Leveraging generative Artificial Intelligence for enhancing the quality of product data

• Autorzy: Niemir Maciej , Mrugalska Beata , Grajewska Dominika , Nitoń Bartłomiej

Identyfikatory

DOI

10.17270/J.LOG.001164

• Języki publikacji: EN

Abstrakty

EN

Background: In an era of digitalization and rapid e-commerce growth, the quality of product data has become a crucial factor for efficient logistics, online trade, and supply chain management. Accurate attributes-such as product name, brand, GTIN, and images-ensure reliable product identification. However, product data often contain errors that disrupt business processes. This study investigates the potential of generative artificial intelligence (GenAI) to improve product data quality. Methods: Generative AI models were analyzed for their potential role in validating product data. To minimize the risk of errors and hallucinations, a supportive validation approach was proposed, in which GenAI provides suggestions for data improvement but does not make automatic decisions. Results: The proposed approach demonstrated strong potential to improve product data quality, particularly in the areas of language validation, brand attribution, categorization, product naming, and image verification. Conclusions: Generative AI shows considerable promise for enhancing product data quality, particularly by automating and streamlining validation. The framework examined here effectively supported error detection and generated actionable suggestions for correction. With further development, such tools could significantly improve data management efficiency in e-commerce and logistics while promoting the standardization of product information management processes.

Słowa kluczowe

EN

Artificial Intelligence; product data quality; data quality improvement; data quality 4.0; electronic catalog; product data management; error detection and correction; product data validation; product data cleansing; product information management

PL

Sztuczna Inteligencja; jakość danych produktowych; poprawa jakości danych; katalog elektroniczny; zarządzanie danymi produktowymi; wykrywanie i korygowanie błędów; walidacja danych produktowych; czyszczenie danych produktowych; zarządzanie informacjami o produktach

• Wydawca: Wyższa Szkoła Logistyki
• Czasopismo: LogForum
• Rocznik: 2025
• Tom: Vol. 21, no 3
• Strony: 317--326
• Opis fizyczny: Bbliogr. 28 poz., fot., tab.

Twórcy

autor

Niemir Maciej

• Poznan University of Technology, Faculty of Engineering Management, Poznań
• Poland Łukasiewicz Research Network – Poznan Institute of Technology, Poznań, Poland

• https://orcid.org/0000-0002-1054-4285

autor

Mrugalska Beata

• Poznan University of Technology, Faculty of Engineering Management, Poznań, Poland

• https://orcid.org/0000-0001-9827-9449

autor

Grajewska Dominika

• Łukasiewicz Research Network – Poznan Institute of Technology, Poznań, Poland

• https://orcid.org/0009-0000-1234-6728

autor

Nitoń Bartłomiej

• Łukasiewicz Research Network – Poznan Institute of Technology, Poznań, Poland

• https://orcid.org/0000-0003-3306-7650

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2026).