Edge computing delivering operational intelligence

In an increasingly competitive business environment, producers need flexibility, increased productivity, the capability to improve efficiency and reduce downtime. Mitsubishi’s Chris Evans argues that meeting these requirements needs a focus on connected production systems, intelligent control and edge computing.

Massive amounts of data collected from production sites need filtering to extract only that needed for analysis. When processing all data through cloud services, the load on communications and the time required for responses becomes a problem. For many manufacturers, cloud-based processing is unsuitable for supporting applications that demand a real-time response. For them edge computing is often more appropriate.

Converging at the edge

Using edge processing, filters aggregated data from intelligent devices for pre-processing and analysis directly from within the automation platform. This eliminates the lag and latency of the cloud, making the data immediately available to the processes that need it. Furthermore, forwarding it to higher level enterprise systems helps supply chain optimisation, improved production control and plant operation simulations.

The benefits of processing large amounts of repetitive data created by machines and automated systems and processing locally are huge. Edge computing takes the pressure off existing networks, data storage, potentially costly cloud services and software applications. Data becomes useful information closer to its point of origin and more importantly, in real time.

Edge computing delivering operational intelligence

What makes edge computing most valuable to an organization though is achieving operational and logistical efficiencies through real-time analysis. Making it happen will become easier thanks to emerging products and open technologies which will bring with them further benefits.

Edge computing uses factory floor technology to provide streamlined information. This becomes operational intelligence once interpreted and displayed by crossover applications sitting in the “Edge” layer. The Edge layer provides data analytics, artificial intelligence (AI) and is the gateway to the higher IT level applications and cloud services that can truly deliver Smart Manufacturing and embrace the principles of the Industrial Internet of Things (IIoT).

Implementing edge computing is not difficult for manufacturers already using factory automation. Existing products such as Mitsubishi Electric’s C-Controller module for its iQ-R Series PLC platform already provide Edge computing functionality. Products offering database connectivity and data management combined with standardised connectivity such as OPC UA provide reliable and secure data communications between the manufacturing-level and IT-level systems.

In addition to its global network of automation system integration partners, Mitsubishi Electric is a member of the EDGECROSS consortium. The group includes major suppliers of IT and factory automation infrastructure including NEC, Oracle, IBM and others. It is responsible for creating an open edge-computing software platform that will provide a universal interface between industrial networks and edge computing functions such as real-time data processing, data model management, security and various application development tools.

OT and IT convergence

Keeping time-critical information within the automation environment and pre-processing information needed by higher-level or cloud systems also reduces bandwidth requirements for the IT infrastructure. It gives greater assurances of security and removes issues of regional data handling and storage compliance regulations. Furthermore, it provides a strong incentive for OT and IT teams to coordinating and develop an overarching strategy.

Aggregating and analysing production data close to the source and only sending relevant information to the higher levels is where edge computing fits in. They deliver a lot of system improvements and efficiency gains from a modest investment.