The Intelligent Edge: Bringing Transparency to the Factory with Lee Jaderborg (Part 2)
We’re back with our sixth installment of The Intelligent Edge, continuing our conversation with manufacturing engineering manager and quality manager, Lee Jaderborg. Didn’t get a chance to read Part 1? Catch up here! In our previous post, we connected with Lee to discuss his work on the ZumIQ application environment and the purpose of intelligent monitoring. In Part 2, he continues the ZumIQ conversation on its applications, and noting what’s next for FreeWave and the IIoT industry. FreeWave: You previously told us about how ZumIQ can help capture data and translate it visually. Why is this important? Lee Jaderborg: This gives a view of the manufacturing floor you can’t get by looking down the production line. It determines the collective state and efficiency of each part in the system. We’ve been trying this out on a few of our SMT, or pick-and-place, machines. We looked at the historical data on the machines’ part usage to see what parts and reels could be adjusted or replaced for increased efficiency and production capacity. We had perceptions, but didn’t have any data points for how much change this would result in. And it’s difficult to act on a perception because you don’t know how accurate you are. We realized by taking an in-depth look at the data output throughout the day, the machines weren’t running to their full potential. By changing the way SMTs operated, we saw a 10% increase in initial capacity, but without the data we wouldn’t have reached the benefits. FreeWave: What’s the “perfect storm” situation in which ZumIQ’s capabilities could be utilized fully? Lee: It could apply to any place where things go wrong and have a severe impact on people. Nuclear power plants, wastewater treatment plants and the water supply coming out of that, oil and gas refineries. Especially for oil and gas, you need sensors to detect leaks. You see disasters caused by natural gas and find out there was no sensor to detect a methane leak. Companies need to introduce networks of sensors that can relay data to an app environment like ZumIQ to monitor and track things like leak pressures, so in case something goes wrong, it can send out alerts and auto shutdown systems before anything bad happens. FreeWave: What excites you about the future of FreeWave Lee: There’s a lot of opportunity and paths we can take with our new products we’re developing. We have a lot of work ahead of us, especially as we look to upgrade our networks and existing technology to adapt to the future of IIoT. It’s exciting because we’ll be working on our newest innovations alongside our legacy products and seeing where gaps may exist. That’s the biggest puzzle to solve – we’re dealing with technology with new capabilities and parts, like radio-frequency identification on chips, compared to older technology which in some instances required tuning to get the correct signal. FreeWave: What about the Industrial Internet of Things as a whole? Lee: I think the promise of sensors and the data they transmit is exciting. If you think about it, there’s a piece of equipment in every place in the world – highways, oil and gas, utilities, etc. – that’s measuring something. A lot of major companies are starting to head in the direction of wanting to get data sooner than later to be analyzed and acted upon. Increasingly bringing intelligence to the edge of the network lets you decide and modify in real time; it lets you make important decisions. FreeWave: Any final words of wisdom? Lee: Our operations director likes to say, “Just because something’s the way it is doesn’t mean that’s the way it should be”. I think that can be highly applied not only in business and technology, but also in one’s personal life. You have to continue learning and innovating or else you’ll fall behind. ______ Interested in what our other experts have to say? Read the first, second, third and fourth installments of The Intelligent Edge. We’ll be back later this month with more insights and interviews with our team!
