Forbes readers’ most frequent requests center on which companies are the best to work for in emerging technology fields, including IoT. The Computer Reseller News’ Internet of Things 50, 2019 list of companies is used to complete the analysis as it is an impartial, independent list created by CRN. Using the CRN list as a foundation, the following analysis captures the best companies in their respective areas today.
IoT startups have received $3.6B in funding this year alone, according to IoT Analytics’ estimates.
Manufacturing is attracting the highest percentage of vertically-focused IoT startups at 30%.
43% of all IoT startups are founded in North America, the leading region globally of startup activity.
7 of the top 10 IoT startups primarily focus on AI, Analytics, and Data Science.
46% of all IoT startups tracked by IoT Analytics primarily focus on AI, Analytics, and Data Science.
These and many other fascinating insights are from IoT Analytics’ recently published IoT Startups Report & Database 2019. IoT Analytics found that there approximately 1,018 startups creating Internet of Things (IoT) products or services today. They have defined one of the most thorough methodologies in IoT research to identify the top 10 IoT analytics startups worldwide. To qualify, startups have to be older than 6 years and fit the definition of the Internet of Things, and methodology and criterion explained at the end of this post.
“The hot IoT startups today have a strong focus on data analytics and AI and are increasingly targeting industrial and manufacturing clients. It remains to be seen how much of the analytics technology that today’s startups are building will be scalable across IoT use cases and industries. For now, most of the IoT startups are adding value in specific industries or for specific use cases,” said Knud Lasse Lueth, Managing Director of IoT Analytics.
The following are the top 10 IoT Startups Of 2019 from IoT Analytics:
Arundo Analytics is a hot IoT Startup that provides analytics software for industrial and energy companies. The company has formed several strategic alliances, e.g., with Dell Technologies and WorleyParsons. Arundo has also formed a joint venture with DNV GL to provide stream data analytics for maritime companies. The board of directors includes Tore Myrholt, Senior Partner at McKinsey and Thomas Malone, the founding director of the MIT Center for Collective Intelligence. Recently, Arundo launched several applications incl. machine monitoring and fuel efficiency.
Bright Machines is currently the fastest growing IoT Startup, has grown from virtually zero at the beginning of 2018 to almost 200 employees a year later (April 2019). The firm focuses on “micro-factories” made up of its software and robot cells as well as new software tools that make manufacturing more efficient. The leadership team is filled with former executives from Autodesk, Flextronics, and Amazon including Amar Hanspal (CEO), Brian Mathews (CTO), Tzahi Rodrig (COO) and Nick Ciubotariu (SVP, Software Engineering). The company recently entered into a strategic partnership with BMW i Ventures.
Dragos (IoT Middleware & Software Infrastructure)
Dragos is a cybersecurity startup that offers a software-defined security platform for manufacturers. The company has seen a 300%+ growth in headcount the last two years and collaborates with GE, Deloitte, OSIsoft, ThreatConnect, Crowdstrike, and several other companies. The company recently acquired Atlanta-based NexDefense and collaborates with Waterfall Solution for a joint solution.
Element (IoT Middleware & Software Infrastructure)
Element (also known as Element Analytics) is a fascinating IoT Startup that focuses on industrial analytics software such as Digital Twins, particularly in heavy industries. The company counts an impressive list of investors, including Kleiner Perkins, GE, Honeywell, and ABB. Element partners with Microsoft, Uptake, OSIsoft, and Radix (consulting).
In recent years, US-based startup FogHorn has gained an excellent reputation with leading manufacturers and oil and gas organizations around the world for its real-time edge computing and analytics software. The company has seen an 89% employee growth in the past two years and has secured partnerships with 50+ industrial solution providers, OEMs, gateway providers, and consultants/SIs, including AWS, Google Cloud, Microsoft, Cisco, HP, NTT Data, and more. FogHorn is also a member of LF Edge, an umbrella organization to drive an open, interoperable framework for edge computing to accelerate deployment among the growing number of edge devices. Investors in FogHorn include The Hive, Bosch, Dell, GE, Honeywell, Intel, Saudi Aramco, and Yokogawa.
Iguazio is a hot startup that provides a state-of-the-art data science platform for various verticals, including Industrial IoT, Smart Mobility, and Telecommunications. The company recently entered into collaborations with NVIDIA, Microsoft, and Google. Iguazio markets its Nuclio platform product as a “serverless” framework for multi-cloud environments and is thus well-positioned for the next wave of cloud computing.
