Research on the Mechanism of Data Intelligence-Driven Collaborative Management Across the Entire Value Chain in Manufacturing and a Hybrid Data Model

In the real economy, manufacturing occupies a core position and is regarded as a crucial force and support driving national economic growth. Under the wave of a new round of industrialization, the innovation and transformation of traditional manufacturing have increasingly attracted attention. In the operational processes of manufacturing, various stages such as raw material and finished product supply, marketing strategy planning, and customer service maintenance generate vast datasets. These data hide complex cost and value-added relationships. By comprehensively reviewing the business data across the entire value chain of manufacturing, meticulously analyzing the characteristics and interconnections of data from each stage, and implementing data collaboration management strategies, valuable insights can be provided for enterprise decision-making. However, given the common challenge of missing dynamic real-time data in practical operations, enterprises inevitably experience lags when performing real-time data analysis and work scheduling, posing challenges to the digital transformation of manufacturing enterprises. This study aims to explore the operational mechanisms of the entire value chain in manufacturing, providing collaborative management solutions for manufacturing enterprises. It proposes a data intelligence-based collaborative management framework for the entire value chain in manufacturing, namely the Data Fusion Model, and accordingly designs an information collaboration management system for the entire value chain in manufacturing oriented towards a microservices architecture.