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Tag: IIoT

  • Accelerating IoT by Switching Gears to 5G

    Accelerating IoT by Switching Gears to 5G

    Unprecedented breakthroughs in technology have often resulted in the emergence of disruptive technologies and outstanding innovations. A major development in one technology always fuels the growth and advancement of several other technology domains and industries that take advantage of it, resulting in a need to transform businesses to address new opportunities.

    The evolution and development of various generations of mobile wireless technologies—mobile phones, mobile application development, eCommerce, banking, transportation, hotels, etc.—have seen a cascading impact on the growth and advancement of various applications. We are about to witness a similar multi-dimensional growth in several industries with the advent of the 5G wireless technology. With speeds that could be at least 10 times faster than 4G networks, 5G is expected to create pathbreaking opportunities for innovation and development in a variety of domains including but not limited to education, IoT, artificial intelligence (AI) and health care.

    5G networks offer three major benefits, bigger channels, reduced latency and scalability, which allow the connection to a large number of sensors and devices. This combination will enable the government and other industries to introduce advanced digital initiatives involving AI, machine learning (ML), Industrial IoT (IIoT) and a variety of other data intensive applications.

    This article focuses on the effect of 5G networks on IoT, and the role that DevOps could play in this mix.

    Where Does 5G Fit with Regards to IoT?

    At a high level, the focus areas for the five generations of mobile network technologies can be summarized as: 1G, voice; 2G, voice and texting; 3G, voice, texting and data; 4G, everything as in 3G but much faster; and 5G is expected to be everything in 4G but extremely faster. 

    IoT devices employ a wide range of wireless technologies such as WiFi and Bluetooth, in addition to cellular technologies such as GSM, LTE, 4G and 5G. In the field of IoT, cellular technologies such as 4G, have helped a great deal with regards to device provisioning and device management. These technologies have also improved and ensured coverage, performance, reliability and security of IoT devices and applications.

    5G brings in a variety of other benefits that are not available with 4G. The speed, higher capacity and low latency of 5G will make it more ideal for simultaneously connecting a large number of devices. Further, additional enhancements made by some 5G service providers aim at expanding the frequencies for data transfer by digital cellular technologies. These features will also make 5G a major enabler in applications that use IoT where real-time performance and decision making are critical. Remote medical diagnosis and remote surgery are some examples where 5G’s ultra-low latency will help improve the virtual and augmented reality experience, making them more powerful tools in the medical industry—especially at a time of crisis like the currently prevalent COVID-19 pandemic. 

    The bandwidth reliability and resiliency offered by 5G will pave the way for several new applications for IIoT systems. For example, factors such as operator productivity in manufacturing, reduction in cost of quality management and enhanced quality through continuous monitoring and improvement, are some examples where IIoT can be made more effective with the use of 5G.

    The volume of real-time data exchanges with IIoT devices will be greatly increased with 5G. This helps with obtaining more frequent and reliable data on several KPIs in manufacturing, worker productivity, root-cause analysis, etc. Predictive maintenance, which is turning out to be a major use case for 5G and IIoT, aims at predicting failures before they occur by leveraging AI.

    The Difference that 5G Makes on IoT Data 

    Prior to the advent of 5G, technology required to offer a higher bandwidth and reliable network connectivity were quite limited and have further prevented from realizing the fullest potential of several edge devices. These devices can potentially generate and transmit large volumes of data that can be processed and analyzed by cloud-based applications. With 5G, there will be a sharp increase in the capture and use of valuable data from edge devices leading to greater insights and predictive analytics on IoT implementations.

    5G technology offers a significant increase in transmission bandwidth, theoretically up to 10 Gbps, as opposed to 4G which only offers 100 Mbps. This increase in bandwidth will result in significant growth in the volume and quality of IoT data and a proportional growth in AI applications leveraging ML and big data. The mass volumes of data created by IoT under the influence of 5G have the power to revolutionize everything, from education to healthcare to manufacturing and even autonomous vehicles.

