Bridging The Gap: How Industry 4.0 Will Offset The Impacts Of Workforce Retirements

According to the Manufacturing Institute, by 2035, retirement-age Americans will outnumber Americans under the age of 18 for the first time in U.S. history. What this means for the manufacturing industry, which already suffers from labor shortages, is simple and worrisome: a highly skilled, experienced workforce is on its way out and taking its knowledge with it.

While younger workers may lack their predecessors’ deep understanding of equipment and operations, they will have a potent tool on their side: data. Smart plants running on analytics and Industry 4.0 will help bridge the labor and knowledge gap as older workers retire and new, inexperienced hires take their places.

Labor Transitions And Knowledge Loss

Due to the technical nature of manufacturing production work, the industry suffers acutely from labor shortages in an environment of population aging and labor market tightness. Many retiring engineers and operators have grown up with their plants and have intimate knowledge of the sounds, smells, temperatures, and sights of optimal operations. You cannot replace so much experience with one hire. The potential adverse effects of this labor transition are sobering: increased downtime, wrong diagnoses of issues, increased time to solve problems, and a lack of understanding of how hardware can and should operate.

Firms are rightfully worried their older workers will retire before passing their knowledge to the next generation, but even as jobs are left unfilled and workers age out of the industry, ongoing technological advances, like robotics and artificial intelligence, are transforming the use of labor. They are also exacerbating the skills gap by requiring continuous training, but the benefits of new technology will help solve the labor crisis in manufacturing by driving agility and productivity.

How Data Can Help

To transition to a smart plant, plant operators need easily translated and reviewable information. Data-rich companies will also attract the best new engineers, who live and learn in a data-driven world. Engineering colleges often include data analysis courses, and the application of data is a growing expectation of new graduates.

There is a myriad of benefits to harnessing the data at your plant:

• Power data shows you how and where power is being drawn, helping you make upgrade decisions, diagnose manufacturing line issues, and more.
• Monitoring plant performance helps find problems (batch quality is off, a line is not making enough product, etc.).
• Deploying vibration, temperature, sound, and vision sensors can augment knowledge lost from retiring skilled workers.
• Visible data can reduce downtime and the time spent troubleshooting.
• Advanced analytics, such as PID loop analytics, predictive maintenance, and model predictive control, can improve plant efficiency.

These analytics tools are part of Industry 4.0, the digitization of manufacturing, which is altering the competitive landscape of manufacturing because it improves financial, operational, and brand value across a range of functions and departments.

Where To Start With Industry 4.0

Having a smart plant means starting with the metrics that matter most to your plant. Industry 4.0 is about investing in information. The history of data you accumulate will help you better understand and run your plant. Essentially, you need to start collecting data now that will be valuable to the next generation of workers. Thinking long-term and considering the (possibly inexperienced) operator or plant manager two or three years into the future is key. What vital information will they need to solve problems? What can you do today to set them up for success?

In summary, applying analytics and Industry 4.0 can serve as a method to offset the impacts of the ongoing retirement crisis in manufacturing. Diving into analytics now, before the major labor transition of retiring workers hits the industry even harder, will help your plant remain agile, efficient, and productive.

Read more on this topic in Automation World.