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Guest post by By Eddie Amos, General Manager and VP of Industrial Applications, GE Digital 

2017 was a transformative year for the industrial world. Among the highlights: GE Digital released the most comprehensive Asset Performance Management (APM) solution on the market, as recognized by Gartner. ServiceMax was recognized (for the third year in a row) as the leader in field service management. And, Apple rolled out a native SDK for Predix. We watched machines become more productive and reliable, while the sensor networks tying them together grew smarter and more ubiquitous. 

As the pace of digital industrial innovation continues to accelerate, 2018 promises to be an even more exciting year. Here are three trends to keep an eye on in the New Year.

IIoT success will hinge on OT expertise

Industrial IoT is not like consumer IoT. Monitoring a $10 million wind turbine is infinitely more complicated than tracking a person’s footsteps, and the stakes are higher. To succeed in IIoT, operators will need to partner with a provider that not only offers best-in-class sensors and software, but also has decades of operational expertise and a deep understanding of the industrial landscape. Modeling a digital twin to accurately reflect the traits and performance of a physical asset requires comprehensive knowledge of the asset, including proprietary design information available only to the original manufacturer.

As operators move beyond the basics of connecting machines to the IIoT, they’ll face the much tougher challenge of gleaning actionable insights from their data. The sheer volume of raw telemetry streams can be overwhelming, even for sophisticated companies, but with the right software and deep OT expertise, organizations will begin leveraging data to streamline asset operations and drive more informed decision making.

Augmented reality goes mainstream

Augmented reality (AR) has already established itself as an entertainment medium thanks to the success of games like Pokémon Go and Apple’s Animoji feature. In 2018, AR will finally evolve from toy to productivity tool as the underlying technology advances to the level of an enterprise-grade solution.

For industrial organizations, AR will revolutionize the delivery of field services. Technicians who operate in some of the world’s harshest environments will be able to do their jobs more safely and efficiently through the use of AR-powered mobile devices and headsets. AR, coupled with real-time data captured via IIoT, will enable field service professionals to perform inspections without needing to physically access an asset. This not only reduces downtime, but also greatly mitigates the safety hazards facing workers every day.    

Big data gives way to big insight

The digitization of industry has created vast data lakes of asset information. Most organizations lack the tools to effectively parse these large asset datasets for actionable insights or use them to drive smarter decision making. As operators complete asset digitization efforts and move on to more advanced stages in the digital journey, having the tools and knowledge to effectively use these datasets will be critical to an organization's success.

Artificial intelligence (AI) and machine learning algorithms will help standardize the flow of data from disparate locations and streamline the process of industrial data analysis. This will enable operators to not only glean deep insights into asset performance, but also lay the groundwork for the automation of everyday decision-making. Manual tasks like work order scheduling will no longer require human intervention as AI algorithms leverage real-time analytics to optimize maintenance logistics and practices.

What digital industrial technology are you most looking forward to in 2018?

This article originally appeared here.

 

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A couple of weeks ago, I spent a few hours at GE Digital’s headquarters in San Ramon, CA. It was a great overview by several executives of how GE is using their Predix platform to create software to design, build, operate, and manage the entire asset lifecycle for the Industrial IoT.  A big part of this transformation for GE involves hiring tons of software developers, acquisitions, and partnerships.

One of those partnerships is with Silicon Valley based FogHorn Systems (GE Ventures, Dell Ventures, March Capital and a few others are investors). FogHorn is a developer of “edge intelligence” software for industrial and commercial IoT applications. FogHorn and GE are working very closely on many IIoT customer use cases, across verticals, bolstered by the integration of FogHorn with Predix.

I turned to FogHorn Systems CEO David C. King to learn more about edge intelligence software for the Industrial IoT. David has been at the helm of FogHorn since 2015, a year after its founding. Prior to FogHorn, David co-founded AirTight Networks, Inc., a technology leader in secure cloud-managed Wi-Fi. Before AirTight, he served as Chairman, President and Chief Executive Officer of Proxim Inc., a pioneer in WLANs and the first publicly traded Wi-Fi company, from 1993-2002.

Lots of talk about the edge in IoT. It’s my smartphone and my doorbell, as well as the sensor on a traffic light or a wind turbine. What exactly is the edge of the network and how do you define it?

We define edge as the closest compute point that can process real time streaming data. So in your case, all three -- phone, doorbell, sensors -- are edges because you can bring compute to the data on any of these platforms. The question is what compute is possible? The single variable filtering that you can do on a sensor is very simple when compared to the complex Machine Learning models that can execute on your phone.   

Analytics is done in the data center or cloud. You claim to do this at the edge now.  Please describe your offering.  

FogHorn has developed a tiny footprint complex event processor (CEP) that provides advanced streaming analytics, and machine learning capabilities at the edge.  This powerful combination of being able to pre-process, cleanse the data and execute ML models, all in real-time, brings the power of big data analytics to the edge. The FogHorn software platform is highly flexible and can be easily scaled to optimize for footprint and/or feature needs.

Tell us about a customer you’re working with and how they are applying your technology.

FogHorn Lightning is an extensible platform currently used by customers from Manufacturing, Oil & Gas, Power & Water, Renewable Energy, Mining, Transportation, Smart Buildings/Cities and other industrial verticals. The deployment patterns range across gateways, PLCs, to ruggedized servers in production, at Fortune 100 sites. A common implementation of FogHorn Lightning is product quality inspection, predictive maintenance, real time health monitoring. Customers are seeing immediate business value; e.g. identifying defects in the early stages of manufacturing reduces, scrap and increases yield. Additionally, there is a trend to FogHorn to generate new streams of revenue by providing real-time smart maintenance for their end customers.

