645 526
Full Length Article
American Journal of Business and Operations Research
Volume 9 , Issue 2, PP: 41-49 , 2023 | Cite this article as | XML | Html |PDF

Title

Intelligent Decision Support System for Optimizing Inventory Management

  Mona Mohamed 1 * ,   Nissreen El Saber 2

1  Higher Technological Institute, 10th of Ramadan City 44629, Egypt
    (mona.fouad@hti.edu.eg)

2  Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt
    (naelsaber@fci.zu.edu.eg)


Doi   :   https://doi.org/10.54216/AJBOR.090205

Received: September 11, 2022 Accepted: January 25, 2023

Abstract :

Inventory management (InvM) is a critical aspect of supply chain management (SCM) and optimizing inventory levels can lead to significant cost savings and improved customer satisfaction. Hence, business organizations have recently worked to increase the value of their operations by utilizing modern and digital technologies as industry 4.0 (Ind 4.0). In the era of Ind 4.0, the technologies as Internet of Every Things (IoET), AI, BDA…etc. Recognizing the significance of InvM in a supply chain (SC), motivated us to volunteer technologies of Ind 4.0 as machine learning (ML) techniques to boosting Decision Making (DM) process to optimize InvM. Subsequently, this study is constructed for providing an intelligent Decision Support ML framework for automating the process of optimizing inventory management. Our constructed framework ensembles powerful ML prediction algorithms for inventory management, such as Artificial Neural Networks (ANNs), Random Forest (RF), and Support Vector Machine (SVM) for building robust sales regressors.  The extensive experimentations on a case study of Walmart suggested that the proposed system has the potential to transform inventory management and improve supply chain performance, but further research is needed to address the challenges of data availability and quality.

Keywords :

Machine Learning; Decision Support Systems; Inventory Management; Inventory Optimization

References :

[1]        Y. Mashayekhy, A. Babaei, X. M. Yuan, and A. Xue, “Impact of Internet of Things (IoT) on Inventory Management: A Literature Survey,” Logistics, vol. 6, no. 2, 2022, doi: 10.3390/logistics6020033.

[2]        I. Giannoccaro and P. Pontrandolfo, “Inventory management in supply chains: a reinforcement learning approach,” Int. J. Prod. Econ., vol. 78, no. 2, pp. 153–161, 2002.

[3]        C. Deng and Y. Liu, “A Deep Learning-Based Inventory Management and Demand Prediction Optimization Method for Anomaly Detection,” Wirel. Commun. Mob. Comput., vol. 2021, no. Im, 2021, doi: 10.1155/2021/9969357.

[4]        C. Jiang and Z. Sheng, “Case-based reinforcement learning for dynamic inventory control in a multi-agent supply-chain system,” Expert Syst. Appl., vol. 36, no. 3, pp. 6520–6526, 2009.

[5]        T. Loya and G. Carden, Business intelligence and analytics. 2018. doi: 10.4324/9781315206455-12.

[6]        M. Deb, P. Kaur, and K. K. Sarma, “Inventory control using fuzzy-aided decision support system,” in Advances in Computer and Computational Sciences: Proceedings of ICCCCS 2016, Volume 2, 2018, pp. 467–476.

[7]        G. González Rodríguez, J. M. Gonzalez-Cava, and J. A. Méndez Pérez, “An intelligent decision support system for production planning based on machine learning,” J. Intell. Manuf., vol. 31, no. 5, pp. 1257–1273, 2020, doi: 10.1007/s10845-019-01510-y.

[8]        K. A. Eldrandaly, N. El Saber, M. Mohamed, and M. Abdel-Basset, “Sustainable Manufacturing Evaluation Based on Enterprise Industry 4.0 Technologies,” Sustain., vol. 14, no. 12, 2022, doi: 10.3390/su14127376.

[9]        A. Diez-Olivan, J. Del Ser, D. Galar, and B. Sierra, “Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0,” Inf. Fusion, vol. 50, pp. 92–111, 2019.

[10]      A. Oroojlooyjadid, M. Nazari, L. V Snyder, and M. Takáč, “A deep q-network for the beer game: Deep reinforcement learning for inventory optimization,” Manuf. Serv. Oper. Manag., vol. 24, no. 1, pp. 285–304, 2022.

[11]      richard oliver, dalam Zeithml., “Solving semi-Markov decision problems using average reward reinforcement learning.,” Angew. Chemie Int. Ed. 6(11), 951–952., pp. 2013–2015, 2021.

[12]      K. L.Lee, C. K., Lv, Y., Ng, K. K. H., Ho, W., & Choy, “Design and application of Internet of things-based warehouse management system for smart logistics.,” Int. J. Prod. Res. 56(8), 2753-2768..

[13]      B. Diez-Olivan, A., Del Ser, J., Galar, D., & Sierra, “Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0.,” Inf. Fusion, 50, 92-111..

[14]      Y.Choi, T. M., Wallace, S. W., & Wang, “Big data analytics in operations management.,” Prod. Oper. Manag. 27(10), 1868-1883..

[15]      A. Gabrel, V., Murat, C., & Thiele, “Recent advances in robust optimization: An overview.,” Eur. J. Oper. Res. 235(3), 471-483..

[16]      R. Singi, S., Gopal, S., Auti, S., & Chaurasia, “Reinforcement Learning for Inventory Management,” Proc. Int. Conf. Intell. Manuf. Autom. ICIMA 2020 (pp. 317-326). Springer Singapore.


Cite this Article as :
Style #
MLA Mona Mohamed, Nissreen El Saber. "Intelligent Decision Support System for Optimizing Inventory Management." American Journal of Business and Operations Research, Vol. 9, No. 2, 2023 ,PP. 41-49 (Doi   :  https://doi.org/10.54216/AJBOR.090205)
APA Mona Mohamed, Nissreen El Saber. (2023). Intelligent Decision Support System for Optimizing Inventory Management. Journal of American Journal of Business and Operations Research, 9 ( 2 ), 41-49 (Doi   :  https://doi.org/10.54216/AJBOR.090205)
Chicago Mona Mohamed, Nissreen El Saber. "Intelligent Decision Support System for Optimizing Inventory Management." Journal of American Journal of Business and Operations Research, 9 no. 2 (2023): 41-49 (Doi   :  https://doi.org/10.54216/AJBOR.090205)
Harvard Mona Mohamed, Nissreen El Saber. (2023). Intelligent Decision Support System for Optimizing Inventory Management. Journal of American Journal of Business and Operations Research, 9 ( 2 ), 41-49 (Doi   :  https://doi.org/10.54216/AJBOR.090205)
Vancouver Mona Mohamed, Nissreen El Saber. Intelligent Decision Support System for Optimizing Inventory Management. Journal of American Journal of Business and Operations Research, (2023); 9 ( 2 ): 41-49 (Doi   :  https://doi.org/10.54216/AJBOR.090205)
IEEE Mona Mohamed, Nissreen El Saber, Intelligent Decision Support System for Optimizing Inventory Management, Journal of American Journal of Business and Operations Research, Vol. 9 , No. 2 , (2023) : 41-49 (Doi   :  https://doi.org/10.54216/AJBOR.090205)