Quick, when you think of self-driving cars and trucks and other news of autonomous vehicles, what comes to mind? OK, maybe an unfair question today given the Waymo v Uber lawsuit trial that began yesterday. But most of us think in terms of passenger cars rather than industrial uses.
PwC worked on a study and Bobby Bono (pictured), Carolyn Lee, and Todd Benigni all of PwC wrote a blog post, Can you be a first mover in industrial mobility? discussing the investment in manufacturing outdistancing the investment in passenger vehicles.
PwC Bobby Bono
When it comes to self-driving vehicles, passenger cars may grab most of the headlines, but they aren’t capturing most of the investment in the space. According to a PwC analysis, of the $6.8 billion raised by autonomous-transport startups since 2012, about 62% has gone to companies working on technology for vehicles ranging from drones to unmanned forklifts and tractor-trailers, all pieces of the larger ecosystem of industrial mobility.
Significantly, these investments in the pioneers of industrial mobility have been accelerating in recent years. From 2012 to 2014, companies working on automobiles received about as much investment ($660 million) as those building non-auto solutions ($702 million). But from 2015 to 2017, non-auto investment increased five-fold to $3.5 billion, while investment in companies working on tech for passenger cars rose a comparatively modest 188% to $1.9 billion.
Why does this matter? The rapid growth in capital pouring into startups working on industrial mobility reveals that hefty bets are being placed on the prospect that the impact of autonomous vehicles may well first made more forcibly upon industrial applications – even as self-driving passenger cars continue to capture consumers’ imagination.
Attitudes toward self-driving trucks are a good example of this cautious approach. Nearly two-thirds of respondents in the survey said they’ll wait and see how the technology evolves before adopting it. That’s especially interesting, given that most all survey respondents estimated that autonomous trucks could slash transportation costs by up to 25%. In a nutshell: they see the potential, but aren’t quite ready to jump in.
Cost is arguably the most important factor keeping manufacturers on the sidelines. The high cost of autonomous technology was the most frequently cited barrier to adoption in our survey, with nearly six in 10 respondents identifying it as a hurdle. At the same time, 86% said advanced industrial mobility’s ability to deliver a cost advantage was among the factors most likely to prompt them to embrace the technology.
With investment in industrial mobility surging, it’s a fair bet that businesses may see autonomous technology’s value proposition start to seem more attractive (and proven) sooner rather than later. And, it only stands to reason that some early adopters – and the early-stage companies developing the technology they implement – will score a competitive edge while their peers loiter on the sidelines.
An interesting, and at times intense, discussion has risen over the past couple of years in information communication circles between OPC UA and MQTT proponents. Some see a competition between the technologies while others (me) see complimentary technologies enabling engineers the flexibility to develop the communication application that best suits their needs.
Kepware, a PTC business, is a leading supplier of OPC development tools. Its newly released version 6.4 of KEPServerEX now includes an MQTT Client driver. Inclusion of this new driver enables users to collect data from sensor networks and other devices that utilize MQTT—and make that data available to the industrial automation devices and applications they rely on to run their plants efficiently.
“Many KEPServerEX users are now acquiring industrial data in their operational environments through new intelligent sensors and open-source or lightweight devices,” said Jeff Bates, Kepware Product Manager. “The MQTT Client driver and KEPServerEX seamlessly integrate data from these devices—enabling users to access new real-time data and provide a robust view of their plant floor operations.”
The MQTT Client driver included in KEPServerEX version 6.4 offers users a commercially available out-of-the-box MQTT to OPC UA translator. It uses innovative parsing tools to enable users to create tags from popular devices that utilize MQTT. With this new driver, KEPServerEX is able to securely subscribe to MQTT topics through any MQTT broker, receive updates as new device data is published, and make that data available over a variety of protocols.
“The enhancements in KEPServerEX version 6.4 are extremely valuable to any customer whose devices utilize the MQTT protocol, including customers of Wzzard Wireless Sensing Solutions,” said Mike Fahrion, CTO and VP of IoT Technologies at Advantech B+B SmartWorx. “There are significant benefits to making IoT Sensor data available in traditional industrial automation applications, and that is now possible with KEPServerEX.”
