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Industry analysis: 8 technologies that will transform manufacturing

June 21, 2018 by Sam Francis

Advisory firm ABI Research has listed what it regards as the most transformative technologies in manufacturing.

The company outlines how the technologies fit together with each other in its report Smart Manufacturing.

The eight transformative technologies the report highlights are:

  1. Additive manufacturing
  2. Artificial intelligence and machine learning
  3. Augmented reality
  4. Blockchain
  5. Digital twins
  6. Edge intelligence
  7. Industrial Internet of Things platforms
  8. Robotics

The manufacturing sector has already seen increased adoption of IIoT platforms and edge intelligence, says ABI. 

Over the next ten years, manufacturers will start to piece together the other new technologies that will eventually lead to more dynamic factories less dependent on fixed assembly lines and immobile assets. Each step in this transformation will make plants and their workers more productive.

Pierce Owen, principal analyst at ABI Research, says: “Manufacturers want technologies they can implement now without disrupting their operations.

“They will change the way their employees perform jobs with technology if it will make them more productive, but they have no desire to rip out their entire infrastructure to try something new.

“This means technologies that can leverage existing equipment and infrastructure, such as edge intelligence, have the most immediate opportunity.”

The transition towards a lights-out factory has started, but such a major disruption will require an overhaul of workforces, IT architecture, physical facilities and equipment and full integration of dozens of new technologies including connectivity, additive manufacturing, drones, mobile collaborative robotics, IIoT platforms and AI.

IIoT platforms must support many of these other technologies to better integrate them with the enterprise and each other. Those that can connect and support equipment from multiple manufacturers, such as PTC Thingworx and Telit deviceWISE, will last.

After decades of producing little more than prototypes, the additive manufacturing winter has ended and new growth has sprung up.

GE placed significant bets on AM by acquiring Arcam and Concept laser in 2016, and Siemens announced an AM platform in April 2018. Other leading AM specialists include EOS, Stratasys, HP and 3D Systems.

ML capabilities and simulation software have made digital twins (illustrated in the main image) extremely useful for product development, production planning, product-aaS, asset monitoring and performance optimization.

Companies with assets that they cannot easily inspect regularly will significantly benefit from exact, 3D digital twins, and companies that manufacture high-value assets should offer digital twin monitoring as-a-service for new revenue streams. Innovative vendors in digital twins and simulation software include PTC, SAP, Siemens, and Ansys.

The above technologies have already started to converge, and robotics provide a physical representation of this convergence.

Robotics use AI and computer vision and connect to IIoT platforms where they have digital twins.

This connectivity and AI will increase in importance as more cobots join the assembly line and work alongside humans. The robotics vendors that can integrate the most deeply with other transformative technologies have the biggest opportunity.

Such vendors include the likes of ABB, Kuka, Fanuc, Universal Robots, Rethink Robotics and Yaskawa.

Owen says: “The vendors that open up their technologies and integrate with both existing equipment and infrastructure and other new transformative technologies will carve out a share of this growing opportunity. Implementation will go step-by-step over multiple decades, but ultimately, how we produce goods will change drastically from what we see today.”

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Filed Under: Features Tagged With: ABI Research, additive manufacturing, artificial intelligence, augmented reality, blockchain, digital twins, edge intelligence, industrial internet of things, machine learning, manufacturing, robotics

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