Manufacturers have long understood the benefits of moving towards an enterprise resource planning (ERP) system that is integrated with the plant floor systems, but it’s been hard to get past the cultural and technological barriers of doing so. As a result, especially for many midmarket manufacturers, the ERP system still sits in a silo, separate from the systems that govern the activities on the plant floor. Operational technology (OT) and transactional data have largely lived separate lives, rarely connecting to spark efficiencies and crucial insight across the business.
With the new imperative to embrace the internet of things, OT and IT systems — and the people who manage them — are being encouraged to become friends like never before. Advancements in technology have democratized the ability to launch IoT projects and the business benefits of integrating systems are too great to ignore. It’s no wonder, then, that the manufacturing industry leads the way in IoT investments, with IDC reporting that it spent a collective $178 billion in 2016.
IoT-enabled assets and strategies, of course, hold the most promise when the data they generate can deliver insight across the business. Yet only 16% of manufacturing companies consume IoT data in their ERP systems, according to a recent study by IFS (registration required). That’s regardless of company size, IoT adoption or digital transformation projects.
A similar trend can be found in a recent survey among midmarket manufacturers who said that the main advantages of their ERP systems were mostly found in their financial and accounting related processes.
Yet, those same midmarket manufacturers can benefit from real-time, actionable insight by integrating data from OT with transactional ERP systems. Consider the power of a machine alerting a plant manager when a valve was about to go bad, triggering the procurement system to order the necessary part and scheduling a service technician to fix it.
In a quest to get their hands around IoT, many midmarket manufacturers realize that this is an evolution impacting the entire global industry and they must start this transformation journey if they are going to remain competitive. It is not as important where a manufacturer starts, but just that they start!
Creating an enterprise IoT strategy is important. To get started with industrial IoT projects that lay the groundwork for IoT to use enterprise systems and transform business processes, manufacturers should consider the following:
Start on the plant floor. Tap into the knowledge of those who have the most experience solving problems and coming up with the best solutions. Find out how that knowledge can be automated to augment the way employees work while empowering them in their roles to work under the best possible conditions. Getting insight and buy-in at the grassroots level pays dividends, not only in terms of accelerating projects, but laying the foundation to ease change management when it comes time to deploy the solutions.
Choose a pilot or proof of concept. Pick a key manufacturing process, for example, packaging line, that allows the organization to learn fast and pick up speed. Be sure to identify the desired business outcome. Asset tracking, predictive maintenance and automation are some of the most popular IoT use cases for manufacturers. But before diving in, consider some key questions: What is the business outcome your organization is trying to achieve? Is it to increase throughput or uptime? Is it to increase the efficiency of an asset, or decrease the cost of maintaining it? What are the key metrics and KPIs to track in that regard? Let the business outcome drive the use case.
Make sure the technology will scale. One of the key challenges in implementing IoT strategies is doing so in a manner that will scale both securely and efficiently. Midmarket manufacturers should look to implement technology that will ease the process of communicating with equipment with different protocols. Cloud-based systems provide the scalability at a fraction of the costs of on-premises systems.
Start learning about and advancing real-world implementations of IoT. The IoT world is active with industry consortiums that offer guidance, help develop and advance standards, and provide valuable connections to get started with industrial IoT projects. Consider the benefits and value in investing time to be part of one.
IoT isn’t just for mega-companies. Midmarket manufacturers can take steps toward launching IoT pilots now and begin to realize the advantages of the cutting-edge technology.
All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.
A machine that gives advance notification to the owner about an imminent breakdown. Smart glasses that allow field technicians to work hands-free while remote supervisors walk them through solutions. Intelligent factory floors connected to the cloud to obtain the status of raw material progress or assembly line production in real time.
All of these are examples of how factories of the future will work. There is little doubt that IoT is already, and will continue to be, the single biggest driving force behind Industry 4.0. According to McKinsey, IoT will have a potential economic impact of up to $6.2 trillion by 2025. A research study by IoT Analytics expects manufacturing to be the biggest IoT platform segment, reaching $438 million by 2021. And a Genpact research study states that almost 81% organizations globally believe successful adoption of IIoT is critical to future success — even more so for high-tech and large enterprises.
IIoT: Opening new possibilities and growth areas
There is more than one way in which IoT can enable industries to accelerate growth, transform economies and achieve a new level of competitiveness. With IIoT, manufacturers can ensure greater efficiencies across the value chain, ranging from operations and services to engineering and product supply.
1. Predictive maintenance: Every year, manufacturing units across the world suffer from huge losses due to faulty machines and equipment, rework and machine failures. IoT can help factory owners predict a solder defect or a type of component defect before it has occurred. This helps owners identify a potential issue and take urgent action to address it, thereby ensuring that there are no faulty pieces. The eventual outcome is that manufacturing time decreases, there is no repeated work and the amount of scrap reduces significantly. By aggregating real-time data from sensors installed on the machinery and equipment, manufacturers can track current machine operational status and also receive alerts if there is a possibility of any failures or unplanned downtime. All these factors enhance operational efficiencies and help aid in increasing revenues.