    With 5G, there will be a surge in the volume of data generated by sensors, cameras, drones and edge IoT devices. Integration of data from different sources will lead to the development of several large data-intensive applications that help draw valuable insights regarding the domains in which they are implemented. The convergence of 5G technology, IoT and data analytics will also pave the way for innovation and several new business opportunities. IoT applications leveraging 5G technology will proliferate wearables, smart home appliances, industrial automation and also future self-driven vehicles.

    All Things Considered

    There is absolutely no doubt that IoT is already taking the world by storm. The power of IoT, when harnessed with the power of 5G technology, will certainly lead to several innovative approaches and open up new business opportunities. Overall, the speed, reliability and low-latency offered by 5G makes it ideal for connecting a large number of IoT devices.

    IIoT devices will find a great use for 5G as they can now collect and report large volumes of real-time data from manufacturing units and sensors, helping companies obtain valuable insights and take well informed decisions. The large bandwidth supported by 5G additionally provides edge devices the ability to transmit larger volumes of data more frequently to applications that can process and use the data effectively for AI and ML.

    We will definitely see the industry witness a sharp growth in IoT, AI and ML applications relatively soon, given the fast growth of 5G and the enhancements that several service providers are already making in this technology.

  • 4 Challenges OEMs Face When Implementing New Technology

    4 Challenges OEMs Face When Implementing New Technology

    Like many others, the manufacturing industry is moving to a subscription-based business model. This new way of doing business is being referred to as servitization and it is forcing manufacturers to no longer strictly sell new products, but instead sell access to and the outcome those products deliver. This is fundamentally changing the way manufacturers have traditionally done business and is not only encouraging a shift to subscription-based pricing models, but also Products-as-a-Service.

    As manufacturers implement the processes and resources necessary to make servitization a reality, implementing new technology will be key to success–laying the foundation for success today and in the future. More specifically, machine learning and predictive analytics will become especially important for predictive maintenance, process optimization and supply chain management. While there are opportunities to capture a significant competitive edge using these technologies, there are also significant challenges specific to the manufacturing industry. Some of these noteworthy challenges include:

    Efficient and Secure Data Connectivity

    Complex, capital equipment (think construction equipment, agricultural equipment or industrial machinery) is built and designed to last, on average, 30 to 40 years. As a result, these machines must be very reliable. Today, however, the majority of equipment in service has upwards of 20 to 30 years remaining in its lifespan. However, almost none of these products were built or designed for IoT connectivity–creating the challenge of retroactively fitting existing equipment with IoT-enabled parts.

    In addition, a majority of this equipment operates in some of the harshest and most rugged environments in the world. Whether it’s an electro-submersible pump 3,000 feet below the surface of the ocean or a mining truck in a remote mine with little to no cellular connectivity, efficiently connecting this type of equipment to collect relevant data is a difficult challenge and oftentimes cost prohibitive.

    Even if manufacturers manage to connect this equipment, there is an added challenge of doing it in a way that ensures cybersecurity. Rogue elements that could gain malicious access to this equipment could have serious safety and economic consequences–and could even implicate national security in some instances.

    Sparse Data

    Newer machine learning algorithms, such as Deep Learning and Recurrent Neural Nets, have been groundbreaking for capabilities like image and voice recognition. However, these algorithms are also very data hungry, requiring millions–if not billions–of similar data points from millions of users.

    Because complex industrial equipment is intended to last for decades, failures are generally rare. Think about it: There have been just a handful of actual engine failures during flights for the entire global commercial fleet, which amounts to nearly 30,000 aircraft. Assuming an average of 2.5 engines per plane, the global commercial aircraft engine fleet is less than 100,000. While certainly a large number by itself, it is relatively small when considering these engines vary greatly in design and thus any single engine type that could be modeled together would be even smaller.

