A Strategic Inventory Management Model base on Deep Learning

Subject Areas :

Hamidreza Hajali ¹ , Mohammad Ali Afshar Kazemi ² , Adel Azar ³ , Abbas Toloie Eshlaghy ⁴ , Reza Radfar ⁵

1 - PhD Student, Department of Industrial Management, SR.C., Islamic Azad University, Tehran, Iran.
2 - Professor, Department of Industrial Management, CT.C., Islamic Azad University, Tehran, Iran (Corresponding Author)
3 - Professor, Department of Industrial Management, Tarbiat Modares University, Tehran, Iran.
4 - Professor, Department of Industrial Management, SR.C., Islamic Azad University, Tehran, Iran.
5 - 5..Professor, Department of Industrial Management, SR.C., Islamic Azad University, Tehran, Iran.

Received: 2024-11-06 Accepted : 2025-03-12 Published : 2025-03-12

Keywords: Inventory Management, Big Data, Artificial Intelligence, Deep Learning, Strategic Model.,

Abstract :

Objective: To present a strategic model for inventory management using deep learning (LSTM) to utilize non-linear and qualitative industrial big data that is unusable by classic methods.

Research Methodology: In this research, a Long Short-Term Memory (LSTM) deep learning model was designed. Data was prepared for training the network using the traditional methods of Economic Order Quantity (EOQ), Economic Production Quantity (EPQ), and ABC analysis.

Findings: After construction and training, the LSTM model achieved a prediction accuracy of 93%, demonstrating the success of integrating traditional approaches with AI for big data analysis in inventory management.

Originality/Scientific Value Added: The novelty of this research lies in combining classic inventory management methods with advanced deep learning techniques, providing a practical solution for the challenge of forecasting based on non-linear data in the supply chain.

References:



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