The Intelligent Edge: Bringing Transparency to the Factory with Lee Jaderborg (Part 1)
“Quality is never an accident. It is always the result of intelligent effort” – John Ruskin. This rings true in the world of IIoT. For Lee Jaderborg, who wears several hats at FreeWave, from engineering, to quality management and process development, the concept of intelligence-driven quality is what inspired him to learn everything he’s accomplished in his 40-year career, as well as what he’s brought to the future of the factory floor. In our fifth installment of “The Intelligent Edge,” we connected with Lee to discuss his work on the ZumIQ application environment and the purpose of intelligent monitoring. FreeWave: Lee, tell us about your role at FreeWave. Lee Jaderborg: Sure! I don’t have one specific job; I wear five hats. Coming up on my fourth year at FreeWave this April, I’m the manufacturing engineering manager, quality manager, sustaining mechanical support, and I oversee process development and design for new products. I also write SQL for our databases. I manage the procedures for a Printed Circuit Board assembly and Surface Mount Technology (SMT) manufacturing line, ensuring the entire process flows through production to the backdoor to ship. I also handle statistical process control and root cause analysis throughout the manufacturing process, all while monitoring for major operational KPIs. FreeWave: Was IIoT operations always your focus? Lee: No, I began college at age 17 as a drama major. Everything I’ve learned about engineering and management since then has been self-taught. During and after college, I worked in various engineering-type jobs, like designing tools to fabricate jet engine exhausts and helping build Colorado’s Eisenhower Tunnel. Along the way, I became exposed to SQL and got a master’s certificate in 6Sigma for project management. Continuous learning has helped advance me to where I am now. FreeWave: What are you currently working on? Lee: Optimizing FreeWave’s ZumIQ for better data visualizations on the manufacturing floor – a major focus at last year’s annual IMPACT Manufacturing Summit. A panel, which included the director of manufacturing for Rolls Royce, discussed transitioning their workforce to better accommodate millennials, who learn better with visual feedback. So, they put an IO on a light stack – like a stoplight that tells you whether a machine is ready – to give a real-time view of various data points. FreeWave: How exactly would they capture that data? Lee: This is where something like ZumIQ comes in – you need to tie into analog signals to continuously record this data. You do this by having the light stack’s sensor communicate its status to an app programmable device, whose data is then collected by the ZumIQ app environment. ZumIQ gathers and tracks data over time to determine both real-time status and historical trends. This gives a view of the manufacturing floor you can’t get by physically looking down the production line. It determines the collective state and efficiency of each part in the system. Interested in more insights by Lee? We’re continuing the conversation for the next Intelligent Edge blog.
Manufacturing in the Age of IIoT
Few industries can claim such a foundational impact on the United States as the manufacturing industry. Modern manufacturing began with the birth of the assembly line and the transformational effect it had on the automobile industry. Companies then adopted that approach to product manufacturing and logistics. The early phases of the next generation of manufacturing appeared as machine-to-machine (M2M) communication, a forbearer of the concept behind the Internet of Things (IoT). Eventually, IoT became so broad that specific designations were needed to differentiate between the consumer and industrial side of things, thus paving the way for the Industrial IoT (IIoT). Today, manufacturing companies, while often on the leading edge of automation technology, are still scrambling to adapt to the explosion of sensors, communication platforms, big data and high-speed analytics to maximize efficiency and future-proof their products or designs. Some companies are touting the idea of retrofitting – a concept that has existed for some time – but some plant engineers may be wary of the need for continual updating to a system that is bound to become irrelevant at some point. Still, the process can be relatively painless, and is quickly becoming necessary, as Plant Magazine notes: … Most food manufacturing and processing plants have motors powering essential equipment such as mixers, conveyors and packaging machines. But they’re just motors. They don’t play in the same league as other intelligent devices. With years of service to go, it’s difficult for plant managers to justify replacing motors that work just to make an upgrade with smart features. But motors can connect to the IIoT without a complete overhaul. Instead of investing in new, more intelligent/smart equipment, consider investing in sensors that provide similar functionality to connected devices. Smart sensors attach to almost any standard low-voltage induction motor. Sensor technology is sophisticated enough to be small, functional and energy efficient. For certain kinds of manufacturing plants, a complete overhaul may not be necessary, and a ‘simple’ retrofitting process might easily solve the first part of the problem. The second part of the problem, or challenge, is that along with smart hardware, plants also need the software and data processing capabilities to keep pace. Some plant engineers are solving these challenges by deploying programmable radios capable of hosting third-party applications so that the data can be transmitted in smaller, highly specific packets, making the transport both fast and easier to push into predictive analytics platforms. From there, software companies are building in the ability to process data in the cloud, essentially running all critical data and software operations through either a fog or cloud computing process. Cloud software services have the potential to be highly customizable based on the needs of the manufacturing plant. These technologies are good examples of the ongoing convergence between traditional information technology (IT) and operations technology (OT) needs in industrial markets. Currently, the manufacturing industry is sitting in an interesting spot: leaders in the M2M world, but still adapting to the IoT world. Where the industry ends up in the next 10 years could be a strong indicator of the economic and financial temperature of the domestic and international marketplaces.
