to hasten the goal of producing fusion energy at a commercial scale. CFS “will be able to compress years of manual experimentation into weeks” with the AI assistance, said its CEO.

Nuclear fission, which splits atoms to produce energy, is already in use in power plants, reports The Wall Street Journal (Jan. 7, 2026).  But many companies see fusion, the energy process that powers the sun by joining atoms together, as a longer-term bet because it can provide much more energy in a cleaner process. Nuclear energy appeals to tech giants because it releases minimal carbon emissions while providing round-the-clock power—particularly as they look to fuel their AI ambitions.

CFS said it was working with Google on an AI project, and explained that that effort has created something like a co-pilot for its fusion machine, while the digital twin plan “is the virtual airplane.” Google also recently signed a power purchase agreement with CFS to secure energy from what could be the first grid-scale fusion plant.

“The race is on for AI. Everyone is trying to get to the next frontier,” said Nvidia’s CEO.

Classroom discussion questions:

  1. Provide other examples of how digital twins can be used.
  2. Why is this fusion project so important as an OM tool?

OM in the News: What Is a “Digital Twin”?

A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making.

NASA tested an early iteration of a digital twin in response to the Apollo 13 disaster in 1970, using training simulators to match the conditions on the crippled spacecraft and test potential strategies for bringing the astronauts home safely. Today’s digital twins are much more advanced, writes The Wall Street Journal ( March 20, 2023). Not only do they pull in real-time data, but also use AI to capture insights and make predictions, such as identifying potential problems before they happen. The technology also can eliminate the need for physical prototyping of products such as automobiles, and offer a way to test different configurations for spaces such as warehouses and stores, potentially saving time and money.

Companies in every industry are looking at the technology to help them improve processes, reduce costs, conserve resources, boost employee safety and productivity: 17% said they have or plan to deploy digital twins.

San Francisco Airport’s digital twin of its Terminal 2.

For example, the massive San Francisco Airport relies on a digital twin to keep the facility running smoothly. It is a 3-dimensional virtual replica of the airport that is continuously updated with data gathered from embedded sensors throughout the airport. If the maintenance team were to receive a request to change door locks, for example, it could consult the digital twin to find the locations of all the doors that need service.

Another growing area is construction. Modern buildings are already layered with sensors and data-gathering systems that building operators can combine in a digital twin to help them improve a structure’s efficiency, sustainability and security. Building managers can use digital twins to keep track of systems—such as EV charging, smart glass that darkens to reduce energy costs and even soap dispensers with built-in sensors that know when it’s time for a refill—all in one place.

Other complicated systems might benefit from connected digital twins, too. A collection of twins representing everything from stadiums to freeways to public parks has the potential to change the way governments build cities and provide services. Cities might use the technology to create more efficient trash-pickup schedules and routes, for example, or to change traffic patterns when there is a spike in additional people getting on the road from, say, a stadium event.

Classroom discussion questions:

  1. How might a digital twin be used at your university?
  2. Why are twins so useful?

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:

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