OPTIMIZATION OF FUZZY INVENTORY MANAGEMENT IN INDUSTRIAL PROCESSES USING DEEP LEARNING ALGORITHMS: A HYBRID APPROACH FOR ENHANCING DEMAND FORECASTING AND SUPPLY CHAIN EFFICIENCY
In today’s dynamic business landscape, effective inventory management is essential for minimizing costs and maximizing profitability. Traditional models like EOQ and JIT often fall short in handling demand and supply uncertainties due to their reliance on precise data. This paper introduces a no...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
The Association of Intellectuals for the Development of Science in Serbia – “The Serbian Academic Center”
2024-12-01
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| Series: | Advanced Engineering Letters |
| Subjects: | |
| Online Access: | https://www.adeletters.com/journals/2024/ADEL0056.pdf |
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| Summary: | In today’s dynamic business landscape, effective inventory management is
essential for minimizing costs and maximizing profitability. Traditional
models like EOQ and JIT often fall short in handling demand and supply
uncertainties due to their reliance on precise data. This paper introduces a
novel approach that combines fuzzy logic and deep learning to address
these limitations. Fuzzy logic offers a robust framework for decisionmaking under uncertainty, while deep learning improves predictive
accuracy by identifying complex patterns in historical data. By
transforming data into fuzzy sets and applying neural networks for
demand forecasting, the proposed model optimizes inventory levels to
reduce costs and prevent stockouts. A mathematical model and
algorithmic implementation demonstrate the approach’s effectiveness and
a numerical example highlights improvements in inventory control,
including reduced holding costs. This study underscores the potential of
integrating AI techniques for adaptive, data-driven inventory management
with broad applications across various industrial processes. |
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| ISSN: | 2812-9709 |