Tech Brew keeps business leaders up-to-date on the latest innovations, automation advances, policy shifts, and more, so they can make informed decisions about tech.

Tech Brew

Automation

Jordyn Grzelewski

techbrew.com

Fleet safety company Netradyne’s solutions are backed by AI—as well as by a decidedly less technological tool: positive reinforcement.

The San Diego-based company, founded in 2015, is built on the idea that focusing on the positive over the negative—with cutting-edge tech to help—can help make roads safer.

“Why not tell someone they’re doing a good job every day?” Adam Kahn, Netradyne’s chief business development officer, told Tech Brew. “If I can, every day, put a little star next to your name and say you did a good job that day, you’re shifting the safety culture to a reward-based safety culture versus a punitive-based safety culture—and that has monumental increases across fleet results.”

Safety is a pressing issue in the trucking industry and in transportation overall, given the steep number of 

Netradyne’s solution is a safety platform equipped with “cutting-edge HD video safety and management technology powered by advanced AI,” according to the company. The platform uses vision-based object detection to analyze “every minute of drive time with up to 99% accuracy.” The company has analyzed more than 20 billion driving miles.

One of the ways Netradyne uses positive reinforcement is through its DriverStars program, which as of July had awarded more than 100 million events. The program “utilizes real-time analysis and edge computing to capture positive behaviors and reward drivers,” according to a news release.

The program includes a “GreenZone” score that provides rewards based on performance, a way of gamifying safety improvements. The company claims that improvements to this score correlate to reductions in crashes.

“We’ve seen some really great results where drivers and fleets that embrace this philosophy of ‘High five, fist bump, good job,’ versus, ‘Got a minute? I’ve gotta talk to you,’” Kahn said. “Nobody likes that.”

He pointed to four risky driving behaviors that tend to cause crashes: speed, distraction, driving too closely behind the vehicle in front of you, and ignoring traffic signals.

“What we started to do was track essentially the full data set around all those activities. Instead of saying, ‘Hey, driver, you had three minutes of driving distracted,’ I really want to look at the amount of time that you were driving non-distracted,” Kahn said. “If I can create awareness around your compliance, the next thing I can do is try to incentivize the improvement of that compliance.”

In January, Netradyne closed a $90 million Series D funding round. Kahn said that the fresh funding will support customer care, the company’s ongoing expansion into new global markets, and R&D.

Netradyne is introducing some predictive elements to its platform.

“Instead of waiting for a vehicle or a driver to do something, based on where they’re at, I can start to forecast and assist the driver in moments where maybe there’s an ice storm,” Kahn said. “I think there’s an opportunity where we can get in front of some of these issues versus reacting to them.”

Netradyne CEO and co-founder Avneesh Agrawal previously 

 that the company plans to leverage its data to train a driving model that could be used by 

 companies. And he said the company is investing more in generative AI to enhance its copilot tool.

“Three years ago, if we talked about AI versus today, vastly different. Three years from now when we talk about AI versus today, it’ll be vastly different,” Kahn told us. “And we’re very committed to being on the leading edge of innovation.”

Netradyne is using AI to reward good driving behavior

Netradyne uses AI-enabled dashcams to monitor driving behaviors and surrounding conditions.

How this fleet safety company is using AI to reward good driving behavior

President Donald Trump has touted his tariff regime as a way to bring back domestic manufacturing jobs—but experts say it’s not that simple.

 on all vehicle imports is that they’ll prompt automakers to invest in US manufacturing, and therefore create new jobs in an industry that has suffered steep job losses over the last few decades.

But a number of factors stand in the way of this vision. Not only would it take years and billions of dollars to build new auto plants in the US, but the industry already struggles to attract the skilled labor it requires—and in recent years has turned to automation to meet its needs.

“Ostensibly, the automotive tariffs are designed to reshore manufacturing, and this, in theory, would lead to more jobs,” Mika Takahashi, a technology analyst at IDTechEx, told Tech Brew via email. “However, the situation is more complicated than that for several reasons.”

One of the major barriers is the time and investment required to move production to the US. Takahashi noted that, given the “significant amount of capital” needed to build new plants, many auto executives are opting to hold off until there’s greater policy certainty.

Swamy Kotagiri, CEO of automotive supplier Magna, 

 at an event in April that the manufacturer’s global footprint was unlikely to change in the near term, emphasizing his view that the industry shouldn’t be reactive.