Top Industrial IoT News Roundup
There is a lot happening in the industrial IoT (IIoT) space lately, as evidenced by all the recent news announcements, analyst insights and business transactions occurring on the daily. Some say there is a foggy forecast for the industrial internet of things, mainly because the success of cloud computing must extend beyond data centers, but real world use cases should continue to pave the way. In some respects, perhaps it’s just the fact that the ROI from the IIoT is still in its infancy, but many are clamoring that a more standardized infrastructure is needed to help solve the unique complexities that IIoT presents. In this week’s IIoT news roundup, you’ll find a little bit of everything – from oil and gas and manufacturing to fog computing, drones and sensors. Dive in and see if you have any other articles that you think are worth adding! And don’t miss the bonus update at the end of the news roundup. Deloitte: End-to-End Automation Real Value of IIoT Technology By @KarenBoman | Published on @Rigzone “Industrial Internet of Things (IIoT) technologies such as machine learning and drones are now available, but the real value lies in linking these technologies together to allow for end-to-end automation, a Deloitte executive told attendees at the Internet of Things Oil and Gas Conference 2016 Wednesday in Houston.” Is Now the Time to Apply Fog Computing to the Internet of Things? By Dr. Vladimir Krylov @Artezio | Published on @IoTEvolution “With fog computing, latency is minimized if one uses fog nodes for data analysis without sending it to the cloud. All event aggregation in this case has to be performed in the distributed architecture deployed in the network where devices (sensors) and fog nodes are located. Thus, fog architecture moves the capacity question from the cloud to the network implementation.” Manufacturing firms investing in IIoT data analytics – even if other areas are slowing down By @James_T_Bourne | Published on @IoTTechNews “The research, the findings of which appear in the report ‘Data’s Big Impact on Manufacturing’, found that of the more than 200 North American manufacturing executives polled, 70% said investing in data analytics would lead to fewer equipment breakdowns, while less unscheduled downtime (68%), unscheduled maintenance (64%), and fewer supply chain management issues (60%) were also cited.” Go Ahead, Fly a Tiny Drone. The Man Doesn’t Have to Know By @luxagraf | Published on @WIRED “THE WILD WEST days of drone flight came to end earlier this year when the FAA began requiring that pilots register their aircraft with the agency. If you want to use your Unmanned Aircraft System (as the FAA calls them) for anything remotely commercial, you’ll need to go a step further and pass a test.” Could Optical Fibre Sensors Save Lives? By @loctier | Published on @euronews “This edition of Futuris looks at how optical fibre sensors could help monitor the stability of roads, buildings, bridges and other constructions – and save lives.” Discovering Value in the Age of IIoT By @lasher64 | Published on @automationworld “The solutions of tomorrow will be much more integrated between implementation tiers on the plant floor to the enterprise and beyond. Therefore, it is imperative that these solutions give strong consideration to network architectures and cybersecurity. As we continue to move forward, you will hear more about operational technology (OT).” IoT is not about radios; it’s all about data By Alan Carlton | Published on @NetworkWorld “The initial challenge for the Internet of Things (IoT) was how to provide physical connectivity of small and often remote devices to the Internet. This issue has basically been solved with the plethora of wireless connectivity solutions. The real challenge for IoT is data organization, sharing and search on an unprecedented scale.” BONUS NEWS This week, FreeWave announced a contest at a chance to win FreeWave’s award-winning WavePro WP201 shorthaul and Wi-Fi solution. Contest entrants must provide a high-level account of the application of the WavePro, along with a description of the need for the platform. Winners will be announced at the close of the entry period. To enter the contest, please visit: http://go.freewave.com/wavepro-network-giveaway. Submissions are due by September 30!
