Abstract: Under intelligent manufacturing era, there is pressing demands from discrete manufacturing enterprise to utilize big data (BD) technologies for enhancing the level of production management and control (PM & C). The BD driven intelligent PM & C in discrete manufacturing process is studied. Based on the determination of characteristics and demands for PM & C, the architecture of BD driven PM & C is firstly constructed, which the main flow is "collection-processing-analysis-service" of manufacturing BD. Based on the closed-loop mechanism "progress prediction-bottleneck discovery-anomaly tracing-decision making" for PM & C, the key technologies are respectively proposed, which are: "A stacked sparse auto-encoder model for production progress prediction", "The parallel gated recurrent units model for shifting bottleneck discovery", "The density peak-weighted fuzzy C-means method for anomaly tracing" and "The multi-agents reinforcement learning for production decision-making". Finally, an aircraft discrete manufacturing workshop is selected as the application scenario to verify the developed prototype system.