Using neural networks to examine trending keywords in Inventory Control

• Autorzy: Sadowski Adam , Sadowski Michał , Engelseth Per , Galar Zbigniew , Skowron-Grabowska Beata

Identyfikatory

DOI

10.30657/pea.2023.29.52

• Języki publikacji: EN

Abstrakty

EN

Inventory control is one of the key areas of research in logistics. Using the SCOPUS database, we have processed 9,829 articles on inventory control using triangulation of statistical methods and machine learning. We have proven the usefulness of the proposed statistical method and Graph Attention Network (GAT) architecture for determining trend-setting keywords in inventory control research. We have demonstrated the changes in the research conducted between 1950 and 2021 by presenting the evolution of keywords in articles. A novelty of our research is the applied approach to bibliometric analysis using unsupervised deep learning. It allows to identify the keywords that determined the high citation rate of the article. The theoretical framework for the intellectual structure of research proposed in the studies on inventory control is general and can be applied to any area of knowledge.

Słowa kluczowe

EN

inventory control; neural network; bibliometric analysis; keywords

PL

sieci neuronowe; analiza bibliometryczna; słowa kluczowe; kontrola zapasów; logistyka

• Wydawca: Oficyna Wydawnicza Stowarzyszenia Menedżerów Jakości i Produkcji
• Czasopismo: Production Engineering Archives
• Rocznik: 2023
• Tom: Vol. 29, Iss. 4
• Strony: 474--489
• Opis fizyczny: Bibliogr. 52 poz., rys., tab.

Twórcy

autor

Sadowski Adam

• University of Lodz, Faculty of Management, ul. Matejki 22/26, 90-237 Łódź, Poland

autor

Sadowski Michał

• Jagiellonian University, Faculty of Mathematics and Computer Science, 6 prof. Stanisława Łojasiewicza, 30-348 Kraków, Poland

autor

Engelseth Per

• Tromso UniversityTromsø School of Business and Economics, UiT The Arctic University of Norway, Narvik Campus, Lodve Langes gate 2, 8514 Narvik, Norway

autor

Galar Zbigniew

• Bayer, Al. Jerozolimskie 158 02-326 Warszawa Poland

autor

Skowron-Grabowska Beata

• Czestochowa University of Technology, Faculty of Management, ul. Armii Krajowej 19b 42-200 Czestochowa, Poland

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).