Friday Top 5 IIoT News Roundup
It’s time to nominate our Friday top five Industrial IoT news articles of the week. Much like the weather in Boulder this week, we couldn’t decide on just one vertical focus, so we cast a wide net of IoT topics. In this week’s roundup, you’ll find a splash of fog computing, manufacturing, smart grid, security and overall IoT updates. Dive in and see if you agree with our picks. Don’t miss the Friday bonus at the end of this short roundup. Making fog computing sensors clearly reliableBy @Patrick_Mannion | Published on @ednmagazinehttp://www.edn.com/design/sensors/4442602/Making-fog-computing-sensors-clearly-reliable“As fog computing rolls in, the onus is upon designers to figure out how much intelligence should be at each node of the system for optimal performance. This implies then that sensors will need to start being more intelligent, with some level of built-in processing, storage, and communications capability.” Army needs wide-area electro-optical sensors for manned and unmanned aircraftBy @jkeller1959 | Published on @IntelligentAerohttp://www.intelligent-aerospace.com/articles/2016/08/ia-wami-sensors.html“Army researchers are interested in moderate-resolution persistent-surveillance electro-optical sensors that operate during the day and at night over large areas to detect vehicles and people on foot. Researchers want to develop a sensor that consists of an imaging sensor, as well as a storage and processing unit.” Five essential IIoT DefinitionsBy @MMS_MattDanford | Published on @MMSOnlinehttp://www.mmsonline.com/blog/post/5-essential-iiot-definitions-“The idea is not just to exchange and collect data, but to act on that data to make things better. (One commonly cited example is a “smart” thermostat.) IIoT is the same concept applied to industry. Examples range from “smart” buildings and power grids to “smart” transportation networks. IIoT might initially take the form of a machine tool status monitoring system.” What makes a grid smart?By David Shadle | Published on @tdworldmaghttp://tdworld.com/grid-opt-smart-grid/what-makes-grid-smart“My point, however, is that the critical consideration is not the number of sensors, controls or data storage components we add to our system when we decide to move ahead with smart grid applications. The focus also needs to be on mastering the integration of these systems, many times across traditional IT and OT lines, to allow them to achieve their potential for intelligence.” Top ten security predictions through 2020By @Gartner_inc | Published on @Forbeshttp://www.forbes.com/sites/gartnergroup/2016/08/18/top-10-security-predictions-through-2020/#4d8ba8073cbe“Through 2018, more than 50% of Internet of Things (IoT) device manufacturers will not be able to address threats from weak authentication practices.” Friday Bonus! FreeWave Technologies announces partnership with Solis Energy By @SolisEnergy and @freewavetech | Published on @SolarNovus http://www.solarnovus.com/freewave-technologies-announces-partnership-with-solis-energy_N10256.html “Both companies are excited about the partnership and are already working through high profile opportunities to take advantage of the growing demand for smart systems and industrial connectivity.”
(Industrialized) IoT App Development
Has IoT app development begun to take the globe by storm? A few weeks ago we discussed the growing need for more third-party app creation for the Industrial IoT industry. This week, we dive deeper and focus on those early adopters of industrialized IoT app development and what industries these “bleeding edgers” are serving. We all know by now the number of connected things is projected to grow massivelyover the coming years. Injecting new software applications into the industrial IoT world creates even more monitoring, control and usage of devices and data at the edge. Some would call this influx of software with industrialized hardware a modern marriage. The manufacturing sector, for example, seems to have found a use for implementing next-generation hardware to improve and automate operations, especially along the assembly line. At the same time, cloud-based software solutions are being leveraged to improve data analytics, thus improving actionable intelligence in real-time. What’s more is this new environment is incentivizing industrial manufacturers to cultivate new business models as they are finding that solutions they have developed in-house are as valuable as the hardware they manufacture. By tracking the performance of manufactured products in the field, manufacturers gain faster feedback loops and insights from customers. For example, instead of waiting months or even years for performance feedback, the integration of cloud-based software and modern hardware provides manufacturers this information in what is approaching real-time. This allows them to respond quickly with fixes, advice or, when needed, replacement equipment. As we enter into uncharted territory for many in this new interoperable, connected tech world, we have to also consider the cybersecurity measures in place and how it will combat any vulnerabilities as the surge of new, industrialized software applications enter our critical infrastructures. Security must be manufactured into the product from the very beginning – this includes tamper-proof hardware, authentication protocols, data encryption and more. What’s Next? Big companies like AT&T and Microsoft are joining forces for the good of the developer. We all can agree software is taking hold of certain business operations, so it is only natural companies would seek an easy solution for enterprise to bring about this change. The industrial side may appear to move slower when it comes to implementation, but that is only because of the various moving parts – machine-to-machine (M2M) devices, sensors and wireless technologies – that must sync with precision without missing a beat. Software is the enabler of this interoperability. So what is the next step in this industrialized development? Jeff Dorsch with Semi Engineering believes that, “Industrial Internet of Things (IIoT) applications proliferate in critical infrastructure, such as the power grid and water supply, the importance of the underlying software and the availability of an open-source platform for app development is coming to the forefront.” This fully-functioning data driven ecosystem will have to decide if open or closed systems are the best for their needs. Google and Apple, for example, have provided internet enabled ecosystems of devices. The problem is that they are closed ecosystems that limit which devices and which data can speak to each other. If industrial players want to take advantage and accelerate their own digital transformations, market opportunities and revenue, then they must take a closer look at open and secure technologies and start innovating for IIoT today. So as we all start to dip our toes in the industrialized software development pond, be sure to consider how your desired outcome matches the factors of delivering business value – customer responsiveness, security, revenue generation and operational efficiency. All are important in and of themselves, but different business models drive different decision-making. Embracing the IIoT app development opportunity early on might prove to be the smartest investment from a competitive advantage standpoint – being able to answer the “why” question is what will eventually separate the high-performers from the rest.