2. Enhanced field service: Companies that have their products and equipment distributed worldwide — for example, elevators or large healthcare equipment — often face a challenge where after-sales service or troubleshooting are concerned. Usually these companies have field service executives that visit different locations for troubleshooting or servicing these pieces of equipment. Often these field executives or technicians must spend a great amount of time going back and forth with back-office experts for more complex problems or must refer to technical guides or tedious repair manuals. IoT can help field service executives by using predictive maintenance systems. Field service executives can identify potential issues before they blow up into a major problem, thereby ensuring quick fixes before creating major inconveniences to the customers. By using smart glasses, technicians can work hands-free and even work remotely with back-office experts. Field technicians can also share real-time photos and videos, annotate on the shared visuals and even create handholding mechanisms with back-office staff, thereby making troubleshooting quick and seamless.
3. Energy management: It is estimated that IoT can help manufacturing units reduce energy bills by up to 20% through energy efficiency measures. By using smart meters, manufacturing units can track how resources are distributed and consumed, lower operating costs, reduce thefts and improve forecasting. Compressors if left on but not used can consume up to 70% of their full power. When identified, such unnecessary energy consumption can be drastically reduced by remotely managing manufacturing assets using connected sensors. Energy management systems can also optimize energy consumption to reduce CO2 emission and manufacturing operation costs.
4. Asset tracking: Digital asset management (DAM) is rapidly becoming one of the mainstays of retail and logistic verticals. In fact, the retail industry is expected to hold the largest share in the asset management system market. DAM offers unparalleled benefits, like real-time asset tracking, greater accountability and enhanced asset management, which ensure better customer service and increased organizational efficiency. Manufacturers, suppliers and end customers can track location and status, as well as condition of items at every step — from the time assets leave the warehouse to their final destination. If at any point any of the items are damaged or are in danger of being potentially damaged due to weather conditions, temperature changes, poor handling or are at a risk of being stolen, manufacturers and suppliers, as well as customers, get instant alerts, enabling them to take preventive or immediate action. Some of the key applications of IoT-driven DAM include inventory management, shelf stocking, check-out process management and counterfeiting elimination. Real-time location systems are expected to grow at the highest rate between 2016 and 2022.
The inherent benefits of IIoT, including asset optimization, smart monitoring, predictive maintenance and, most importantly, intelligent decision-making, are rapidly making it an irreplaceable technology. An amalgamation of different factors like machine learning, big data, sensor data, M2M communication and automation, IIoT is no longer the “next big thing.” We are well into the IIoT age.
All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.
Edge computing is rapidly being recognized as a necessity for implementing several industrial IoT use cases. Take offshore oil platforms, for example. It is enormously valuable to be able to predict when critical components are likely to fail. When failures are predicted, repairs can be made before human safety and the environment are put in jeopardy.
To do failure prediction, companies first feed historical data from components and sensors into a machine learning algorithm, with markings about whether the component was healthy or failing. The machine learning algorithm uses this data to create a “predictive model,” which is an application that can be fed real-time data and make judgments on whether the component being monitored is healthy or likely to fail.
While the machine learning techniques to build and deploy predictive models have been around for years, in practice network connectivity is typically what stands in the way. Building and operating predictive models for industrial equipment is data intensive, requiring collection of data points from a multitude of components and sensors (an oil platform may have thousands) at least once per second. Offshore oil platforms at best have cellular or satellite connections, which means bandwidth is scarce and downtime is frequent, making it impossible to send all sensor data at full fidelity to a central site or cloud.
Because of this, a prominent design pattern has emerged called “learn globally, act locally.” In this scenario, it means building predictive models at a core location (like the cloud) where compute power and data are plentiful, and deploying those models to data systems that reside at the edge (like the oil platform). Once deployed, these data systems collect data from all local controllers and sensors at full fidelity, evaluate that data against the local predictive models in order to detect possible impending failures, and take action without waiting on any data to be sent or received from the core.
In this design, it’s still important to have a network connection between the edge and the core, but it isn’t critical for the connection to be active all the time, since it’s out of the critical path of detecting and acting on possible issues. Instead, it’s more of a convenience channel to communicate filtered, summary and critical data from the edge to the core for the purposes of tuning the predictive models over time, and sending new models in the reverse direction.
It isn’t hard to imagine other ways artificial intelligence and edge computing help save lives and the environment, from autonomous cars to smarter air traffic control systems. The question is no longer how, but when?
All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.
Today’s faster-paced business requirements for all things automated, surveilled and customer-centric have focused C-level executives squarely on the ability to innovate using enterprise-level IoT. Everyone is looking for the next big disruptor. The complexity of an IoT strategic effort faces the most demanding requirements of compliance and security. Layered onto and around this are the extended requirements for partner compliance, converged cyber and physical protections, and other cloud automated incident response of the extended modern-day supply chain.
The implications to these IoT demands are for organizations to build modular functionality, establish pull marketing and demonstrate platform management that is cost-effective and low-risk. Operationalizing these business and functional requirements translates into managing interconnected personalized and partner information in a dynamic compliance environment.
As “surveillance capitalism” extends its reach, more and more organizations face the dilemmas of customized platforms and partnering to share, sell or gain information and data. C-level executives and their boards face the dilemmas of dynamically evolving issues related to data liability and privacy generated autonomously.
Expanding guidance and standards
In 2018, federal contractors and global companies are straddling the two big hurdles of the European Union’s (EU) General Data Protection Regulation (GDPR) and Controlled Unclassified Information (CUI) and the associated Defense Federal Acquisition Requirement Supplement (DFARS) requirements for doing business with the government. IoT and associated AI capabilities face the same compliance requirements as well as other NIST guidance and guidelines from the Federal Trade Commission (FTC).