When compared to software-defined IIoT smart gateways, there are still millions more hardware-defined M2M gateways out there. At what point do we cross the chasm to smarter gateways, and where are we now in this cycle?

We are still very early in adoption of IIoT technologies. Understandably, typical industrial sectors are conservative, and have much longer adoption curves. However, we are beginning to observe that it the ROI from edge intelligence is accelerating customer demand for FogHorn. We will cross the chasm once industries identify key use cases that generate new revenue streams, which is still about 3-5 years away.

You can’t talk about IoT without talking about security, and it’s even more important in the industrial sector. How do you address security concerns for your customers and what does the industry need to do to make IoT more secure?

Yes, you are right. When you think of IoT, especially IIoT, security is a top concern. Hacks such as “Devil’s Ivy” will become everyday events with increasingly connected devices. At FogHorn, our edge intelligence software runs very close to the data source, and is local to the asset. This implies that we are secure (like the assets) behind firewalls, and in a DMZ layer. And because most of our processing is done locally, we are less vulnerable to malicious hacks that occur when connected.

Because IIoT is still such a nascent set of technologies, we caution users to deploy solutions after thoroughly weighing the business value, and convenience versus security risk factors. My guiding question before any deployment: “Can I do this locally, without connecting to an external network?”. The answer is usually yes, and if otherwise, you should probably talk to us.

How can companies make their industrial processes better?

We understand that today’s industrial processes are highly complex and advanced, with many moving parts. While it may seem humanly impossible to optimize it any more without help from technology, we believe that a key asset is still untapped: your operator! Companies will start seeing incredible improvements once they translate the tribal knowledge on the plant floor into actionable insights. This can be further supplemented by techniques from machine learning, and artificial intelligence, to tease out the known unknowns, and also, the unknown unknowns.

Anything else you’d like to add?

FogHorn is redefining edge intelligence for IIoT. A year ago, we started our journey as a company that did analytics on tiny footprint devices. Today, we have accelerated the transition to Machine Learning at the edge, and are very are excited about the market validation. With our Operational Technology focus, we are looking forward to defining new business models, and delivering transformational value for our industrial customers.

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IoT Big Swings

Last week Tom Davenport, a Distinguished Professor at Babson College, wrote about “GE’s Digital Big Swing” in the Wall Street Journal. As he cites in his latest piece, there are many others taking big swings in digital and IoT overall. (BTW - If you’re not following Tom, you really should do so now. His thoughts are a perfect mix of research and practice covering big data, analytics and changes in the digital landscape.)

During my time at Pivotal, I was witness to the digital big swing that GE took and saw the energy, effort and resources they were committing to make sure that whatever they made that could be connected to the Internet - jet engines, power plants, surgical image machines - would capture all data to improve products and the customer experience. I don’t think GE watchers - investors, competitors, partners - fully understand yet the enormity of this bet.

They keep making moves. This week the company announced the creation of GE Digital, a transformative move that brings together all of the digital capabilities from across the company into one organization.

Jeffrey Immelt, Chairman and CEO of GE, said, “As GE transforms itself to become the world’s premier digital industrial company, this will provide GE’s customers with the best industrial solutions and the software needed to solve real world problems. It will make GE a digital show site and grow our software and analytics enterprise from $6B in 2015 to a top 10 software company by 2020.”

GE, the industrial giant, a Top 10 software company? That’s taking GE’s slogan “Imagination at Work” and making it real.

Much like the cloud trend before it, the IoT trend is something where all major vendors are investing.

Yesterday at Salesforce’s behemoth customer conference Dreamforce, the company announced the Salesforce Internet of Things Cloud. Based on a home-grown data processing technology called Thunder, Salesforce touts their IoT Cloud as empowering businesses to connect data from the Internet of Things, as well as any digital content, with customer information, giving context to data and making it actionable—all in real-time.

With perhaps a nod of guilt to marketing hype, other notable big swings include:

  • IBM - The company has created an Internet of Things business unit and plans to spend $3 billion to grow its analytics capabilities so that organizations can benefit from the intelligence that connected devices can provide. According to IBM, as much as 90 percent of data that is generated by connected devices is never acted on or analyzed.

  • Cisco - Its approach focuses on six pillars for an IoT System - network connectivity, fog computing, security, data analytics, management and automation and an application enablement platform. You can buy all the pieces of the system from Cisco, of course.

  • Samsung - They are betting on openness and industry collaboration. By 2017, all Samsung televisions will be IoT devices, and in five years all Samsung hardware will be IoT-ready. They also recently open sourced IoT.js, a platform for IoT applications written in JavaScript, and JerryScript, a JavaScript engine for small, embedded devices.

  • Monsanto - Their near billion dollar purchase of The Climate Corporation is combining The Climate Corporation’s expertise in agriculture analytics and risk-management with Monsanto’s R&D capabilities, and will provide farmers access to more information about the many factors that affect the success of their crops.

In the wake of these giant big swings will be new and exciting startups - sensor companies, chip players, software, analytics and device makers. If you know of a compelling start-up in the industrial IOT space, drop me a line at [email protected]. We would love to hear from you.




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