Manufacturing the Future
It’s no secret that the industrial revolution was directly born from the development of specialized machinery, thus providing the means of manufacturing a new path in history. Industrialization marked a societal shift through the development of these new systems, which also opened new ways of doing business. The principles and practices from these transformations continue to have a long-lasting ripple effect on the world today. It may come as a surprise that America manufactures more today than we ever have before in the country’s history. The advancements in manufacturing have spurred the next era of global growth and innovation. As a local manufacturer for the past 20 years in Boulder, Colorado, FreeWave has a unique understanding of how producing goods locally actually improves the bottom line, as compared to sending the work offshore. The Manufacturer is Evolving According to a major report from the McKinsey Global Institute, manufacturing continues to evolve in many ways. Some of the key findings to note were: Manufacturing’s role is changing. The way it contributes to the economy shifts as nations mature: in today’s advanced economies, manufacturing promotes innovation, productivity, and trade more than growth and employment. In these countries, manufacturing also has begun to consume more services and to rely more heavily on them to operate. Manufacturing is not monolithic. It is a diverse sector with five distinct groups of industries, each with specific drivers of success. Manufacturing is entering a dynamic new phase. As a new global consuming class emerges in developing nations, and innovations spark additional demand, global manufacturers will have substantial new opportunities—but in a much more uncertain environment. The report also highlights two very critical priorities for the future: “Companies have to build their R&D capabilities, as well as expertise in data analytics and product design. They will need qualified, computer-savvy factory workers and agile managers for complex global supply chains. In addition to supporting ongoing efforts to improve public education—particularly the teaching of math and analytical skills—policy makers must work with industry and educational institutions to ensure that skills learned in school fit the needs of employers.” IoT and Smart Manufacturing Whether it’s called smart manufacturing, Industry 4.0 or Industrial IoT, even the casual observer of the industrial landscape can see how manufacturing is changing. Being driven by new technologies and rapidly evolving customer demands manufacturers have needed to respond with mass customization – the concept of building flexibility into mass production. Through the adoption of the Internet of Things (IoT), factory and plant settings are becoming more outfitted with advanced instrumentation and being interconnected for a holistic approach to the modern assembly line. IoT provides the ability to gain valuable data off of all the “things” along the manufacturing process. From the condition of assets and equipment to quality and yield metrics, IoT provides live, real-time data from the manufacturing environment to our fingertips. In addition, new data sets (and perhaps more importantly data analytics) are changing the way we see our machines, our processes and our business operations. Analytics can identify patterns in the data, model behaviors of equipment, and predict failures based on a variety of variables that exist in manufacturing. As more factories and equipment are instrumented with the IoT, data volume will only grow larger. In Closing America is still making plenty of “things” and thanks to the latest advancements in technology, is still the leader in many of its fields of expertise. Below is a throwback video from PBS to remind us how the manufacturing sector continues to produce not just products, but ingenuity. Video courtesy of PBS.org
Berg Insight: Bright Days Ahead For Wireless Automation
