Although digitization and artificial intelligence (AI) are key steps toward achieving advanced automation in manufacturing, there is significant resistance among workers to adopting these new technologies.
However, it’s becoming increasingly clear that these advanced technologies not only reduce production time but also help to lower costs and streamline processes. So how can manufacturers overcome the hurdle of employee resistance to fully embrace digitization?
Identify 3D Tasks to Help Increase Productivity
Employers know that their employees do not aspire to work on 3D tasks—dangerous, difficult, demeaning—like battery inspections, which are notoriously dangerous to perform. However, it’s important for employers to help their employees identify these elements of their manufacturing process.
In showing a more system-level appreciation for their work and a willingness to eliminate such tasks, workers are demonstrating that they have what it takes for additional training and advancement beyond easily automatable work.
Creating Opportunities for Upskilling
It’s important to note that apprehension around AI replacing manufacturing jobs is relatively misguided. In fact, labor-based jobs that are replaced by autonomous manufacturing don’t disappear entirely. We are seeing a shift from repetitive, injury-prone labor to jobs that capitalize on non-physical strengths, including creativity, critical thinking and problem-solving. Furthermore, humans will always play a role in the maintenance and monitoring of autonomous equipment.
On the other hand, employee resistance to digitization can partially be attributed to the fear that AI will reduce their decision-making power. However, it’s important to note that the types of decisions made by AI are not always the types of decisions that humans can make. Instead of AI replacing the employee, AI will compliment employees’ skills.
In addition to opening up more strategic jobs for workers to move away from repetitive labor, digitization will also create many opportunities for those with advanced skills. There will be an increased need for in-house AI and machine learning (ML) specialists with more advanced education and background who can address technology issues in real-time or communicate problems to autonomous providers for remote troubleshooting.
Armed With Information
By evaluating and understanding the big picture in the production process, data can guide employees in gaining deeper insights into how to improve processes. For example, if there is a quality-control issue at one station, the root cause may lie several stations further upstream. A root cause analysis can be accomplished in minutes with AI but would take human ingenuity and time to interpret and implement an appropriate fix.
“Gamifying” the work experience can entice workers to do the 3D jobs they typically wish to avoid. For example, a factory can offer bonuses when certain goals are met, thus enabling workers to see their job as more colorful rather than monotonous. It also fosters employee growth by enabling workers to view their positions within the context of the whole process.
The Tasks: Complex and Rewarding
AI provides feedback about the various tasks that lead to a project’s completion; it can also offer insights into how to improve a process. Therefore, even if an employee doesn’t upskill, they can still understand their role in the larger picture, making them feel more engaged.
The absence of a bigger picture limits workers’ insights into the manufacturing process as well as an understanding of how their position plays an integral role. For example, a line worker has made three improvement suggestions over the last six months in response to what the AI is telling them. They bring this information to management, who note that the line worker has the right mindset in wanting to improve processes, thereby increasing that line manager’s odds of growth within the company.
Barriers to Adoption
In a typical manufacturing setting, information sharing is limited. After all, allowing employees to have a view of the entire manufacturing process means they can reveal trade secrets if they leave the company. However, employees motivated by more rewarding work are less likely to resign, and AI can quickly make many processes across otherwise disparate manufacturing verticals standardized.
AI can address the cultural resistance of “this is how we’ve always done things” by providing unique data on production processes and their effectiveness. For example, instead of performing re-work to deal with quality issues at the end of a line, AI can quickly and accurately identify the root cause of the issue upstream. This minimizes re-work at end stations and ensures that all workers have the opportunity to positively contribute to higher-quality products.
Identifying the Core Issue
There’s an old saying in manufacturing that bottlenecks never disappear, they just move. If a worker has a poor reputation for being behind their station, AI may reveal the reason is a problem occurring three or four stations upstream. AI gives workers the opportunity to provide feedback that can be utilized across the team.
When an employee is late three out of five weekdays, colleagues may believe that they are not dedicated. However, AI can show that the worker has been consistently late because of back aches; perhaps they are performing a movement incorrectly, doing a task they shouldn’t be doing at all or compensating for another colleague.
AI can help make issues more transparent and show that problems might not be where you think that they are; it provides facts devoid of emotion.
The Bottom Line on Manufacturing Automation
Automation and digitization in manufacturing is ultimately going to be valuable; you’ll spend less time chasing phantoms and spend more time fixing the things that are actually wrong.