"Industry 4.0 is the concept of taking all of this AI—the capability of prediction—combining it with what we have already in terms of the automation systems, and then making smart factories that can come together and make decisions on the fly," Boqaileh said. "They can help and assist by taking in the predictive information it has taken and giving you those predictions, and then making sure those systems and automated systems are working better across the board."
More data
Boqaileh noted that AI was created in 1955, but didn't really pick up any traction until more recently. There are two reasons for that.
First is the amount of data that is being created and collected daily. For AI to function properly, it needs a lot of data to start. And it turns out, we have a lot of that data now.
It is estimated, Boqaileh said, that 90 percent of the world's data was created in the last two years.
"We are producing more data than we ever have before," Boqaileh said. "Anytime you get on your phone, every time you log into Google, every time you log into Facebook—all your current machines—they all have sensors on them that now are starting to collect data."
Moreover, the cost of storing all that data has gone down, and it's also an awful lot cheaper to crunch that data. It's certainly more profitable to do so, particularly in the materials space, he said.
New materials development is difficult, expensive and time intensive. But the underlying question is "why?" What is the industry doing—what manufacturing and testing processes are we using—and how does that impact the end result?
"We looked at what customers were doing," Boqaileh said. "… What we find is that people were going through a very repetitive, circular process."
That process boils down to making a material, manually testing that material and then manually analyzing data from the testing phase to determine the next course of action regarding the material development.
This space—or any place where employees are engaged in circular, repetitive tasks—is where automation and AI can have the biggest impact.
It may start with hardware and software components that test and analyze tensile or viscosity with little more than an assist from technicians. But it doesn't have to end there because automation and AI can be applied in a variety of areas.
"Imagine if you did that with a lot more of your repetitive tasks," Boqaileh said. "You start having a technician where you can get them to do high value-added jobs and free them up to do quite a bit. You have less people in the lab doing a lot more great work as opposed to repetitive, redundant work."
To understand the benefits of this time savings, Boqaileh pointed to a study of LabsCubed products that indicated an 85 percent time savings for technicians and a 40 percent increase in efficiency.
"This actually is (from) a white paper study we did with a whole bunch of customers in terms of the potential savings that they saw on time saved, consistency and additional benefits," Boqaileh said. "But you can actually take this and extrapolate it into any automated system you are more likely to implement into your lab. So these are the kinds of numbers that you can start to aim for."
When to invest
Automation in the lab also allows for more consistency—both in the testing itself and the data collected. Boqaileh contends any increase in consistency of testing is a selling point for material makers because it allows for more consistent data that can be shared with customers. This, he said, emphasizes the producer's ability to manufacture quality, consistent products.
Boqaileh believes that the industry is moving toward automation, and while it may not be something that a company sees itself doing today or even a few years from now, chances are that at some point in the future, automation will be the baseline expectation for the laboratory setting.
"I hate to say this, but the rubber industry isn't all that sexy," Boqaileh said. ".. So how do we attract young people (to the industry)? They need to know that we are looking at the future and how they implement their skill sets in the elastomer field will directly help them in the future. They are looking at automation, they are looking at AI."
Phasing in automation may be one way to tackle this.
"You look at the lowest hanging fruit," Boqaileh said. "What are the things you can automate today—where the ROI is going to be fantastic on them, they can save time, they can free up technicians to do other things?"
When considering if automation works for your company, Boqaileh suggests a three-question test to properly evaluate the work you're already doing.
- Are the tests you conduct repetitive?
- Are they creating bottlenecks?
- Are those tests prone to human error?
Answering any of those questions in the affirmative is an indication that automation may help to streamline your operations, he said.
"To be clear, it doesn't mean that you have to automate it today," Boqaileh said. "It could be prohibitively expensive, it might (have) an ROI of 10 years on it, and it might not make much sense. But it's still something to consider."
