Against the backdrop of rapid development in big data and intelligent algorithms, intelligent production environments serve as the forefront of manufacturing enterprises. Under the guidance of artificial intelligence, these environments require precise control and intelligent management of production systems and processes to maximize corporate value. Based on this, a management approach is proposed for intelligent optimization and control in smart factory operations, grounded in the theory of shared value networks. Building on this, by calculating the earliest start time and earliest completion time for workpiece processing, a processing time matrix is derived for each product, thereby establishing a flexible scheduling optimization decision-making model. The simulated annealing genetic algorithm is employed to solve the flexible scheduling optimization decision-making model. The results indicate that the widespread adoption of flexible production and the enhancement of flexible expansion levels can generate a sustained driving effect on the intelligent upgrading of manufacturing, while improvements in technical flexibility levels can only promote the intelligent upgrading of manufacturing in the short term but will significantly inhibit the intelligent upgrading of the manufacturing sector in the medium to long term.