“Is there a chance in the short to medium term to look at balancing capacities where possible? Yes,” he said. But building a new plant would take about two years, and Magna was not considering any long-term strategy changes at that time.

 that most companies are unlikely to reshore supply chains because of the steep costs of doing so.

, which represents hundreds of thousands of autoworkers in the US, has called on domestic automakers to crank up some of the unused capacity in their US plants. The union recently published a white paper citing industry data indicating that the US auto industry had the capacity to build more than 14.7 million vehicles last year, but ultimately only built 10.2 million.

“We don’t need to break ground on a single new plant to rapidly grow auto manufacturing capacity—it’s already right in front of us, in the plants we've built, the skills of our members, and the communities that depend on these jobs,” UAW President Shawn Fain said in a statement. “Instead of offshoring jobs to low-wage, high-exploitation countries, auto companies must invest here at home and rebuild the middle class with union labor.”

Some experts, however, say it’s not so simple.

“Just because the capacity is there doesn’t mean [it] could be filled,” Sam Fiorani, VP of global vehicle forecasting at AutoForecast Solutions, 

 the Detroit Free Press. “Much of the current level of employment in the industry is due to the increased automation, a point that the UAW doesn’t want to acknowledge. It takes fewer people to make a vehicle today.”

Another one of the challenges is the cost of labor, and a lack of skilled labor in a low-unemployment economy.

“US labor remains much higher cost, and it may simply be uneconomical even with tariffs to produce low-cost models in the US,” Takahashi said.

More than a fifth of respondents to CNBC’s survey said that finding skilled labor was a roadblock to reshoring supply chains. More than 80% of respondents said they would use automation more than human workers if they were to boost US manufacturing.

Kotagiri, too, acknowledged this challenge and noted that it was a 

 for the industry at the start of the coronavirus pandemic.

“Over the last 10 years we’ve done a lot in robotic programming, controls, and mechatronics, and [it’s] still not enough for us. And during Covid, when we started having the churn of people, it became a huge issue,” he said. “That is a huge, huge challenge that’s not being talked about.”

Already, factories are heavily automated and employ robotics alongside human workers. A recent survey from QNX found that 30% of respondents in the North American auto industry use robotics in their organization. In 2024, the US auto industry’s installations of industrial robots climbed 10.7%, according to the International Federation of Robotics.

Collaborative robots “enable these companies to scale operations more efficiently and cost-effectively, helping bridge the gap between labor shortages and growing production demands,” Takahashi said.

Gene Berdichevsky, co-founder and CEO of 

, told Tech Brew that he doesn’t believe the tariffs will lead to a boom in general assembly manufacturing jobs.

“It’s a joke, and I don’t think anyone in the administration or in the world assumes that we’re going to have Americans screwing together iPhones. That’s not what’s going to happen,” he said. “General assembly will have to be automated if it’s going to reshore.”

Car workers fitting bonnets to cars on production line in car factory. 

Why Trump’s tariffs may not lead to a domestic manufacturing renaissance

“The automotive tariffs are designed to reshore manufacturing,” according to one analyst. “However, the situation is more complicated than that.”

AI hype train, meet an actual train maker.

Tech Brew recently caught up with Allison Whitesell, head of talent acquisition at Siemens Mobility North America, about the ways that the division of the multinational conglomerate is integrating AI into its workplaces and talent searches.

Whitesell, who will participate in Tech Brew’s 

, Onboarding Your Favorite New Coworker: AI, has a background in finance but found herself working in recruitment during the Great Recession. She’s now been with Siemens for 13 years and last year she took on the talent-acquisition role, where she oversees numerous recruiters. Right now, her team is focused on staffing a new manufacturing facility in North Carolina.

This conversation has been lightly edited for length and clarity.

What are some of the ways that Siemens Mobility is integrating AI into its recruitment and hiring processes?

We do have several different pieces of AI that we incorporate into our entire recruiting process, starting with the job description.

We use some AI tools…to help craft the job description, especially if we’re working with a new hiring manager or they’re hiring for a brand-new position that we’ve not worked with before. So we get on a Teams call with that manager and we ask them questions about the skills that are needed for the role, and the [tool] will build that job description.