Along with the MQTT Client driver, KEPServerEX version 6.4 includes:
- Siemens TCP/IP Ethernet Driver Read/Write Enhancements: Enables users of Siemens TCP/IP Ethernet drivers with Siemens S7-400 and S7-1500 controllers to perform read/writes more efficiently by configuring their Packet Data Unit (PDU) size up to the maximum levels supported by the controller. Users can now easily monitor high-fidelity data with high tag counts and high data change rates.
- Store And Forward Capabilities With The ThingWorx Native Interface: Enables users to reliably transmit data between KEPServerEX and ThingWorx—even in the event of network instability. During communication disruptions between KEPServerEX and ThingWorx, the store and forward service collects data that ThingWorx had been requesting. Upon reconnection, the stored data is automatically forwarded to ThingWorx.
- CODESYS Ethernet Driver Tag Browsing Capabilities: Users of the CODESYS Ethernet driver now have the option to select and import only relevant tags into their KEPServerEX projects. This enables users to more efficiently connect to and start streaming data from CODESYS devices.
The Industrial Internet Consortium (IIC) has been incredibly active over the past month. While I’ve been traveling, news releases and interview opportunities have been pouring in.
- IIC and Avnu Alliance Liaison
- IIC and the EdgeX Foundry Announce Liaison
- IIC Develops Smart Factory Machine Learning for Predictive Maintenance Testbed
- IIC Publishes Edge Computing Edition of Journal of Innovation
See my white paper on OPC UA and TSN. I wrote this following interviews at Hannover for the OPC Foundation and subsequent travels to see people. I think this is a powerful combination for the future.
Why it’s important:
These news items when viewed collectively show momentum for what is happening with the Industrial Internet—or as some say the Industrial Internet of Things. These technologies are soon to be powerful business drivers for a new age of manufacturing.
Liaison with Avnu Alliance
The Industrial Internet Consortium (IIC) and Avnu Alliance (Avnu) have agreed to a liaison to work together to advance deployment and interoperability of devices with Time Sensitive Networking (TSN) open standards.
Under the agreement, the IIC and Avnu will work together to align efforts to maximize interoperability, portability, security and privacy for the industrial Internet. Joint activities between the IIC and the Avnu will include:
- Identifying and sharing IIoT best practices
- Realizing interoperability by harmonizing architecture and other elements
- Collaborating on standardization
“Both Avnu and the IIC are well aligned to pursue the advancement of the IIoT. An example of this is Avnu’s participation in the IIC TSN testbed where members have an opportunity to try their equipment and software on the testbed infrastructure. This provides the participants with the ability to discover what’s working and what is not and provide feedback that helps speed market adoption,” said Gary Stuebing, IIC liaison to Avnu. “The lessons learned in our TSN testbed fuel the ability of both of our organizations. TSN could open up critical control applications such as robot control, drive control and vision systems.”
“Our liaison agreement and work with the IIC TSN Testbed demonstrates real-world applications and solutions with TSN and helps to accelerate readiness for the market. The testbed stands as a showcase for the value that TSN standards and ecosystem of manufacturing applications and products bring to the market, including the ability for IIoT to incorporate high-performance and latency-sensitive applications,” said Todd Walter, Avnu Alliance Industrial Segment Chair. “Our collaboration with IIC and the work coming out of the TSN Testbed is already having a direct impact on suppliers and manufacturers who see the technology as a value add for their system structure.”
Avnu and IIC are meeting for a TSN Testbed plugfest later this month to evaluate and trial TSN device conformance tests that are being developed as a baseline certification in the industrial market.
Avnu creates comprehensive certification tests and programs to ensure interoperability of networked devices. The foundational technology enables deterministic synchronized networking based on IEEE Audio Video Bridging (AVB) / Time Sensitive Networking (TSN) base standards. The Alliance, in conjunction with other complementary standards bodies and alliances, provides a united network foundation for use in professional AV, automotive, industrial control and consumer segments.
Agreement with EdgeX Foundry
The Industrial Internet Consortium and EdgeX Foundry, an open-source project building a common interoperability framework to facilitate an ecosystem for IoT edge computing, announced they have agreed to a liaison.
Under the agreement, the IIC and the EdgeX Foundry will work together to align efforts to maximize interoperability, portability, security and privacy for the industrial Internet.
Joint activities between the IIC and the EdgeX Foundry will include:
- Identifying and sharing best practices
- Collaborating on test beds and experimental projects
- Working toward interoperability by harmonizing architecture and other elements
- Collaborating on common elements
- Periodically hosting joint seminars
“We are excited about working with EdgeX Foundry,” James Clardy, IIC liaison to EdgeX Foundry. “And we look forward to leveraging the experiences of the IIC to help further accelerate the adoption of the industrial Internet.”
