Advanced industrial manufacturers around the globe continue to be challenged by workforce and labor woes1 that are uprooting the industry’s long-relied-upon norms of consistent labor supply and certain skill level expectations. Despite advances in technology (such as gen AI and new approaches to crew scheduling), manufacturers are still struggling to increase productivity, a challenge driven in large part by a younger, less experienced labor force. The critical solution may be reducing time to proficiency—that is, the time it takes for an employee to reach the specific level of skill required for their specific role and contribute effectively.
Our latest research confirms what the advanced industrial manufacturing market has been feeling more broadly: a significant gap in levels of proficiency. Difficult and physically taxing skilled trades (such as welders, electricians, and machinists) take a significant amount of time and training to master. However, seasoned workers are retiring at rapid rates, leaving manufacturers with a shortage of experts who can bring new joiners up to speed. Advanced industrial manufacturers, who frequently build complex products with long cycle times, often forecast labor demand years before a bid is won and base it on assumptions of labor proficiency, which is now being fundamentally uprooted by the realities of labor supply. For example, an aerospace and defense manufacturer wanted to relaunch a product line of legacy weapon systems, but because its current talent base wasn’t trained to manufacture these legacy products, the company needed to reach out to and rehire retired employees.
As this challenge continues to grow, improving time to proficiency becomes an imperative for advanced industrial manufacturers. However, the manufacturing industry is, by design, more insular than other sectors. Organizations are slower to adopt new tools or ways of working because their priority is high innovation at the product level. Organizations find themselves trying to remedy a lack of workforce proficiency reactively and without addressing the true root cause. Performance management, for example, plays a significant role in productivity. But for many advanced industrial manufacturers, the nature of career paths and intentional silos mean managers might not have visibility outside of their direct work areas. This limits their perspective and exposure to broader solutions. A slow time to proficiency also means that talent isn’t earning as much as fast due to the traditional relationship between tenure and pay grade, which could affect employee motivation and satisfaction.
Despite these obstacles, organizations can still boost productivity with the talent they have. Even when existing labor proficiency is low, manufacturers can activate a variety of levers, such as performance management and talent acquisition and development, to increase talent’s time to proficiency. Winning advanced industrial companies are outpacing competitors by aligning the trifecta of business units, operations, and HR to make bold, targeted, ROI-driven investments that build a more agile, capable, and productive workforce.
The evolving labor environment and its growing challenges
In the decade leading up to the COVID-19 pandemic, US manufacturing’s total factor productivity decreased by an average of 0.3 percent per year.2 Driven in large part by an aging workforce, employers are experiencing an increasing hurdle: The proportion of manufacturing employees over the age of 55 has more than doubled in the past 20 years and retirement rates have surged, removing a significant pocket of veteran employers with deep, institutional knowledge (Exhibit 1).
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Two sets of line charts show the proportion of the US manufacturing workforce over age 55 and retirement rates among manufacturing employees. The line chart on the left shows the proportion of manufacturing employees over the age of 55 from 1995 to 2025, with the total US manufacturing workforce in millions highlighted underneath the chart. It shows that since 1995, the proportion of manufacturing employees over 55 increased from about 10% to about 25%, while the total manufacturing workforce decreased from 20.5 million to 15.0 million over the same period.
On the right, the second line chart shows retirement rates among manufacturing employees from 2014 to 2024, with the COVID-19 pandemic and postpandemic era highlighted from 2020 to 2024. Retirement rates have remained roughly steady at between 1.6% and 2.0% over the past decade, with a spike to 2.2% in 2022.
- Manufacturing includes both durable goods (eg, mineral products, metals, machinery, electrical equipment, and transportation equipment) and nondurable goods (eg, food and beverage, textiles, chemicals, and petroleum).
- Retirements are measured using the US Bureau of Labor Statistics (BLS) de¬finition of “other separations,” which includes retirements as well as transfers to other locations, deaths, or separations due to employee disability. Retirement rate is calculated as the number of other separations divided by the total manufacturing workforce.
Source: “Employed persons in nonagricultural industries by age” from Current Population Surveys, BLS, 1995–23; Job Openings and Labor Turnover Survey, BLS, 2014–22
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As a result, employers are facing a “brain drain” because of knowledge not being passed down to new joiners, which affects employers’ overall productivity.