And then we also run it through Grammarly and Textio, and those tools will help to select the right verbiage, make sure that we are inflecting the right tone in order to attract as many applicants as possible. And then, once the role is posted, we utilize another tool called Eightfold, which is bolted onto our applicant tracking system, and it will assess the job description and the candidate’s resume and assign a match score. It ranges from zero to five. And while we don’t completely rely on that match score, it’s just one of the tools in the toolbox that we use to review the candidate and their relation to the job description.

We also use [Microsoft] Copilot, as far as making sure that we’re attracting the right candidates in our system.

How are you thinking about implementing this in a responsible, thoughtful way while moving quickly so you’re not behind?

We recently attended a conference for the Great Place to Work certification, and one of the speakers there advised everyone in attendance that you should be spending at least 30 minutes a day just playing around with AI tools so you don’t get behind. While I think it is important to stay on top of technology, there are also sometimes results that you need to validate. During one of our other trainings that we do, there was an example where an attorney [looked up] some cases on an AI tool…in order to use in the courtroom, and all of those cases were fraudulently generated…So it’s important to validate those results in order to communicate correct information. But again, it’s just another tool in the toolbox that we have the ability to use.

What are some of the skills shortages that you’re encountering, and do you see AI playing a role in helping to close some of those gaps?

I do, and I will reference our project in Lexington, North Carolina. Doing some studies with regard to the current workforce in that location, their primary industries are tobacco, textiles, and furniture. In Mobility, we make trains. So there is a definite skills gap there, in order to reskill and upskill their current workforce.

So we will be using some automation tools and working with a local community college in that area to help craft and mold their curriculum so that those workers can upskill themselves based upon what our needs are. So it’s more of an aptitude and willingness to learn, and we do have the tools to teach them—it’s just contingent upon the human effort as well. There’s AI, yes, but we also need the human component.

Is the ability to leverage some of these tools something you’re looking for in your talent searches?

It’s definitely part of the conversation. We want to look at the candidate as a whole and not just any one particular skill, but the skills that they do bring to the table, plus what else we can impart upon them and their willingness to learn and fill in that gap. So we are definitely willing to invest in our workforce, our current workforce plus our future workforce.

There’s so much hype around this technology right now. How monumental of a shift is this in terms of how you and your employees do your jobs?

It’s definitely a shift—and one that we are kind of anticipating and excited about because there are some administrative aspects to the recruiter’s day-to-day position. And if it’s something that could automate some tasks and free up time to identify those unicorns or purple squirrels out in the workforce, then all the better. Because there’s a lot of reporting we do…and if it becomes more automated, I welcome it.

Graphic featuring a headshot of Siemens Mobility North America's Allison Whitesell.

Siemens Mobility exec says AI is reshaping hiring, but leaves room for human touch

Allison Whitesell, Siemens Mobility North America’s head of talent acquisition, sat down with Tech Brew ahead of her appearance at our June 26 event in New York.

The coronavirus pandemic. Houthi attacks on Red Sea shipping routes. The Baltimore bridge collapse.

This is merely an abbreviated list of events the automotive industry and its byzantine supply chains have weathered in the last few years. Individually, they are challenging; taken together, they reflect what one expert told Tech Brew is the auto industry’s “decentralization and fragmentation” and the need to overhaul logistics strategies as a result.

“Automotive is a far cry from the Model T days where they had vertical integration all the way back to the trees for the rubber,” said Jason Clark, global VP of manufacturing and energy for supply-chain management company Exiger. “We’ve gone to the opposite end of that spectrum…and now we’re feeling the pain of that as geopolitical tensions as well as some of these supply-chain shocks highlight the fragility of the network.”

But thanks to emerging technological capabilities and a heightened focus on supply-chain management in the wake of the pandemic, businesses may be better positioned to weather these types of crises.

As director of intelligence solutions for global supply-chain insights and risk analytics firm Everstream Analytics, Mirko Woitzik has had a front-row seat to this evolution. What once was a niche function is now a subject of boardroom discussions and earnings reports, he told Tech Brew.

Everstream provides real-time supply-chain monitoring and risk assessments, mostly for multinational companies with tens of thousands of suppliers, helping guide logistics and procurement decisions.

