Research on material demand forecasting based on comparative analysis of two forecasting models: take D company as an example

Feiyan Zhong

doi:10.1117/12.2685851

28 July 2023 Research on material demand forecasting based on comparative analysis of two forecasting models: take D company as an example

Feiyan Zhong

Author Affiliations +

Proceedings Volume 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023); 1275636 (2023) https://doi.org/10.1117/12.2685851
Event: 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), 2023, Tangshan, China

Abstract

The IT-based and state-of-the-art inventory management tools help in collecting the historical data of materials accurately and easily, laying a solid foundation for material demand forecasting. An accurate and science-based material demand forecasting however depends on the effective use of material demand forecasting models. To improve the accuracy of material demand forecasting in manufacturers and reduce the procurement cost and even the overall operation cost of the supply chain, this paper, with the Company D as a case study, adopts information technology to collect the historical data of the company's important material, and employs the cubic exponential smoothing method as well as the Winters linear and seasonal exponential smoothing forecasting method to forecast the material separately, Finally, SPSS statistical method is used to compare and analyze the prediction results of the two methods.. As a result of the study, it is found that the cubic exponential smoothing method is the optimal method for forecasting the demand of material color powder, which serves as a reference for the material demand forecasting of other materials of the company and even other enterprises of the same type.

Citation Download Citation

Feiyan Zhong "Research on material demand forecasting based on comparative analysis of two forecasting models: take D company as an example", Proc. SPIE 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023), 1275636 (28 July 2023); https://doi.org/10.1117/12.2685851

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