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Top 5 industrial automation trends in 2020: Craig Resnick, ARC Advisory Group


January 2, 2020
By Craig Resnick

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Photo: metamorworks/Getty Images

In the upcoming issue of Manufacturing AUTOMATION, we asked several industry experts to share their top five trends in industrial automation for the coming year.

It’s a new decade – read on for what Craig Resnick at ARC says manufacturers should expect in 2020, and check back for more insights from other experts over the coming days.

Craig Resnick, ARC Advisory Group

Craig Resnick is vice-president at ARC Advisory Group and supports both automation supplier and financial clients. He has more than 30 years of hands-on experience in marketing, business development and strategic planning. Resnick graduated from Northeastern University with an MBA and BS in Electrical Engineering. 

Craig Resnick ARC

Craig Resnick, ARC Advisory Group

1. Deploying Industrial IoT edge 2.0 solutions

The edge of industrial internet-enabled architectures is becoming increasingly important, largely due to their often-critical role in determining the success of digital transformation strategies. Initially focused on delivering timely, clean data to cloud-based applications, the edge is emerging as an entirely new ecosystem within the overall enterprise architecture. Solution architects now rely on the edge not only for cloud integration, but also as a solution to address manufacturers’ concerns in areas such as latency, security, cost containment and isolation for production environments.

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Edge computing applications – particularly high-value analytics and artificial intelligence (AI) delivered via machine learning (ML) – allow data to be processed near its source. Recognition of the rising importance of the edge will be reflected in the spike of investments targeted at this space. IT and OT suppliers alike are introducing new Industrial IoT edge hardware, software and solution offerings – what we call “Industrial IoT Edge 2.0” – representing important improvements in ease-of-use, self-service and turnkey operation, while emphasizing business outcomes and application-specific solutions versus pervasive infrastructure.

Industrial IoT Edge 2.0 offerings will place greater emphasis on turnkey solutions that address specific outcome-oriented use cases, shifting away from a simple “run the operations” mentality to use of real-time data analytics to rethink competitive fundamentals.

2. Increasing use of cyber-physical systems

While manufacturers ramp up to meet demand for the growing “smart product” market, they are facing challenges developing and manufacturing new and more complex products and systems. These require tight integration between the computational (virtual) and the physical (continuous) worlds. To meet these complexity and integration requirements, there will be more cyber-physical systems deployed using advanced simulation platforms that cover model-based mechatronic systems engineering, embedded system design integration, and simulation models that validate product and system design in the physical world.

Cyber-physical systems are an engineered system or mechanism that is controlled or monitored by computer-based algorithms and is tightly integrated with both the Internet and its users. In cyber-physical systems, physical and software components are deeply intertwined and get much of their intelligence from the use of AI and ML. Factory production lines, process plants for energy and utilities, and smart cities will depend on cyber-physical systems to self-monitor, optimize and even run infrastructure, transportation and buildings autonomously.

In the future, cyber-physical systems will rely less on human control and more on the intelligence embedded in the AI-enabled core processors. These will run the devices, products and systems that will be a pervasive part of the industrial world that produces them.

3. Accelerating development of open process automation systems

Advances in hardware, software, networking and security, along with increasing global competition and cybersecurity risks, will accelerate the development of open process automation systems, driven by the collaboration of users, such as ExxonMobil, Aramco, BASF, ConocoPhillips, Dow Chemical, Georgia-Pacific and Linde. These companies are members of the Open Process Automation Forum (OPAF), established by The Open Group for identifying and selecting standards to be used for technology and systems. The Open Group works with users and suppliers of technology products and services, and with consortia and standards organizations to capture, clarify and integrate current and emerging requirements, establish standards and policies and share best practices.

The goal of this collaboration is to accelerate creation of a standards-based, open, interoperable and secure automation architecture that addresses both technical and commercial challenges of current systems. A recently developed test bed for use and testing by the collaboration partners will act as the foundation for testing the performance and operation of individual components and standards. The collaboration partners will nominate and prioritize new components, standards and system features to be added and tested. The results from the test bed will be shared with all collaboration partners and create a foundation for the development for future solutions.

4. Digital transformation: the shift from digitization to digitalization technologies

Digital transformation is pivoting from “digitization” to “digitalization technologies.” Digitization focuses on technology and infrastructure, and involves creating digital versions of previously analog data, such as replacing paper-based work orders with digital work orders. It involves replacing legacy analog technology with digital technology, such as the transition from analog field instrumentation and control systems to digital instrumentation and control systems.

However, digitalization involves using digital data and technologies to improve business or work processes. For example, using data from a digital work order to improve maintenance work processes and execution, or using digital twins to improve asset information and/or engineering processes. Digitalization uses digital technologies and data to improve the way people work, collaborate and get things done within a plant, across a company or the value chain by using, for example, augmented reality (AR) for assembly and maintenance, and virtual reality (VR) for training and simulation.

Successful digital transformation does involve both digitization and digitalization: digitization makes it easier to capture, organize and manage a variety of data; while digitalization makes it possible to gain more value from all data, focusing on multi-process disruptive change and how to implement these changes throughout an organization. It engages an entire company and its people, rather than just processes and data.

5. Applying systems engineering practices to industrial cybersecurity

Ensuring the cybersecurity of information systems and associated networks has always been challenging. Serious vulnerabilities are identified on a regular basis and new threats continue to emerge to exploit those vulnerabilities. Industrial systems share many of the same vulnerabilities and are subject to the same threats. However, the consequences may be very different and, in some cases, more severe. This makes cybersecurity an imperative for the asset owner, who ultimately must bear the consequences of an adverse event.

The threat is ongoing and evolves constantly, so cybersecurity should not be viewed as a one-time “project” with a defined beginning and end. Since there is no such thing as being fully secure, the preferred approach should be ongoing, similar to the approach used for safety, quality and other performance-based programs. Similarly, it is not sufficient to focus on specific elements.

Instead, asset identification and management, patch management, threat assessment, and so on are all parts of a broader response that must address all phases of the life cycle. This response begins with identifying principal roles and assigning responsibilities and accountability for each stage of the system life cycle. With these addressed, the well-established systems engineering discipline can provide effective tools and methods to help define, plan, and conduct the response.