Customers “not only see exactly where the ships are, but they also see whether there might be a port closure or whether there’s a strike at a different port or whether there’s a hurricane likely causing some delays at an airport,” Woitzik said. “We have that additional risk layer, so you can take the time to reroute ahead of time.”

Companies are reckoning with the fact that the days of unimpeded global trade are in the rearview mirror, Clark said, with realities such as heightened geopolitical tensions and sourcing and human-rights regulations that have led them to scrutinize every aspect of their supply chains.

Experts told us they don’t expect any significant changes to the “just-in-time” supply-chain model that’s employed widely across the industry (where parts are delivered to assembly plants as they’re needed for production rather than maintaining stockpiles), but there are other ways manufacturers can protect themselves from disruptions.

Companies may look to bring a larger portion of product engineering in-house, Clark said, or to localize production. They may renegotiate capacity commitments with logistics providers. And they could re-evaluate their existing inventory policies, as they did during the pandemic.

They might conduct sourcing analyses to help ensure they know exactly where components and raw materials that are needed for production come from, set up early warning systems to flag potential disruptions, and increase the level of dual sourcing (having two suppliers for a component).

“Each of those have different time horizons for when they can be affected, but some of the things that I’ve been hearing is that [companies are] pursuing them all in parallel,” Clark said, “realizing that some of them will take some more time to pull through.”

In a surprise to no one, AI has entered the supply-chain logistics chat, with companies using the tech to look deeper into their supply chains.

A recent report by ecosystem integration software company Cleo 

 that 33% of businesses “are turning to generative AI specifically to combat supply chain disruptions in 2024,” Frank Kenney, director of industry solutions, said in a statement provided by spokesperson Sarah Medina.

Everstream uses AI models trained on historical and customer data to identify events that could disrupt supply chains and track down hard-to-find information about sub-tier suppliers.

Analysts “can’t possibly figure out 10 to 15 events that are potentially impacting their supply chain,” Woitzik said. “We use AI to make those severity assessments, looking at huge troves of data. [It gives] our analysts the best possible information so that we can filter that down and only focus on the ones that have the big impact—like an earthquake, like the Baltimore bridge.”

Supply-chain visibility company project44 used predictive analytics after the bridge collapse in Baltimore to estimate what percentage of goods were rerouted to alternate ports.

“Through advanced analytics, automotive companies can forecast the impact of disruptions on their inventory in real-time—assessing the amount of inventory that’s at risk, where the company has safety stock padded, and where inventory will need to be redistributed to prevent delays,” Eric Fullerton, project44’s senior director of product marketing, said in a statement provided by spokesperson Hanna Bautz.

Still, there could be opportunities for companies to more effectively leverage technologies like AI.

 that despite the potential benefits, less than a third of CFOs are looking to leverage AI for procurement.

“It’s no longer an option for companies (whether a startup or a mature company) to use obsolete technology,” Nari Viswanathan, Coupa’s senior director of supply strategy, said in a statement provided by spokesperson Katie Murphy.

Woitzik would like to see better integration of the various supply-chain management systems businesses use.

“I think most of the efforts right now and in the next five years will go into making these systems talk to each other,” he said. “That’s definitely something that still needs to be figured out.”

How data and technology like AI can help mitigate supply-chain crises

“Automotive is a far cry from the Model T days where they had vertical integration all the way back to the trees for the rubber,” one logistics expert told Tech Brew.

Jordan McDonald

The tech world is having a rough time with slowing sales, 

, but Siemens seems to be having a comparatively smooth ride with its digitization-focused businesses.

The industrial conglomerate released its fiscal year (FY) Q1 2023 earnings report this month, where it posted an all-time record profit for its Industrial Business segment in the first quarter, with €2.7 billion ($2.9 billion) in profit. Total company revenue climbed 10% year over year to €18.1 billion ($19.3 billion), up from €16.5 billion ($17.6 billion).

Siemens’ FY Q1 performance wasn’t just a bright-spot blip—it represents a continuation of the company’s strong fiscal 2022. In FY 2022, it 

 posted a record-high profit of over €10 billion ($10.7 billion) in its Industrial Business section, which includes its Digital Industries, Mobility, and Smart Infrastructure divisions. The digital transformation-focused conglomerate offers a range of tech-focused products and services to customers, serving industries from transportation to healthcare.

