{"id":11638,"date":"2026-08-06T12:12:41","date_gmt":"2026-08-06T12:12:41","guid":{"rendered":"https:\/\/www.creactives.com\/2026\/08\/06\/how-to-find-duplicate-materials-with-ai-in-global-inventory\/"},"modified":"2026-08-19T10:18:26","modified_gmt":"2026-08-19T10:18:26","slug":"how-to-find-duplicate-materials-with-ai-in-global-inventory","status":"publish","type":"post","link":"https:\/\/www.creactives.com\/it\/2026\/08\/06\/how-to-find-duplicate-materials-with-ai-in-global-inventory\/","title":{"rendered":"How to find duplicate materials with AI in global inventory"},"content":{"rendered":"\t\t
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Supply chain directors can reduce working capital by utilizing Deep Learning and Knowledge Engineering to identify duplicate materials hidden across fragmented, multi-language ERP environments. By establishing a semantically connected <\/span>data foundation<\/span>, organizations can harmonize material master data to uncover redundant safety stock and consolidate global inventory levels. This process shifts the focus from manual, lexical searching to automated, semantic identification, enabling the reallocation of existing assets and the prevention of unnecessary procurement. <\/span><\/p><\/div>

The challenge of fragmented material master data in global operations<\/span><\/h3><\/div>

In large multinational organizations, the accumulation of duplicate materials is rarely a result of simple clerical errors; rather, it is a structural consequence of decentralized ERP systems and linguistic diversity. When plants in different regions operate on siloed instances of SAP, Oracle, or legacy systems, the same physical item is frequently recorded under distinct part numbers and descriptions. <\/span><\/p><\/div>

Traditional inventory management relies on exact string matches or basic fuzzy logic, both of which fail to bridge the gap between technical descriptions written in different languages or following disparate naming conventions. For instance, a French facility might record a “Ruptor 400V Siemens,” while an English-speaking plant registers a “Circuit breaker 5SY4310-7 400V.” To a standard database, these are unique entries; to a supply chain director, they represent a missed opportunity to optimize stock. <\/span><\/p><\/div>

Consequently, working capital remains trapped in redundant safety stock. Without a unified view, procurement teams continue to purchase new items that are already sitting idle in a warehouse in a different region. This lack of transparency is fundamentally a <\/span>data foundation problem<\/span> rather than a simple analytics deficiency.<\/span><\/p><\/div>

How deep learning identifies hidden duplicates across languages<\/span><\/h3><\/div>

Deep learning models represent a significant advancement over manual audit processes by shifting from lexical matching to semantic understanding. While traditional tools look at letters and symbols, AI trained on procurement-specific datasets understands the underlying engineering intent of a material description. <\/span><\/p><\/div>

Semantic vs. lexical matching<\/span><\/h3><\/div>

Lexical matching focuses on character patterns. If a part number is missing a hyphen or a brand name is abbreviated differently, the match fails. In contrast, deep learning analyzes the context and technical attributes of a description. By training on millions of industrial records, specialized AI\u2014such as the proprietary models developed by <\/span>Creactives<\/a><\/span>\u2014can recognize that “Moteur \u00e0 induction” and “Induction motor” refer to the same functional entity, regardless of the language or syntax used.<\/span><\/p><\/div>

The role of knowledge engineering<\/span><\/h3><\/div>

To achieve the precision required for global supply chain optimization, deep learning must be paired with knowledge engineering. This involves a pre-defined industrial taxonomy\u2014often encompassing over 30,000 categories\u2014that provides the AI with a logical framework. This combination allows the system to: <\/span><\/p><\/div>