Intelligent Decision Optimization System for Enterprise Electronic Product Manufacturing Based on Cloud Computing

With the rapid development of the electronic product manufacturing industry, enterprises face growing challenges in improving product quality while maintaining strict cost control. In complex multi-stage production environments, key decisions, such as whether to inspect or disassemble spare parts, semi-finished products, and finished goods, are interdependent and directly affect production efficiency, cost, and product quality. Against this backdrop, the importance of enterprise information management is becoming increasingly prominent. Effective information systems can integrate data from various production stages, enabling real-time decision-making, traceability, and process optimization. To address these challenges, this study proposes an Intelligent Decision Optimization System for Enterprise Electronic Product Manufacturing Based on Cloud Computing. Leveraging cloud computing’s scalability and high-performance data processing capabilities, the system applies simulation-based machine learning algorithms to optimize inspection strategies across the manufacturing stages. The research is grounded in a real-world dataset from 2011 to 2014, collected from an electronics manufacturing enterprise based in Shenzhen. The dataset includes detailed records of inspections, disassembly actions, defect rates, production volumes, costs, procurement prices, and inspection fees across different production stages. By simulating the production workflow and constructing a cost analysis model under known defect rates, the study concludes that the most cost-effective strategy is to inspect all spare parts while avoiding inspection and disassembly of semi-finished and finished products. This strategy not only reduces total production cost but also enhances system stability. The proposed system underscores the critical role of intelligent information management in manufacturing and provides a foundation for future research involving deep learning and multi-objective optimization in enterprise quality and cost management.