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Pervasive deployment of architecturally consistent workflow applications creates the enterprise environment for manufacturing intelligence.
The capability of automated defect classification embedded in AOI equipment is the key factor to empower manufacturing intelligence.
SM also encompasses the sophisticated practice of generating and applying data-driven Manufacturing Intelligence throughout the lifecycle of design, engineering, planning and production.
As far as product data are concerned, data exchange, 3D visualization, and communication are crucial processes for reusing manufacturing intelligence across lifecycle stages.
Data-rich manufacturing environments provide a broad stage on which advanced data analytics play leading roles in creating manufacturing intelligence to support operational efficiency and process innovation.
The adoption of advanced manufacturing intelligence technologies requires managing the interaction of information in Product-Service Systems (PSS) by combining Product (PLM) and Service Lifecycle Management (SLM).
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Effective data management underpins one technology that will shape the future of manufacturing: artificial intelligence (AI).
The fourth industrial revolution is called as Industry 4.0 by Germany that is the intelligence manufacturing [2].
This scheme was defined by Meziane et al. [16] when identifying different areas of manufacturing where Artificial Intelligence (AI) was being applied.
On the business analytics side, Dr. Johnson helps companies translate their complex internal data sets into strategic, actionable information across a variety of business settings including human resources, finance, marketing, manufacturing, and business intelligence.
Machines are learning to do the jobs of manufacturing workers; artificial intelligence-based tools are mastering the jobs of call-center and knowledge workers; and cars are beginning to drive themselves.
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