“EdgeX Foundry’s primary goal is to simplify and accelerate Industrial IoT by delivering a unified edge computing platform supported by an ecosystem of solutions providers,” said Philip DesAutels, senior director of IoT for The Linux Foundation. “Formalizing this liaison relationship with the IIC is fundamental to unlocking business value at scale. Together, we will provide better best practices that will drive the unification of the industrial IoT.”
Hosted by The Linux Foundation, EdgeX Foundry has an ecosystem of more than 60 vendors and offers all interested developers or companies the opportunity to collaborate on IoT solutions built using existing connectivity standards combined with their own proprietary innovations. For more information, visit
Smart Factory Machine Learning for Predictive Maintenance Testbed
The Industrial Internet Consortium announced the Smart Factory Machine Learning for Predictive Maintenance Testbed. The testbed is led by two companies, Plethora IIoT, a company, designing and developing cutting-edge answers for Industry 4.0, and Xilinx, the leading provider of All Programmable technology.
This innovative testbed explores machine-learning techniques and evaluates algorithmic approaches for time-critical predictive maintenance. This knowledge leads to actionable insight enabling companies to move away from traditional preventative maintenance to predictive maintenance, which minimizes unplanned downtime and optimizes system operation. This would ultimately help manufacturers increase availability, improve energy efficiency and extend the lifespan of high-volume CNC manufacturing production systems.
“Testbeds are the major focus and activity of the IIC and its members. We provide the opportunity for both small and large companies to collaborate and help solve problems that will drive the adoption of IoT applications in many industries”, said IIC Executive Director Dr. Richard Mark Soley. “The smart factory of the future will require advanced analytics, like those this testbed aims to provide, to identify system degradation before system failure. This type of machine learning and predictive maintenance could extend beyond the manufacturing floor to have a broader impact to other industrial applications.”
“Downtime costs some manufacturers as much as $22k per minute. Therefore, unexpected failures are one of the main players in maintenance costs because of their negative impact due to reactive and unplanned maintenance action. Being able to predict system degradation before failure has a strong positive impact on machine availability: increasing productivity and decreasing downtime, breakdowns and maintenance costs,” said Plethora IIoT Team Leader Javier Diaz. “We’re excited to lead this testbed with Xilinx and work alongside some of the leading players in IIoT technologies. This is a unique opportunity to test together machine learning technologies with those involved in the testbed at different development levels starting from the lab through production environments, where a real deployment solution is utilized. As a result, from these experiences, we can significantly reduce the time-to-market of Plethora IIoT solutions oriented to maximize smart factory competitiveness.”
”Xilinx is committed to providing the Industrial IoT industry with our latest All Programmable SoC and MPSoC platforms – ideal for sensor fusion, real-time, high-performance processing, and machine learning from the edge to the cloud,” stated Dan Isaacs, Director of Corporate Strategic Marketing and Market Development for IIoT and Machine Learning at Xilinx. “The combination of these highly configurable capabilities drives the intelligence of the smart factory.”
Additional IIC member companies participating in this testbed are: Bosch, Microsoft, National Instruments, RTI, System View, GlobalSign, Aicas, Thingswise, Titanium Industrial Security, and iVeia. They provide technologies to enable the Smart Factory Machine Learning testbed, including:
- Factory automation
- OT and IT security
- Edge to cloud machine learning and analytics
- Time-sensitive networking (TSN)
- Data acquisition
- Smart sensor technology
- Design implementation
- Embedded programmable SoC technology
- Secure authentication
Journal of Innovation
The Industrial Internet Consortium (IIC) has published the fifth edition of the Journal of Innovation with a focus on edge computing. The Journal of Innovation highlights the innovative ideas, approaches, products, and services emerging within the Industrial Internet, such as smart cities, artificial intelligence, the smart factory, and edge computing.
Edge computing promises to bring real-time intelligence to industrial machines at the edge of the network, where data can be processed closer to its source. Edge computing provides businesses with a cost-effective means to transmit and analyze large quantities of data in real-time, enabling them to reduce unplanned downtime, improve worker safety and enhance asset performance.
