Data Sharing Needed for Sustainable Energy

Sustainable energy can be profitable. That, in a nutshell, is the finding of a GreenBiz Research survey presented in the 2019 Corporate Energy & Sustainability Progress Report from Schneider Electric. And an important key to those profits is sharing data.

“Companies agree that sharing data is important, with those that share the most seeing significant benefit,” the report said. This importance of data sharing stands out in the context of the overall report findings, which are broken up into 5 main topics:

  • Funding: Executives that demonstrate ROI (return on investment) and provide strong leadership can overcome perceived obstacles, such as insufficient capital.
  • Data: The challenge is to ensure the quality of collected data, and to share it effectively.
  • Goals: Setting public targets or goals for energy conservation and sustainability drives motivation and success.
  • Energy: Strategic sourcing optimizes usage, yielding significant cost savings in a volatile energy landscape.
  • Technology: Energy efficiency and renewables, based on data-driven technologies, are a leading source of ROI.

Ultimately, for a sustainable energy project to succeed, it must provide a solid return on investment. This report affirms the experience of our customers in wind and solar that the better the quality of their data, and the more they are able to share it, the higher their ROI.

For example, a wind farm doesn’t operate in isolation. In addition to the electrical power it sends to the grid, each wind turbine also sends data for its rotor speed, operating state, power output, and more out to control engineers and automated systems to optimize performance. This data can also be integrated with other data arriving in real time. Weather and climate conditions can be introduced, along with real-time market pricing, to generate live, real-time cost/benefit analyses.

Seeking ways to share data

Sharing data like this takes both cooperation and technology. The various players involved have to agree on what to share and how. Reviewing last year’s survey, the report noted that “respondents indicated that 80% of their companies had energy and sustainability data collection projects underway.” And this year “the research finds that more companies are now seeking the most efficient ways to share the data that has been collected.”

We are pleased to see this growing level of awareness of the need for data sharing. At the same time, we actively encourage executives, managers and engineers who are looking for more efficiency in their data sharing practices to consider our approach. It could be just what they need to boost the ROI of their sustainable energy projects.

Embarking on the Journey of Digital Transformation

A Skkynet team attended the 23rd Annual ARC Industry Forum last week in Orlando, Florida, themed “Driving Digital Transformation in Industry and Cities”. They came back with a vision of how the digital transformation journey is shaping up—for those who are in the driver’s seat.

The main takeaway was that among this group of C-Level executives, VPs, directors and managers of some of the top manufacturing and process industries worldwide, everyone is on the journey. Some are starting out, others well underway, while still others have already completed some significant milestones. “People are realizing that this is something they have to do,” said Michael Quartarone, Skkynet’s Director of Channel Sales. “Everyone was interested in two things: What does the future state look like? and What can we learn from others that will help us on the journey?”

Expert guidance

Guiding them on this journey were digital transformation experts from major corporations like AVEVA, Ford, Schneider Electric, BP, Dow, and GE. In a series of keynotes on the first day, these seasoned veterans of IoT implementation shared their experiences of how they turned a lofty vision into concrete action. They told their stories of how they got started, where they got stuck, who helped them, what resources they tapped into, and what are the business cases that validate their efforts.

David Kramer of Ford gave a particularly compelling description of how they are taking a long view of digital transformation, the steps they are taking and how they look for quick wins as they execute on their vision. Steve Beamer at BP shared their challenges of ensuring that their digital transformation efforts deliver key process improvements where they can impact things like worker safety.

Forum Focus

The bulk of the conference consisted of forums that covered topics like IoT data communications, security, edge processing, OT-IT convergence, and more. The Skkynet team remarked on how receptive the industry leaders they met were to our technology, and how well they grasped the value of what we are doing. “There is a noticeable change in perception of IoT and its value,” said Xavier Mesrobian, Skkynet’s VP of Sales and Marketing. “People now understand more clearly the challenges of providing secure, remote access to OT systems in real time, and they appreciate what we offer.”

“Some big vendors are taking this journey,” said Quartarone. “Microsoft, Intel, SAP, GE, and other players are actively engaged in in the IoT space delivering innovation and solutions. They all had booths at the conference, demonstrating their level of commitment. We had some very fruitful and enlightening conversations.”

