Steel multi-variety demand prediction method based on intelligent supply chain

Abstract

The invention relates to a steel multi-variety demand prediction method based on an intelligent supply chain. The method comprises the following steps: 1), obtaining a demand data time sequence of steel multi-variety based on industrial product production data of an intelligent supply chain system; 2) constructing an SARIMA time sequence model based on the demand data time sequence of multiple varieties of steel; 3) inputting a to-be-tested training sample set to the SARIMA time sequence model, and obtaining a steel multi-variety demand prediction result; 4) constructing an SARIMAX time sequence model based on the demand data time sequence of multiple varieties of steel; 5) inputting a to-be-tested test sample set to the SARIMAX time sequence model, and obtaining a demand quantity prediction result of multiple varieties of steel after algorithm correction; and 6) obtaining a final prediction result according to a result output by the SARIMA model and a correction result output by the SARIMAX model. Compared with the prior art, the method has the advantages that the time sequence is more stable, the prediction accuracy is higher and the like.

Description

technical field [0001] The invention relates to the technical field of supply chain production management, in particular to a method for forecasting demand for multiple varieties of steel products based on an intelligent supply chain. Background technique [0002] With the development of the industrial Internet, the downstream supply chain of the iron and steel industry is also undergoing transformation and upgrading, and the production organization method of its main raw material steel also needs to be changed accordingly. Transformed into a flexible production and sales organization mode. It is necessary to use the information exchange with the downstream supply chain enterprises in the process of manufacturing industrial products, combined with effective technical means, to dig out the laws of industrial product production data, and predict the demand for multiple varieties of steel in the downstream supply chain of the steel industry in advance for a certain period of ti...

AI Technical Summary

Problems solved by technology

The current technical means and methods of demand forecasting are time-consuming, poorly systematic, low in integrity, low in accuracy, and slow in response and output

[0004] To sum up, the existing multi-variety steel demand forecasting methods cannot improve production efficiency and capacity resource utilization, and cannot meet the intelligent linkage from the production of industrial products in the smart supply chain to the multi-variety demand storage and production scheduling of steel production enterprises.

Images