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Academic Journal of Computing & Information Science, 2023, 6(8); doi: 10.25236/AJCIS.2023.060816.

Research on material demand analysis of manufacturing industry based on time series model—ARIMA


Changsheng Li, Sujun Liu, Guoying Yang

Corresponding Author:
Changsheng Li

College of Information Engineering, Lanzhou Petrochemical University of Vocational Technology, Lanzhou, 730060, China


In order to solve the problem of mismatch between material production plan and actual demand, this paper analyzes and forecasts the material demand of manufacturing industry. Firstly, the material demand frequency at different time points in the historical production data of a manufacturing industry is calculated by statistical method. Secondly, it quantitatively analyzes the change trend of the total sales volume of each material at different time points to the unit price of the material. Thirdly, with the quantity, frequency, total sales and unit price of materials as characteristic factors, through relevant statistical analysis, six kinds of materials are synthesized. Finally, according to the demand data of these six materials, the time characteristics are transformed into weekly characteristics, and the time series model ARIMA is applied to construct the weekly prediction model of material demand, and the performance of the prediction model is evaluated. The results show that the comparison between the forecast results and the actual values of the weekly forecast model passes the test and has a good application prospect.


Time series model, Prediction Model, Feature selection, Correlation

Cite This Paper

Changsheng Li, Sujun Liu, Guoying Yang. Research on material demand analysis of manufacturing industry based on time series model—ARIMA. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 8: 131-137. https://doi.org/10.25236/AJCIS.2023.060816.


[1] Hu Xinbing. An introduction to cost control in manufacturing enterprises [J]. China Township Enterprise Accounting, 2020(02):121-122. 

[2] Chang Fang, Xinbao Liu, Jun Pei, et al. Optimal production planning in a hybrid manufacturing and recovering system based on the internet of things with closed loop supply chains[J].Operational Research, 2016.DOI:10.1007/s12351-015-0213-x.

[3] Li Changsheng, Yang Guoying, Liu Zongcheng. Research on course data analysis based on time series model and clustering algorithm [J]. Information and Computer (Theory Edition), 2021, 33(14): 79-82. 

[4] Fan Yingbing, Lu Bin. Research on China's gold reserve forecast based on time series model [J]. Software Guide, 2021, 20(04):96-100. 

[5] Shan R, Wang SHH, Gao DFL, Gao JH. Research on combined prediction model based on time series model and gray model [J]. Journal of Yanshan University, 2012, 36(01):79-83. 

[6] Lin Z. Analysis Research on Joint Development of Regional Manufacturing and the Logistics Industry——Based on Coupling System of Supply and Demand[J]. Technoeconomics & Management Research, 2016. 

[7] Zhang Wanlin. Forecasting analysis of Ningbo port logistics demand based on GM(1, 1)-ARIMA combined model[J]. Logistics Engineering and Management, 2023, 45(01):32-35. 

[8] Guo Shunsheng, Wang Lei, Huang Kun. Research on predicting automobile sales based on time series model [J]. Mechanical Engineer, 2013(05):8-10. 

[9] Lan H, Liao C-M, Zhao Y. Power output prediction of photovoltaic power plants based on ARMA model [J]. Electrical Measurement and Instrumentation, 2011, 48(02):31-35. 

[10] Bolotova Y V, Novakovic A M. An Empirical Analysis of Pricing in the United States Cheese Industry: 2000-2010[J].Ssrn Electronic Journal, 2011.DOI:10.2139/ssrn.1865314.

[11] Yao JH, Zou JJ. Construction and numerical simulation of SVM-ARIMA model for CPI forecasting [J]. Statistics and Decision Making, 2022, 38(21):48-52.