“Our world-class supply-chain teams continue to make a clear difference in a competitive environment, building on long-term trustful relationships with our suppliers and partners,” Roland Busch, president and CEO of Siemens AG, said on the company’s most recent earnings call. “This close collaboration currently matters most in industrial electronics, where supply is still tight.”

Siemens’s strong performance comes amid a broader tech downturn that has hit consumer electronics manufacturers and ad-based platforms particularly hard.

In its Digital Industries business, which helps companies integrate technologies like AI, edge computing, and industrial 5G into their business, revenue climbed from €4.3 billion ($4.7 billion) in FY Q1 2022 to €5.1 billion ($5.4 billion) in FY Q1 2023, an increase of 18%. The company attributed the increases to its automation business, while other key subsegments like software declined due to “higher expenses related to cloud-based activities, including the transition to software as a service.”

“In Digital Industries, we saw continued robust demand at high levels across our core end markets, leading to sequential order growth,” Ralf Thomas, chief financial officer of Siemens AG, said on the earnings call. “Investment sentiment continues to be healthy, not only in discrete industries, including automotive, machine building, and aerospace, but also in hybrid verticals such as food and beverage or pharma.”

Siemens’ Smart Infrastructure business, which specializes in tech like intelligent grid control, electrification, and building automation, posted its best quarter ever too, increasing revenue 20% from the same time last year to €4.6 billion ($4.9 billion) from €3.8 billion ($4 billion).

Within Smart Infrastructure, electrification and electrical products led growth in the business. Orders increased 16% in the electrification business on the back of new contracts in the semiconductor industry, while revenue increases stemmed from electrical products and electrification for data centers.

The company anticipates its success will continue this fiscal year, despite the headwinds facing the tech world: On the earnings call, Siemens raised its guidance for 2023, projecting single-digit percentage increases in revenue, particularly for its Digital Industries, Mobility, and Smart Infrastructure businesses.

Creative graphic of robotic process automation at work (with a physical robot)

Siemens records record-high profit in Industrial Business segment

The digitization-focused business is performing well despite a broader tech downturn.

Siemens records record profit in Industrial Business segment

We may be waiting for the consumer metaverse to fully materialize, but in some pockets of industry, digital replicas have been a thing for decades, enabling businesses to see virtual models of products, tools, and even cities.

Take the aerospace industry, for example. In the business of designing planes, each component and part must be tested and certified for safety before the plane is ready for service, which can be very expensive. As a result, some in the industry have turned to 

 over the last two decades as a way to potentially help lower costs, speed up testing cycles, and model aircraft before building and testing physical prototypes.

To do this sort of in-depth virtual testing, many aerospace companies look to external organizations like the National Institute for Aviation Research (NIAR), the aerospace R&D arm of Wichita State University.

NIAR works with government agencies, eVTOL manufacturers, and commercial aircraft OEMs like Boeing to test parts for compliance with FAA regulations, and with the FAA itself on certification by analysis methodologies for airframe crashworthiness and ditching, according to Gerardo Olivares, senior research scientist and director at NIAR. The industry has outsourced parts of these processes to organizations like NIAR in an effort to lower costs.

Olivares told Emerging Tech Brew that NIAR uses digital twins for flight testing, design, and test safety in devices like pilot seats, and to assist in FAA certification. He said its digital twin tech is developed with the help of Altair, a tech company that specializes in simulation software, among other things. 

, digital twins “can accelerate time to market by up to 50%,” as companies can spend less time building and testing physical models.

“[Digital twins are] a game changer for the aerospace industry, because now you don’t have to spend many millions of dollars building physical prototypes in order to be able to develop, and then test new aircraft concepts,” Olivares said.

The institute, founded in 1985, has worked with some version of digital-twin tech for decades.

In the earlier days of the tech, NIAR used what Olivares called “data-driven” digital twins, which he said “are very good for processes where you have repetitive tasks,” like a plane flying one specific route, and used data from thousands of sensors in aircraft.

As data was gathered through precise repetition, NIAR was able to extrapolate and use it through AI and machine learning tools to try and model future outcomes, schedule maintenance programs, and gain a better understanding of when to replace certain parts.

But since 2007, as computing, sensor, and connectivity tech has improved, NIAR has been shifting its operations to what Olivares called “physics-based” digital twins, which are full, high-fidelity models of every part and piece of an aircraft, and which can simulate specific conditions like weather or repeated use or inform potential maintenance issues like when to replace certain parts virtually.

