Abstract
With the development of information technology and global economic integration, supply chain management has become a research hotspot in global management science. This article uses an electronic product as a supply chain management system. Based on the mathematical model and algorithm of supply chain design, the experiment designs an experiment based on traditional predictive models and optimized models to predict and analyze the sales of a certain electronic product. Experimental data shows that in order to adapt to the characteristics of supply chain demand forecasting in a big data environment, the proposed optimization model and method are reliable and practical. The experimental results show that the nine-month sales of the traditional prediction model are: 32, 38, 58, 57, 66, 73, 81, 90, 93, and the sales of the prediction model optimized by the cross genetic algorithm are: 41, 58, 72, 84, 95, 104, 103, 107, 92, to make up for the shortcomings of the model’s insufficient generalization ability under the high-dimensional situation, reduce the error of demand forecasting, and provide supply chain demand forecasting under the background of big data Effective forecasting methods have greatly alleviated the defects in supply chain management.
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Huang, S. (2021). Research on Basic Mathematical Models and Algorithms of Large-Scale Supply Chain Design Under the Background of Big Data. In: Xu, Z., Parizi, R.M., Loyola-González, O., Zhang, X. (eds) Cyber Security Intelligence and Analytics. CSIA 2021. Advances in Intelligent Systems and Computing, vol 1342. Springer, Cham. https://doi.org/10.1007/978-3-030-70042-3_42
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DOI: https://doi.org/10.1007/978-3-030-70042-3_42
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