As previously reported, companies risk considerable value if workforce gaps go unaddressed. In aerospace and defense, for instance, a midsize company could avoid more than $300 million in costs and impact to the bottom line by addressing workforce gaps.3
McKinsey research shows that productivity gaps between high and low performers increase by as much as 800 percent as a task increases in complexity.4 Even manufacturers operating in the lowest-complexity jobs still experience a 50 percent premium between high and low performers (Exhibit 2).5
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A horizontal bar chart shows the productivity gap between average performers and high performers, broken down by job complexity. As job complexity increases, the gap in productivity between highly skilled and lower-skilled workers rises dramatically, with the productivity gap for low complexity jobs at 50%, medium complexity at 85%, high complexity at 125%, and very high complexity at 800%.
Source: McKinsey Global Survey: War for Talent 2000, refreshed in 2012
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This trend is expected to persist as employers learn to operate in an environment in which critical knowledge and experience are leaving the workforce at a much faster rate, and new talent is coming in much faster. But some players have shifted this paradigm, and our new research shows that those who move fast to transform their approach to talent are gaining a competitive edge.
Investing in talent and unlocking proficiency
The advanced industrial leaders who are realizing value target investments to three different time-to-proficiency categories.6 McKinsey research shows that compared with industry averages, the advanced industrial manufacturers with the highest levels of labor productivity also have the highest levels of TSR, with a difference of eight percentage points on average.7
To prioritize investing in talent and realizing an attributable ROI, advanced industrial leaders need to bring together all the organizational capabilities (for example, operations, engineering, HR, and IT) to embrace and solve a core problem statement: “How can we decrease time to proficiency to increase resulting productivity?” McKinsey analysis shows that there are three primary time-to-proficiency levers, each with unique problem statements and supporting sublevers—as discussed in the following sections—that can deliver outsize productivity gains in both direct and indirect ways (Exhibit 3).
Image description:
A flow chart shows the effect of talent levers on time to proficiency. From left to right, the objective of investing in talent to increase productivity flows into three main levers of talent acquisition, talent development, and performance management. Each main lever includes a set of sublevers, which are identified as having a direct or indirect role on time to proficiency. Under talent acquisition, sourcing candidates with higher skills, sourcing candidates with higher learning potential, and reducing time to hire have a direct effect on time to proficiency. Under talent development, increasing existing talents’ skill and upskilling highly adjacent talent have a direct effect on time to proficiency, while redeploying similar talent and reskilling moderately adjacent talent have an indirect effect. Last, under performance management, increasing talent performance has a direct effect on time to proficiency, while reducing attrition has an indirect effect.
Source: Ezra Greenberg, Asutosh Padhi, and Sven Smit, “2024 and beyond: Will it be economic stagnation or the advent of productivity-driven abundance?,” McKinsey, January 12, 2024
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Talent acquisition
Talent acquisition has been a struggle for the advanced industrial manufacturing industry because of a shrinking talent pool in certain areas and competition from other sectors. Additionally, younger talent may often be unaware or uninterested in manufacturing jobs. Manufacturers can drive a more effective talent acquisition strategy by solving for the following problem statements:
- How can we better acquire new talent with a higher skill level or who can achieve proficiency faster?
- How can we reduce candidate time to hire?
One industrial manufacturer answered the first question by increasing the sourcing of candidates with higher learning potential.
The manufacturer identified the external sources of talent (such as specific trade schools) that historically produced the most productive hires, with productivity quantified as “cost per productive hour,” and realigned the sourcing strategy to target them. These measures increased hiring of about 70 percent more skilled-trade labor at a lower cost per productive hour with no increase to the HR budget.
Talent development
Talent development is critical for the industry to keep pace with evolving technologies, fill skill gaps, and sustain productivity. However, many employers use a traditional approach for the training component of talent development. Those organizations on the bleeding edge of talent development are considering holistic programs that build capabilities through coaching, apprenticeship, feedback, and on-the-job learning to tackle the following problem statements:
- How can we expand the pipeline of “ready” talent by upskilling or reskilling those in roles and in adjacent roles?