A recent report published by Berg Insight details the bright future ahead for Industrial IoT through the implementation of wireless automation technologies. Berg Insight senior analyst Johan Svanberg made note that higher levels of automation and IoT solutions enable “shorter lead times, lower inventories, increased throughput as well as more flexibility and the ability to respond faster to changing customer needs.” The wireless IoT device market is served by a multitude of players from various backgrounds including global automation solution providers, automation equipment and solution vendors, industrial communication specialists and IoT communication specialists. This new report from Berg Insight informs us that: 2015 estimate of wireless devices for industrial automation applications reached 4.8 million units worldwide. Wireless devices installed for industrial applications have a forecasted growth rate of 27.7 percent from 14.3 million connections at the end of 2015 to 62.0 million devices by 2021. Key Findings from Berg Insight: Wireless connectivity is instrumental in the Internet of Things era and the use of wireless solutions in industrial automation is increasing rapidly at all levels of automation systems. Industrial automation systems utilize wireless communication to connect remote and local facilities and equipment to increase operational efficiency. A wireless automation system contains a mix of network technologies, equipment and systems including enterprise and automation systems, network equipment, control devices and field devices. The most common wireless technologies in industrial automation include cellular, 802.11.x Wi-Fi, proprietary unlicensed ISM radio, Bluetooth, various LPWAN technologies and 802.15.4 based protocols such as WirelessHART, ISA100.11a and ZigBee. Berg Insight estimates that shipments of wireless devices for industrial automation applications, including both network and automation equipment, reached 4.8 million units worldwide in 2015. Growing at a compound annual growth rate of 25.1 percent, shipments are expected to reach 18.3 million by 2021. The installed base of wireless devices in industrial applications is forecasted to grow at a compound annual growth rate of 27.7 percent from 14.3 million connections at the end of 2015 to 62.0 million devices by 2021. Wi-Fi is widely used for backbone communications as well as in monitoring and control applications within factory automation where Industrial Ethernet has got a strong foothold. Bluetooth is also popular – often as a point-to-point wire-replacement between for example a mobile HMI solution and a field device or control unit. 802.15.4 networks are often used to connect wireless sensors and instrumentation in process automation. Cellular connectivity is typically used for backhaul communication between plants, connecting remote devices in long haul SCADA applications and for third party access to machinery and robots. LPWAN technologies are increasingly used in certain low data, long range applications. Most of the major vendors of wireless IoT devices in industrial automation offer a wide range of devices with various wireless technologies in order to support many different applications. Key Takeaways, According to Berg Insight: Companies are now deepening the integration between industrial automation systems and enterprise applications and the promise of IoT is getting more tangible by the day. Large multinational corporations are beginning to systematically develop and adopt best practices to maximise the benefits of IoT technology in every part of their organisations. IT/OT convergence, smart factories, Industry 4.0 and the Industrial Internet of Things are concepts which are part of the ongoing evolution of industrial automation. Innovation in sensors, wireless connectivity, collaborative robots, big data and cloud solutions along with seamless exchange of information between devices, systems and people paves the way for improved performance, flexibility and responsiveness throughout the enterprise value chain. For more information, read the full report from Berg Insight.
