Summary

This blog was originally posted by ARC Advisory Group. 

Over the last several years, there has been significant advances in and adoption 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 some popular consumer technology. This 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 this relates to the acceptance of 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.

In no particular order, here are five key technology trends that will have a major impact on both process and discrete automation in 2018:

1. Intelligence at the edge

As more data-intensive compute 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 that had previously been 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 on-site operators to connect in real time with off-site experts to more quickly resolve, or even avoid, downtime events. This will help to free operations people and IT staff to perform their respective roles, making best advantage of their specific expertise.

2. Advances in industrial cyber security management

Additional advances in industrial cyber security 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 cyber security management solutions, but in a manner that limits any negative impacts on control system operation.

More importantly, these new industrial cyber security 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 cyber security regulations, such as NERC CIP, and leverage new integrated strategies that combine IT, OT, and IIoT security efforts, maximizing the use of all corporate cyber security resources.

3. Open process automation (OPA) vision gains traction

The OPA vision will gain additional traction, with the OPA 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, establish standards for, and ultimately build commercial open process automation systems that minimize vendor-specific technologies and increase overall return on system investment, while maintaining stringent safety and security. This would be achieved by specifying highly distributed, modular, extensible systems based 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. These systems will better empower technical personnel, reducing the level of training required and facilitating additional benefits through collaboration.

4. Merging of virtual and physical worlds

New technologies are accelerating the merging 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 along with 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, as well as for machine maintenance. Thanks to IIoT, cloud, big data, and operational analytics; AI-based machine learning (ML) solutions can be used to make operational changes without the need for programming.

5. 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 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 ways to reaching new ones. These 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.

Recommendations

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 (APM) (including avoiding unscheduled downtime) is a great place on which to focus.

End users and OEMs alike should embrace, rather than resist, digital transformation. While the increasing convergence of operational technology (OT) and information technology (IT) serves 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 both an open mind for emerging technologies, approaches, and business models; and close collaboration between OT and IT groups at the respective operations and enterprise levels, as well as with technology suppliers and industrial and governmental consortiums.  While not all technologies, solutions, and approaches will be right for all companies, it’s important to understand what’s going on, what’s available today, what’s likely to be available tomorrow, and what peer organizations are doing to be able to determine where to best focus your limited human and financial resources.  

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About the author

Craig Resnick

ARC Advisory Group

Craig Resnick covers the PLC, PAC, HMI, OIT and Industrial PC markets as well as the Packaging, Plastics and Rubber Industries for ARC.  He is the primary analyst for many of ARC’s Automation Supplier and Financial Service clients.  Craig’s focus areas also include Production Management, OEE, HMI Software, Automation Platforms, and Embedded Systems.  Craig has 30 years’ experience in sales, marketing, product development, and project management in the industrial market, gained with major suppliers of PLCs, process control systems, power transmission equipment, and field devices.  Craig is a graduate of Northeastern University with an MBA and BS in Electrical Engineering.

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