Is Manufacturing Ready for the 2030 Workforce Gap?
- GPA
- May 13
- 5 min read
The manufacturing workforce gap is often discussed as a future problem, but for many manufacturers, the pressure is already showing up on the plant floor.
According to the National Association of Manufacturers (NAM), 3.8 million manufacturing roles will need to be filled over the next decade.
The Manufacturing Institute also predicts the U.S. could face about 2.1 million unfilled manufacturing jobs by 2030.
The numbers point to a workforce challenge that reaches beyond open roles. Manufacturers are also preparing for the loss of experienced knowledge, changing skill requirements, aging systems, and a growing need for reliable operational data. Together, these pressures are shaping how the next generation of manufacturing work will be supported.
As Bill Medcalf, GPA’s VP of Digital Transformation and Innovation, explained,
“We’re losing a lot of engineers just because they’re retiring. That workforce is retiring. Our senior operators are retiring.”
How the Workforce Gap was Created
The workforce gap did not happen overnight. There was a period when engineering was a strong career path, followed by a period where engineering roles were reduced and fewer students entered the field. That left a gap in the talent pipeline. Bill described it this way:
“There was a hole where there were not a lot of engineers. Now we’re starting to build engineers, but they’re coming in and they’re only at a point of just a few years of experience.”
That gap is now showing up between experienced operators, engineers, and maintenance professionals who have spent decades learning the plant, and newer employees who are still building that depth of operational experience.
A Generation of Knowledge is Leaving
For many manufacturers, the most valuable operational knowledge has lived inside the minds of experienced people. It was built through years of troubleshooting, adapting, and solving problems under real conditions.
Bill described the experienced employee as “that single source of knowledge.”
That kind of knowledge is powerful, but it becomes risky when it is not documented or translated into systems others can use. When those employees retire or leave the workforce, they take more than job experience with them. They take context.
“As they leave that workforce, they’re taking that tremendous amount of experience and knowledge with them,” Bill said.
That is why the workforce gap is also a knowledge gap. Manufacturers are not only trying to replace people. They are trying to preserve decades of decisions, instincts, lessons learned, and system understanding.
The Workforce Evolved, but the Industry Did Not
Today’s workforce is more comfortable with connected tools, visual dashboards, digital workflows, and immediate access to information.
Bill noted that today’s workforce is “more technology forward,” “more used to seeing infographics,” and “more data driven.”
The problem is that many manufacturing environments have not evolved at the same pace. A new employee may come into the workforce expecting connected systems and accessible data, only to find equipment that was designed decades ago.
“The equipment that we’re dealing with is twenty years old,” Bill explained. In many cases, that equipment was built “before the internet or just at the very inklings of the internet even being a thing.”
This disconnect does not mean legacy equipment has no value. It means manufacturers need a practical strategy for connecting, documenting, and modernizing operations in a way that supports both current production and future workforce needs.
Is It Really a Skills Gap?
The phrase “skills gap” is accurate, but it can oversimplify the problem.
Manufacturers need people who understand not only data, automation, connected systems, and modern technologies, but also legacy equipment, process behavior, plant realities, and the practical details that are rarely taught in a classroom. Bill explained,
“Manufacturing is ten years behind, twenty years behind in technology. And so the people that are going to school today are going to be very data driven, very data forward. And the legacy equipment is not that.”
That creates a skills gap in both directions. New employees may understand modern tools, but they may not be prepared for older systems. Experienced employees may understand the equipment deeply, but they may need support adapting that knowledge into more connected, data-driven environments.
What Role Will AI Play?
AI will play a role in the future of manufacturing, but it is not a shortcut around the work manufacturers still need to do. Bill said,
“I am 100% for AI, whether it’s in school or in the workplace.”
He also pointed to the need for governance, especially around privacy, company secrets, and intellectual property.
AI can support faster learning, better decision-making, and more consistent access to information. But it depends on the quality, structure, and reliability of the data behind it. Bill made the foundation clear:
“You have to have it standardized and documented before you can even start to see the benefits.”
Time is Working Against Us
The concern is not just that the workforce gap exists. The concern is that there may not be enough time to address it if manufacturers wait too long. Bill put it plainly:
“My fear is that we are so far behind the curve in standardization that we will not be able to fill the void by 2030.”
That void is not limited to staffing. It includes:
documentation
data structure
system connectivity
training
operational visibility
These are not problems that can be solved immediately once the pressure becomes obvious.
From Experience-Driven to Data-Driven Operations
Manufacturing has always depended on experience. A senior operator may know something is wrong before a system alarms. They may hear a sound, feel a vibration, or notice a subtle change that others would miss.
Bill described this as “a hunch” or “a gut instinct” built through experience.
That experience should be captured in a way that preserves its value and makes it easier to share across the operation.
With the right technology foundation, manufacturers can begin to translate that experience into data. Instead of relying only on what one person hears or feels, the operation can monitor speeds, vibration, and other performance indicators in real time.
“With AI, we can actually monitor the speeds and the vibration,” Bill explained, allowing decisions to be made “based on data rather than gut instinct.”
This is not about removing people from the process. It is about giving people better information, preserving expertise, and making critical knowledge more accessible across the organization.
Should Manufacturers Invest in Skills or Technology?
The better question is how to invest in both in a way that supports the same goal. Bill said,
“I think that it needs to be equal effort given to both.”
Manufacturers need knowledgeable people involved in building the next generation of digital intelligence. The people who understand the operation should help shape the systems that will support it moving forward.
“I need to have knowledgeable people as I’m building my digital intelligence,” Bill said. “I want these people to build into that digital intelligence and be a part of that.”
That is where the path forward becomes practical.
Capture the knowledge that is leaving.
Standardize what has become inconsistent.
Build a data foundation that supports better decisions.
Train people to work confidently across both legacy and modern environments.
Then, introduce technologies like AI in a way that is grounded in real operations.
At GPA, digital transformation is not treated as a future concept or a one-time technology upgrade. It is a disciplined approach to helping manufacturers create visibility, structure, and trusted operational intelligence across the enterprise.
The workforce gap is real, but it is not only about the number of people available to fill roles. It is about whether manufacturers are building the systems, skills, and knowledge foundation needed to support the people who will lead the next generation of operations.
Because 2030 is not that far away, and the work to get ready has already started.