IIoT Top News: Machine Learning
Machine-to-machine (M2M) learning is an integral apart of the expanding world of Industrial IoT. Over the past few months we have given attention to manufacturing and its current digital disruption, but have failed to show the direct impact smart M2M and IoT technology is having on the industry. So, this week we are diving deeper into the term machine learning and how it connects to manufacturing both today and in the future. Before we get to our news round up let’s start by re-defining M2M, to ensure we are all on the same page with its purpose and meaning. Gartner has defined machine-2-machine communications as “something used for automated data transmission and measurement between mechanical or electronic devices.” Now, that we have defined M2M, its time to check out our top news round up for the week on how M2M applies to both manufacturing and IoT. 10 ways machine learning is revolutionizing manufacturing Machine learning is poised to improve manufacturing by streamlining the process of OT and IT, thus increasing efficiency and lowering overall operation costs. Louis Columbus at Forbes believes that “Every manufacturer has the potential to integrate machine learning into their operations and become more competitive by gaining predictive insights into production.” IoT will recharge Machine Manufacturers Manufacturing can look to software companies as an example of how IoT can implement creating a smarter M2M network. Timothy Chou with CFO.com writes, “Today, manufacturers of machines — whether seed drills, chillers, or CT scanners — can leverage the path paved by the software product companies through three new business models: service and support; assisted services and machine-as-a-service.” Climbing the IoT Mountain–by adding M2M to manufacturing Manufacturing is only at the beginning of its ascent into IoT and M2M, so there are many more bumps and obstacles a long the way for the industry to fully integrate. Ronnie Garrett with Supply & Demand Chain Executive describes IoT and M2M manufacturing implementation as, “Standing at the foot of Mount Everest, ready to climb the world’s tallest mountain. You know you want to get to the top but you aren’t really sure how you will get there or what obstacles you’ll encounter along the way.” Cybersecurity is manufacturing’s biggest risk factor Manufacturing needs to continue to add M2M automation and big data analytics to the shop floor, but a threat to the overall industry is manifesting itself in the cybersecurity world. Ian Wright with Engineering.com informs writes, “A new report from BDO indicates that 92 percent of manufacturers cited cybersecurity concerns in their SEC disclosures this year. According to BDO, this represents a 44 percent increase compared to the first Manufacturing Risk Factor report in 2013.” As we wrap up our top news for the week, we realize the need to fully implement advanced machine learning across the manufacturing world will take more than a simple flick of the wrist. With that said, we leave you with a cautionary tale of when automation goes wrong. It was recently discovered an airport in India had an sign translated with automation software which read, “eating carpet strictly prohibited” — of course this was not the translation they had meant to display. Regardless, as we move towards a fully integrated M2M world, we will have to adjust our equations depending our our intended outcome, much like the world is finding with the love/hate of language automation. Hope you have enjoyed this week’s top news, as always tell us your thoughts on M2M and how it might impact your world!
Fog Computing: Answering the IoT Challenge
Fog Computing is being touted as the data communication solution our Internet of Things (IoT) devices are asking for by bringing the power of cloud computing closer to the end user. The fact is, the number of connected devices is going to continue to grow exponentionally. In fact, Gartner predicts that by 2020 IoT will include 26 billion connected things. Consider the impact that amount of data collected and processed will have. The Challenge Naturally, with billions of devices all connected to the cloud for manufacturing, oil and gas, utilities, municipalities and enterprise, to name a few, the data transmission and processing rate is bound to slow down – especially if the current cloud architecture is upheld. Some IoT devices use the cloud to store data long term, where other connected things send data to the cloud to be analyzed and sent back to the devicewith operational instructions. Ahmed Banafa with SemiWiki explains, “As dependence on our newly connected devices increases along with the benefits and uses of a maturing technology, the reliability of the gateways that make the IoT a functional reality must increase and make up-time a near guarantee.” What is Fog Computing? Fog Computing is a term coined by Cisco, that offers a way to analyze the data closer to the IoT device, thus saving valuable milliseconds. It may be hard to believe, but a millisecond has the power to prevent a M2M line shut-down, increase the speed at which power is restored to utilities and prevent an oil rig from leaking, just to name a few. An easy way to visually understand where Fog Computing fits in our IoT world, is by looking at the diagram above. It clearly shows that Fog Computing hangs between the cloud and the device, much like the fog on an early San Francisco morning. Fog Computing operates at the network edge, extending the cloud capabilities closer to the source (IoT device). Each IoT connection works with what’s called Fog Nodes to digest the intelligent data and then coordinate operational next steps, whether that be acting directly and or transmitting results to the cloud. The diagram below covers the types of response times IoT devices face from both Fog Nodes and main cloud locations. Fog Computing Brings Efficiency to Enterprise A recent report by Machina Research highlights the companies that pioneered Fog Computing and those poised to capitalize on the benefits in their near future. These companies are able to collect, protect, transport and control the data via IoT devices at the edge of the network, saving time and creating a more stream-line approach to sending and receiving data efficiently and more securely. Overall, as our need to connect explodes, we will not only need to think about IoT, but also the way in which intelligent data is processed from the critical infrastructure and back to the cloud. Fog Computing will continue to open more efficient channels across our IoT, as long as we allow it.
