Manufacturing 2020: 5G, AI, IoT And Cloud-Based Systems Will Take Over

2020 will be a year when manufacturers move deeper into digital production systems. New technology will blend with existing tech to digital production that requires a skilled workforce.

Technology vendors expect that 2020 will be a big year for manufacturing plants to onboard digital systems. But will it happen? While digital systems – IoT, machine learning, 5G, cloud-based systems – have proven themselves as worthwhile investments, they may not get deployed widely.

For insight on what to expect in 2020, we turned to Rajeev Gollarahalli, chief business officer at 42Q, a cloud-based MES software division of Sanmina. Gollarahalli sees a manufacturing world that will take solid steps toward digitalization in 2020, but those steps are likely to be incremental rather than revolutionary.

digital manufacturing, IoT, IIoT, artificial intelligence, AI, machine learning, ML, 5G, big data, data analytics
We're going to see manufacturers make progress on digitizing their factories in 2020, but it won't happen as quickly as vendors would like. (Image source: digital.gov)

5G On The Plant Floor

Design News: Will 5G increase the pace of digital factory transformation, and where it will have the most impact?

Rajeev Gollarahalli: We’ve started to see a little of 5G popping up in the factory, but it’s limited. It’s mostly still in the proof-of-concept stage. It will be some time before we see more, probably around the end of next 2020.

DN: Will 5G increase the pace of digital transformation?

Gollarahalli: Undoubtedly. Yet one limit is that in order to make accurate decisions, you need to be able to ingest high volumes of data in real-time. That’s been one of the limitations in infrastructure. When you can use 5G across the factory, you’ll have considerable infrastructure. That challenge with data is solved by 5G.

DN: What still needs to be done in order to deploy 5G?

Gollarahalli: You have the 5G service providers and 5G equipment manufacturers working together. Both are developing capabilities in their own silos. What has not yet matured is putting these together, whether it’s in health, discreet manufacturing, telecom, or aerospace. The use cases haven’t matured, but we are seeing more use cases piling up.

DN: What could spur equipment vendors and telecom to work together?

Gollarahalli: I think we’ll see an industry consortium. That doesn’t exist now. There are partners that are starting to talk. Verizon is working with network providers. You’re going to see two or three different groups emerge and come together to do standards. With the advent of 5G, and the emergence of IIoT, they are all going to come together. One of the limitations is the volume. We generate about a terabyte of data with IoT. The timing will be perfect for getting 5G utilized for IoT and get it widely adopted.

The Emerging Workforce Skilled In Digital Systems

DN: What changes in the plant workforce can we expect in the coming year?

Gollarahalli: The workforce will need a completely different set of skills to drive automation on the factory floor, and industry has to learn how to attract those workers People are saying manufacturing is contracting, but I’m not seeing it. Manufacturing seems to be stable. As for skills for the factory of the future, we need to be re-tooling our employees. The employees today don’t have the technical skills, but they have the domain skills. We need to get them the technical skills they need.

DN: Will the move to a workforce with greater technology skills be disruptive?

Gollarahalli: You’re not going to see mass layoffs, but you’re going to see retooling the skills of the employees. We can’t get them trained at the speed that technology is increasing. We’re going to see more employees getting ready in trade schools and with degrees. What you’re seeing is a convergence of data skills with AI and domain skills. An ideal skillset is someone who understands manufacturing and knows the data. For several years kids were moving away from STEM, wanting to learn the sexier stuff. But I think STEM is coming back.

Cloud-Based Systems For Security

DN: Will cloud-based systems be the go-to for manufacturing security versus on-premises security?

Gollarahalli: Five years ago, when I talked about cloud with customers, they asked whether it was real-time. That was when the infrastructure was not as secure. I have a network at home. That was unheard of 10 years ago in factories. Now that the infrastructure issue has been solved, the next step is security. I have always countered that you can’t secure data on premises as well as you can in a cloud. A lot of money has poured into cloud-based security. No single company can match that. It’s almost impossible to do it on premises.

AI, Machine Learning and Big Data Analytics

DN: Will advances in AI, machine learning, and analytics?

Gollarahalli: We’re seeing AI and ML (machine learning) is some areas. We’re seeing it implemented in some areas at 42Q. Most use cases are around asset management and quality. It’s used to predict the quality of a product and to take preventive actions in asset maintenance. AI and ML are also popping up in supply chain management. 2020 will be the year of AI and ML. It’s getting embedded into medical products. You’ll see it pop up everywhere, showing up on the factory floor as well as in our consumer products.

DN: Is AI and machine learning going mainstream yet or is it mostly getting deployed by large manufacturers who are typically the early users?

Gollarahalli: You’re going to see it move down the supply chain to tier 2 and tier 3 suppliers. I don’t think it’s just for the elite any more. It’s getting adopted quickly, but it is not happening as quickly as I thought it would.

The Role Of IoT In Manufacturing

DN: Will we see growth in IoT’s role in measuring and providing closed loop controls?

Gollarahalli: We’re going to see it in manufacturing, regulating the humidity in the room or the temperature on the floor. They need closed loop from IoT. They’re measuring with IoT, but the closed loop as not been adopted as quickly. We don’t have the right standards. How do you do close loop with a system that is throwing off data in milliseconds. You must be able to use the IoT and those algorithms. If you can make them more efficient for closed loop control, you’ll see a lot more of it going forward.

Rob Spiegel has covered automation and control for 19 years, 17 of them for Design News. Other topics he has covered include supply chain technology, alternative energy, and cyber security. For 10 years, he was owner and publisher of the food magazine Chile Pepper.

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