Edge computing devices are generally devices that provide entry points into enterprise networks. This means the average router or switch used in white-collar offices are actually edge computing devices. While popular hardware may work excellently in controlled environments, they will struggle to function as industrial edge computing hardware in factories or facilities where heavy-duty equipment work round the clock or where physical contact is constant. In fact, challenges in finding applicable, long-lasting hardware are one of the reasons why 41% of stakeholders struggle with adopting industrial cloud within their facilities.
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The task of integrating cloud computing in manufacturing or industrial processes is solely being driven by software and communication service providers. This is why all the news coming out of the industrial cloud niche are focused on 5G connectivity, OPC UA over TSN protocols, and IIoT interconnectivity. While the focus on the flashy aspects of industrial cloud and its benefits have created a market share of $9billion, it has relegated the hardware that will drive a more complete adoption to the back. But statistics from Mckinsey show that this should not be so. This is because edge computing can potentially increase the hardware market to a market size of $215billion by 2025 if hardware design is done correctly.
For industrial edge computing hardware to realize its potential, the challenge of developing durable industrial hardware that can withstand the rigors of shop floor operations while delivering edge computing must be overcome. Other challenges edge computing devices face is matching functionality and durability with aesthetics. A scenario that highlights this challenge is the design of IoT applications and devices for industrial use. Although the app will function on the ultra-slim devices or hardware that dominate today, that device is unlikely to last for more than a year in the heat and oil dominated industrial world. This is why 37% of manufacturers struggle with cloud adoption or getting cloud solutions to function properly.
To increase the adoption rate of cloud computing and edge computing within industrial facilities, vendors and service providers must hack the development of applicable industrial edge computing hardware. Only then, will the flashy computing resources and intuitive app interfaces being developed in squeaky clean labs be easy to use or adopt across diverse manufacturing industries.
To design industrial edge computing hardware that meets the challenges of industrial application, different requirements must be taken into consideration. These requirements include:
Buttressing the highlighted points, IDC analyst Ashish Nadkami recommend that engineers should abstain from picking custom edge hardware tied to a specific vendor’s applications or ecosystems. Instead, engineers and IT managers should focus on choosing or customizing industry-standard hardware that is durable, non-proprietary, and last for the long haul. The EXOR JSmart series of human-machine interfaces are examples of industrial edge computing hardware that meet these recommendations.
Aesthetically pleasing industrial edge computing hardware with intuitive user interfaces simplify the process of capturing data and computing in diverse ways. An example is the use of 2D or 3D visualization techniques to explore captured data and relevant information. A visual representation of shop floor relationships and variables, as well as, visualized instructions from centralized cloud platforms makes it easy for employees to understand tasks and take action.
Visualization also simplifies the process of installing edge hardware in shop floors. Thus eliminating the difficulties many manufacturers say they experience with setting up cloud services to function as expected. The ability to integrate emerging features such as speech recognition, and computer vision tools within industrial edge computing hardware will also be selling points for many manufacturers which will lead to an increase in cloud adoption rates.
The positive symbiotic relationship between hardware, software and other interrelated technologies is expected to drive the adoption rate of edge computing. With the right hardware, ML, AI, and computer vision, and speech recognition tools can also be integrated into industrial edge computing hardware. Thus, the dynamism of edge computing provides enterprises with the opportunity to explore how emerging technologies can advance industrial processes especially with respect to the hardware community.