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Manufacture-ML, AI, and the Smart Manufacturing of Smart Products- B-AIM Pick Selects

The IoT (Internet of Things) is dramatically changing how industries work. In the past decade alone, one of the defining trends in the global industrial sector has been the push for digital transformation and Industry 4.0. Manufacturing, in particular, is undergoing mass change as IoT technologies become more affordable, more accessible, and more accepted as the way of the future.

By 2020, BI Intelligence projects the installed base of manufacturing IoT devices to reach 923 million, and this investment in the IoT by manufacturers will translate to billions of dollars in spending globally. Spending will also ramp up as manufacturers graduate from more simplistic IoT solutions to more complex ones.

One example of this comes from AT&T, which announced earlier this year its goal to achieve “Zero Waste” at 100 facilities by the end of 2020. Some of the strategies include reducing waste and increasing recycling and composting.

Across the company, it is also working to make its network, fleet, and operations more efficient as well. Further, it is helping its customers reduce carbon emissions and use the IoT for Good, by connecting everything from trucks, to farm equipment, to city infrastructure, to manufacturing, and more.

Industrial automation through the use of technologies such as ML (machine learning) and AI (artificial intelligence) will be an important driver of digital transformation in manufacturing. Factories that were once defined by a complex system of buildings, machinery, and human workers are now being defined by automation and intelligence. In smart factories, industrial robots are replacing “boots on the ground” as AI-enabled systems become capable of managing an increasing number of core operations.

As they do, manufacturers will benefit from increased efficiencies resulting from streamlined processes and fewer errors. The growing demand for visibility throughout the manufacturing process, as well as safe, well-tested products will continue to push the space forward in the transition to Industry 4.0, but barriers to IoT implementation—including cybersecurity risks, difficulty determining ROI (return on investment), technical integration, and the threat of job loss—remain.

The Peggy Smedley Show: AT&T Business Summit

Peggy Smedley is joined by Chris Penrose, president, Internet of Things, AT&T Business, and James Brehm, founder and chief technology evangelist, James Brehm & Associates, who discuss the verticals seeing the most IoT (Internet of Things) penetration. They also discuss the AT&T Foundry and why it is important to brainstorm and change how companies operate businesses. Finally, they talk about the trends going forward, including the fact that every business will change the way they operate by introducing IoT technologies.

Machine Learning and Artificial Learning

Thorsten Wuest, assistant professor of smart manufacturing at West Virginia University, says data analytics, ML, and AI are key to realizing smart manufacturing and the concept of Industry 4.0. “The ability not just to collect (large) amounts of manufacturing data but also to effectively and efficiently analyze it to learn previously unknown insights in the processes can not be valued high enough,” Wuest says. “And this is just one application where AI and ML are deemed valuable. In the future, we will see more automated design adaptation, again stressing the personalized product that essentially requires ‘batch size 1’ and ‘mass personalization’.”

Jagannath Rao, head of data-driven services at Siemens, says the biggest game-changing aspects of the IIoT (industrial IoT) include the ability to integrate disparate data sources in a manufacturing plant with ease and, using the latest sensor technologies, the ability to measure almost anything in an unobtrusive way.

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“The combination enables the generation of large datasets with diverse features, and to these we can even add external data sources like weather, logistics, dynamic electricity pricing, etc.,” Rao says. “As a result, we can now apply cutting-edge modern technologies like machine learning, deep learning, AR (augmented reality), visual analytics, etc., on these datasets and build far more real models of any predictive aspect of manufacturing and process optimization.

This not only enables accurate decision making but also reduces the response time to minutes and hours compared to traditional methods.”

What’s more, these technologies create the opportunity to gather insights into the entire manufacturing value chain

Running algorithms to find clusters in a dataset can indicate trends or process deficiencies, says Rao, thereby enabling changes in operations that can lead to productivity gains and/or reduced operations costs.

“The ability to monitor and analyze in near-real time also enables businesses to embark on new business models which are more output based and customer centric,” he adds.

Becoming more customer centric and leveraging data to increase profitability are two areas of gain with the successful implementation of smart manufacturing technologies. And what makes smart manufacturing “smart”?

Machine learning and AI are in many ways responsible; for instance, these technologies can help increase the efficiency and, therefore, the speed of manufacturing, which can create ripple effects like lower labor costs and reduced equipment downtime.


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