Optimization of Process, Knowledge, and Manufacturing Management in Customized Production: A Graph-Based Approach for Manufacturing Planning

This research paper describes the design and implementation of a graph-based database using Neo4J, specifically tailored to optimize process, knowledge, and manufacturing management in the custom manufacturing environment of control cabinet manufacturing. Given the challenges of digital transformation and increasing data complexity, traditional database systems often fall short in terms of scalability, flexibility, and visualization. This study addresses these gaps by leveraging the capabilities of graph databases. The paper presents a systematic methodology that includes abstraction, data preparation, integration, analysis, and validation against real-world parts lists. The research underscores the potential of graph databases to improve decision-making, streamline workflows, and maximize data-driven resources in an industry characterized by diverse production requirements without excessive manual data generation and management.