The complexity of these compliance standards tends to focus on technical issues without regard to the organizational impacts such as privacy liabilities and data management issues. The Draft guidance of NIST SP 800-53, Rev. 5 establishes these liabilities on the near-term horizon with wording that addresses systems, not just information systems, as well as the new security control families related to individual consent and privacy. Most IoT technologies rely on the cloud to operationalize capabilities and transform massive amounts of data into intelligence and predictive sales and solutions bringing Federal Risk and Authorization Management Program (FedRAMP) compliance into the picture as well.
In the face of growing intelligent things, smart automation and self-regulating devices, CEOs and C-suite leaders should prepare to answer inquiries related to privacy effectiveness and technical information and data protection. As geolocation implications of cookies, IP address mapping and other customized digital marketing transform the public and private marketplace, compliance parameters will require key performance indicators both internally and externally.
Apply top-down, bottom-up risk decisions
Consulting with and adapting the engineered decision trees of new intelligent things after the fact exponentially increases operational costs and may generate repetitive penalties for privacy and consumer fraud infractions. In some instances, the violations could result in the loss of viability for the organization.
To adequately address the cyber, governance and compliance changes introduced by IoT, organizations will need to apply a top-down, bottom-up risk decision capability. Defining shared risks across the IT, financial centers, marketing and supply chain is the next big change in this cyber and compliance space as organizations integrate liabilities between corporate centers for better management and executive decisions. (The top-down governance strategy!)
Utilizing inherent operational security and integrated compliance with IoT begins with incorporating cloud, CUI and DFARS standards such as those incorporated already into FedRAMP templates. As IoT efforts expand, it is predictable that the value of using FedRAMP and 3PAO consulting and testing will expand as SecDevOps benefits from accepted government-wide program lessons learned and processes. For many companies, FedRAMP services to minimize penalties related to compliance — both cyber and privacy.
IoT and digital transformation bring the possibility of the newest risk of all: the inability to physically document — even using automation — the true organizational risks of these new standards. Working with providers who are already certified and can demonstrate their security controls and policies related to data backup, encryption, authentication, data ownership and deletion can give organizations a head-start on the long road to IoT, AI and cloud security.
If you are worried about compliance, here are the steps to take now
IoT compliance will be a challenge. The compliance and cyber challenges are nuanced and may impact each organization differently. Invest in being a leader in risk mitigation by using existing certified components where possible.
The essential steps are:
Understand your obligations: Current requirements — FTC precedence, CUI/DFARS, FedRAMP and UK’s GDPR — offer actionable advice on how to strike the balance of continuing the adoption of IoT, cloud and AI while maintaining proper information protection standards. Although not every organization has to follow all of these standards, it’s important to consider them for future planning and market awareness. In competitive situations, you may end up losing business to companies that check more compliance boxes, so business leaders have to determine their cost-benefit analysis now.
Document your known risks and identify a plan of action: You may not need to solve for every compliance challenge right now, but awareness of your areas of weakness is critical for your long-term success. Essential to this is understanding how data flows and who controls the flow internal to the organization and externally. Data and intelligence handoffs may include direct and indirect customers, as well as your supply and support chains. Looking ahead, regulations and standards are likely going to be tighter and more costly, so having a plan of action is going to help you be proactive and possibly offset some of the costs of compliance.
The hype surrounding IoT platforms is reaching a crescendo, and at 500+ IoT platforms (by some estimates), we have a dizzying array of choice to contend with. Most of these platforms offer varying levels of functionality across the entire spectrum of the IoT value chain. Some operate in distinct niches (connectivity, edge, analytics, etc.), while the bigger ones seem to be targeting an all-encompassing proposition.
One thing remains common, though, across all these IoT platforms. The focus and fascination with getting the sensors to spew out their emotions (aka measurements) constantly and the ability to visualize that real-time live “in the moment” continues unabated. Customers too are driving this frenzy and the demonstrable use cases they are demanding focus on pulling streaming data feeds off the sensors, ingesting the same and visualizing the same real time with basic trending and exception analysis. Clearly there is a lot more to the IoT business value than real time-data acquisition and visualization that seems to be getting all the attention in the IoT revolution.
In our eagerness to get IoT tech adopted, the table stakes of IoT seems to be obfuscating the real business value equation. Are we really missing the forest for the trees? Is our obsession with connectivity and visualization causing us to miss out the big picture on how IoT will really generate business value? In short, the key question is “Are we putting the IoT cart before the horse?”
Gartner predicts that by 2020, 80% of all IoT projects would have failed at the implementation stage. The obsession with getting data across the finish line in the hope that it will shine a spotlight and create an IoT utopia seems clearly misplaced. Sensors have been there for a long while. No doubt they are getting cheaper, abundant and more capable owing to the integration, compute and bandwidth advancements, but the real power of the IoT platforms would be better tapped in delivering actionable insights on an ongoing basis which delivers the real business value. Clearly there is no one single silver bullet, but a multitude of factors in long road ahead toward the business value realization from IoT.
So, what is the ultimate holy grail of IoT-driven business value? What is it in the medley of data, connectivity, compute, algorithms, talent or process that would enable us to harness the true business potential of IoT? To answer that question, let’s explore each of these factors and the value each brings.
