The Rise of AI in Manufacturing

About 43% of workers across generations are worried that another employee with better skills in generative artificial intelligence could replace them in their role in the next year, according to a Feb. 12, 2024 report from D2L, a learning technology company.

Generation Z workers appear to be most worried about this. While 52% of Gen Z workers said they’re worried about being replaced within the next year, 45% of millennials and 33% of Generation X workers said the same.

“What this new data suggests is that there’s an opportunity for employers to help workers better prepare themselves for the future and to give people the confidence that they can continue to make a meaningful contribution in their roles,” Sasha Thackaberry, vice president of Wave at D2L, said in a statement. “Skills development — whether it’s on using generative AI more efficiently or simply upskilling to stay ahead of change — is crucial for workers to keep up with the rapidly changing landscape of work.”

In a survey of 3,000 full-time and part-time U.S. employees, 60% said they want to use generative AI tools more frequently at work during the next year. About 49% said they’re already using AI tools at least once a week at work, and 52% said they’re using the tools outside of work. At the same time, 37% said they never use AI tools.

In preparation for the future workplace, younger workers were more likely to say they plan to take multiple professional development courses during the next year. About 26% of Gen Z workers and 24% of millennials said they plan to enroll in 6-10 courses in the next 12 months, as compared with 12% of Gen X workers.

Despite ongoing unease about AI in the workplace, worker confidence may be growing compared to previous years, according to a Robert Half report. HR and tech professionals, in particular, believe generative AI tools will create more demand for their skills.

However, mid-career professionals and managers (between ages 25-54) appear to be most at risk for generative AI disruption, according to an Indeed report. Many of these positions are considered “exposed” to disruption, where AI can perform a high percentage of the required skills in a “good” or “excellent” manner.

From an HR perspective, AI tools have the potential for “massive gains,” an HR software expert told HR Dive. AI can help with predictive text and scheduling functions, for instance, which may assist with talent acquisition. AI may also account for human bias and improve diversity and inclusion objectives, she said.

Dive Brief:

• Mercedes-Benz and robotics company Apptronik have agreed to deploy humanoid robots at the automaker’s manufacturing facilities, Apptronik announced March 15.
• As part of the agreement, both companies will study the potential use cases for Apollo humanoid robots in logistics, such as delivering parts to assembly line workers.
• The robots can help automate tasks that are physically demanding, repetitive or hazardous for humans. They can also address labor shortages by performing less desirable tasks.

Dive Insight:

Advances in robotics technology can help make workplaces safer for humans and increase efficiency. In January 2024, BMW announced a similar project at its manufacturing facilities in South Carolina. Tesla is also considering deploying its AI-powered, humanoid “Tesla Bot” in its factories to work alongside humans.

"Mercedes plans to use robotics and Apollo for automating some low skill, physically challenging, manual labor – a model use case which we'll see other organizations replicate in the months and years to come,” Apollo CEO Jeff Cardenas said in the press release.

Apptronik was founded in 2016. The company spun out of the Human Centered Robotics Lab at the University of Texas at Austin. The humanoid Apollo robot resulted from Apptronik’s experience building over 10 previous robots, including the Valkyrie robot for NASA. 

The Apollo robot is around the size of a human worker, standing 5 foot 8 inches tall and weighing 160 pounds. It can lift objects weighing up to 55 pounds and work alongside humans in industrial settings, performing more physically demanding tasks in factories. The robot can operate with its onboard battery pack for up to four hours.

“This is a new frontier and we want to understand the potential both for robotics and automotive manufacturing to fill labor gaps in areas such as low skill, repetitive and physically demanding work and to free up our highly skilled team members on the line to build the world's most desirable cars,” Jörg Burzer, who’s in charge of the Production, Quality & Supply Chain Management division, said in the press release.

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On one filling line each day, Medivant Healthcare produces 20,000 single dose injectable vials. These vials end up at patient bedsides, on hospital crash carts and in the emergency room, often for patients needing an anesthetic. 

The FDA requires that each vial be visually inspected before leaving the plant. Currently, Arizona-based Medivant uses humans to verify that there are no visible particulates or label defects that would render the vials unusable. 

