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Manufacturers see varying levels of success with edge computing initiatives using industrial IoT (IIoT), sensors, robotics, automation, and cloud-scale data management. They face multifaceted challenges to enable smart manufacturing, including:
The growing number of IIoT devices, sensors, smart meters, and other edge endpoints escalate security and data management complexities around the volume, velocity, structure, and data stream formats generated by disparate sources. Moving this data to the cloud for analytics is expensive, and this limits the real-time insights that are needed by the technicians on the factory floor for equipment efficiency, predictive maintenance, product quality, and yield optimization.
The proliferation of disparate edge devices crowds factory floor space and conflates the technology architecture. The sprawl of piece-meal edge initiatives limits the ability to apply a unified strategy to scale up smart manufacturing.
Every edge endpoint added to the infrastructure increases the threat of attacks to the security perimeter and the potential for data loss to hackers. This impedes the scope and scalability of new edge applications for IT-OT convergence.
While smart manufacturing creates more demand for edge compute, the limited compute, storage, and virtualization capabilities of the legacy systems limit the adoption of artificial intelligence (AI) and machine learning (ML) applications and real-time analytics at the edge. Organizations must balance the right level of compute from the factory to the public cloud.