Research on Intelligent Supply Chain Optimization and Operational Performance Improvement of Media Enterprises Using Industrial Engineering Methods

Abstract

The rapid development of digital technologies and artificial intelligence (AI) has fundamentally transformed the marketing environment and operational models of media enterprises. Traditional marketing processes, which are often characterized by fragmented information flows, limited audience interaction, delayed decision-making, and inefficient resource allocation, increasingly struggle to meet the demands of personalized communication and data-driven market competition. This study investigates the reengineering of marketing processes and the optimization of operational performance in media enterprises within the context of digitalization and artificial intelligence. Drawing on the perspectives of digital transformation, intelligent decision-making, and marketing process management, the study develops an analytical framework that integrates data collection, audience profiling, intelligent content distribution, marketing decision-making, customer interaction, and performance evaluation. Artificial intelligence technologies, including machine learning, data analytics, natural language processing, and intelligent recommendation systems, are incorporated to improve the automation, precision, and adaptability of marketing activities. Furthermore, the study examines the relationship between marketing process reengineering and operational performance from multiple dimensions, including marketing efficiency, customer engagement, resource utilization, decision-making quality, and organizational responsiveness. The proposed framework emphasizes the transformation of traditional linear marketing processes into dynamic, data-driven, and intelligent operational systems. The findings suggest that the effective integration of digital technologies and AI can enhance the coordination of marketing activities, improve the accuracy of audience targeting, strengthen customer interaction, and support continuous performance optimization. This study provides a theoretical and practical reference for media enterprises seeking to redesign their marketing processes and improve operational competitiveness in the era of digital and intelligent transformation.