The Future of Manufacturing: AI and People

The longstanding, widespread perception that automation and robots are putting manufacturing jobs at risk is simply wrong: humans will always be a necessary part of the manufacturing process. As facilities continue to evolve and connect more of their assets, people will be needed to process the vast amounts of data generated from the floor so that companies can build things faster, better, and cheaper. For this to be possible, that data must first be converted into a shape and scope that humans can digest.

 

Enter artificial intelligence (AI), which can transform large amounts of raw data into information that a human can read and interpret. Without AI, people would have to learn how to gather data from multiple database systems, connect them together, know which features to investigate, extract those elements manually, and then interpret the results. This process is not only cumbersome and tedious but also very prone to errors—and by the time all of that data is prepared the analysts have usually already run out of time (and patience) to examine it. With the help of AI, those individuals no longer have to spend their time manually crunching numbers but can instead focus on drawing useful intelligence from that data and putting it into action.

 

Recent research from AT Kearney and Drishti presents further evidence to dispel the myth that AI and automation are taking jobs away from people. Their survey found that “72 percent of the tasks in a factory are performed by humans” and that humans continue to drive significantly more value than their machine counterparts. Even as more parts of the manufacturing process become automated, no machine will ever be able to replace human judgement and intuition.

 

As demonstrated by past industrial revolutions, innovations in technology always generate new positions. For example, one PwC report estimates that over the next twenty years in the United Kingdom, robotics and AI will produce as many jobs as they will displace. AI, machine learning, and automation create jobs because specialized skill sets are required to support and maintain them. After machines start generating troves of data, software is needed to analyze that data and convert it into human-readable information. In order to leverage this information, companies still need humans to leverage that data in meaningful ways.

 

Filling a Void

 

To fill these roles, organizations will need to draw on younger workers. However, the job profiles that appeal to this talent pool differ greatly from the job profiles that are currently available on the factory floor. The members of today’s younger generations are accustomed to getting everything on demand—including information. Therefore, digitizing the manufacturing business is a strong first step toward connecting with these potential employees as they enter the workforce.

 

Younger generations are showing an increased interest in science and engineering careers. According to the National Science Foundation, “S&E bachelor’s degrees have consistently accounted for roughly one-third of all bachelor’s degrees for at least the past 15 years.” Additionally, the percentage of doctorates in science and engineering has increased during that time and in 2015 amounted to 64 percent of all doctorate degrees granted in the USA that year.

 

More and more students are demonstrating an interest in science and engineering, and the pursuit of careers in advanced technologies is a logical progression of that interest. As AI and machine learning continue to mature, they will become more mainstream components of technology-focused college curricula and training programs.

 

AI Is a Key Component to Manufacturing’s Future

 

Manufacturing processes generate an immense volume of data. But raw data by itself is useless: there is little point in gathering it unless it is used to learn something. Unfortunately, there is simply too much data for any one person or even an entire team of people to analyze—which again demonstrates the need for AI and machine learning. They were made to analyze huge amounts of information, identify the trends within them, and enable business leaders to make more informed decisions faster.

 

Manufacturers are always looking for ways to improve margins. The information generated by AI is key to operating a leaner facility and addressing potential concerns quickly and effectively. (After all, the longer a condition persists, the more money a manufacturer flushes away.) For example, because it is paramount that facilities know how their machines are operating, if they’re suddenly consuming too much power or if are headed toward a malfunction, the ability to notice and act on key indicators (such as temperature and pressure) proactively can prevent costly downtime.

 

AI is already helping organizations stay ahead of their assets’ performance and have positive impacts on their bottom lines. This continuing trend will also help create more jobs that will attract the next generation of talent, which is keen to work with the latest technologies. As the industry continues to change, the roles supporting it will change as well. The future workforce will not only focus on keeping machines running but will also learn from the data that those machines generate, which will position them to make better decisions for their companies. AI and machine learning technologies will empower humans, not replace them.

 

Prateek Joshi is the founder and CEO of Plutoshift, which provides process and asset performance monitoring for industrial process facilities to increase energy efficiency while maximizing throughput. Joshi is an artificial intelligence researcher, the author of nine books, and a TEDx speaker.