(PDF) IJERT-Inventory Management using Machine Learning
Inventory Management Using Machine Learning Project
[PDF] Inventory Management using Machine Learning
Inventory Optimization Using Machine Learning
(PDF) Predicting Material Backorders in Inventory Management using
(PDF) Decision Support Tool for Dynamic Inventory Management using
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Inventory management using Machine Learning - ResearchGate
This paper presents a case study for the assembling company on inventory management. It is proposed to use inventory managementin order to decrease stock levels and to apply an agent...
A Deep Learning-Based Inventory Management and Demand ...
Based on this consideration, this paper proposes a deep inventory management (DIM) method using the long short-term memory (LSTM) theory of deep learning (DL) . DIM intends to predict customers’ demands, according to which the intelligent decisions for inventory management can be made.
Benefits, challenges, and limitations of inventory control ...
This article presents a comprehensive review of the literature on the benefits, challenges, and limitations of using machinelearning (ML) algorithms in inventory control, focusing on how these algorithms can transform inventory management and improve operational efficiency in supply chains.
Optimizing inventory control through a data-driven and model ...
Inspired by the work done in the field of machinelearning, the proposed approach exploits transfer learning to relate demand patterns and key inventory policy hyperparameters with total inventory cost and, based on the relationships learned, optimize stock control.
Smart Inventory Optimization using Machine Learning ...
In this study, ML algorithms are tailored for intelligent inventory management. This research centers around the application of ML techniques within the framework of the ABC Inventory Classification methodology.
Applications of Artificial Intelligence in Inventory ...
The results revealed that machinelearning methods are generally used in demand forecasting and classification, while reinforcement learning algorithms are preferred in inventory problems. In terms of methods’ variety, the field of demand forecasting is quite rich, with 39 techniques.
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This paper presents a case study for the assembling company on inventory management. It is proposed to use inventory management in order to decrease stock levels and to apply an agent...
Based on this consideration, this paper proposes a deep inventory management (DIM) method using the long short-term memory (LSTM) theory of deep learning (DL) . DIM intends to predict customers’ demands, according to which the intelligent decisions for inventory management can be made.
This article presents a comprehensive review of the literature on the benefits, challenges, and limitations of using machine learning (ML) algorithms in inventory control, focusing on how these algorithms can transform inventory management and improve operational efficiency in supply chains.
Inspired by the work done in the field of machine learning, the proposed approach exploits transfer learning to relate demand patterns and key inventory policy hyperparameters with total inventory cost and, based on the relationships learned, optimize stock control.
In this study, ML algorithms are tailored for intelligent inventory management. This research centers around the application of ML techniques within the framework of the ABC Inventory Classification methodology.
The results revealed that machine learning methods are generally used in demand forecasting and classification, while reinforcement learning algorithms are preferred in inventory problems. In terms of methods’ variety, the field of demand forecasting is quite rich, with 39 techniques.