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AI Can Empower Manufacturing HR Teams to Counter Labor Shortage Issues

AI 可以協助人資部門招募新血、培育員工並留住人才,下列為人資部門使用 AI 的 4 個應用方向

Manufacturing's Labor Shortage Problem

The manufacturing industry faces a significant labor shortage, and HR managers struggle to fill open positions. A study from 2018 found that more than 8 million global manufacturing jobs could go unfilled by 2030.

According to a forecast by the National Association of Manufacturers, 2.1 million manufacturing jobs in the US may remain vacant by 2030. The economic implications of these unfilled positions could be substantial, potentially resulting in a loss of $1 trillion or more for the industry. Additionally, a study conducted by Deloitte and The Manufacturing Institute in the previous year revealed that the industry had a record number of job openings, which cannot be solely attributed to the pandemic.

In this article, we look at some of the main reasons driving the labor shortage problem in manufacturing and how HR teams can use AI to alleviate these issues.

Reasons Why Manufacturing Has a Labor Shortage Problem

1.Workplace Safety Concerns

Entry-level manufacturing jobs are often filled by inexperienced workers, and this lack of experience significantly raises the risk of injury. Even individuals with prior manufacturing experience may face an elevated risk of injury if they have been out of work, are working with unfamiliar machinery, or are working longer hours. Their physical conditioning may have diminished during the pandemic-induced break, leading to a higher likelihood of workplace injuries.

Since the pandemic, many workers are now re-evaluating their life priorities and prioritizing health and safety way more than before.

2.Mental Health Concerns

Throughout the pandemic, workers in the manufacturing sector encountered a plethora of challenges, which led to staff mental health complications. Some experienced frequent shutdowns and temporary layoffs, while others were required to work long hours due to staffing shortages and heightened consumer demands. These challenges have become so pervasive that one study reported an 86% increase in burnout among manufacturing workers in 2020.

These mental health challenges have, in some cases, resulted in workers not returning to their jobs. For instance, 34% of manufacturing companies in the United Kingdom have reported an employee quitting due to a lack of attention to their mental well-being.

3.Higher Demand For Tech-Related Skills

Manufacturing jobs often have a poor perception of being unskilled low-level labor. In fact, modern machines used in manufacturing are extremely advanced, many are wired in order to gather data and improve reliability and are also programmable for automation. Operators need to be both skilled and educated.

Take for example a stationary power tool used to cut, shape, or remove material from an object. To use this kind of machinery, one needs an understanding of the relationship between the machine's operating parameters and the vibration behavior of the cutting-edge, which requires an education in math, geometry, and physics.

The staff shortage in manufacturing is not just an issue of lack of available staff, but moreover, staff with the skills and training to operate modern machinery.

4 Areas Where AI Can Empower Manufacturing HR Managers to Solve Staff Shortage Issues

1.Employee Recruitment

AI recruitment tools can help manufacturing HR managers save time and identify the most qualified candidates, meaning they can be more efficient when filling open positions.AI software can scan resumes, conduct initial interviews, and even analyze candidate responses to assess their suitability for the role.

According to a 2022 PWC survey, 39% of HR managers are already using AI in recruitment, making it one of the main areas of initial adoption.

2.Employee Retention

AI tools can improve manufacturing employee retention rates by analyzing the reasons why employees might leave and taking early measures to address them.

At Profet AI's 2022 Crossover Talks, Kenny Chiu, deputy general manager of Shuttle Service, the chosen logistics partner for Taiwan’s high-tech sector, explained how his company uses AI to highlight the characteristics of drivers that have resigned and which present drivers have a high probability of leaving. They then reach out to those drivers in advance and see if there is any way to help them and improve their situation.

3.Health and Safety

As mentioned, health and safety concerns are some of the main reasons why workers are passing over manufacturing jobs or those already in the industry looking to leave.

AI-powered tools can help to improve the health and safety of employees and alleviate these concerns. By analyzing data from multiple sensors and devices, AI has the ability to detect safety hazards in real time. This empowers organizations to proactively identify potential accidents and implement preventive measures before they occur. AI-powered devices can also help to monitor employees' health and well-being, providing alerts and recommendations to prevent injuries and illnesses.

4.Training and Development

The labor shortage problem is mostly a lack of skilled workers who can match the challenges of today’s advanced manufacturing machinery.

If companies are unable to find skilled workers to hire, they may resort to hiring less experienced workers who require additional training and onboarding to become competent. AI can help to give personalized digital work instructions and can help these novice or less-skilled workers become proficient more quickly by tailoring the guidance to their skills, training, and job performance. This individualized performance support can bring various benefits beyond just faster onboarding and increased proficiency, such as greater compliance with safety and quality protocols, reductions in defects and waste, and improvement in the effectiveness of on-the-job training.

Manufacturers can also use AI to create a framework for both personnel succession and the handover of knowledge, which will make them less vulnerable when processes need to be replicated or there needs to be a handover of know-how if senior staff retire.



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