Data: Data has indeed proliferated exponentially, and so has the ability to capture that data meaningfully at a fraction of the cost. According to some estimates, the amount of data actually put through analysis prior to the large-scale advent of the IoT revolution was a mere 2%. Technology is definitely getting better at amassing and analyzing a higher percentage of this treasure trove of data, and definitely as data science would tell us the more the data, the better. Data may be the new oil, but the texture and chemistry of this oil is becoming ever more complex with varied unstructured data sources constituting the lion’s share of data processing. This new data oil will also need massive refining, blending and distribution (to the right stakeholders at the right time in the right context) to deliver the goods.
Compute: If data is the oil, compute is the power to keep the engine humming. Compute has seen rapid advancements and the mainstream adoption of GPU-based architecture and massively distributed parallel processing has ushered in a revolution in compute capacity. Compute has also enabled a level playing field through availability of cloud-based environments on tap which eliminate any potential hindrances and scale related challenges for even a startup with a brilliant idea.
Connectivity: Connectivity still remains a spaghetti of sorts, but constitutes the essential plumbing to keep the IoT engine humming. Multiple standards proliferate both at the radio and network level, and there is no one size fits all. We are however blessed to be in an era where connectivity is no longer a constraint though. Multiple competing standards and protocols may delay our journey towards standardization, but connectivity still is the umbilical cord which binds all the elements together.
Algorithms: Algorithms are purportedly the icing on the cake and the most hyped of the lot. Algorithms are the cool stuff, the ultimate nirvana in IoT land, but algorithms rest on a solid foundation of data and compute and draw their power from the same. Algorithms also need to deal with multiple aspects like precision, recall, false positive/negatives, and despite the hype surrounding them have multiple challenges to contend with. Developing algorithms in an isolated data science classroom-based environment is one thing, deploying them in a production environment and using them in context to derive and then apply insights is yet another. Context also changes too fast and algorithms need to keep pace through continuous refreshes to stay ever relevant.
Talent: Like in any field, having the right talent is the absolute bedrock for true success in the IoT world too. Easier said than done, though, since we need a multitude of very diverse skills to successfully deploy IoT in a production landscape and deliver the goods. IoT needs a cross-disciplinary approach and an amalgamation of engineering, domain, IT, OT, data science skills and a really strong cross-domain program management perspective to integrate the multiple disciplines. Good talent (especially the cross-disciplinary ones to help integrate diverse perspectives) remains a challenge at least in the foreseeable future.
Process: IoT still remains a relatively new field, and the processes and methodologies for production-level deployments at scale are still evolving. There are arguments both in favor of and against adoption of agile-based methodologies for IoT deployments which have proven so successful in the IT realm. The IoT methodologies and best practices development remains a complex endeavor with multiple moving parts and entirely different legacies and philosophies on the IT and OT camps.
IoT platforms thus need to broaden their horizons and evolve into richer IoT ecosystems which can orchestrate these diverse aspects aptly and provide integrated value-driven technologies. For the real potential of IoT to be unleashed and for it to realize the vision of trillions of dollars of business impact in the coming decades, it will clearly take a “systems thinking” approach to integrate all the above perspectives into an integrated whole. That is how we will get the IoT horse before the cart!
The 21st century saw the inception of the internet of things. Many industries adopted IoT to improve efficiencies and meet increasing customer demands by using real-time data for prompt decision-making.
IoT began its journey on the factory shop floor with technologies like RFID helping to advance the manufacturing process. Then came consumer IoT applications, where homes started seeing a huge level of automation with lights or air conditioners operating remotely. After shop floors and homes, IoT was examined to help improve processes in the enterprise supply chain.
Initially, supply chain professionals and IoT technologists believed that by connecting goods-carrying fleet around the world, they could obtain real-time visibility of their shipments. Therefore, the first move in IoT supply chain was to develop technologies to track “vehicles.”
GPS vehicle tracking became the preferred method to gain shipment visibility, causing thousands of companies to launch with new technologies in this space. Wired to the vehicles’ batteries, GPS trackers provide data analytics about the fleets’ locations and movements.
GPS tracking analytics include location, speed, route, acceleration and driving patterns. However, you cannot obtain data on the condition of your goods — and this is a major limitation with fleet management systems.
Challenges with the ‘connected fleet’ approach
While GPS vehicle tracking works well for personal cars and car services, the “connected fleet” approach is not effective for supply chain professionals wanting to stay abreast about the holistic health of their goods, and it poses several challenges:
1. Inability to monitor LTL shipments and market vehicles
Whether the shipper is a large multinational or a small business, companies rarely ship goods using their own vehicles — especially on intercity and transnational routes.
Typically, companies use logistics service providers (3PLs) to arrange for a pickup at the origin. The 3PL then sources a vehicle on a by-trip basis to haul the goods to its destination.
This type of vehicle, known as a market vehicle, constitutes more than 80% of surface hauls around the world.
Since market vehicles seldom return to the shipment origin after delivering the consignment to the destination because they are likely sourced by another 3PL for another shipper, it’s extremely difficult to secure the visibility of goods. You will have no insight into whether the market vehicle has a GPS tracker. Even if it does, you would need to obtain the data from the truck owner which is difficult, time-consuming and often impossible.
2. Inability to track multi-modal and trans-shipped cargo
A large percent of shipments in the world travel through more than one mode of transport during transit. A shipment can move by surface, then by rail and even hop on a vessel for a journey across the ocean.
By tracking the vehicle alone, you cannot monitor your goods’ journey end to end.
3. There’s more to a shipment than its location — its health condition
If your shipment in question is perishable ice creams or pharmaceuticals, it’s imperative for them to be preserved at an acceptable temperature range. Simply knowing the truck’s, flight’s or vessel’s location and movement will not provide you with the ability to rectify a condition anomaly that could impact its quality or integrity.
