From product specifications to smart manufacturing: Generative AI-driven process innovation for BOM digitization and its applications

Abstract

As global manufacturing advances toward digital transformation and intelligent operations, seamless integration between product specification documents and Bills of Materials (BOM) has become increasingly critical. Conventional BOM creation processes rely heavily on manual interpretation. This often leads to inconsistencies in part numbering, redundant records, and extended engineering cycles, especially when documentation is unstructured and multilingual. To address these challenges, this study proposes a generative AI–driven framework that integrates large language models (LLMs) with retrieval-augmented generation (RAG) for intelligent BOM digitization. The framework establishes a closed-loop pipeline. It converts unstructured product datasheets into rule-validated and semantically consistent part numbers through multi-stage document parsing, knowledge retrieval, and constrained decoding. A human-in-the-loop verification mechanism is incorporated to ensure semantic precision and traceability. By leveraging the multimodal capabilities of the Qwen model, the proposed framework effectively parses and understands bilingual (Chinese–English) technical documents. This enhances cross-lingual semantic comprehension and data accuracy. The validated outputs are consolidated into an executable Electrical and Digital BOM (x-EDBOM). This x-EDBOM serves as a unified source of truth across Product Lifecycle Management (PLM), Enterprise Resource Planning (ERP), and Manufacturing Execution Systems (MES). Empirical validation at a mid-sized electronics manufacturer demonstrates a 12.5× reduction in processing time (25 to 2 min), an average accuracy of 91%. It also indicates estimated monthly cost savings of approximately USD 0.7 million. Furthermore, the x-EDBOM enables comparative analysis of identical components across multiple suppliers. It integrates their price, lead-time, and specification data within a unified system, thereby supporting data-driven sourcing optimization and supplier management. Beyond efficiency improvements, the proposed framework supports cross-lingual data normalization, supplier benchmarking, and post-merger system harmonization. It provides a scalable foundation for intelligent product data management and smart manufacturing transformation.