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Home Industry Manufacturing

Edge, AI and IoT combine to drive the hyper-automation for manufacturing

Scot Kim by Scot Kim
December 27, 2021
Photo by ThisIsEngineering from Pexels: https://www.pexels.com/photo/engineers-in-workshop-3862627/

Photo by ThisIsEngineering from Pexels: https://www.pexels.com/photo/engineers-in-workshop-3862627/

With Edge hardware costs coming down, AI/ML becoming prevalent at the atomic level, and IoT connectivity, Hyper-automation is becoming a thing for manufacturers to increase productivity with optimization.

Today’s supply chain disruptions, labour shortage, and macroeconomic turmoil happening in 2021 and may continue throughout 2022, manufacturers are ready to make aggressive investments to modernize their factories with EdgeAIoT technologies to transform into a “lights-out” factory.  However, is EdgeAIoT ready for the challenge? 

The answer is yes, it is ready for the challenge because of the mass adoption of IoT in the past 10 years.  I am witnessing many manufacturing CIOs inquiring about edgeAIoT as a part of their hyper-automation roadmaps.

What manufacturing CIOs should be doing to embrace edgeAIoT:

  1. Speak with your cloud provider to see if they have an edge AI on-ramp capabilities.  What are the data requirements, data sharing transmission costs and types of training and inference is done in the cloud vs the edge?
  2. Is IoT installed at key points of the shopfloor and PLC controllers?  Is the digital thread established and its associated governance model?
  3. What closed-loop tasks and information are being relayed back to the data lake and machines?  Predictive maintenance?  Asset optimization?

2022 is going to be a wild and interesting year for smart manufacturing.

First published on Gartner Blog Network

Related:  Human-centric strategies essential for manufacturing’s future
Tags: Artificial IntelligenceEdgeAIoTGartnerhyper-automationmachine learningsmart manufacturing
Scot Kim

Scot Kim

Scot Kim is senior director analyst with Gartner. He is a member of the Advanced Manufacturing and Transportation group within Gartner's CIO research organization. He has over 20 years' experience working in technology strategies. His areas of specialization include industrial IoT platforms, IoT analytic applications, manufacturing data management/visualization/insights. Furthermore, emerging business models like Equipment-as-a-service (EaaS) and consumption-based services are other areas of coverage for both CIOs and vendors. Kim advises both end-user organizations with their IoT vendor selections process and purchasing decisions to best help solve their IT problems. But also, he helps vendors know which types of business model and IoT features are desired by end-user companies. He currently evaluates Industrial IoT platform companies in the latest Gartner Magic Quadrant and Critical Capabilities. Furthermore, Kim helps vendors build, organise and measure competitive intelligence functions. He focuses on issues related to understanding and adapting marketing and sales, to understand the competitive landscape, who the competitors are, and how to exploit the competitor's weaknesses by countering with your company's strengths.

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