The same applies to fragile shipments that are sensitive to shock or humidity.
The conditions surrounding the shipment, such as temperature, pressure, humidity and shock (its handling), are just as important as the location itself, and fleet-based tracking cannot provide you this information.
The solution: An IoT ‘connected things’ supply chain model
To achieve effective supply chain visibility beyond intel on vehicles’ whereabouts, let’s spotlight the “things” in IoT. By monitoring the things or parcels themselves, and not just tracking the fleet that are moving the goods, your supply chain can receive real-time data enabling you to make better decisions, reduce risk and increase business efficiencies.
Portable wireless monitoring device hotspots and BLE beacons working on hybrid IoT technology (a combination of GPS/GSM/BLE/Wi-Fi) when attached to packages, boxes, pallets, containers or loads track and monitor the condition of the individual packages in transit and at the warehouse.
By using such a hybrid IoT technology and portable devices, the location as well as the health of the shipment can be monitored in real time. You can implement visibility across your enterprise without relying on the fleet owners or the logistics service provider, making the model scalable and providing you with the power of integrated data across all your shipments. The collected data can be used to analyze trends such as past shipping patterns. With insights into past shipments, real-time information about the location and the condition of the shipment, predictions are more reliable and actionable.
For example, if the temperature of your consignment is rising in the summer months and it hasn’t moved for more than an hour from a location where it shouldn’t have stopped in the first place, it could indicate an issue. If real-time temperature data was not known (as in the case of fleet tracking systems), you would be unaware that your goods were in jeopardy.
The only challenge with portable device deployment to monitor things is about managing their reverse logistics. Most portable shipment monitoring devices may not be economical to dispose after use on a single trip. Therefore, before using a portable device, it’s important to ascertain if a tight reverse logistics system is in place for the retrieval of your IoT devices.
The best route is to work with an IoT provider who handles the reverse logistics and provides a completely managed plan with no device ownership or Capex.
When reviewing technologies and services, keep in mind to evaluate shipment visibility from a granularity perspective — package, pallet, load or container — and choose one that can manage the entire gamut of things.
I was recently asked what my biggest takeaway was from this previous year, as well as what I thought might happen in 2018. In a nutshell, it comes down to this: With all the hype around IoT, I believe technology firms invested ahead of actual demand. This resulted in a number of products and solutions on the market that don’t yet address the actual needs of the customer’s business.
In 2017, we saw several distinct categories of IoT technologies emerge:
- Hype-chasing fad products, like IoT coffeemakers and home security systems;
- Hardware and software infrastructure products that provided the basis upon which customers could build their IoT solutions;
- Packaged IoT platforms; and
- Development tools and environments.
As customers grapple with determining what technology will actually suit their business needs, I think 2018 will be a year where vendors either drop off or seek to merge and/or partner together for solutions that show a clear return on investment.
Here’s what’s coming
My bet is that we’re going to see a lot of the following this year:
- Customers with a pinpointed focus on IoT projects that can produce measurable success around very specific business outcomes, such as driving out cost, increasing operational efficiencies, or reinventing the customer experience to increase demand.
- Increased interest in a stable, dependable infrastructure, built on open standards, on which to implement IoT projects.
- The need to connect new data sources into existing infrastructure while ensuring smooth integration and data management capabilities
In addition, I expect we will see potential IoT customers asking themselves these very strategic questions:
- Am I willing to change my business processes to match how a cloud-based or off-the-shelf IoT solution works?
- Does an IoT infrastructure built on open source afford me greater flexibility and will it allow for changes as well as growth into the future?
Supporting data from customers
I have had the opportunity to work with dozens of customers over the past few months, ranging from telecommunications companies and as-a-service providers to G8000 enterprises and government agencies. Their industries included transportation, healthcare, utilities services and retail. In my discussions with them, the following points became clear:
- Many have strategically adopted open source technologies for interoperability needs.
- They are concerned about the security of their data in the public cloud, as well as vendor lock-in.
- IoT is a strategic investment for them, and many already have a pilot or production architecture in place.
- They are looking for modularity and deployment flexibility.
- They want to reduce risk and complexity.
- End-to-end analytics and features designed to provide end-to-end security are essential.
One of the most important takeaways from all of this is the need for an enterprise-worthy, end-to-end open architecture that customers can rely on for hosting their specific IoT projects. What’s needed is an architecture that enables them to integrate what they have today and evolve as they digitally transform their business.
New year, new ways to address the challenge
IoT projects need to be focused on generating business results. Those who wish to implement IoT know their business, but few have the skills required to develop an entire IoT infrastructure. They need complete platform solutions, and I would argue that they’ve known this from the start.
But early adopters of IoT who invested in proprietary platforms are now finding themselves frustrated by limited functionality, locked into a particular vendor and rethinking their choices. The value of open source alternatives is recognized as a hub for continuous innovation, but it is a significant challenge to manage multiple open source projects, validate that they work together, integrate them to provide the right functionality and ensure future enhancements.
Envisioning the future
So, what would an end-to-end, open source architecture for IoT look like? Here are key components I think are important and should be included in an IoT architecture:
- Connected “things” that generate device data, a connection designed to be more secure and seamless connectivity.
- An intelligent gateway stack that simplifies data flow management and offers intelligence and analytics capabilities at the edge.
