With the intensification of global business competition, enterprises are increasingly focusing on improving operational efficiency to improve performance. Operational efficiency refers to the ratio of output to input in the utilization of resources by a company. Performance evaluation measures the performance of a company in achieving goals and providing value. Nowadays, improving operational efficiency and accurately evaluating performance are crucial for business success. Cost management is one of the important methods to improve operational efficiency and achieve excellent performance. Through cost management, enterprises can better understand resource utilization, identify waste and redundancy, and make targeted improvements. This article used the EVA (Economic Value Added) method to study the impact of cost management on operational efficiency and performance evaluation. Through data analysis, it was found that the economic value after cost management was 1.07775 million yuan. This numerical value was positive, demonstrating the value and effectiveness of the constructed model and method in operational efficiency and performance evaluation. Cost management could also optimize resource allocation, reduce costs, and improve operational efficiency and performance evaluation.

In this study, a firm's scheduling problem optimized using the genetic algorithm method and it aimed to reach the schedule that gives the smallest time in the production schedules. Considering the scheduling of the solenoid part produced by the company, a schedule with a shorter production time than the current production time of the part obtained and the production times of the company were improved. A genetic algorithm developed to solve the parallel batch processing problems. The developed genetic algorithm is an effective heuristic algorithm for the flexible flow type problem. Parameter optimization study carried out to improve the solution performance of genetic algorithms. Genetic operators examined in detail and compared with each other, and the most appropriate parameter set was determined because of research and experiments. The best parameters found for each problem with suggested algorithm. In order to reach the optimum solution of the part to produce in the scheduling problem, chromosomes created and sequence sizes randomly assigned. These assigned dimensions are in ascending order and converted to actual rows. Then, the total production times were determined by generating solutions sequentially from the generated chromosomes.

Intelligent manufacturing is the deep integration of new generation information technology and advanced manufacturing technology, which is of great significance for improving industrial technological level and production efficiency. Intelligent manufacturing includes fields such as industrial internet, industrial robots, semiconductors, and so on. This article analyzes the development status of these three fields in China, pointing out that the market prospects for these fields in China are broad, the development speed is fast, and the level is constantly improving. On this basis, it analyzes the challenges facing intelligent manufacturing in China, including the shortage of high-end talents, some fields still at junior or intermediate stages, core technology still needs to break through and innovate, and the need for further attention to industrial internet security issues. Finally, some targeted solutions are proposed, hoping to improve the development and construction level of intelligent manufacturing in China to a certain extent.

With the continuous development and improvement of digital technologies such as the Internet, information and data resources have become the key factors determining market share and play a core value role in enterprise development. Traditional manufacturing enterprises are facing the dilemma of gradually falling into market passivity, and their traditional business and production models are difficult to sustain. Therefore, they need to use digital technology to complete enterprise transformation in order to achieve sustainable development. This paper introduces the concept and motivation of Digital transformation of manufacturing industry, analyzes the current status and challenges of Digital transformation of manufacturing industry, studies the optimal application of digital technology in the production and operation of manufacturing industry, summarizes the trend and five optimization paths of Digital transformation of manufacturing industry, and hopes to have a certain impact on the exploration and development of Digital transformation of manufacturing industry.

The unit production theory is applied to the production practice of water meter enterprises, and the case study is carried out. The radar map is used to systematically evaluate the management level of water meter production site, and the water meter production site is comprehensively improved from the aspects of unit operation mode, multi-function worker training, workbench design, 5S management and Kanban management in combination with the unit production theory, which effectively improves the production efficiency of water meter and the flexible manufacturing ability of the enterprise, and realizes the goal of the water meter enterprise to deal with the multi-variety and small-batch market.