Inventory classification with artificial intelligence: Conceptual framework
DOI:
https://doi.org/10.31181/jdaic10029092024kKeywords:
Supply Chain Management, Inventory Classification, Artificial IntelligenceAbstract
In today's world where competition is increasing harshly, it is important to achieve sustainable profits and keep costs competitive. Under these conditions, the importance of supply chain elements is increasing day by day. Management of inventory costs, which constitute a large volume among cost items, affects the performance of companies. The first step to focus on to keep inventory costs under control and improve is inventory classification. Inventory classification, which is at the top of the supply chain elements and closely affects the subsequent phases, it is critical in determining supply chain performance. Thanks to inventory classification, material groups are determined and stock strategies for these groups are clarified. Incorrect inventory classification causes materials to be assigned to incorrect groups, which negatively affects inventory costs and subsequent phases of the supply chain, causing an increase in costs. The most used methods for inventory classification are ABC analysis, multi-criteria inventory classification and optimization. However, the increasing momentum in artificial intelligence studies in recent years has also closely affected inventory classification. The advantages brought by artificial intelligence methods have also created distinctive contributions to inventory classification studies. This study provides a conceptual framework that examines artificial intelligence methods in the field of inventory classification.
Downloads
References
Agarwal, R., & Mittal, M. (2019). Inventory Classification Using Multi-Level Association Rule Mining. International Journal of Decision Support System Technology, 11(2), 1-12.
Cui, L., Tao, Y., Deng, J., Liu, X., Xu, D., & Tang, G. (2021). BBO-BPNN and AMPSO-BPNN for multiple-criteria inventory classification. Expert Systems With Application, 175, 114482.
İsen, E., & Boran, S. (2018). A Novel Approach Based on Combining ANFIS, Genetic Algorithm and Fuzzy c-Means Methods for Multiple Criteria Inventory Classification. Arabian Journal for Science and Engineering, 43(6), 3229–3239.
Kaabi, H. (2022). Comparative Analysis of MultiCriteria Inventory Classification Models for ABC Analysis. International Journal of Information Technology and Decision Making, 21, 1617-1646.
Kabir, G., & Hasin, M. A. A. (2013). Multi-criteria inventory classification through integration of fuzzy analytic hierarchy process and artificial neural network. International Journal of Industrial and Systems Engineering, 14, 74-103.
Kartal, H., Oztekin, A., Gunasekaran, A., & Cebi, F. (2016). An integrated decision analytic framework of machine learning with multi-criteria decision making for multi-attribute inventory classification. Computers and Industrial Engineering, 101, 599–613.
Khanorkar, Y., & Kane, P. V. (2023). Selective inventory classification using ABC classification, multi-criteria decision making techniques, and machine learning techniques. Materials Today: Proceedings, 72, 1270–1274.
Lei, Q., Chen, J., & Zhou, Q. (2005). Multiple criteria inventory classification based on principal components analysis and neural network. Lecture Notes in Computer Science, 3498, 1058-1063.
Li, B., Zhao, Z., & Duan, T. (2004). Hybrid predictive method for multi-criteria inventory classification. Computer Integrated Manufacturing Systems, 10, 594-599.
Lolli, F., Balugani, E., Ishizaka, A., Gamberini, R., Rimini, B., & Regattieri, A. (2019). Machine learning for multi-criteria inventory classification applied to intermittent demand. Production Planning and Control, 30(1), 76–89.
Lolli, F., Ishizaka, A., & Gamberini, R. (2014). New AHP-based approaches for multi-criteria inventory classification. International Journal of Production Economics, 156, 62–74.
Lolli, F., Ishizaka, A., Gamberini, R., Balugani, E., & Rimini, B. (2017). Decision Trees for Supervised Multi-criteria Inventory Classification. Procedia Manufacturing, 11, 1871–1881.
López-Soto, D., Angel-Bello, F., Yacout, S., & Alvarez, A. (2017). A multi-start algorithm to design a multi-class classifier for a multi-criteria ABC inventory classification problem. Expert Systems with Applications, 81, 12–21.
Mohamadghasemi, A., Hadi-Vencheh, A., Hosseinzadeh Lotfi, F., & Khalilzadeh, M. (2019). Group multiple criteria ABC inventory classification using the TOPSIS approach extended by Gaussian interval type-2 fuzzy sets and optimization programs. Scientia Iranica, 26(5), 2988–3006.
Mohammaditabar, D., Hassan Ghodsypour, S., & Obrien, C. (2012). Inventory control system design by integrating inventory classification and policy selection. International Journal of Production Economics, 140(2), 655–659.
Šarić, T., Pezer, D., & Simunovic,G. (2014). Inventory classification using multi – criteria abc analysis, Neural networks and cluster analysis. Tehnicki Vjesnik, 21, 1109-1115.
Šimunović, G., & Šarić, T. (2009). Application of Artificial Neural Networks to Multiple Criteria Inventory Classification. Strojarstvo, 51, 313-321.
Svoboda, J., & Minner, M. (2022). Tailoring inventory classification to industry applications: the benefits of understandable machine learning. International Journal of Production Research, 60(1), 388-401.
Tsai, C. Y., & Yeh, S. W. (2008). A multiple objective particle swarm optimization approach for inventory classification. International Journal of Production Economics, 114(2), 656–666.
Xue, D. J. (2014). Inventory classification and management strategy of components and parts in assembly workshops. Applied Mechanics and Materials, 687–691, 5028–5031.
Yu, M. C. (2011). Multi-criteria ABC analysis using artificial-intelligence-based classification techniques. Expert Systems with Applications, 38(4), 3416–3421.
Zhang, Q., Zhao, Q., & Li, Y. (2018). Integrating Replenishment Policy with GSAA-FCM Based Multi-Criteria Inventory Classification. International Journal of Computational Intelligence Systems, 11,248-255.
Zhang, Z.L., Wang, Y.F., & Li, Y. (2019). Inventory control model based on multi-attribute material classification: An integrated grey-rough set and probabilistic neural network approach. Advances in Production Engineering & Management, 14, 93-111.
All site content, except where otherwise noted, is licensed under the 