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Data-Driven Supply Chain Performance Optimization Through Predictive Analytics and Machine Learning
- 1 University of Technology Sydney, Sydney, Australia
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Abstract
The paper discusses the use of predictive analytics and machine learning techniques like the LSTM neural network in order to enhance supply chain effectiveness. Economic Order Quantity (EOQ), JIT, and linear programming are well known for their supply chain optimization approaches, but they rarely take the challenge of an evolving supply chain into account. Such methods ignore real-time information and the non-linear dynamics of supply chain variables. Instead, the LSTM model takes historical and real-time data and calculates demand with high precision, making it easier to manage your inventory, avoid stockouts, and improve customer experience. As shown in the paper, LSTM provides an accuracy rate of 91% for forecasting demand, which is superior to the use of other conventional statistical methods like Moving Averages and Exponential Smoothing. The impact of this advanced machine learning technology for optimizing supply chain performance is huge, it reduces the operating cost, optimizes the use of resources, and delivers the best service. This work demonstrates how predictive analytics and machine learning can enable supply chain management to be flexible, effective and adaptable to market shifts.
Keywords
Data-driven, Supply Chain Optimization, Predictive Analytics, LSTM, Demand Forecasting
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Cite this article
Wang,Z. (2024). Data-Driven Supply Chain Performance Optimization Through Predictive Analytics and Machine Learning. Applied and Computational Engineering,118,30-35.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Software Engineering and Machine Learning
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