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Smart factory trends identified for next year

Smart factory trends identified for next year
Needless to say, factories have become smarter in the wake of IIoT and data. That said, the smart factory concept will continue next year, with the use of AR/VR, cybersecurity and digital twins cited as some of the trends to keep an eye on for.
Needless to say, factories have become smarter in the wake of IIoT and data. That said, the smart factory concept will continue next year, with the use of AR/VR, cybersecurity and digital twins cited as some of the trends to keep an eye on for.
That was the conclusion reached in a blog post by ARC Advisory Group, which has identified some of the smart manufacturing trends as follows.

Greater adoption of augmented reality

As an older generation of workers retire and a new generation of workers step in, knowledge transfer becomes key. In this regard, augmented reality can play a part.
“As an example, for maintenance and service operations, AR devices provide maintenance and service technicians with detailed workflows and procedures, such as asset diagnostics, work order information, recording capabilities, and a platform to contact remote experts for assistance,” the post said. “AR users can share their video feed with a mentor and that remote expert can overlay annotations or feed the user with manufacturing/maintenance details for better contextualization. Companies that employ AR achieve faster throughput, reduce rework, and lower downtime.”

Increased use of virtual reality

According to the post, VR is a powerful tool for creating immersive experiences and lends itself to applications such as product and process design or training simulations. “VR can provide a highly realistic virtual training environment with contextualized, real-time data overlaid. This enables operators, maintenance technicians, and plant engineers to explore a variety of plant and field scenarios in a safe, off-line environment and prepare for the real-world environment with reduced unknowns,” it said. “VR enables near-limitless creation of training scenarios with zero risk of disrupted operations. The VR training method is gaining traction in the process industries, where competency requires familiarity with equipment and operational and maintenance procedures.”

Combining cloud and edge computing

Edge computing allows data to be collected from devices in near real-time, helping operators make production-related decisions in a timely manner. The data is then sent to the cloud, producing valuable insights that can be leveraged by IT or management. Combining these two, then, is a trend that will last in the years to come.
“By combining both edge and cloud technologies, industrial and infrastructure organizations can provide appropriate personnel with actionable information to support real-time business decisions, leveraging asset monitoring, analytics, machine learning (ML), and artificial intelligence (AI) to make sense of and act on complex data patterns,” the post said. “This will help organizations better identify process and other operational inefficiencies and pinpoint potential safety, production, or environmental issues.”

IT/OT cybersecurity converging

While cybersecurity is seen as a top priority for manufacturers, integrating information from sensors within and outside control systems creates confusion in IT/OT responsibilities, and adding more suppliers further complicates enforcement of security requirements for new assets, the post said. “To help combat this, companies will converge their IT and OT cybersecurity efforts, which will help to clarify responsibilities and remove security gaps. It will also help ensure more consistent security levels across entire organizations. Combined, this will help to reduce the organization’s overall cyber risk,” it said.

Digital twin technology

According to the post, more and more plant and other assets will come with digital twins that provide a virtual representation of the asset. “These digital twins contain an archive of asset-related information, such as drawings, models, bills of material, engineering analysis, dimensional analysis, manufacturing data, and operational history. This historical information can be used as a baseline when benchmarking asset performance,” it said. “The digital twin will also have an archive of real-time data acquired via integrated sensors or external sources that can be used for condition monitoring, failure diagnostics, and both predictive and prescriptive analytics.”

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