    Manufacturers of industrial equipment or durable goods also tend to be reluctant to share data, especially if their competition could potentially benefit from it. The challenges of efficient connectivity, rare failures and relatively small numbers of similar equipment in a fleet leads to the sparse data problems in the Industrial IoT (IIoT) realm.

    Domain Expertise

    While domain expertise to successfully apply machine learning and predictive analytics is required in the consumer world, in the manufacturing space, it is much more critical. Machines and processes are designed using physics and engineering principles. And, the context in which machines operate and behave is very important for accurate training models and analyzing results. Without appropriate context, there becomes a “garbage in, garbage out” problem as sensor data and machines alone do not capture or provide any context. This is where domain experts who can make sense of historical data for accurate model training as well as interpreting results are critical.

    The impact of making an incorrect decision can have expensive–and sometimes catastrophic–results that aren’t seen in the consumer-facing domain. For example, re-routing an aircraft mid-flight could cost millions of dollars for an airline. If that decision is made because of bad data and an incorrect prediction, it would be a very costly mistake. For us as consumers, we have some aspect of domain expertise for problems in the consumer world, whereas a trained pool of engineers with the relevant design, operational and maintenance expertise is a very small pool. And this, combined with the problem of baby-boomer retirement where expertise is lost at a significant rate, there is a real challenge to capture the knowledge and best practices required to deliver value.

    Data Ownership

    Currently in the manufacturing sector, there aren’t industry standards for data security, ownership or accessibility. Currently, many manufacturers are wrestling with who owns and has access to the data. Is it the manufacturer who designs and builds the equipment? Or, is it the third-party entity that rents and operates it? Or, is it the financial entity that may be the legal owner? What about the supplier that provides a component that is part of a larger piece of equipment (e.g. an aircraft engine that works in conjunction with the aircraft, with each component manufactured by a different entity)?

    Manufacturers being risk-averse in nature, are being very conservative in interpreting and applying the myriad patchwork of regulations being brought in other industries, including data security standards. This is limiting them in adopting more connected processes and strategies. The industry must accelerate efforts to establish industry-wide standards as well as regulatory frameworks for data ownership, data-sharing, interoperability and testing of systems and end-to-end security to overcome the roadblocks for faster adoption of these technologies and realize the promised benefits.

    The shift to servitization will require manufacturers to completely re-think how they operate–new organization structures and skilled resources, new incentive models, new KPIs to measure success and new processes. They will have to become data-driven organizations investing in technologies to connect and track products, collect data and efficiently analyze massive amounts of operational and service data, using technologies like IoT, machine learning and predictive analytics. This will strain manufacturers’ existing organizations and IT infrastructures, necessitating investment in highly scalable, cloud-based solutions to lay the foundation for a successful future. Manufacturers that embrace these changes will be the winners, while others will struggle to stay relevant. In fact, the ones that can successfully adapt to these paradigm shifts will be able to gain significant competitive advantage and market share.

    — Vivek Shah

  • Fear of Being Left Behind Driving IIoT Investment

    Fear of Being Left Behind Driving IIoT Investment

    It’s said that the industrial internet of things (IIoT) will help enterprises improve business performance and enjoy growth from new products, enhanced and new customer experiences and even new business models. The challenge, as we covered in “The Industrial Internet of Things Still More Promise Than Reality,” is that business leaders still don’t have a clear plan forward when it comes to implementing and benefiting from these new connected technologies.

    Dave Murray, director of thought leadership at BPI Network

    With that in mind, we reached out to Dave Murray, director of thought leadership at the BPI Network, which conducted the survey of 350 global IoT leaders that formed the basis of “The Impact of Connectedness on Competitiveness: Creating Business Value From Operational Insight Driven by the Industrial IoT” report.

    Below is our short conversation.

    Leadership Suite: Based on your research for “The Impact of Connectedness on Competitiveness,” where do most executives stand today when it comes to their vision of the IIoT?

