March 2018


Automation Strategies: Top technology trends in automation for 2018

The last several years have seen significant advances in, and acceptance of, new automation technologies.

Resnick, C., ARC Advisory Group

The last several years have seen significant advances in, and acceptance of, new automation technologies. This rate of change and subsequent adoption will continue to ramp up in the coming year. Many of the recent advances include industrializing popular consumer technologies, which helps accelerate the ongoing convergence of information technology (IT) and operational technology (OT) to support digital transformation.

In 2018, there will be an acceleration of this IT/OT convergence, particularly as it relates to the acceptance and proliferation of Industrial Internet of Things (IIoT)-enabled solutions, cybersecurity, edge computing, augmented reality (AR), artificial intelligence (AI), analytics, digital twins and progress on the open process automation (OPA) front.

Five key technology trends that are expected to have a major impact on both process and discrete automation in 2018 are discussed here.

Intelligence at the edge

As more data-intensive computing workloads are pushed to the network edge, real-time remote management and a simplified edge infrastructure are crucial for success. Operational issues, such as managing asset performance to improve production while reducing unplanned downtime, will drive end users to deploy edge computing.

Companies that take advantage of self-managed, edge computing infrastructures will be able to unlock additional data stranded inside machines and processes. They will also be able to more quickly identify production inefficiencies; compare product quality against manufacturing conditions; and better pinpoint potential safety, production or environmental issues.

Remote management will enable onsite operators to connect in real time with offsite experts to more quickly resolve, or even to avoid, downtime events. This will help free operations personnel and IT staff to perform their respective roles, thereby utilizing their specific expertise to the best advantage.

Advances in industrial cybersecurity management

Additional advances in industrial cybersecurity management solutions will be deployed to address the unique requirements of industrial automation equipment, applications and plants—particularly as these relate to the stringent constraints on system updates and network communications. These advances will incorporate commercial-type IT cybersecurity management solutions, but in a manner that limits negative impacts on control system operation.

More importantly, these new industrial cybersecurity management solutions will extend this functionality to include unique, non-PC-based industrial assets and control system protocols. These solutions will also recognize and manage industry-specific cybersecurity regulations, such as NERC CIP (North American Electric Reliability Corp. critical infrastructure protection), and leverage new integrated strategies that combine IT, OT and IIoT security efforts, thereby maximizing the use of all corporate cybersecurity resources.

Open process automation vision gains traction

The OPA vision will gain additional traction, with the Open Process Automation Forum adding new end user and supplier members.

Initiated by ExxonMobil and managed by The Open Group, this initiative aims to build a proof-of-concept prototype and establish standards for, and ultimately build, commercial OPA systems. These systems will be designed to minimize vendor-specific technologies and increase overall return on system investment, while maintaining stringent safety and security. These goals would be achieved by specifying highly distributed, modular, extensible systems reliant on standards-based architecture for interoperable components, with intrinsic cybersecurity.

The objective is to eventually replace large-CAPEX automation retrofit programs with smaller OPEX programs that require less analysis, engineering and planning. Updates to these new, open systems will be managed as a maintenance activity. These new systems will consist of smaller, more modular and more easily distributed components. They will better empower technical personnel, reducing the level of training required and facilitating additional benefits through collaboration.

Merging of virtual and physical worlds

New technologies are accelerating the merger of the virtual and physical worlds, enabling the creation of new business models. Manufacturers are introducing new business models under which they sell digital services along with products. Examples include digital twins, which are a virtual replication of an as-­designed, as-built and as-maintained physical product. Manufacturers augment the digital twin service with real-time condition monitoring and predictive analytics. Customers use the equipment and products, as well as maintenance and operational optimization services, based on predictive and prescriptive analytics.

AR technologies are used to connect virtual design to physical equipment for operator training and visualization, and for machine maintenance. With help from the IIoT, the cloud, big data and operational analytics, AI-based machine learning (ML) solutions can be used to make operational changes without the need for programming.

Distributed analytics

IIoT-enabled distributed analytics will further extend data processing and computing close to or at the data source, typically through intelligent, two-way communication devices, such as sensors, controllers and gateways. In many instances, the data for distributed analytics comes from IIoT-connected devices located at the edge of the operational network.

These devices can be located near, or are embedded in, a wide variety of edge machines and equipment, such as robots, fleet vehicles and distributed microgrids. The analytics can be embedded within distributed devices or created in a cloud environment and then sent to the edge for execution. From an operational perspective, security, privacy, data-related cost and regulatory constraints are often the reasons cited for keeping the analytics local.

Distributed analytics can help support revenue generation from new methods of serving existing customers and encouraging ways to reach new ones. These methods include asset optimization through improved, proactive and highly automated management of infrastructure and resources; higher satisfaction and retention by engaging customers with high-value products and services where and when they need them; and improved operational flexibility and responsiveness through better and faster data-driven decisions.


Successful digital transformation will be a prerequisite for industrial organizations to compete effectively and maximize business performance. When looking for a place to start the digital transformation process, asset performance management (including avoiding unscheduled downtime) is a good place to focus.

End users and original equipment manufacturers (OEMs) alike should embrace, rather than resist, digital transformation. While the increasing convergence of OT and IT serves as an enabler, this digital transformation must still embrace legacy assets, as plants will not “rip and replace” old (but otherwise well-functioning) equipment without financial cause. Legacy assets must remain a part of, and be integrated into, the solutions for digital transformation wherever possible.

Succeeding here will require an open mind for emerging technologies, approaches and business models. It will also require close collaboration between OT and IT groups at the respective operations and enterprise levels, as well as collaboration among technology suppliers and industrial and governmental consortiums.

While not all technologies, solutions and approaches will be right for all companies, it is important to understand what is going on, what is available today, what is likely to be available tomorrow and what peer organizations are doing to determine where to best focus limited human and financial resources. HP

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