With the rise of Industry 4.0 and the Industrial Internet of Things, the integration of operational technology (OT) systems with informational technology (IT) systems is becoming a necessity. Increasingly, IT systems are being combined with OT systems to monitor devices, assets, processes, and events while making adjustments in industrial operations. As this transition continues, enterprises need better visibility of their OT and IT assets from a single vantage point.
This desired convergence, however, comes with its own challenges, particularly as the ISA 95 automation levels become increasingly blurred. These challenges include reliability, security, and interoperability – as well as determinism. As end users move from the enterprise level down to the monitoring & supervising level, and to the production level, the requirements for time determinism grow increasingly stringent. A robotics or motion control application, for example, requires lower latency and jitter than an ERP application.
Georgia Pacific (GP) — one of the world’s leading makers of tissue, pulp, packaging, building products and related chemicals – is deploying IIoT solutions at scale in order to improve production efficiency and quality at its 150 manufacturing locations in North America. GP has been working in partnership with Intel on IoT workload consolidation at the enterprise level. GP’s Edge initiative was developed on Intel architecture, and has already been deployed at multiple sites. This GP architecture consolidates IoT workloads using containers and virtual machines to run concurrent IoT solutions in a production environment — improving scalability, maintenance, and security.
Intel Unified Edge Framework: A Set of Guidelines That Help Enterprises to Define Their Own IoT Unified Edge Architecture
GP’s solution was developed using the Intel IoT Unified Edge Framework, which is a set of guidelines for architecting enterprise class edge systems to deploy IoT workloads across different vertical markets using off the shelf software and hardware ingredients. The Intel framework enables enterprise-level users to consolidate multiple IoT applications onto edge devices and servers using commercially available software. The business value of this workload consolidation is that it reduces edge stack complexity, enables workloads to be efficiently distributed across different architecture tiers and devices, and lowers TCO.
Instead of using multiple edge devices to handle each distinct vendor solution, Intel’s framework guidelines enable the different edge devices to be containerized, with the workload split between a unified field edge device and edge servers. Creating a smaller edge device footprint reduces both the overall network complexity and the operational security risks for GP. It also facilitates improved workload optimization because different solutions are consolidated to improve efficiency.
GP is using consolidated workloads to optimize numerous IIoT production systems, such as machine vision and environmental sensors. Intel’s unified framework has, for example, helped GP improve its machine vision-based anomaly detection system. The system uses fixed cameras to monitor and identify changes during production. GP uses machine vision in production for everything from automatically tracking production materials and ensuring delivery to the proper destination to identifying whether a machine is operating outside of its specified parameters. This automated quality control improves output by reducing production-related errors and minimizing downtime.
Intel’s Industrial Edge Insights Software: On-site Production Ready Middleware to Enable Industrial Analytics & Insights
Intel’s Unified Edge Framework is a set of guiding principles that help enterprises to define their own IoT architecture. Intel has also developed Industrial Edge Insights Software, a product-quality software stack designed to accelerate the development and deployment of Industrial IoT solutions.
Industrial Edge Insights Software is designed to enable OEMs, systems integrators and software developers to securely ingest, process, store, orchestrate, and manage data across a diverse set of operating systems and industrial protocols. The software stack facilitates the development and deployment of workloads and applications across a variety of hardware nodes in an IIoT production environment, and also enables near real-time event driven control.
This software also supports the management of a wide range of computational, storage, and device solutions. It is currently optimized – using both time series data and video/image data – for industrial applications, such as quality control, predictive maintenance, worker safety, and factory optimization. In a production environment, it can be used to support high performance inferencing, in conjunction with the Intel® Distribution of OpenVINO™ toolkit – which is imperative for real-time machine vision defect inspection, equipment maintenance, quality control, and security monitoring. OpenVINO-based algorithms are proven tools for the deployment of machine learning and deep learning artificial intelligence applications throughout the industrial factory production process.
More tools like Intel’s edge framework and the Industrial Edge Insights Software are in the works, and Intel is developing IIoT hardware and software solutions for enabling real-time control of industrial applications. This includes new solutions that manufacturers can implement for optimizing the use of robotics, motion control, and virtualization of protection relays.
Connected solutions deliver a competitive advantage by enabling process optimization, new business models leading to additional revenue streams, and predictive maintenance to maximize equipment life. As Georgia Pacific’s experience underscores, when it comes to powering the intelligent factory, Intel IIoT solutions are streamlining production processes, from the edge to the cloud.
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