Some in the manufacturing industry are focusing on new and exciting technologies, such as predictive maintenance, artificial intelligence and robotics, but others are raising concerns about robots replacing human activity, thus spurring unemployment. But few people take a step back to look at the bigger picture and ask how these technologies can work together with employees in a meaningful way to improve efficiencies and productivity across the business.

Those who do recognise this potential are embracing the concepts of Industrial Internet of Things (IIoT) and readdressing their technology requirements to ensure that they are ready to transition to smart manufacturing.

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In addition to technology considerations, companies and especially Information Technology (IT) and Operational Technology (OT) managers will need to ensure long-term quality, reliability and lifecycle of solutions, in a wide range of rugged environment or industrial use cases.

Since the 1980s and 90s, annual productivity gains within manufacturing have tended to be minimal. Fortunately, that is set to change with IIoT and the advent of new technologies which, powered by the cloud, will drive improved efficiencies within factories and throughout the supply chain. By feeding data from numerous sensors, vast quantities of data can be gathered in just moments.

But smart manufacturing is not just about gathering large data sets through connected devices. At the heart of smart manufacturing is the ability to use that data effectively to improve and make automated decisions, predictions and actions in real-time to optimise industrial output. This requires enormous processing power and it should therefore not be a surprise that smart manufacturing will increase the requirements not just for cloud computing, where the demand for longer-term analytics continue to rise, but also for more processing and storage capabilities at the edge, a trend referred to as edge computing.

What is edge computing?

While edge is not a new concept in computing, over recent years it has become the most convenient solution to play a pivotal part in the smart manufacturing formula to accelerate digital transformation. Edge computing helps manufacturers turn vast data sets, generated by machines, into insightful and actionable data. It does so by utilising resources connected to a network, such as temperature sensors, alarms or motor drives. This enables big data analytics to take place at the source of the data.

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‘Edge’ refers to the computing infrastructure that resides closest to the sources of data, such as robotic arms and automated heavy-lifting machinery. These are considered at the ‘edge’ as they tend to exist furthest from the heart of the computing infrastructure, which is available in the cloud.

As seen in the image above, a typical smart factory deployment consists of highly heterogeneous devices, including brownfield installations, comprising new hardware and software that coexist with legacy IT systems.

An industrial IoT gateway is a way to connect these devices and bring them to the Internet Protocol (IP) domain for further edge processing or backhaul to the cloud.

Industrial IoT gateways need to be able to support standard Ethernet, Controller Area Network and other industrial protocols and may also support many wireless protocols such as cellular, WiFi or Low-Power Wide-Area Network (LPWAN) such as Bluetooth, Zigbee or LoRa.

Industrial gateways are designed to always be on and are ruggedised to withstand harsh environments that may be encountered on the smart manufacturing workshop floor. And since these gateways may be connecting a factory’s internal infrastructure to the outside world, they must be able to support high cyber security features.

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The gateway was once somewhat limited in its function of aggregating data streams, but this is no longer the case. With more intelligence being shifted from the cloud to the edge, the role of the industrial IoT gateway will become increasingly important as not only a gateway but also as an edge server integrated solution.

Therefore, IT and OT managers can opt for either a standalone edge gateway and server in deployment (as shown in figure above), or in some cases a higher end IoT gateway that comes with integrated server functionality. These will provide sophisticated features and functionalities, such as:

Interoperability: IoT gateways can provide the necessary protocol translation for communications to be established between devices that are not able to communicate with each other in a factory.

Offload computing tasks: Edge servers or higher end IoT gateways can offload computing tasks from smart devices by caching/ storing information and acting as a private cloud that can be accessed remotely.

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Quality of service: IoT gateways can maximise the effectiveness of bandwidth while minimising endpoint bottlenecks.

Security: IoT gateways can implement much more sophisticated security solutions than those implemented on each individual endpoint, creating a good defensive, in-depth strategy for the whole factory network.

Local storage: Storage at the edge helps save transmission costs by only sending relevant data to the cloud. For instance, it is not ideal to send high frequency captures that are large in data size to the cloud for analysis. Instead, it is more efficient to have the gateway act as the computing node to capture the data and make analytical decisions locally.

In this way, only summarised data is sent to the cloud. In addition, in environments where connectivity is intermittent, data can be collected and saved locally, ensuring that all the data is eventually captured.

Why memory selection matters

For both industrial IoT gateways and edge servers in smart manufacturing deployments, memory selection is taking on a more important role. IT and OT decision makers working on edge computing deployments will need to consider a comprehensive evaluation of their hardware components and software to determine if obsolescence is a concern, and how to guarantee performance and reliability over the useful life of the device in the factory to ensure lowest TCO.

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For example, commercial solid state drives move to next generation silicon lithography every 18 to 24 months. This is not ideal in a smart manufacturing environment because planning and execution can take longer than the duration of the typical mainstream component availability.

Smart manufacturing IT deployments require some of the industry’s most robust solutions. Edge servers are typically based on high-end x86 processors and need to meet NEBS shock and vibration requirements as well as a temperature rating of -40 to 55 centigrade or higher.

Meanwhile, IoT gateways require high speed connections through any combination of cellular, Wi-Fi and copper fibre, and need to withstand harsh environmental conditions. IT and OT decision makers will need to identify a vendor that truly understands the unique requirement of edge servers and industrial IoT gateways.

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Both standalone industrial IoT gateways and edge servers, and consolidated IoT gateways alike are going to form a critical element of a smart manufacturing deployment.

Choosing the right memory and storage solutions that provide application tailored reliability, quality, extended temperature range, data loss protection, security, performance and longevity, will be key to business success, not just providing a quick return on investment, but by providing a significant competitive advantage through efficiency gains.

Sourced by Taufique Ahmed, from Micron Technology‘s embedded BU