Conversations on Edge AI: Seven Takeaways from A3’s Intelligent Edge Webinar Series With Intel Corporation

Most of today’s industrial manufacturing relies on technology designed around 50-year-old standards. Such fragmented, proprietary, and highly inflexible architectures fail to meet modern production needs due to the lack of interoperability between systems, proprietary interfaces, and data integration complexities.

“Enabling the Intelligent Edge for Industrial Applications,” a seven-part webinar series sponsored by Intel, outlined new architectures -- Dubbed Intelligent Industrial Edge Platforms – that enable the next generation of manufacturing leveraging artificial intelligence (AI), machine learning (ML), edge and cloud processing, and connectivity. These advances give manufacturing offers enhanced scalability, flexibility, transparency, and security, while reducing time to market. You can watch the entire webinar series here.

Here are seven key takeaways from the webinar series on how to use industrial edge to deploy Industry 4.0 and IIoT at scale:

Interoperability Creates Competitive Advantage

One of the hot discussions right now in the industrial IoT space is the willingness of companies to explore how to connect formerly proprietary systems to talk with one another to make operations more efficient. During the webinar, “From the Edge to the Cloud, Smart Technologies Create Competitive Advantage.” Jonathan Luse, general manager of product planning for Intel’s IIoT Group, gave an example of the problem regarding interoperability that he has seen in the pharmaceutical industry.

Luse used the example of a time-to-scale gap, where the lab environment to the production environment has a lull because they have to take the recipe from the lab and re-tune it and revalidate it --  which takes potentially months to happen.

“If we can figure out how to go from lab data set integrating seamlessly to a production environment moving data securely and accurately across those environments, the net result of that is you get production of that pharmaceutical or drug faster,” Luse said. “That will have a benefit on society that is well beyond the economics of the production factory.”

Don’t Let Data Stay Locked Away in Legacy Systems

Modern production operations rely more on integrated data and computing power from the cloud. Yet, most legacy industrial solutions are built on siloed, proprietary, and generally inflexible systems that make retrieving data to send to the cloud for processing difficult. Adding to the complexity are new processes that require faster processing at the edge, which could mean former processes that were handled in the cloud now need to be managed closer to the devices producing the data.

“The story of manufacturing through digital transformation is a data story,” said Charlie Sheridan, Google Cloud’s global technical director of manufacturing, automotive, and energy during the webinar “Creating an Architecture that Supports Smart Solutions from the Edge to the Cloud.”

A single machine could generate five gigabytes of data per week, Sheridan said, and a typical smart factory can produce five petabytes per week. So there could be a wealth of information inside this data.

Manufacturers should find partners to help them unlock their data from legacy machines and then incorporate that into an edge-to-cloud and cloud-to-edge infrastructure, depending on specific AI-driven optimization use cases.  Examples given of these use cases can include visual inspection AI, time-series predictive analytics, digital twin visualization and simulation, machine-level anomaly detection, predictive maintenance, and root cause identification.

Edge Compute Speeds Decision Making

Deploying edge processing can speed decision-making and enable timely intelligence in cases where it isn’t feasible to bring data back and forth from the cloud.

“We often see industrial use cases where manufacturers or OEMs tell me that they have to make that entire round trip, including the network, in a very small number of milliseconds,” says Rita Wouhaybi, Senior AI Principal Engineer at Intel, said during the webinar “Artificial Intelligence and Machine Vision: Moving from Operation to Automation.”

“It makes it impossible against the law of physics to actually send that request to the cloud,” she said.

Additional stakeholders that should be included in edge AI discussions include subject matter experts for the process being automated (including factory floor workers who would monitor the processes), systems integrators and application creators in addition to any data scientist teams. Everyone on the panel agreed that data science needs to be brought to the level of every employee, instead of relying on specific data scientists to explain or operate everything.

Connectivity is the Nervous System of Your Factory

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Digital transformation will rely on moving computing and machine learning technologies from cloud environments closer to the machines processing critical data to ensure that data from those devices can properly reach the computing resources, facilities must focus on the right networking technologies, whether wired or wireless.

Panelists from Siemens and Intel mentioned the importance of having an edge device that’s able to gather data and communicate with the cloud in order to have successful connectivity during the “Convergence of Connectivity and Compute Unleashes Intelligence at the Industrial Edge” webinar.

Although Wi-Fi is easier to set up and deploy – and may be suitable for many applications -- 5G can allow you to simultaneously connect multiple applications across a single network, especially when collecting data from machinery, equipment, and field devices across enterprise locations for local processing on a private cloud.

Software-Defined Control Models Gain Momentum

The conversion of industrial control systems to a software-defined model – a digital transformation that moves proprietary hardware to software systems -- has been discussed for many years, but only until recently has the idea gained momentum. These advances are empowering new levels of flexibility, innovation and interoperability.

One of the biggest ways companies can begin moving to transform their industrial control systems to a software-defined model is to collaborate and get IT officials and OT officials in the same room. Educating IT officials about operations and OT officials about technology was a big step. 

Panelists from ExxonMobil and Intel discussed the limitations of proprietary systems and the advantages of software systems, IoT, edge computing and ease-of-use in the webinar, The Transition to Software-Defined Control Systems.

Open Standards are Driving Industrial Control Transformation

As companies look to adopt an Industry 4.0 approach to their industrial control systems and operations, they are increasingly looking to open, interoperable and portable technologies to help upgrade or replace their legacy systems.

“There’s a large need for multi-vendor interoperability and portability in today’s distributed control system infrastructure, and they really want to future-proof their lifecycles,” says Kirk Smith, Director & PE of Industrial Systems & Solutions Architecture at Intel during the “Foundational Standards and Open Source to Realize the Promise of Industry 4.0” webinar.

The two panelists from ExxonMobil and Intel shared examples of companies that are already creating test beds, prototypes and field trials of the open standards initiative. Both panelists agreed that the best way to address skeptics on the transformation of industrial control systems is to provide successful examples in the space. “We address the skeptics’ concerns by showing what’s possible and doing it,” says Bartusiak. We can show what's possible by demonstrating the stuff that is working in practice.”

Driving Sustainability Will Increase Profitability

Corporate efforts to fight climate change and create more sustainable processes were once thought of as a “nice to have” checkbox -- but a growing number of companies are now finding value and profitability through such efforts, which also happen to provide a greater good for the planet. While companies taking a more altruistic approach to the problem is one of the contributing factors, so are digital transformation efforts that provide a balance between becoming more sustainable and finding optimizations that improve the bottom line.

In the webinar, “Getting to the Greater Good — Digital Transformation Drives Profitability and Sustainability,” panelists agreed that companies must look beyond just reducing their carbon footprint in sustainability efforts – such as reducing water consumption (or even becoming water-positive), engaging with partners to ensure they are sourcing sustainable power and materials, and reusing or recycling materials better. All of which could lead to a better bottom line and a better planet.

For more advice on working with partners and suppliers to get up to speed on edge technologies, check out the entire Intel webinar series. It’s a great way to explore the industrial edge’s  capabilities and impacts and hear about use cases that demonstrate IIoT 4.0 at scale. You’ll find an informative look at edge AI, the stitching between edge and cloud, and IIoT connectivity with Intel and its partners. View all discussions at www.automate.org/intelligentedge.