IoTium (IoT Connectivity)
IoTium is a quickly upcoming IoT startup from the Silicon Valley area that focuses on software-defined network infrastructure in manufacturing and related verticals. The company has seen a 100%+ growth in headcount over the last two years and now counts John Chambers, former Cisco CEO, as an investor along with other well-known corporate investors incl. Juniper, Qualcomm, SafeNet, and Wind River. The company is also very active in the EdgeX Foundry and recently joined the Siemens’ MindSphere partner program as a gold member.
Preferred Networks is one of Japan’s IoT hotshots, focused on applying real-time machine-learning technologies to new Internet of Things applications. The company has seen a 100%+ employee growth in the last two years and now collaborates with world-leading organizations incl. Toyota Motor Corporation, Fanuc, and the National Cancer Center. The company is also very active in developing the deep-learning framework Chainer™ together with IBM, Intel, Microsoft, Nvidia.
READY Robotics (IoT Hardware)
READY Robotics is a rare robotics startup that is looking to benefit from the increasing automation and flexibility of manufacturing processes around the world. The company emerged from the cutting-edge robotics research at Johns Hopkins University to develop its industrial robotic software called Forge. The company has seen a 150%+ growth in headcount in the last two years and is now producing roughly 15 robot systems per month.
SparkCognition excels in AI-powered analytics, particularly in manufacturing and related verticals. SparkCognition has seen a 100%+ growth in headcount over the last two years. The company has launched Skygrid, a joint venture with Boeing and it has partnered with Siemens as part of its Mindsphere program. The company is also a Google Cloud Technology Partner and works with IBM as a trusted partner.
The full 62-page report (+ 1,018 line-item database) titled “IoT Startups Report & Database 2019” is available for purchase here.
Industrial Internet of Things (IIoT) presents integration architecture challenges that once solved can enable use cases that deliver fast-growing revenue opportunities.
ISA-95 addressed the rise of global production and distributed supply chains yet are still deficient on the issue of data and security, specifically the proliferation of IIoT sensors, which are the real security perimeter of any manufacturing business.
Finding new ways to excel at predictive maintenance, and cross-vendor shop floor integration are the most promising applications.
IIoT manufacturing systems are quickly becoming digital manufacturing platforms that integrate ERP, MES, PLM and CRM systems to provide a single unified view of product configurations.
The following are the key takeaways from the study:
Capturing IIoT’s full value potential will require more sophisticated integrated approaches than current automation protocols provide. IIoT manufacturing systems are quickly becoming digital manufacturing platforms that integrate ERP, MES, PLM and CRM systems to provide a single unified view of product configurations and support the design-to-manufacturing process. Digital manufacturing platforms are already enabling real-time monitoring to the machine and shop floor level. The data streams real-time monitoring is delivering today is the catalyst leading to greater real-time analytics accuracy, machine learning adoption and precision and a broader integration strategy to the PLC level on legacy machinery. Please click on the graphic to expand for easier reading.
Inconsistent data structures at the machine, line, factory and company levels are slowing down data flows and making full transparency difficult to attain today in many manufacturers. Smart machines with their own operating systems that orchestrate IIoT data and ensure data structure accuracy are being developed and sold now, making this growth constraint less of an issue. The millions of legacy industrial manufacturing systems will continue to impede IIoT realizing its full potential, however. The following graphic reflects the complexities of making an IIoT platform consistent across a manufacturing operation. Please click on the graphic to expand for easier reading.
Driven by price wars and commoditized products, manufacturers have no choice but to pursue smart, connected machinery that enables IIoT technology stacks across shop floors. The era of the smart, connected machines is here, bringing with it the need to grow services and software revenue faster than transaction-based machinery sales. Machinery manufacturers are having to rethink their business models and redefine product strategies to concentrate on operating system-like functionality at the machine level that can scale and provide a greater level of autonomy, real-time data streams that power more accurate predictive maintenance, and cross-vendor shop floor integration. Please click on the graphic for easier reading.
Machines are being re-engineered starting with software and services as the primary design goals to support new business models. Machinery manufacturers are redefining existing product lines to be more software- and services-centric. A few are attempting to launch subscription-based business models that enable them to sell advanced analytics of machinery performance to customers. The resulting IIoT revenue growth will be driven by platforms as well as software and application development and is expected to be in the range of 20 to 35%. Please click on the graphic to expand for easier reading.
Global spending on IIoT Platforms for Manufacturing is predicted to grow from $1.67B in 2018 to $12.44B in 2024, attaining a 40% compound annual growth rate (CAGR) in seven years.
IIoT platforms are beginning to replace MES and related applications, including production maintenance, quality, and inventory management, which are a mix of Information Technology (IT) and Operations Technology (OT) technologies.
Connected IoT technologies are enabling a new era of smart, connected products that often expand on the long-proven platforms of everyday products. Capgemini estimates that the size of the connected products market will be $519B to $685B by 2020.