But soon, the company’s vial inspection machine will take over that process. “People get tired and bored,” and can miss some defects, while the machines do not, said Medivant CEO Viraj Gandhi.

As part of the machine’s AI training process, Medivant sets standards for what the banned particles look like, to use in the algorithm training, Gandhi added that AI is seeping into many parts of the drug manufacturing process, something the FDA is monitoring. Earlier this year, the agency released a discussion paper, Artificial Intelligence in Drug Manufacturing, requesting comments from stakeholders and the public on how the technology can be best used in the sector.

Integrating AI into drug manufacturing is difficult because the industry is highly regulated, said Thomas Hartman, president and CEO of the International Society for Pharmaceutical Engineering. Any process changes, including the direct or indirect introduction of AI, can impact product quality, requiring the manufacturer to go through a comprehensive change control procedure, which is time and resource intensive.

How drug manufacturing currently uses AI

In addition to quality control, AI can help manufacturers understand how to optimize production processes. AI algorithms can help companies understand how trends and facility temperatures can impact the manufacturing process, including the impact on product quality or yield, said Sue MarchantEVP of product at MasterControl, which sells software for quality management and manufacturing execution. 

Some manufacturers use AI to focus on a single manufacturing step prior to commercialization, according to Marchant. During the R&D phase, they run through the manufacturing process changing one step at a time to determine if that improves the quality, results or efficiency. 

AI can be used to augment a company’s visibility into operations, such as an unplanned deviation on the manufacturing floor. It can also provide contextual data, like sharing other times something similar happened at the facility and what caused it. 

“We’re providing [workers] information to potentially guide their actions and assess risk,” Marchant said. “But we’re not yet prescriptively saying we believe you should take this action, so it’s just introducing AI into those processes in a very careful way.” 

The technology can be integrated on the quality side, to make it easier for manufacturers to comply with regulations and ensure their practices are safe and secure, by reviewing data and highlighting at-risk areas, Marchant noted. 

Challenges implementing AI in drug manufacturing

The biggest roadblock to implementing AI in drug manufacturing is cost, Gandhi said. Medivant spent millions of dollars on its semiautomatic vial inspection, which he says is 10 times more than the cost of human labor for the same job. 

“Everyone wants to do this, but you need enough capital,” Gandhi said. 

Medivant bought its machine four months ago and is training and validating it with sample vials for four to five months, then will test it with commercial batches. After putting it into production, the company will also perform manual observations for six to nine months, to ensure there are no false negatives in the inspections. Then it will become the manufacturer’s primary inspection method. 

Investing in automation and AI is difficult for smaller manufacturing companies like Medivant, which makes generic, low-cost drugs like epinephrine and lidocaine. The company’s products mostly sell for dollars per vial, Gandhi said. 

AI tools are, however, more widely adopted by larger manufacturers. “The big ones typically take a risk in new equipment and see how it goes,” Gandhi added. 

Another limitation to adopting AI is having the appropriate digitized data available to train the algorithms and run the analyses. This can limit the AI options to larger players, Marchant said, as many smaller manufacturers still use paper logbooks or rely primarily on on-premise data storage, rather than cloud services.

Incorporating AI may require that the “entire manufacturing process is digital, you have outlined every step in your manufacturing process, you are capturing every step digitally, you’re capturing the date and time it occurred, who participated and what the parameters of each step were,” Marchant said. Manufacturers may also need to combine and accurately integrate data from disparate sources. 

The FDA’s role

The FDA “has recognized and embraced the potential of advanced manufacturing to bring benefits to patients and consumers,” an agency spokesperson told Manufacturing Dive. 

Its Center for Drug Evaluation and Research established the Emerging Technology Program in 2014 to work with stakeholders on supporting advanced manufacturing, including tech such as AI. CDER recognized that regulatory policies and programs may need to evolve to enable timely technology adoption, according to the spokesperson.

“A lot of what we’ve seen [from the FDA] is them trying to get a handle on it,” Marchant said. “There’s not a lot of strong guidance about what companies should do.” 
The result, she said, is companies may be hesitant about using AI as they are uncertain what it will mean for them in the future.

