NOVI, Mich.—As manufacturers move toward a more automated future, there are plenty of questions to work through, and a plethora of opportunities for those that can.
Automation, machine learning and what to do with all the data produced were some of the issues addressed by a panel of industry experts at the Plastics and Rubber in Automotive conference, held recently in Novi. The end goal for many companies is to create smart equipment, machines that can self-diagnose, self-correct and communicate.
"The idea is not just being reactive and finding out why something occurred on the prior shift," said James Ricci, chief technology officer and co-owner of Harbour Results Inc., who also moderated the discussion. "We actually want to become more proactive and understand and anticipate issues, ultimately getting to something that's totally actionable."
Matt Myrand, director of advanced manufacturing and supply chain at Faurecia North America, an automotive supplier, said his company has a wide diversity of programs at very low volume, meaning it must adapt to customer volumes and products. What that could look like in an Industry 4.0 shop is machines flexing in and out based on the products being made.
Quick, reliable decisions are also a key driver for companies. As turnaround times in the automotive industry become more rapid, companies are turning to automation to keep up.
"One of the biggest drivers is cost and efficiencies," said Shaun Karn, president of Hi-Tech Mold & Engineering Inc. and Baxter Enterprises L.L.C. "One of the other pieces to that is work force development. By utilizing this, we've seen a number of different ways how the flexibility can help us and we've seen how computers and machines can help drive decisions for you."
Michelle Bockman, vice president of commercial development at HP Inc., added that the business drivers behind the movement involve customization and personalization.
"I would say in the last two years, we're seeing the industrial market move to true industrial applications (for 3D printing)," Bockman said. "They're able to use parts in their sub-assemblies and in their vehicles. A lot of this is driven by cost and time to market. We're able to make things a lot quicker that you couldn't do before in traditional design or manufacturing."
Karn said collecting data and using it helps predict pitfalls and make better decisions.
"There are a lot of different data aggregators entering the field," said Mike McGrath, director of automotive and manufacturing at the SAS Institute. "The big challenge companies have is to take as much data as possible off of the machines, combine that information and ultimately glean results and predictive analytics."
One key issue, especially for manufacturers who have been around for awhile, is how to integrate these new features when the shop is still running older equipment.
Myrand said his company has a mix of machines that are decades old and others that have been purchased within the last year. There are both environmental and vibration sensors that can be externally mounted to the older machines, allowing them to gather a sizable amount of data.
McGrath echoed those sentiments, saying that while the data might not be as much as a newer machine, it's enough for manufacturers to get a start and capitalize on the advantages of the new technology.
"Start somewhere," McGrath said. "I think everyone in this room would have certain issues where you could potentially improve yield, ultimately reducing scrap and those kind of things. You know what your pet projects are, I would just start with something. Find a project, start small, start collecting data. There are a lot of cloud-based systems you can leverage to grab analytics tools on demand that are very user-friendly."