The forum was hosted by ARC Advisory Group, who offer advisory services, marketing analytics, and technology selection services at the corporate level for the manufacturing and process industries. Skkynet has been working closely with ARC for years now, and will continue to build our relationship, sharing our perspective and expertise in in this journey of IoT and digital transformation.

Industrial Product Servitization Via the IIoT

Now there’s a ten-dollar word for you: “servitization.” It has emerged from the trend of industrialized societies to move away from manufacturing-based economies towards service-based economies. Applying this trend to products, the term “servitization” was popularized by Tim Baines at Aston Business School, who sees a “product as a platform for delivering services.” IBM shifts its focus from selling computers to selling business services. Rolls Royce sells propulsion instead of jet engines. Alstom ties its railroad maintenance contracts not to reduced equipment failures, but to fewer “lost customer hours.” These are just a few examples of servitization—a transition from selling products to selling services.

In a recent article, Servitization for Industrial Products, Ralph Rio at ARC Advisory Group shows how the trend of servitization is now impacting the factory floor itself. As production machinery grows increasingly sophisticated, plant managers find their staff less able to maintain and repair it by themselves. They need more services from vendors. Machine builders and OEMs are providing more training, more extensive maintenance contracts, and better condition monitoring of the equipment they supply. “Services have become an inseparable component of the product,” Rio says.


The benefits are significant. Predictive maintenance offered as a service means reduced stoppages due to equipment failure, and fewer but more efficient service calls when problems do arise. A growing trend is to provide condition monitoring services, which guide operators to run their machinery more effectively, increasing the lifespan of the equipment and improving output and product quality.

To be most effective, condition monitoring needs to run 24/7 in real time, ideally via a connection to the equipment vendor or supplier. Thus, the Industrial IoT is the logical choice for data communication. “To implement servitization, suppliers will need to adopt Industrial IoT for condition monitoring,” Rio predicts.

Two-way street

As we see it, this level of service works best as a two-way street. Data related to the condition of the machine flows to the supplier, while guidance and adjustments coming from the supplier can flow back the plant staff and equipment. This kind of feedback is invaluable for optimizing machine performance. A one-way IoT model that simply collects data for off-line analysis may not be adequate for many use cases. Technically more sophisticated, bidirectional data flow is useful in many condition monitoring scenarios, and thus has always been an option for Skkynet customers.

If the lessons of the past few decades are any indicator, the servitization trend will continue to grow, both among industrialized and emerging nations. And the Industrial IoT will almost certainly play an important role in providing data communications. As long as those communications are robust and secure, we can expect to see more and more IoT-based industrial product servitization, even though that term itself may never become a household word.

Digital Transformation in Wonderware and AVEVA

This one is local.  Although our DataHub software is running in pretty much every industrialized country in the world, and our SkkyHub service connects plants and offices across nations and continents, next week we will be travelling just down the street to participate in the Wonderware Canada East Knowledge Transfer Event, right here in Mississauga, Ontario.

Skkynet, Cogent (a Skkynet subsidiary), and the DataHub products have a long history with Wonderware.  The first large-scale implementation of DataHub technology, which ran for more than 20 years, was at a chocolate manufacturing plant in Toronto.  Initially tasked with providing a fast and reliable connection between Wonderware InTouch running in Windows and QNX-based supervisory control systems, Cogent introduced the real-time middleware architecture that is the functional precursor of DataHub, SkkyHub, ETK and DHTP technology.

Since that time the Wonderware company was acquired by Schneider Electric, and earlier this year there was a merger between Schneider Electric’s industrial software business and the AVEVA Group, one of the world’s largest providers of engineering and industrial software. One of the primary goals of the merger was to “accelerate how capital-intensive industries achieve end-to-end digital transformation.”

In fact, the theme of next week’s Knowledge Transfer Event is “Increase Your Competitive Edge through Digital Transformation.”  Put simply, digital transformation is how the Industrial IoT and related digital technologies are currently changing the industrial landscape.  AVEVA’s position is that “understanding the technology and driving forces behind digital transformation is the key to mastering the digital future of industry.”

