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AUTHOR(S): Vishal Kumar
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TITLE Harnessing Machine Learning for Anticipating Product Demand Trends |
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ABSTRACT Harnessing machine learning for anticipating product demand trends is a critical aspect of modern supply chain management. This investigation delves into the realm of hybrid demand forecasting methodologies, notably the integration of ARIMAX and Neural Network models grounded in machine learning principles. Through the strategic application of these sophisticated techniques, businesses gain the capacity to augment accurate demand prediction, optimize inventory management, and elevate the overall efficiency of their supply chain operations. The research also addresses the intricacies of data preprocessing, underscoring the significance of mitigating challenges like noisy data and missing values. An exhaustive comparative study is carried out, analyzing the effectiveness of various machine learning models such as Linear Regression, Random Forest, ARIMA, and LSTM Networks. This scrutiny yields valuable insights into the distinctive capabilities of each model. The implications for businesses are profound, encompassing enhanced inventory management practices, streamlined production planning processes, and an overall optimization of the supply chain. |
KEYWORDS Traditional forecasting techniques, Supply chain analytics, Supply chain management, Machine learning, Demand and sales forecasting |
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Cite this paper Vishal Kumar. (2024) Harnessing Machine Learning for Anticipating Product Demand Trends. International Journal of Education and Learning Systems, 9, 13-19 |
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