Siemens – The OM Blog by Heizer, Render, & Munson

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:

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?
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 in the News: AI-Based Robots

Should companies deploy robots at their plant if they could virtually reprogram themselves to perform new and different tasks, asks Industry Week (May 13, 2026)? We’re nearly at the end of the AI hype cycle, when suggestions for how to leverage the technology become less flashy and more realistic.

Now Siemens has just revealed Eigen, an AI agent that can replace manual coding or programming for programmable logic controllers, distributed control systems, and robotics applications, updating code or instructions to reflect new priorities and goals.

Siemens says that engineering and reconfigurations constitute 70% of the entire lifecycle cost of a robot. If, however, an AI agent like Eigen can shorten the time needed to make these adjustments, it makes the robot more efficient, and small and medium-sized businesses might be better able to afford deploying the technology.

“There’s a kind of new age of automation arising, because with AI assistance to program robots and PLCs, it means you could suddenly automate much smaller lot sizes on a good return of investment,” says the firm’s CEO of its automation division.

Eigen can help manufacturers deal with a lack of coders and programmers. Another Siemens exec adds “We don’t attract the best of the programmers to the manufacturing floor. … So getting programmers to come and code our controllers or robotic systems? That was a scale up bottleneck. Bringing in AI to reprogram things, reprogram the whole process, will be more game changing in the U.S. than in Germany, where I see when people with Master’s degrees on the manufacturing floor, which is not the case in the U.S. Humans must always remain in the loop, however. Agentic AI is like an orchestra and humans the conductors.”

In short, Eigen acts as an AI-agent that handles the tedious, expensive back-end coding of robotics, making automation flexible, cheaper and more accessible to smaller firms.

Classroom discussion questions:

What is Eigen‘s role?
What is the roadblock to more robotic use in small manufacturers?

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:

How is PepsiCo employing digital twins?
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:

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

OM in the News: 3-D Printing–or Additive Manufacturing?

Engineering companies now prefer to talk about “additive manufacturing” rather than “3D printing,” writes The Economist (May 3, 2014). One reason is that printing is not quite the right word for some of the technologies given this label. Whereas hobbyist-scale 3D printers typically build a product by squirting out drops of plastic, a newer manufacturing technique called selective laser melting zaps successive layers of powder with a laser or ion beam, hardening only certain bits. Larger firms want to stress the “manufacturing” aspect: that technology has moved beyond the development labs and is now being used on the factory floor to make complex metal parts. In Siemen’s gas turbines, for example, elaborately shaped blade components are hard to design and costly to make. But Siemens is using additive manufacturing machines to cut the cost and the time needed to replace the blades on customers’ turbines when they break– eventually from 44 weeks down to 4.

For simpler mechanical parts, the approach allows designers to imagine shapes that would be impossible to create through older techniques, besides greatly speeding up prototyping—for turbine blades and similar parts, from 16-20 weeks to just 48 hours, Siemens says. Additive manufacturing cuts the cost of tooling and materials: a piece can have all of its holes incorporated into it, with great precision, as it is built up from powder, instead of needing to have them expensively drilled afterwards. Siemens hopes to cut the cost of some parts by perhaps 30%. As it gets easier to make low-volume, specialized parts in-house, Siemens gains bargaining-power when it comes to outsourcing such parts to other firms.

Aircraft engines, subject to even higher standards of reliability than turbines, are another area in which the engineering giants have implemented additive manufacturing. GE is using it to make fuel nozzles for its next-generation Leap engines. GE says the nozzles will be 25% lighter and five times more durable than their predecessors—and since there are 20 or so in each engine, the weight savings are significant.

Classroom discussion questions:

1. Is there a difference between 3-D printing and additive manufacturing?

2. How will 3-D printers change the world of manufacturing?

OM in the News: Germany Exports Jobs Training to the US

Germany’s transplant-factories, like the sprawling VW complex in Chattanooga, aren’t just cranking out cars, machinery and chemicals. They are also bringing, writes The Wall Street Journal (June 14, 2012), a German training system that could help narrow America’s skilled labor gap. VW, which will graduate its first class of U.S. apprentices next year, is one of dozens of companies introducing training that combine German-style apprenticeships and vocational schooling.

These programs are winning adherents as manufacturers grapple with a paradox: Though unemployment remains stuck above 8%, companies can’t find enough machinists, robotics specialists and other highly skilled workers to maintain their factory floors. An estimated 600,000 skilled, middle-class manufacturing jobs remain unfilled nationwide, even as millions of Americans search for work.

“In the U.S. we’ve evolved to the point where we think the only thing people should strive for is a four-year college education, and factory work is seen as dirty, dangerous and repetitive,” says the director of the Aspen Institute’s Manufacturing and Society program. “In Germany, the work that is done on the factory floor and prepared by its vocational education system is highly valued.”

In Germany, 2/3 of the country’s workers are trained through partnerships among companies, technical schools and trade guilds. Last year, German companies took on and trained nearly 600,000 paid apprentices. In the U.S., such close cooperation doesn’t often exist. One stumbling block has been companies’ fear of spending on training, only to see apprentices go elsewhere. Siemens spends approximately $165,000 an apprentice in its new three-year mechatronics training program in Charlotte. VW warns that without training its own skilled workers, it will struggle to expand: As it ramped up production this year, it needed a nationwide advertising campaign to fill 100 of the more specialized new jobs at the Chattanooga plant.

Discussion questions:

1. Why is VW willing to invest so much money in an apprentice?

2. Why are these programs more popular in Germany than in the US?