- How can we use “modern” development approaches that make the learning happen faster and better?
One industrial manufacturer tackled these issues by increasing the skills of existing talent. It created a legacy skills video-based training library where soon-to-retire employees taught skills that were more difficult to pick up. The colleagues demonstrated the skills step-by-step, explaining the “why” behind each action, common pitfalls, and tricks of the trade they had learned over the years. These videos were broken into segments and posted on the learning management system accompanied by documentation.
Performance management
Performance management is traditionally used to increase productivity in the near term—however, it is often applied indirectly. New insights into what matters most to employees and managerial techniques can make it easier to deliver increased outcomes via similar levels of effort and help tackle the following problem statements8:
- How can we manage talent differently to increase either skill level or time to proficiency?
- How can we capture and codify critical knowledge that is at risk of leaving the organization?9
An aerospace and defense supplier increased talent performance to solve the first problem statement. The supplier aspired to increase the productivity of their shop floor, which required understanding each employee’s skill level and increased visibility into the performance management approach. The supplier first assessed what skills were needed in the value chain and then realized that only one person was qualified to do a critical operation in a single shop. What’s more, the employee wasn’t working the correct shift for the work that needed to be done, resulting in confusing delays. By diagnosing the root cause of the productivity issue, the supplier understood that pushing harder wouldn’t help. They needed to find a way to sustainably remove the bottleneck. The supplier made a decision designed for long-term ROI: It temporarily reduced individual productivity by pairing up workers with the person who had the critical skill to train everyone else. Once enough operators were trained to staff both shifts, the site almost immediately realized a 15 percent increase in throughput.
Cracking the proficiency code
Decoding the path to proficiency could result in increased productivity and greater long-term TSR. Previous McKinsey research shows that, for a midsize employer, one hour of unproductive labor per week because of lower-skilled employees could cost companies as much as $5,900 annually.10 The organizations that have been most successful at decreasing time to proficiency have embraced a data-driven, test-and-learn approach that is calibrated by cross-functional teams, a willingness to try new forms of technology (including gen AI 11), and a focus on tailoring strategies to the employee experience.
For employers looking to combat their productivity challenges through proficiency, we see four possible bold moves (Exhibit 4).
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A set of donut charts show four bold moves that manufacturers can follow to deliver productivity through proficiency, including pinpointing value, designing and validating, piloting and scaling, and driving ROI. Together, these four moves make up a cyclical labor pro¬ficiency improvement engine.
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Pinpoint the value
Manufacturers need to start with a rigorous fact base that pinpoints where they need to increase the time to proficiency to capture the most value as well as the root causes of the obstacles. Each organization will have a different fact base that will often offer surprising insights into where the most significant value lies. A fact base can challenge long-held beliefs or biases in an organization. For example, one industrial manufacturer believed that its biggest talent development challenge was not having enough training space, but in reality, it already had capacity in its existing footprint. This process required mapping the end-to-end employee life cycle, from prehire to retire; quantifying the cost of all actions and investments; and understanding the resulting ROI and effect on productivity. Many organizations fall short here because they cannot articulate the skills they require in a truly measurable and quantifiable way.
Rally cross-functional teams
Manufacturers can continue to improve labor proficiency by initiating cross-functional teams to design and validate potential solutions. Typically, organizations assume this responsibility falls to HR or engineering. But to truly solve the proficiency challenge, leaders from all organizational areas need to come together to find new and possibly groundbreaking solutions.
Start small and pilot solutions
Manufacturers will need to test solutions on a small scale to build momentum and secure buy-in from leadership. Changes to employee onboarding or technical training need visible wins for leaders to embrace them and believe in their potential. Not only do pilots need to demonstrate measurable improvements, but they also need to help shift organizational mindset and beliefs about their operations.
Drive ROI
Although ROI-driven decision-making is common sense, it’s often not common practice. Manufacturers need to adopt a relentless focus on ROI and embed an organization-wide expectation to use, understand, and track ROI to make real talent decisions.
While the workforce continues to change, organizations have an opportunity to make bold investments to accelerate time to proficiency. Those who take the leap could enable greater capabilities within their workforce, unlocking a faster, more resilient path to productivity and competitive ROI.
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