KEYWORDS: Raw materials, Optimization (mathematics), Data modeling, Data analysis, Stochastic processes, Factor analysis, Evolutionary algorithms, Data mining, Algorithm development, Algorithms
With the advent of the era of massive data, algorithmic optimization and data mining have been applied in many industries in an unprecedented and innovative way. In the field of enterprise supply chain management, big data and algorithmic optimization technologies have been well applied. The establishment of the innovative model of supply chain management efficiency optimization can improve the management efficiency in the supply process and can greatly promote market development. The use of big data technology can make supply chain management more informative and intelligent. This study proposes a discrete modeling analysis of enterprise supply chain management optimization based on big data analysis. The main use of big data technology is to analyze the obtained data information and quantify the abstract features of the supply process. The discrete dynamic modeling technique is also used to develop a three-level intelligent planning algorithm, focusing on the quality issues in the upstream supply process and the cost issues in ordering and forwarding for management optimization. In addition, this study also have innovatively developed intelligently selected time series for forecasting the supply potential of companies.
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