As an AVEVA partner, with DataHub products listed on the AVEVA Digital Exchange, Skkynet has been a strong supporter and proponent of digital transformation.  Our participation in this upcoming event is focused on educating Wonderware users, distributors and partners on how Skkynet’s DataHub technology can meet the needs for secure streaming of the industrial data involved in digital transformation.

After more than two decades, Skkynet continues to build a relationship with Wonderware that started in real-time industrial data communication, and is now evolving into digital transformation.  What exactly will that look like?  If you happen to be in Mississauga next week, feel free to stop by the event to meet us and find out.

When Edge Computing Makes Sense

As the concept of cloud computing becomes more familiar to industrial automation engineers and system integrators, the discussion has moved from “Whether I should use it?” to “When should I use it?”  In a recent blog, “Edge or Cloud Analytics?“, Michael Guilfoyle at ARC Advisory Group looks at the business case of cloud computing for industrial applications and compares it to edge computing.  It comes as no surprise that in many instances edge computing makes more sense.

So, what exactly is edge computing?  Generally speaking, it is the processing power of the “things” in the Internet of Things (IoT).  It has become an economically attractive complement for the cloud in IoT, thanks to rapid cost decreases for small-scale processors.  And edge computing has additional benefits for Industrial IoT (IIoT) because it means that data can be processed closer to its source.

Six Factors Favoring Edge Computing

Guilfoyle lists six factors that typically favor edge computing:

  • Connectivity: Some industrial systems are located in environments that make it difficult to maintain the regular connections necessary to sustain cloud computing.
  • Immediacy: For any mission-critical system, the closer you can get to real-time decision-making, the better. Running right on the device itself, an edge-processing system can respond in a few milliseconds, compared to a cloud system which would take at least 100 milliseconds, and often longer.
  • Volume: Industrial systems churn out enormous volumes of data, very little of which is of much interest. Edge computing can monitor the data and filter out what is irrelevant. This reduces bandwidth and frees up cloud-computing resources.
  • Cost: Related to volume, feeding large quantities of raw data to the cloud for processing is not cost effective. It is more economical to at least filter the data, or better still process it locally and send the relevant results to the cloud.
  • Privacy: Company policy or government regulations may prevent connecting process data directly to the cloud.
  • Security: Gateway hardware or software at the edge can be used to help control inbound access to the plant. Skkynet’s DHTP protocol, for example, supports outbound-only connections, keeping all firewall ports closed and eliminating the need for VPNs.
Data Abstraction – A Seventh Factor

In addition to these six factors, we would add another important contribution that edge processing can make towards enhancing the value of cloud computing: data abstraction, the ability to generalize data protocols.  The DHTP protocol, in addition to supporting secure connections, also supports data abstraction.  Skkynet’s edge-processing tools, the ETK and DataHub, can convert data from multiple connected protocols into one universal format consisting of name, value, timestamp and quality.  Using DHTP, data abstracted in this form can be transported with minimal overhead across a TCP connection and converted back into its previous protocol, or other protocols, upon its arrival.

Data abstraction solves one of the problems often associated with the Industrial IoT—the wide range of incompatible protocols.  To get all the IIoT devices talking to each other, they need a common language.  Data abstraction implemented at the edge provides a way for each device to share its data with the cloud, and to receive inputs from other devices.

For all of these reasons—connectivity, immediacy, volume, cost, privacy, security, and data abstraction—edge computing makes a lot of sense for IIoT implementations, as it allows data to be processed close to where it is needed, providing the most value at the least cost.

Pairing OPC UA with a Good IIoT Protocol, a leading online publisher of automation-related content, recently ran an article on the value of pairing OPC UA with a good IIoT protocol like DHTP. The article discusses how OPC UA was initially expected to serve as an IIoT protocol, but more recently the trend seems to be towards using OPC UA at the plant level only. Other protocols, such as MQTT and AMQP are being offered as candidates for connecting outside the plant, but they are not ideally suited to IIoT. This article explains why, and introduces 9 criteria for good IIoT data communication.