DIGITAL TRANSFORMATION IN DISCRETE MANUFACTURING

Practice path and effect analysis of the digital transformation of discrete manufacturing enterprises –based on the perspective of production and operation optimization

This study takes a large-scale discrete mechanical manufacturing enterprise as a case study, exploring the implementation pathways and effects of digital transformation from the perspectives of production and operational optimization. The research aims to address three core issues: (1) how to enhance operational efficiency through data mining and business process reengineering; (2) how digital transformation can drive workforce reduction while increasing productivity; and (3) how a systematic analysis of indicator systems can support strategic implementation. The research methodology integrates case analysis, multi-dimensional data collection (such as real-time equipment monitoring via SCADA systems and RFID tracking of material paths), in-depth interviews (to extract key contradictions like process fragmentation and system compatibility), and lean tools (such as value stream mapping and policy X matrices). The findings reveal that the elimination of information silos reduces cross-system data integration time by 80% and shortens management decision-making cycles by 40%; data-driven decision-making improves the timeliness of anomaly response by 50%; and the fusion of lean methodologies with digital technologies boosts production capacity by 20%, achieves annual cost savings of 12 million yuan, and reduces defect rates by 18%. The study innovatively proposes a standardized improvement model combining the policy X matrix with the PDCA cycle, offering a replicable transformation pathway for the discrete manufacturing sector.