- An integration hub to manage disparate devices and control the operational flow of data.
- A comprehensive, centralized advanced analytics and data management platform to enable deep business insights and actionable intelligence and manages IoT data processing, offers persistent storage and machine learning capabilities.
- Application development, deployment and integration services.
The model would essentially look like this:
Packaged IoT platforms that were promoted in 2017 may work well for completely new businesses building a separate IoT infrastructure from scratch. But few companies have the luxury of completely overhauling their infrastructure just for an IoT project. IoT implementations are part of a journey businesses take toward digitalization. There are a lot of already existing components that need to be taken into account. And a lot of future additions and enhancements that require a foundation that is flexible enough to adjust to growth and change.
The modular nature of the open, enterprise solution described above could enable system components to be swapped out as needed. This way, businesses can integrate new technology while at the same time preserving existing investments.
January’s Hide ‘N Seek IoT botnet has reminded us all of the sophistication of attacks that will continue to appear and hamper our collective network management.
These shifting IoT security threats of today will mirror the efficacy and complexity of PC and workstation threats of a decade ago. Having fought this fight before, expectations and game plans of attack and defense (respectively) can at least be estimated and planned for from a reasonably successful template.
In 2018, we can expect IoT deployment of multifactor authentication backed with hardware, even more sophisticated attacks against IoT networks and, for the short term, confusion over standards and certifications.
A trend toward security incident recovery through hardware
With the prevalence of weak passwords and access control on IoT devices leading to a growing number of attacks, there will be a shift toward designing systems to include incident recovery as a core IoT security requirement.
Product designers will begin asking the question, “When this device does get attacked, how do we ensure it can be disabled to prevent further damage and then recovered?” Expect the emergence of design requirements that mandate implementation of key rotation mechanisms at the time of manufacture. Still, during the event horizon of an attack, the existence of purely software key rotation mechanisms can still lead to device identity to be spoofed until after the attack is mitigated.
Designers and engineers should consider adopting multifactor authentication in their device design and making use of a hardware secure module or other hardware-secure security element as an additional factor in their identity and access management implementations.
Existing vulnerabilities will allow single event damage with maximum payload
Hide ‘N Seek and other attacks have shown us that exploits are becoming complex enough to attack a wide variety of device types in varied deployment types, no longer aimed at a specific product, stack or environment.
As a result, a single attack can adapt to maximize its payload and spread faster than device-specific or stack-specific patches and upgrades can be applied comprehensively.
We can expect at least one major attack that causes damage of astronomical, never-before-seen proportions. This attack will affect several architectures and singlehandedly cause damage greater than multiple major prior attacks combined.
How can we prepare? 2018 will be a good time to revisit open ports and services running on devices, and to consider adopting cloud- or controller-based configuration interfaces rather than running administrative services directly on devices.
Confusion over security standards to continue
Several efforts have birthed various certification and self-assessment initiatives; however, with the exception of specific industries, like payments and healthcare, don’t expect to see patterns in adoptions emerge until either regulation or a critical mass of buy-in is reached, both of which take time.
As such, new efforts for security accreditation and standardization won’t have a widespread positive impact by the end of 2018. What can we do in the mean time? Continue to back the certifications and standards that make the most sense for your applications — the more thoughtful voices we have, the sooner that critical mass of adoption and regulation will happen.
Because of the success in mitigating malware on PCs, workstations and phones, the devices the world employs are, quite frankly, assumed to be secure. Blind trust is often attributed to the devices and the data they serve — even life-saving devices. We’re in a battle to succeed and make the internet of things a secure place, whether or not anyone notices.
The real challenge for enterprises in deploying an IoT solution today is the lack of neutral network integrators able to deliver solutions using any combination of LPWAN technologies. The LPWAN community is supported by several specialists using either Sigfox, LoRa, Ingenu or Weightless technologies, each with its strengths, weaknesses and distinct business models. Several mobile operators are promoting their own cellular LPWAN IoT networks including LTE-M (CAT-M1) and NB-IoT (CAT-NB1). Enterprises often require true global coverage and these different LPWAN technologies will likely live alongside each other for quite some time. Traditional IT system integrators have focused on integrating complex components and analytics to deliver a full IoT solution, including Accenture, Deloitte and PricewaterhouseCoopers, IBM and HP.
Wireless 20/20 believes there is a lack of neutral network integrators able to deliver IoT network solutions using multiple technologies. This article focuses on the key role played by IoT connectivity providers that can use any combination of technologies and match the right IoT network service to each use case and application. No one connectivity technology can meet all IoT requirements, and any solution will likely require multiple connectivity technologies just for coverage purposes for applications deployed today. Multiple connectivity solutions may be required just to meet the use case requirements, independent of coverage. Many solutions providers are tied to specific network technologies — and a neutral party is needed to sort through to the right answer.
Low-power wide area network, or LPWAN, is a set of wide area wireless networking technologies that interconnect low-bandwidth, battery-powered devices with low bit rates over long ranges. The leading specialists in the LPWAN technology community include Sigfox, LoRa, Ingenu and Weightless. The primary advantage of LPWAN technologies is the ability to support a greater number of connected devices over a larger area with a lower cost with greater power efficiency than traditional mobile networks. The main challenge for enterprises is that none of the LPWAN networks are mature, so coverage is limited although improving every day.
Here is a quick summary of the strengths, weaknesses and distinct business models for the leading specialists in the LPWAN community — Sigfox, LoRa, Ingenu and Weightless:
- Strengths: Solid technology, large ecosystem, many devices, low cost, low predictable power consumption, managed service.