Marchant said her customers are erring on the conservative side, asking in requests for proposals how AI is used in MasterControl software. They are concerned about whether future FDA requirements will interfere with their processes if they adopt AI tools now. 

“Everyone’s sort of in a wait and see mode,” Marchant noted. “The FDA definitely wants to encourage innovation, because their message is that they’d like to embrace innovation, and yet what does that really mean? That’s what nobody really knows yet.”

The FDA is interested in incorporating AI in drug manufacturing and highlighted for Manufacturing Dive ways it could potentially improve the process: 
•    Identify optimal process design and scale-up strategies that might reduce development time and waste  
•    Gain greater control of product and process quality while reducing human involvement and human error  
•    Better monitor manufacturing processes and detect changes in performance that trigger preventative maintenance activities and reduce process downtime  
•    Prevent more process deviations, and in the case of a deviation allow for better root cause identification of the underlying problem

In addition to requesting feedback from stakeholders and the public, in September the FDA cohosted a workshop with the Product Quality Research Institute on the regulatory framework for the use of AI in drug manufacturing. 

CDER’s goal, the spokesperson said, is to help patients realize the benefits of using AI in drug manufacturing, while minimizing risks, such as those stemming from the use of AI for unethical outcomes. 

The biggest fear, Gandhi said, is that the data can be manipulated. On the inspection side, there is already stringent FDA regulation to validate the accuracy and security of the machine and inspection results.

Dive Brief:

• Employees may be warming up to generative AI: 4 in 10 of the more than 2,500 U.S. workers surveyed by consulting firm Robert Half believe the technology will have a positive effect on their career, the firm announced Aug. 22.
• Attitudes differ by occupation, the May 2023 research found. About two-thirds, 63%, of technology professionals and more than half of HR professionals said generative AI will create more demand for their skills. Age group also plays a role, as 63% of Generation Z respondents and 57% of millennials are optimistic about AI’s benefits, compared to 30% of Generation X respondents and 21% of baby boomers.
• Generative AI’s greatest benefit is automating time-consuming tasks, more than one-third of respondents said, while 30% said the technology increased efficiency and productivity. For now, “embracing generative AI in the workplace will require a clear set of guidelines and openness to its potential,” Trisha Plovie, Robert Half’s senior VP of future of work, said in a press release.

Dive Insight:

The Robert Half findings suggest employees may be becoming more comfortable with the integration of AI into the workplace, a departure from recent studies revealing widespread concern among U.S. adults that AI will take jobs from humans, including theirs.

Recent college grads have also expressed unease about AI, according to a Cengage Group report released in July. Hiring managers surveyed for the report confirmed that AI could replace some entry-level jobs, teams and skills.

Mundane HR tasks are no exception. Earlier in 2023, Microsoft, IBM and Google Cloud announced automated tools that can create job postings and listings, identify and contact potential candidates and manage employee requests. Language-learning models like ChatGPT can take over sending emails and sorting through applications, sources previously told HR Dive.

However, many HR managers have already embraced the tech, according to Robert Half. A separate survey by the consulting firm found that HR managers said they were using generative AI to write job descriptions, find and screen candidates, and evaluate employee performance.

Managers in other industries have welcomed the technology as well, the firm’s research showed: Those in finance and accounting said they’re using generative AI to automate data entry, while managers in administrative and customer support said they were using it to analyze and categorize customer feedback.

Generative AI is also helping tech managers process large volumes of data to improve system performance, and managers in the marketing and creative sectors are using it to write copy for campaigns, social media and communications, Robert Half said.

Employees who know how to use generative AI platforms — including ChatGPT, BERT and Stable Diffusion — have reasons to be confident, a recent Upwork report found. Generative AI jobs were up more than 1,000% on the platform in the second quarter of 2023 compared to the end of 2022, Upwork said.

Dive Brief:

• Tesla is spending $500 million to install a Dojo supercomputer at its Riverbend Gigafactory in Buffalo, New York, Gov. Kathy Hochul said in a Jan. 26 press release.
• Tesla’s Dojo supercomputer, which will be installed over the next five years, will be used to train its artificial intelligence systems that support autonomous driving.
• The Dojo supercomputer is expected to be one of the most powerful computing platforms in the world, with the ability to train machine learning models and process millions of terabytes of data from Tesla’s electric vehicles.