“The Journal of Innovation brings together innovators and thought leaders across the IoT spectrum. In this issue, our experts share their insights on edge computing as a key enabling technology poised to transform the IIoT,” said Mark Crawford, co-chair of the IIC Thought Leadership Task Group and Standards Strategist, SAP Strategic IP Initiatives. “Edge computing is not a new concept, but as IIoT transforms business processes, the need to use data closer to its source, whether that be from a wind turbine, a deep-water well’s blowout preventer, or an autonomous car, is paramount.”
The Edge Computing edition of the Journal of Innovation includes articles contributed by leaders at IIC member companies including:
- Where is the Edge of the Edge of Industrial IoT? · Pieter van Schalkwyk XMPro
- Device Ecosystem at the Edge – Manufacturing Scenario · Sujata Tilak, Ascent Intellimation Pvt. Ltd.
- Edge Intelligence: The Central Cloud is Dead – Long Live the Edge Cloud · Yun Chao Hu, Huawei Technologies Duesseldorf GmbH
- Outcomes, Insights, and Best Practices from IIC Testbeds: Microgrid Testbed · Brett Burger, National Instruments · Joseph Fontaine, Industrial Internet Consortium
- A Knowledge Graph Driven Approach for Edge Analytics · Narendra Anand, Accenture Technology Labs · Colin Puri, Accenture Technology Labs
- Industrial IoT Edge Architecture for Machine and Deep Learning · Chanchal Chatterjee, Teradata Inc. · Salim AbiEzzi, VMWare Inc.
- A Practical and Theoretical Guide to Using the Industrial Internet Connectivity Framework · Stan Schneider, PhD. Real-Time Innovations, Inc. · Rajive Joshi, PhD. Real-Time Innovations, Inc.
Data Science has gotten us to the point of collecting servers full of manufacturing data. We can do some analytics. But there are miles to go before we sleep.
This press release crossed my email stream last week. I haven’t time to interview the founder–that will come later. But here is a teaser.
Data Science Pioneer Drew Conway Closes $2.5M in Seed Funding to Bring Machine Learning to Industrial Operations
New venture Alluvium delivers “Mesh Intelligence” to close the machine-to-human gap
Alluvium, developers of Mesh Intelligence solutions that harness machine learning insights for real-time applications in industrial use cases, today announced $2.5 million in seed funding led by investors IA Ventures, Lux Capital, and Bloomberg Beta. The machine learning venture is running pilot projects of its Mesh Intelligence technology in fleet management, and oil and gas, among other vertical industrial applications.
Alluvium aims to conquer one of big data’s greatest unsolved challenges for complex industrial operations with expert human operators. Alluvium’s breakthrough Mesh Intelligence solution frees the data from these proprietary systems, transforms it into rich information streams, and provides real-time insights to human operators for immediate action.
“The commoditized big data stack is fundamentally broken for complex industrial operations,” said Drew Conway, Founder and CEO at Alluvium. “Modern industrial assets and hardware are continuing to be instrumented by OEMs who have not considered how these heterogeneous streams of machine data should be leveraged in the overall workflow and data strategy of the organization. And the modern analytics ‘stack’ — where data is moved and crunched in back end systems — does not meet the real-time requirements of human operators at the edge.”
Conway, who earned his PhD at NYU, is a leading expert in the application of computational methods to social and behavioral problems at large-scale. He started his career in counter-terrorism as a computational social scientist in the U.S. intelligence community and is known for his venn diagram definition of data science as well as applying data science to study human decision making.
At the core of Alluvium’s Mesh Intelligence platform is unique technology for extracting data from all elements of complex industrial operations — tablets, sensors, as well as industry-specific assets — with no expectations of compute resources or network bandwidth. This breakthrough allows machine learning processing to occur at the edge of systems where human operators need data most — in-real time.
“The early days of big data were about capturing and storing the vast amounts of new information streaming from devices in manufacturing, transport, medicine and more,” said Mike Olson, co-founder and Chief Strategy Officer at Cloudera, and a seed investor in Alluvium. “As that technology has matured, the more important and more interesting problem has become: What can we learn from all that data? Alluvium is focused on extracting meaning from streaming data coming from hardware that instruments all sorts of industries. The company augments human expertise with its powerful machine learning technology to make customers smarter and help them operate better.”
