Industry product iteration speed accelerates, the trend of multiple varieties and small batches strengthens; trade wars and globalization layout increase supply chain management cost pressure, extremely sensitive to manufacturing costs; Customer's SMT workshop has a large number of heavy asset, heavy data equipment, 70% OEE level needs to be improved through data analysis; maintenance engineers perform routine maintenance on time, paper records, material throwing failures frequently occur, unable to analyze statistical data of defects, urgently hope for data analysis.
Application Layer - SMT Line Equipment Predictive Maintenance: Achieve fault prediction models for key equipment such as placement machines, predictive maintenance - TPM Equipment Maintenance: Achieve equipment fault knowledge archives, auxiliary maintenance, and integration with material management, achieve automatic material calling, OEE automatic calculation monitoring model - Production Operation MOM System Integration: Integration of workshop MES and WMS Platform Layer - IoT Platform + Big Data Platform: SMT workshop multiple lines, hundreds of models, 300+ machines, daily N*100+G data volume access and processing. Workshop equipment model configuration modeling Edge Layer/Device Layer - Achieve edge data protocol parsing and processing for commonly used proprietary equipment in SMT industry
