Product order data analysis and demand forecasting based on machine learning

Yue Lu

doi:10.1117/12.3013253

29 November 2023 Product order data analysis and demand forecasting based on machine learning

Yue Lu

Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 129370J (2023) https://doi.org/10.1117/12.3013253
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore

Abstract

In recent years, the external environment of enterprises has been characterized by significant uncertainty; and the increasingly complex situation has also posed numerous challenges to their supply chains. Demand forecasting, as a critical component of the enterprise supply chain, directly affects market performance. However, demand forecasting is influenced by multiple factors, leading to generally low prediction accuracy, with substantial deviations from actual outcomes. To enhance forecasting precision, this study utilizes shipment data directed towards distributors from a large domestic manufacturing enterprise during the period from September 1, 2015, to December 20, 2018. The data undergoes preliminary processing, followed by exploratory analysis and feature engineering. Conventional machine learning models are applied for prediction. By comparing the Random Forest and LGBM models, the LGBM model is selected as the best-performing one for data prediction. Forecasts are conducted at daily, weekly, and monthly granularities for the product demand in January, February, and March 2019.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yue Lu "Product order data analysis and demand forecasting based on machine learning", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 129370J (29 November 2023); https://doi.org/10.1117/12.3013253

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