How Democratized Artificial Intelligence Can Move Manufacturing to a New Evolution Pace
correlation complexity and few device / sensors data consolidation due to small data storage size and computing power . Even if those technologies are innovating fast , the other issue that presents itself is the innovation upgrade ( mostly hardware ) which is limited by the complexity of deployment and maintenance at a large scale . New devices like smart watches , tablets , AR glasses , collaborative robots and 3D printers are now also able to extend their capabilities with a direct access to the cloud and hybrid cloud , using external computing power and services .
2 . In case of brownfield or low computing power capability in the devices , this intelligence can be hosted in the edge computing devices , which are playing the role of automation controller ( PLC , Programming Logic Controller 24 / Motion Logic Controller ). They are now becoming intelligent controllers with a mix of industrial real-time functions , “ human real-time ” smart computing and cloud gateway capability to access external cloud computing power and services . This technology can also help to control and make smarter a group of machines and devices to automate actions and monitor a part of a manufacturing process in a specific location area . The limitations here are the silo scope and the limited local storage capability and analytics computing power .
3 . The third level is the plant level , consolidating data from factory machines , humans , orders , warehouses , logistics , maintenance and quality to better organize the activities using cloud or hybrid cloud AI ( hybrid cloud means hosting a part of the cloud on premise ). The aim of this level is to leverage complex analytics to provide predictive alerts , machine learning and adaptive intelligence to help technicians , workers , managers and operational officers make the best decision in advance or in realtime to improve overall efficiency and excellence . As a plant manager , I want to predict in real-time my future production achievement , my supply , logistic , worker and maintenance plans based on the current situation and predicted situation , and I want to be agile if the situation changes . That is what Cloud / Hybrid Cloud AI is providing .
4 . Finally , the last layer is the ecosystem , regional or worldwide level , consolidating data from your company , getting access to data from suppliers , benchmark data from partners , consulting firm and data suppliers to refine your horizontal and vertical AI analytics to make the best decision . Can I benchmark predictive failure of all my motors across my factories and OEM suppliers to identify specific ( humidity , amperage fluctuation , vibration , etc .) patterns of failure in my conveyors , robot and elevators and avoid hard failure ? How can I predict the impact of
24 https :// en . wikipedia . org / wiki / Programmable _ logic _ controller
- 16 - November 2017