These and many other fascinating insights are from IoT Analytics’ study, IIoT Platforms For Manufacturing 2019 – 2024 (155 pp., PDF, client access reqd). IoT Analytics is a leading provider of market insights for the Internet of Things (IoT), M2M, and Industry 4.0. They specialize in providing insights on IoT markets and companies, focused market reports on specific IoT segments and Go-to-Market services for emerging IoT companies. The study’s methodology includes interviews with twenty of the leading IoT platform providers, executive-level IoT experts, and IIoT end users. For additional details on the methodology, please see pages 136 and 137 of the report. IoT Analytics defines the Industrial loT (lloT) as heavy industries including manufacturing, energy, oil and gas, and agriculture in which industrial assets are connected to the internet.
The seven things you need to know about IIoT in manufacturing include the following:
IoT Analytics’ technology architecture of the Internet of Things reflects the proliferation of new products, software and services, and the practical needs manufacturers have for proven integration to make the Industrial Internet of Things (IIoT) work. IoT technology architectures are in their nascent phase, showing signs of potential in solving many of manufacturing’s most challenging problems. IoT Analytics’ technology architecture shown below is designed to scale in response to the diverse development across the industry landscape with a modular, standardized approach.
IIoT platforms are beginning to replace MES and related applications, including production maintenance, quality, and inventory management, which are a mix of Information Technology (IT) and Operations Technology (OT) technologies. IoT Analytics is seeing IIoT platforms begin to replace existing industrial software systems that had been created to bridge the IT and OT gaps in manufacturing environments. Their research teams are finding that IIoT Platforms are an adjacent technology to these typical industrial software solutions but are now starting to replace some of them in smart connected factory settings. The following graphic explains how IoT Analytics sees the IIoT influence across the broader industrial landscape:
Global spending on IIoT Platforms for Manufacturing is predicted to grow from $1.67B in 2018 to $12.44B in 2024, attaining a 40% compound annual growth rate (CAGR) in seven years. IoT Analytics is finding that manufacturing is the largest IoT platform industry segment and will continue to be one of the primary growth catalysts of the market through 2024. For purposes of their analysis, IoT Analytics defines manufacturing as standardized production environments including factories, workshops, in addition to custom production worksites such as mines, offshore oil gas, and construction sites. The lloT platforms for manufacturing segment have experienced growth in the traditionally large manufacturing-base countries such as Japan and China. IoT Analytics relies on econometric modeling to create their forecasts.
In 2018, the industrial loT platforms market for manufacturing had an approximate 60%/40% split for within factories/outside factories respectively. IoT Analytics predicts this split is expected to remain mostly unchanged for 2019 and by 2024 within factories will achieve slight gains by a few percentage points. The within factories type (of lloT Platforms for Manufacturing) is estimated to grow from a $1B market in 2018 to a $1.5B market by 2019 driven by an ever-increasing amount of automation (e.g., robots on the factory floor) being introduced to factory settings for increased efficiencies, while the outside factories type is forecast to grow from $665M in 2018 to become a $960M market by 2019.
Discrete manufacturing is predicted to be the largest percentage of Industrial IoT platform spending for 2019, growing at a CAGR of 46% from 2018. Discrete manufacturing will outpace batch and process manufacturing, becoming 53% of all IIoT platform spending this year. IoT Analytics sees discrete manufacturers pursuing make-to-stock, make-to-order, and assemble-to-order production strategies that require sophisticated planning, scheduling, and tracking capabilities to improve operations and profitability. The greater the production complexity in discrete manufacturing, the more valuable data becomes. Discrete manufacturing is one of the most data-prolific industries there are, making it an ideal catalyst for IIoT platform’s continual growth.
Manufacturers are most relying on IIoT platforms for general process optimization (43.1%), general dashboards & visualization (41.1%) and condition monitoring (32.7%). Batch, discrete, and process manufacturers are prioritizing other use cases such as predictive maintenance, asset tracking, and energy management as all three areas make direct contributions to improving shop floor productivity. Discrete manufacturers are always looking to free up extra time in production schedules so that they can offer short-notice production runs to their customers. Combining IIoT platform use cases to uncover process and workflow inefficiencies so more short-notice production runs can be sold is driving Proof of Concepts (PoC) today in North American manufacturing.
IIoT platform early adopters prioritize security as the most important feature, ahead of scalability and usability. Identity and Access Management, multifactor-factor authentication, consistency of security patch updates, and the ability to scale and protect every threat surface across an IIoT network are high priorities for IIoT platform adopters today. Scale and usability are the second and third priorities. The following graphic compares IIoT platform manufacturers’ most important needs:
