
Four Manufacturing Jobs that will Soon Look Very Different
Operators will use digital tools like digital twins and AI "recommendation engine" support in the future to proactively identify and fix difficulties
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CIO Applications Europe | Friday, September 03, 2021

Operators will use digital tools like digital twins and AI "recommendation engine" support in the future to proactively identify and fix difficulties.
Fremont, CA: A trend in or soft skills is becoming just as important as technical and manufacturing skills is visible in all of the listed vocations. Several manufacturing professions are shifting as a result of technological advancements. Here are four manufacturing roles that will soon change:
The Production Planner
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Production planners will transition from reactively managing shop floor concerns to much more proactive positions in which they analyze data, manage exceptions, and find possibilities for continuous improvement regularly. They will transition from manual supply and inventory management to predictive analytics and digital twins to build optimized production schedules and proactively handle supply challenges.
The Industrial Engineer
In addition to traditional forms of automation, industrial engineers will increasingly leverage digital twins and other cyber-physical systems to integrate manufacturing processes and optimize shop floor operations. They'll work to improve human-machine interactions and serve as a link between their OT and IT colleagues.
The Operator
Today's operators are more likely to specialize in a single machine or product line, relying on institutional knowledge, supervisor instruction, and personal judgment to monitor devices and processes, which leaves room for human mistakes. Operators will use digital tools like digital twins and AI recommendation engine support in the future to proactively identify and fix difficulties. They will be taught as generalists who will work on a variety of machinery and product lines.
The Quality Engineer
Quality engineers frequently change standards in response to consumer complaints, sub-optimal yields, or defective products. Through the use of digital twins, machine learning models, advanced analytics, and the ability to incorporate smart quality controls, they will monitor processes in real-time, forecast quality concerns before they arise, and swiftly trace and diagnose any issues.
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