- Weaknesses: Relies on ultra-narrowband technology combined with DBPSK and GFSK modulation using 200 kHz of unlicensed spectrum in the 868 to 869 MHz and 902 to 928 MHz bands depending on regions. Limited network coverage in the U.S. since the company did not reach its 2017 buildout goals. Immature ecosystem, but improving rapidly.
- Business model: Business model flaws in subscription-based connectivity at low cost.
- Countries are franchised to a variety of carrier and utility partners.
- Strengths: Solid technology, large ecosystem, but largely private (not generally accessible) but improving, low cost, low not-as-predictable power consumption, it is mostly private and (mostly) not offered as a managed service.
- Weaknesses: No central service manager or clearinghouse to interoperate networks (not a managed service — both a pro and a con), wide-scale coverage in its infancy, needs a more robust open ecosystem. Monopoly (thus far) on chips make devices a little more expensive.
- Business model: Senet is the largest U.S.-based provider of LoRaWAN IoT technology with network coverage in 225 markets. Recently launched low-power wide area virtual network to offer IoT connectivity solutions for cable, CLEC and wireless operators. Comcast is also expanding its LoRa LPWAN-based network to 12 U.S. markets.
- Strengths: Random Phase Multiple Access (RPMA) technology uses the unlicensed 2.4 GHz ISM band. Supports higher speeds, closer to LTE in functionality. Ingenu reports its U.S. network currently covers around 45 million POPs.
- Weaknesses: Expensive network and solution. CEO John Horn abruptly left Ingenu during the summer of 2017. Looks unlikely to survive.
- Business model: Subscription based.
- Strengths: Narrowband modulation scheme, can operate in both sub-1GHz license exempt and licensed spectrum, offers full acknowledgement of 100% of uplink traffic for unmatched QoS in a system operating in unlicensed ISM and SRD spectrum.
- Weaknesses: Guarantee low cost and low risk, and to maximize user choice and ongoing innovation.
- Business model: Open global standard promoted by a SIG rather than a specific company with a proprietary technology.
Here is a quick summary of the strengths, weaknesses and distinct business models for the leading Cellular LPWAN IoT network solutions including NB-IoT (CAT-NB1) and LTE-M (CAT-M1).
- Strengths: Carrier-backed, ubiquitous coverage due to it being an LTE upgrade, with higher data rates possible.
- Weaknesses: More expensive in terms of power consumption and device cost than other LPWAN technologies such as LoRa or Sigfox. The requirement for a SIM and pairing of some sort may also be an issue — compared to LPWANs that are paired at the factory.
- Business model: T-Mobile is the only major U.S. carrier to deploy NB-IoT as its low-power wide area network of choice. Verizon recently completed its first successful NB-IoT Guard band data session trials and plans to deploy an NB-IoT network across Verizon’s nationwide network in 2018.
- Strengths: Becoming pervasive — it offers higher data rates than LPWAN, but at higher cost and power consumption.
- Weaknesses: CAT-M probably provides the high end of the “LPWAN” segment.
- Business model: Verizon, AT&T and Sprint have all opted for LTE-M, another LPWA technology standardized by 3GPP, and have completed their initial nationwide LTE-M launches in 2017.
T-Mobile became the first U.S. operator to launch NB-IoT commercially and has committed to extend coverage to a nationwide NB-IoT network during 2018. This would be a major feat if accomplished, and the announced pricing is aggressive and will give the LPWAN players pause. T-Mobile parent Deutsche Telekom (DT) recently updated its plans for its narrowband-IoT rollout across Europe. DT launched NB-IoT technology in Germany in 2017, and the network is now available in approximately 600 towns and cities across its home market, with more than 200 companies trialing the technology. This DT NB-IoT network deployment in six of its European markets was on track at the end of 2017, including Germany, Poland, Slovakia, Czech Republic, Hungary and Greece.
Wireless 20/20 believes these different LPWAN technologies will likely live alongside each other for quite some time. The major technologies (Sigfox, LoRa, NB-IoT) will live alongside each other for a while, but only one may survive in the long run unless dual-mode chips become common.
Key enterprise verticals and horizontal applications require true national coverage and need to integrate multiple IoT network technologies. Among these, the following have enterprises that can integrate their own IoT network solutions and others that cannot develop their own solutions:
- Shipping and logistics/supply chain: Much of the supply chain is national in scope and needs national coverage — pallets, shipping containers, truck trailers, etc. But a portion is also local. Use case is to track location and sense temperature and shock/vibration. Local = the last mile of product distribution — think local warehouse to convenience store for consumer goods, for example. Multiple modes are needed. Examples: A pallet en route may need to report location only when it starts or stops (LPWAN), but a pallet in a warehouse may need to report its contents to a smartphone (Bluetooth).
- Agriculture: Reasonably localized per customer, but national in scope with big areas in California and the Midwest. Ag requires large coverage areas with modest populations. Use cases = many and varied: tracking, soil sensors, moisture sensors, tank monitoring and many more. Multiple technologies may be required — LPWAN for remote areas and fields, Bluetooth for applications expected to communicate to a smartphone.
- Facilities and spaces: Localized per customer, but national in scope … such as major hotel chains like Marriott or Hyatt. Shopping malls and office complexes and resorts and casinos and theme parks are largely localized, but there may be many facilities under the same owner nationwide. Use cases include a varied portfolio — door open/close, irrigation on/off, soil moisture monitors, water leak detectors, buttons to provide a service and many more.
