Lean Production and Big Data Sports Industry Development Based On Machine Learning and Agent Engineering Cycle

Abstract

The sports industry has become one of the most popular industries in the world. This is a new industry with fierce competition, open space and high profits. In the social economy, the percentage shows an upward trend year by year. Planning the scientific development of sports science is an urgent issue to be solved in this area. Lean production easy to understand the big data plays an important role in the sports industry development needs. Previous methods have not accurately reflected in the sports industry product are smaller cities, commuting patterns due to the lack of precision space. The method is proposed to use advanced machine learning techniques on the Internet of Things, the assumption of normality, the industry's development strategy is optimized, and the model is established under the mechanism of innovation. Decision-making of the sports industry's scientific development strategy is, to some extent, to provide a practical guide for the healthy development of the sports industry. The results show that processes and equipment, product design, supplier relationships, and customer relationships have a positive and significant impact on sustainable performance. It is also interesting to observe that the lean culture presides over the sustainable performance impact of processes and equipment-supplier relationships.