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Industrial sector amped for digital transformation

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Manufacturing often gets dinged for being stuck in another era, but in truth the industrial sector is well positioned to surge ahead with digital transformation, thanks to investments in augmented reality, the “industrial internet of things,” machine learning, and artificial intelligence.

These advanced technologies are already having an impact on how manufacturers design, produce, and service products, according to Joseph Biron, chief technology officer, IoT, at Boston-based PTC, a company with roots in 3D design that now offers software for industrial transformation.

Biron, who spoke at the recent MIT Sloan CIO Symposium, cited data from market research firm IDC estimating that more than 30% of the $1 trillion to be spent on digital transformation in the next year will be earmarked for initiatives in the discrete and process manufacturing sectors.

The industrial IoT, in particular, is a huge area of focus for manufacturing. A PTC survey pegged adoption of IIoT in businesses’ manufacturing functions at 46% — more than two times the rate compared to service (22%) and operations (19%) areas, and well beyond planned IoT implementations in product development groups (4%).

IIoT connectivity is a natural next step for plant floor machinery like robotic arms and conveyors, and in-field assets like oils rigs and wind turbines which are already hooked up to industrial networks and instrumented with sensors to facilitate data collection and automation, Biron said.

Layering in technologies like cloud computing, internet connectivity, and advanced analytics allows manufacturers to move beyond simple automation of work cells and production processes to new use cases in areas like predictive and preventative maintenance as well as optimization of operational performance.

“We’re looking at what to do with modern technology to make those critical systems that run the planet be more well-behaved, operationally efficient, resilient, and reliable,” Biron said.

PTC’s survey data bears that out. Those companies with IIoT initiatives on their docket are moving aggressively, PTC found, with 89% expected to transition use cases from pilot to full production within one year of purchasing the requisite technology.

Biron specifically drew attention to a class of industrial hardware called programmable logic controllers (PLCs), which are the existing engines of automation and a focal point for IIoT enhancement.

“The connectivity already built into those systems can be harnessed to optimize manufacturing,” Biron said.

Product design, transformed

Before a product hits the manufacturing line, it needs to be designed, and digital transformation is upending that process as well. For decades, engineers relied on 3D modeling tools like computer-aided design and computer-aided engineering to help them design more efficiently. Now, advances in high performance computing and generative design software let teams unleash more creativity with a lot less effort.

Thirty percent of the $1 trillion to be spent on digital transformation in 2020 will be earmarked for manufacturing, according to research firm IDC.

For example, engineers are tapping the combination of more computing horsepower and a new class of AI-infused generative design tools to create lattice structures that are optimized for strength but also lightweight, which reduces cost while increasing part performance.

Engineers input data constraints and design parameters into the generative software, which uses algorithms to come up with its designs.

“The machine doesn’t design purely with AI, but the incremental steps actually add up to quite a bit of transformation,” Biron explained.

Proactive and preventive maintenance

Manufacturers have long used historical data and heuristics to determine a recommended course of service for a given piece of equipment, but with IIoT and machine learning, those educated guesses can become intelligent predictions that are meaningful enough to act upon.

By analyzing real-time data on temperature, vibration, humidity, and usage patterns, and combining that trove with historical data and other information sources, manufacturers can zero in on potential failures before they happen, initiating a service fix and avoiding costly down time.

“The revolution we’re seeing is with predictive and proactive maintenance,” Biron said. The combination of heuristics, simulation, and machine learning delivers a formula for when and where a part might fail, eliminating guesswork and unnecessary maintenance.

Upskilling the workforce

Industry researchers like Deloitte are projecting that 2 million manufacturing jobs will go unfilled by 2025 due to the high demand for skilled workers. Digital technologies, including augmented reality, can help close that gap and accelerate the training process, Biron said.

AR, which transposes digital artifacts on physical reality, can deliver step-by-step, hands-free instructions to plant operators as well as to maintenance professionals equipped with an AR headset in lieu of bulky instruction manuals.

“In the industrial world, you can’t follow someone around for six months before you can do repairs,” Biron said. “IoT sensing data and AR give humans a real-time magical combination.”

Related MIT Sloan executive education course: Implementing Industry 4.0: Leading Change in Manufacturing and Operations Nov. 12 — 13, 2019. 

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