Intelligent Material Code Deduplication and Governance Using Tokenization-Based Fuzzy Matching for Industrial Group Collaboration

DOI: https://doi.org/10.62517/jiem.202503106

Author(s)

Chunyan Yu1, Xingyan Zhou1, Jianjia He1,2, Han Xia1

Affiliation(s)

1Business School, University of Shanghai for Science and Technology, Shanghai, China 2Center for Super Networks Research, University of Shanghai for Science and Technology, Shanghai China

Abstract

Ensuring the uniqueness of material codes in group-level material management is essential for integrating business and finance across an industrial supply chain. This study proposes an intelligent deduplication and governance approach by incorporating a fuzzy matching algorithm based on tokenization matrices. The method enhances material management efficiency by computing the similarity between newly applied material codes and historical records, ensuring precise duplication detection. The algorithm tokenizes material names, specifications, and models to construct a tokenization matrix, enabling accurate similarity calculations. It is applied to key approval processes in the aluminum industry, including material code application, deactivation, activation, and merging. Empirical results from a large aluminum industry group demonstrate that the algorithm significantly reduces duplicate codes, improving data accuracy and management efficiency. The findings show that this approach optimizes inventory balancing, minimizes overstocking, and reduces capital occupation, contributing to cost control and operational efficiency. Moreover, it supports cross-subsidiary material sharing, reinforcing data standardization across departments and systems. The proposed method offers a scalable and effective solution for intelligent material governance in industrial groups, highlighting its broad applicability and high promotional value in enterprise-wide material management.

Keywords

Supply Chain Management; Material Coding; Inventory Optimization; Fuzzy Matching

References

[1] Mostofa M G. Store, warehousing and inventory management system in Bangladesh: upgradation approach in Bangladesh Civil Aviation Authority (CAAB). 2024. [2] Wang Z, Sun Z, Yin H, et al. Data‐driven materials innovation and applications. Advanced Materials, 2022, 34(36): 2104113. [3] Bekele A A, Mahesh G, Ingle P V. Enhancing SMCs’ competitiveness through improving material supply chain management practice. International Journal of Construction Management, 2025, 25(1): 77-88. [4] Wang X, Wang P, Hu J, et al. Design and Implementation of a Low-Code Platform in the Power Sector//Proceedings of the 2023 4th International Conference on Big Data Economy and Information Management. 2023: 359-363. [5] Zulkifli A. Accelerating Database Efficiency in Complex IT Infrastructures: Advanced Techniques for Optimizing Performance, Scalability, and Data Management in Distributed Systems. [6] Su A, Wang A, Ye C, et al. Tablegpt2: A large multimodal model with tabular data integration. arXiv preprint arXiv:2411.02059, 2024. [7] Tang Y M, Ho G T S, Lau Y Y, et al. Integrated smart warehouse and manufacturing management with demand forecasting in small-scale cyclical industries. Machines, 2022, 10(6): 472. [8] Nwankwo D C. Improving approaches to material inventory management in construction industry in the UK. University of Wales Trinity Saint David, 2023. [9] Mohammed A R, Abdelkhalek E, Saleh H, et al. Towards construction 4.0: A conceptual uml model for enhancing procurement and inventory management in conventional construction projects. IEEE Access, 2024. [10] Hrytsenko M V. Automated spare parts supply management system for an aviation company. 2024. [11] Martínez-Peláez R, Ochoa-Brust A, Rivera S, et al. Role of digital transformation for achieving sustainability: mediated role of stakeholders, key capabilities, and technology. Sustainability, 2023, 15(14): 11221. [12] Richards G, & Grinsted S. The logistics and supply chain toolkit: over 100 tools for transport, warehousing and inventory management. Kogan Page Publishers, 2024. [13] Tito M. A comparative analysis of good enterprise data management practices: insights from literature and artificial intelligence perspectives for business efficiency and effectiveness. M. Tito, 2023.