Dive Insight:

The data processed by the Dojo supercomputer will help Tesla improve the safety and engineering of its Full Self Driving and Autopilot autonomous driving features and advanced driver assistance systems, according to the press release. 

During Tesla’s Q2 2023 earnings call in July 2023, the company identified four main technology pillars required to enable vehicle autonomy at scale: large, real-world datasets; neural net training; vehicle hardware; and vehicle software. The Dojo supercomputer will support faster and cheaper neural net training for Tesla’s self-driving vehicles, according to the automaker.

“The better the neural net training capacity, the greater the opportunity for our Autopilot team to iterate on new solutions,” Tesla wrote in its Q2 2023 earnings presentation. 

In September 2023, Morgan Stanley analysts led by Adam Jonas wrote in a note that Tesla’s Dojo supercomputer could boost the electric automaker’s market value by $600 billion by supporting AI-powered robotaxis, vehicle software, and advanced computer vision processing, according to a Reuters report.

Dojo can open up new addressable markets that "extend well beyond selling vehicles at a fixed price," the analysts wrote. "If Dojo can help make cars 'see' and 'react,' what other markets could open up? Think of any device at the edge with a camera that makes real-time decisions based on its visual field."

In December 2023, Bloomberg reported that Tesla’s Dojo project leader Ganesh Venkataramanan departed the company. Former Apple Inc. executive and Tesla director Peter Bannon leads the project now.

Injuries are an ever-present threat in the manufacturing world. In 2021, the manufacturing industry was preceded only by the healthcare and retail industries in its rate of nonfatal work injuries and illnesses. 

AI is poised as a way to address many of the hazardous elements of manufacturing workplaces. The technology can help limit employees’ exposure to loud environments, unwieldy machinery and dangerous tasks by streamlining processes and helping workers focus on less physically risky activities. 

In manufacturing, many of AI’s potential benefits are concentrated in replacing the cause of the most common workplace injuries. These include “musculoskeletal disorders, mainly from overexertion in lifting and lowering, and being struck by powered industrial trucks and other materials handling equipment,” according to an OSHA spokesperson. 

There are several ways to reduce those points of risk, with the most dangerous manufacturing tasks standing to benefit the most. 

In industrial settings, autonomous vehicles such as drones and robots can be used to perform dangerous tasks, including inspecting pipelines, tanks, and other structures, said AI startup Alpamayo co-founder Christoph Netsch. Here, hazards due to worker fatigue, forgetfulness or other human error are significantly reduced.

Maintenance activities often bring an increased risk because it can require workers to go outside of standard safety protocol and “improvising,” Netsch said. These processes can be supported by predictive AI, which can monitor asset conditions in real time and reduce the amount of maintenance needed.

For memory chip maker Micron, early detection of a machinery malfunction via thermal imaging can mean the difference between “making a simple repair and replacing an entire piece of equipment,” which besides being costlier, can mean more risks to the worker. 

With some AI tools, a workplace that’s already digitized or is in the practice of collecting data from various sources is necessary to implement these tools, Netsch said. 

He gave the example of one gas turbine factory that remotely operated gas and oil platforms. In such a setting, establishing a predictive maintenance diagnostic service required workers to first “implement a uniform data structure,” so that data coming in from different sources was standardized.

Some of the easiest ways to implement AI in the workplace are systems employees can bring into the field without significant digitization of the organization. Computer vision, for instance, is a standalone tool that can detect if workers aren’t wearing protective equipment, or even if there are anomalies in production environments. It works because automated systems can detect hazards at a rate that humans cannot replicate.

Virtual training is another area in which AI has the capacity to make the process easier to integrate into operations. While AI cannot serve as a true substitute for real-life scenarios, it can still help prepare workers, according to Muilenburg Manufacturing Consulting founder Michael Muilenburg.

“[If you’re a firefighter], you don't want to light a building on fire every single day,” Muilenburg said. “You can use virtual reality to simulate that.” 