    Murray: They are starting to get their feet wet, and are starting the early stages of implementation or their pilot stages. There is just a tremendous amount of interest out there, and I would say, belief that in the next three years the industrial internet of things will have a significant impact on competitiveness.

    At the same time, I think executives are also saying, “We really don’t know what our long-term strategy should be. We haven’t thought through how this is going to change our business models. We don’t have the talent, in terms of technology integration, data integration and analytics, to get the job done.”

    I think there is a certain amount of concern regarding falling behind. So a lot of companies are just starting to incrementally work on IIoT, mostly with their own products. For instance, equipment manufacturers are integrating sensor technology into their products to improve uptime, and preventative and predictive maintenance.

    Leadership Suite: Do you see apprehension among CIOs regarding the IIoT?

    Murray: Over the long term, I think many executives just don’t know where this all leads, and they are concerned that they don’t know.

    There’s also certainly concern about security. We asked, very directly, “What are your top concerns?” The first three are all about security and who owns the data. And the privacy issue about how much vendors are going to know about their customers.

    After those security and privacy concerns, among large companies at least, the top concern is competitive disruption. There’s a concern about what this going to mean for their industry and their company. Am I going to be disrupted by this technology? they are asking themselves.

    Leadership Suite: If I were a CIO, CEO, or executive that would be a big concern of mine: Am I missing something that a competitor is going to see? Am I going to suddenly fall behind a year?

    Murray: Particularly among large companies, that’s a definite concern.

    Leadership Suite: Do you think widespread adoption and related transformation of IIoT will play out as quickly as people expect now, or might it take longer than many assume? An example that I think back to is the mid 1990s: There was a ton of writing about the death of brick-and-mortar retail back then. And a lot of what was predicted to start happening is really just starting to come to fruition in the past couple of years.

    Murray: I agree. I think what we’re going to see is going it’s to be incremental. It can’t happen all at once. They don’t have it figured out. They don’t have the skills and technologies in place to make it all happen, but they are starting to implement. And I think that will just grow over time. There are many issues ahead. There are issues with standards. There are issues with building up ecosystems to create more compelling offerings. But I think that we’re going to see a significant uptake in the next three years.

    I think it was most large companies who said that they thought that it would have a significant to major impact on their industry in just the next three years. Understanding that there is a big different between expectation and reality in technology adaption. I’m not saying that in three years, the world will look very, very different, but I’m saying we will be much farther along.

    Leadership Suite: I suspect we might be further along seeing IIoT capabilities installed in products and applications, and some tactical enterprise deployments rather than big systemic efforts?

    Murray: I think that’s correct. I think we’re seeing that already. That discreet product-level deployment. But I think that as these companies move forward, they’re going to change business models to provide new services, database services. To provide equipment more as a service than a sale. Then I think that over time, you’re going to see very big deployments that change the face of manufacturing.

    We talked to Airbus and they and all the other aircraft manufacturers have deployed sensors into their planes to improve preventative and predictive maintenance. But in addition to that, it will be deployed more across their products, their aircraft, but also Airbus is talking about the factory of the future. The smart factory. IoT is all about the way they are going to design their next manufacturing process in plants.

    Leadership Suite: Based on your survey and enterprise interviews, what’s your advice on how companies should start thinking about IIoT if they haven’t yet? Or, if they have, what should be their focus?

    Murray: My advice is No. 1 that executives need to know that clearly companies are beginning to take action here. And it very well may be a big competitive differentiator over time. I don’t think companies can afford to wait on the sidelines. I think they’ve got to start to deploy this technology and understand how it may change their business models, and how it can affect their business processes. I certainly think it’s time for industrial companies to take action, although on an incremental basis.

    Just as you say, you look at commerce and retail, and you could envision what has taken place back in 1999. But it took many years to get there, but it’s coming. And companies that aren’t prepared and lag behind will suffer competitively.

    — George V. Hulme