The Optimization Strategy and Application Practice of Business Management Supply Chain Based on Artificial Intelligence Technology

Suppliers, producers, distributors, and retailers are among the many stakeholders in the supply side chain. The data silos between the parties are serious, resulting in poor information flow, which affects the accuracy and practicality of the optimization model. To this end, this paper studies the application strategies and practices of artificial intelligence technology and genetic algorithms in business management supply chain optimization. First, the advantages of artificial intelligence-driven solutions in supply chain management are explored, especially in demand forecasting, inventory management, and warehouse process automation. These technologies provide real-time decision support by processing massive data, reducing inventory waste and improving logistics efficiency. Secondly, a supply chain optimization model based on genetic algorithm is constructed, and the objective function and constraints are clarified, covering key optimization goals such as cost minimization, profit maximization and service level improvement. The model continuously iterates and optimizes the supply chain configuration through the core operation steps of genetic algorithm, including population initialization, fitness evaluation, selection, crossover and mutation. The experimental results show that artificial intelligence technology and genetic algorithm can significantly reduce supply chain costs, improve logistics efficiency and enhance service levels, verifying the effectiveness of the model in practical applications.