While the potential benefits of this technology can translate to a reduction in injuries and improved workplace safety, introducing any new element into a manufacturing site introduces a new set of risks that managers should account for.

Whether it’s an industrial robot or automated pallet wrapper, workers should be made aware of the unique hazards associated with working around these machines, said an OSHA spokesperson. Otherwise, they simply risk replacing one source of hazards with another.

Muilenburg said he’s an advocate of a hybrid approach, in which AI and workers can each use their strengths in tandem. He cites one company which opted to have people, not robots, working the assembly line, “frankly because humans were better at it,” while AI-powered robots were responsible for the final inspection of the parts. 

“That approach was really good because the robot was more consistent than the human eye at catching these these little deviations,” Muilenburg said. “So I thought that was a great application of the humans adding the value and the robot simply inspecting and qualifying the final result.”

As food and beverage companies look to artificial intelligence to modernize and streamline their plants, Jonathan Darling believes the technology can be used to bring CPGs closer to their consumers.

Darling, the industrial automation market segment leader for Schneider Electric, told Food Dive, the ultimate goal of his company is to bring tools like AI to companies to streamline their supply chains in an effective way.

One way AI can be effectively utilized by food and beverage companies, he said, is contextualizing data points to center their consumers in the products they create and what consumer groups they should serve — for example, using data that shows if a consumer is more likely to buy Oreos since 70% of their social media followers bought them.

“It can help to analyze data to see if decisions can be made on new equipment investments or, if they need to shift how they're making a product to reduce overuse of ingredients or loss of ingredients through manufacturing issues or efficiencies,” Darling said.

Several food and beverage companies have leveraged and increased their reliance on AI in recent years for new product development, often in burgeoning categories like better-for-you foods. In 2022, Mars entered a collaboration with life sciences company PIPA to advance the development of plant-based ingredients for new products. And in April 2023, beverage giant AB InBev announced the development of a “futuristic beer” — Beck’s Autonomous — which was selected by artificial intelligence after developing millions of flavor combinations. And ADM is partnering with food tech company Brightseed to develop immunity-boosting ingredients.

Darling said that while AI is a game changer, its outputs are only made up of its inputs.

“We understand that the tool is not the answer, but it is the conduit that helps companies move towards the achievement and accomplishment of their goals,” Darling said.

Darling said Schneider Electric works with a variety of CPGs — large breweries, coffee manufacturers and water bottlers — to verify that the machinery in its facilities is not at risk of obsolescence. It then helps companies create a roadmap to implement new equipment into its plants, in terms of budgets and resource allocation.

While there are fears among some observers about the potential for increasing artificial intelligence leading to mass labor displacement, Darling said food and beverage manufacturers can use AI to improve their plants and keep them running. Specifically with the recruitment process, he said, the technology can help ensure candidates are qualified for the positions.

“I think a lot more comfort is going to come in learning how to use it as a tool versus as a solution,” Darling said.

How tech could drive down emissions

Companies looking to lower their carbon footprint over the next decade — particularly indirect scope 3 emissions from ingredient transportation and waste, which make up 87% of industry emissions — will turn to AI to help discover where proper investments can be made.

Darling said the biggest issue automation companies run into when attempting to make food and beverage supply chains more sustainable is working to implement new software and data without any major disruptions to their production.

While food companies may make lofty expectations for their own abilities, Darling said they must be tethered to tangible outcomes. He quoted Bill Gates, who once said people often “overestimate what they can do in one year and underestimate what they can do in ten years.”

But Darling doesn’t think the food industry’s emissions goals are unachievable. He said companies will primarily be concerned about showing a positive return on investment into sustainable practices — something they will need to continue working towards. This includes tailoring strategies to specific locations of facilities and having the right people in charge to course correct.

“What’s needed is an internal audit of where we are at at our current state, and what is actually achievable and actionable that's going to help us move the needle towards that five- or 10-year goal when it comes to reducing emissions,” Darling said.

When Schneider Electric works with food companies, he said, it details risks that currently exist in its supply chain and examines whether new technologies can help mitigate them.