“We can do all the development virtually: Test, fly structures, aerodynamics, and everything so by the time that we start building components, we already have a very high degree of confidence that the system is going to work as intended,” he said. “And the only purpose of the full-scale test is to validate that, in fact, all the simulations that we ran were accurate.”

NIAR can run thousands of virtual missions in a short period of time, amassing 10 to 20 years of real-world testing data within a few weeks, Olivares said.

Olivares said that developing a pilot seat typically takes 24–32 months “from conceptual design to certification,” and can cost up to $3.5 million “between physical prototypes and engineering,” but with digital-twin models, he said that testing and design were done virtually, cost less than $1 million, and took only six months to complete. 

Olivares said that NIAR has been building data-driven models since 1985, but emphasized that without modern high-performance computing, digital twins as we know them would simply not be possible.

“You can have good physics-based models that predict things. But if you’re not able to run them quickly and accurately, then it slows down things. So that’s another advantage of the digital world: If you have enough funds to have a big cluster, you can scale things greatly. You can look at thousands of cases [where] the physical testing [would] be impossible from a planning point of view,” he said.

Update: This piece has been updated 12/09/22 to provide additional context for Altair’s technology use by NIAR.


image of earth with planes flying around it

How NIAR helps the aviation industry with digital twins

For in-depth virtual testing, many aerospace companies look to external orgs like NIAR.

Meet the organization helping aviation companies harness digital twins

Jenn Brice

Though large, fully crewless ships are still uncharted territory, semi-autonomous vessels might be catching on.

Lured by the promise of lower labor costs and decision assistance, some commercial shipping companies are looking to explore autonomous navigation systems. In one of the latest and most ambitious experiments with the technology, a natural gas tanker named 

 from Texas to South Korea in May, completing about half of its 33-day test journey on June 2 without the help of a human crew.

Avikus, a Hyundai subsidiary, is the company behind the voyage. It equipped the boat with its autonomous navigation system, HiNAS 2.0, which still requires a 

 in crowded sea areas, docking, and near ports. Although the tech is still in its early stages, the system will be 

 by the end of 2022, and the company’s VP of business development, Carl Johansson, told us Avikus has made “a couple hundred” sales of its system already, although he declined to name a specific figure.

Avikus works directly with its parent company’s shipbuilding division, Hyundai Heavy Industries, which is one of the largest shipbuilders in the world. Johansson declined to disclose what percentage of new ships built by HHI will employ its autonomous technology, but he said they “intend that it will come as a standard for new builds through HHI.”

Avikus also sells its navigation system and “usually some additional cloud services and fleet management services on top of that,” to commercial shipping companies like ferries, container ships, and cruise ships. Korean lines Sinokor, SK Shipping, and Hyundai Glovis, plus Greek ship management companies Capital and Metrostar are among the customers Avikus said it’s working with.

Shipbuilders and shipping lines are interested in the technology because it can reduce human error and supports the human crew, Johansson said. But, the technology’s biggest selling point, according to Johansson, is that it can increase safety and reduce the skill level necessary for seafarers. In 2020, there were 

 can involve human error,” according to Allianz reports.

Avikus isn’t alone in developing this tech—companies from IBM to specialized startups like Sea Machines Robotics and Orca AI are also working on autonomous navigation tech.

Before autonomous navigation software is available on the market, it undergoes research and regulation through the International Maritime Organization, the special agency under the United Nations that regulates shipping, explained Natasha Brown, head of public information services for the IMO.

“International sailing requires international regulations, but you can have local [regulations] within one country’s domestic waters, and then that country decides the rules., There it will happen much earlier,” Johansson said. He cited the Nordics, South Korea, and Singapore as major test areas, plus “a little bit in the US.”

The IMO plans to develop a mandatory code for autonomous navigation software that will go into effect in 2028 and would be enforced by individual nations or ports, Brown said.

Looking ahead, Johansson told us that the technology for entirely crewless, international ships will be ready in 2030, at the very earliest.

“It’s not about full autonomous decision-making right now, particularly around larger shipping. said Gordon Meadow, chair of the Institute of Marine Engineering, Science and Technology’s MASS special interest group. “It’s about providing decision support capability to the human in the loop.”