Independent research and surveys show the massive economic opportunity for IoT and machine learning across industrial use cases. A report by Jabil found that “$1.9 trillion dollars of economic value could be created by the use of IoT devices and asset tracking solutions.” For U.S. oil and gas suppliers — an industry where Alluvium has had significant early traction — the daily cost of unplanned downtime at a refinery can reach $1.7 million per day, and the daily cost of unplanned downtime for liquid natural gas drillers can top $11 million per day. A recent McKinsey report found that “car data monetization could be as high as $750 billion by 2030” — which has far-reaching implications for fleet management. Analyst firm Gartner forecasted more than 6 billion connected devices will be in use worldwide in 2016 supporting more than $265 billion in services. And in a 2015 “Moving Toward the Future of the Industrial Internet” report by GE and Accenture, 84% of executives expected Big Data to shift the competitive landscape within the next year.
“Bringing machine intelligence into the physical world is an incredibly difficult task,” said Shivon Zilis, partner at Bloomberg Beta. “We were excited to back Alluvium because of their unique insights into how complex industrial systems could be transformed by predictive engines.”
Read Alluvium Founder’s Perspective on Starting the Company
Software platforms that provide specific “apps” for industrial applications was the theme of the week for me. I received a better look at Siemens’ Mindsphere along with a competitor’s app that I’ll discuss in a later post. Tuesday and Wednesday this week found me in Las Vegas at the 2016 Automation Summit—Siemens US users group. There were many sessions and quite a lot of training for customers.
The keynote was given by Klaus Helmrich, a member of the managing board of Siemens. He continued the theme repeated during Hannover Messe—digitalization. His point was that digitalization enhances competitiveness, time to market, flexibility, quality, efficiency. You design in the virtual world; take it to real world; receive feedback from real world to the virtual world to assure design is current to reality.
Although I’ve been told that Europeans are not fond of the term “ecosystem” in this context, Helmrich uttered the “e-word”. The Digital Enterprise Ecosystem enables customers toe realize their wish to interact with the production process making their product.
Memorable quote—“using software is key to realization of Industry 4.0.”
Maintenance and Reliability
Terry O’Hanlon CEO of ReliabilityWeb.com and Uptime magazine invited me to a panel presentation he was on. From the description in the program, I’d probably have never looked a second time. Plus, I’m not fond of panels. Usually each one talks for 10-15 minutes and then there is 10-15 minutes at the end for questions.
This one went against that grain. Each panelist gave about 2 minutes of their interest in the topic, then moderator Bob Vavra, editor of Plant Engineering magazine, proceeded directly to asking questions of the panel. The panel did not just sit back but each chimed in appropriately.
They did hope to hold questions to the final 15-20 minutes of the 105-minute session, but the audience would have none of that and started waving hands to ask follow up questions soon after the beginning.
The other panelists were Jagannath Rao, President of Siemens Industry Services; Brian Clemons, process automation manager at Dow Chemical; and, Keith Jones, of Prism Systems—an integrator.
It was a wide-ranging discussion. So, here are some quotes that capture some of the flavor of the discussion.
O’Hanlon, “What maintenance delivers is capacity.”
Clemons, “We bring a new process into the plant, but we’re still dealing with the same people.”
Clemons, Reliability usually talks MTBF, but what is really important is MTTR (repair or recover).
Rao, “Technology Suppliers more than component sellers, but look at larger solution.”
Jones, “Big data going to analytics is a difficult proposition—both doing and defining.”
O’Hanlon, “You need sensors that are appropriate to the health of the asset. That’s why you need predictive analytics.”
Jones, “IoT increasing traffic on network is a burden and sometimes affects production.”
O’Hanlon, “Reliability as a function of the business case.”
Data Analytics — Mindsphere
MindSphere is Siemens Cloud for Industry built on SAP HANA. It is a platform, which Siemens, customers, and OEMs can build software apps (App Store) on top of.
Speakers acknowledged that some customers are still uncertain about the cloud, but the cloud is where analytics run.
One app already developed is control loops. Customers can connect selected control loops, send data to cloud, analytics check for status of tuning and other things. The customer gets a dashboard. The analytics can even see stiction in valves.
This solution (like many) moves the software expenditure from CapEx to OpEx (note: look for this as a theme for how technology suppliers are beginning to price software).
Domain Knowhow + Context Knowhow + Analytics Knowhow = Customer Value
is the foundation of app development.
Siemens has a product “MindConnect” secure data acquisition box. This is a similar idea to the Dell IoT Gateway or Advantech. These edge computing and communicating engines are the current IoT trend.
Current apps include: