According toThe Manufacturing Institute, by 2030 one in five Americans will be 65 years or older. By 2035, for the first time in U.S. history, retirement-age Americans will outnumber Americans under the age of 18. 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, in many cases, they are taking their knowledge with them.
而年轻员工可能会缺乏predecessors’ hard-earned know-how and 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, by necessity, 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 manufacturing sector suffers acutely from labor shortages in an environment of population aging and labor market tightness. This effect is deemed the manufacturing “skills gap.” 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 include increased downtime, incorrect diagnoses of issues, increased time to solve problems, and a lack of understanding of how hardware can and should operate.
Many firms are rightfully worried that their older workers will retire before passing their entire body of knowledge to the next generation. This is especially concerning for organizations whose cultures rely on passive information transfer and interpersonal connections to share knowledge. If organizations don’t have a playbook for institutionalizing and preserving such information or fail to utilize mentorship and apprenticeship programs properly, this vital knowledge may be lost when older workers depart.
At the same time that 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
Many organizations are transitioning to become smart plants to better deal with labor and skills shortages. To do this, plant operators need easily translated and reviewable information. Using data and analytics to run plants more efficiently can also ease the burden of the labor shortage because data-rich companies will attract the best new engineers. Today’s students 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. Data is quickly becoming a native method for new engineers to troubleshoot issues.
In addition to preparing your factory for the future, following are just a few of the operational benefits of harnessing your plant’s data:
- Power data can help you know how and where power is being drawn. This can help you make upgrade decisions, diagnose manufacturing line issues, and more.
- Monitoring plant performance will help you dig into problems, such as why batch quality is off, a line is not making enough product, etc.
- Deploying vibration, temperature, sound, and vision sensors can augment the knowledge lost from retiring skilled workers.
- Visible data, put in the context of each manufacturing role, can reduce downtime and the time spent troubleshooting.
- More advanced analytics, such as PID loop analytics, predictive maintenance, and model predictive control, can improve plant efficiency.
Where to start
Ultimately, Industry 4.0 is about investing in information. That’s why creating a smart plant means starting with the metrics that matter most. As time passes, the history of data you accumulate will help you better understand and run your plant. You can then use that data for reporting to troubleshoot, identify, and resolve issues. 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 the problems of the plant? What can you do today to set them up for success?
Dan Riley is the Analytics Manager atInterstates, a certified member of theControl System Integrators Association(CSIA). For more information about Interstates, visit its profile on theIndustrial Automation Exchange.