- Industrial: Usually localized coverage when talking about the factory or enterprise, but national when talking about supply chain. Use cases are varied: track inventory or tools within a factory facility, fuel tank levels, panic buttons for personnel, personnel tracking for safety, etc.
Traditional IT system integrators, including Accenture, IBM, HP, Deloitte and PricewaterhouseCoopers have focused on integrating complex components and analytics to deliver a full IoT solution. Most of the traditional large system integrators focus on the higher end of the technology stack — connectivity is usually assumed to exist. Certainly, some of these companies can do large-scale Wi-Fi implementations and have solid experience with cellular services, but the core of their offerings focus on analytics and business process integration with legacy ERP and other systems. Newer connectivity solutions such as LPWAN are not yet fully integrated into their domain expertise.
The current need for IoT network integrators is driven precisely by this need — the capability to determine the right network connectivity solution for lower tier access in terms of power consumption and cost is nascent, and this very capability is what determines whether new projects can be delivered at a reasonable ROI.
IoT network integrators and connectivity providers can use all these technologies and match the right IoT network technology to each use case and application. IoT is not a one-size-fits-all environment in terms of connectivity. Every application and use case is different and connectivity requirements need to be evaluated on an individual use case basis. There are many parameters — cost, power consumption, latency, range, application, etc. Bluetooth may be the right answer for some, Wi-Fi may be the answer for others, LPWAN for still others.
In our next blog post, Wireless 20/20 will examine how some of the leading neutral IoT network integrators can provide a deep and rich set of services to ensure the highest quality of service for mission-critical IoT network management solutions in the U.S. market.
5G — or “fifth generation” internet — is the catalyst industrial mixed reality technologies need for today’s primary use cases to take hold. With 5G’s promise of virtually eliminating latency, augmented reality (AR) and virtual reality (VR) will finally thrive within the enterprise in instances when time is of the essence.
Two primary use cases established for mixed reality in an industrial context are informational overlay utilizing AR (think technician repairs on- or off-site), and training and education in high-stress or dangerous situations (think training for an oil spill without the risk). While both of these use cases have seen success in experimentation and small-scale use, operators are still challenged by achieving real-time data flow to complete tasks in virtual and real-world environments — especially in areas of low signal.
For example, a surgeon is needed to operate on a patient, but not at her hospital; the patient is halfway around the world. Utilizing a virtual reality headset, haptic gloves and virtual twin robotics onsite, the surgeon is able to operate without being physically present. This theoretical healthcare industry use case is only possible if there is zero lag between the actions performed by the surgeon and the effects happening in real time across the globe. For now, it’s relegated to training scenarios only.
Or, consider a construction company throwing out its training and operator manuals and relying on augmented reality glasses, like Microsoft Hololens, to deliver all technician information in real time as needed to assist in repairs, part replacements, on-the-job training and more. This will only achieve operational efficiencies if employees’ AR hardware works appropriately in all locations served, sending and receiving data with ease even in the most rural of locations. This is especially true in an emergency scenario where real-time information is critical to reducing equipment damages or human danger. See what Caterpillar is doing along these lines today.
With 5G connection, the aforementioned use cases — and many more — are not only made possible, but made better, with less margin of error. This is due to one of 5G’s greatest promises: low end-to-end latency that increases the amount of interactivity possible. According to Qualcomm, truly interactive remote experiences require low end-to-end latency, often ranging from 40 ms to 300 ms. 5G offers potential to bring over-the-air latency down to 1 ms.
Plan for the industrial mixed reality impacts of 5G now
Though we may not see mass U.S. availability of 5G at a consumer level until 2020 (at the earliest!), both B2C and B2B enterprises are beginning to experiment today with prototype devices and simulation networks. If you are utilizing AR and/or VR within your organization, it’s time to start considering the operational and managerial impacts 5G will have on your current mixed reality implementations and initiatives:
- Increased hardware needs: When more employees can access information on-demand using AR glasses, goggles or helmets — in any situation, anywhere — expect to feel the effect on your IT budget. Continually measure the increased efficiencies achieved to justify expenditures.
- Greater visibility into operations: Alongside faster internet connections and increased bandwidth comes the potential to increase the information garnered from onsite sensors within your industrial IoT network. Consider the software and hardware implications in storing, analyzing and accessing the extra data quickly and seamlessly through a new tactile internet — as well as the ability to build applications on the fly.
- New revenue streams and scalability: Once 5G is available on a global scale, the potential scalability of workers in virtual environments will bring on new business models. Think of the possibility of scaling one worker’s abilities to hundreds of locations without expenses of travel. Or, scaling training initiatives using AR and on-demand educational content.
- Renewed focus on change management: As with any new technology implementation in the enterprise, change management principles guide employees into newly supported “digital first” roles. Internal education, along with an acceptance of failure and learning by experimentation, can help your workforce understand the benefits of utilizing mixed reality to perform their duties.
5G’s effect on industrial AR/VR programs is one of many ways we’re beginning to see mixed reality take a stronger foothold in employee operations, site and product maintenance, training, development, sales and more. It’s an enabling technology that serves as the foundation for increased use of mixed reality, as well as other IIoT implementations that rely on fast, reliable data connections, coverage and speed.
How are you planning for 5G’s role in your mixed reality and IIoT initiatives?