Innovation and collaborative, synchronized program management for new programs
Enterprise Manufacturing Intelligence extends the Siemens Manufacturing Operations Management (MOM) solution by providing the capability to apply analytics and reporting techniques to operations and business data at the site, regional and global levels.
Enterprise Manufacturing Intelligence integrates, connects and unifies data sources such as Manufacturing Execution System (MES), Quality Management System (QMS), Advanced Planning and Scheduling (APS), Laboratory Information Management System (LIMS), Enterprise Resource Management (ERP) and others – into one accessible analytical data model providing capabilities to explore and drill down into contextualized data.
Enterprise Manufacturing Intelligence is used at the plant level to improve collaboration and data exchange between the shop floor and enterprise systems, and/or at the enterprise level to benchmark and compare production runs or predict various plant operations. As data from different sources are combined, they can be put into a new context and provide users with a different and more complete perspective of manufacturing operations regardless of where the data originated.
The Valor False-Call Reduction Predictive Analytics Solution uses AI model training based on historical AOI pass/fail information to predict with high accuracy whether each failed board has real errors or pseudo-errors, reducing manual review work by up to 60%.
Modern architecture of manufacturing intelligence supports a wide range of technology platforms, and is multiplied by huge functional capabilities.
Data exposition of manufacturing intelligence can be emphasized through its context-driven data navigation and openness in selection of the visualization tool.
Manufacturing Data Warehouse (MDW) represents the physical implementation of the Manufacturing Analytical Model (MAM) based on ISA-95 International industry standard.
The Valor-Vanti Predictive Analytics solution collects data from test machines early in the production line and, using machine learning, predicts which units will fail the next test. Predicting faulty units earlier significantly reduces the cost of error.
The Valor-Cybord Predictive Analytics Solution dramatically reduces costs due to compromised or counterfeit and compromised components and improves product reliability. It visually inspects every placed component and uses AI and library comparisons to provide full authentic and homogenous component traceability information.
The tremendous engineering capabilities of Manufacturing Intelligence (MI) cover nearly every need to prepare manufacturing data for analyses.
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