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

In the context of global manufacturing accelerating towards intelligent and digital transformation, discrete manufacturing enterprises are confronted with the dual challenges of intensified market competition and diversified customer demands. Traditional production models, plagued by issues such as information silos and delayed data analysis, struggle to support efficient operations and agile responses. 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.