Product Volume Forecasting Model Based on Integrated Learning and EOQ

DOI

10.2991/978-94-6463-542-3_36How to use a DOI?

Keywords

ARIMA-LSTM-XGBoost combination model; Joint order point method and periodic order method; “modern” EOQ model; Dynamic Adjustment inventory strategy (s,S); Genetic ant colony algorithm

Abstract

With the rapid development of China’s e-commerce industry, how to effectively manage commodity inventory costs has become one of the core issues to be solved. This paper presents a comprehensive solution to this challenge. First, through the analysis and processing of sales data, the combined model of ARMI-LSTM-XGBOOST is used to predict the sales volume of 1996 commodities during the period from May 16 to June 2, 2023, providing basic data for inventory management. Secondly, the optimal stock strategy (s, S) for each day of the period from May 16 to May 30, 2023 is determined by combining the joint order point method and the regular order method. Finally, the model of commodity replenishment cost is established and solved by genetic ant colony algorithm, and the optimal replenishment plan to meet the inventory demand at the lowest cost is obtained. Taking a product (PRODUCT_994-East China) as an example, the lowest cost of replenishment was determined to be 964 yuan. The results of this study provide important guidance for e-commerce enterprises, which can help them manage inventory more effectively, reduce costs, and improve competitiveness.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Hongwei Liu
AU  - Jing Li
AU  - Junyu Yang
AU  - Lingli Zhang
PY  - 2024
DA  - 2024/10/15
TI  - Product Volume Forecasting Model Based on Integrated Learning and EOQ
BT  - Proceedings of the 2024 2nd International Conference on Management Innovation and Economy Development (MIED 2024)
PB  - Atlantis Press
SP  - 276
EP  - 294
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-542-3_36
DO  - 10.2991/978-94-6463-542-3_36
ID  - Liu2024
ER  -