Product Volume Forecasting Model Based on Integrated Learning and EOQ

Hongwei Liu, Jing Li, Junyu Yang and Lingli Zhang ()
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Hongwei Liu: Chongqing University of Arts and Sciences, College of Mathematics and Artificial Intelligence
Jing Li: Chongqing University of Arts and Sciences, College of Mathematics and Artificial Intelligence
Junyu Yang: Chongqing University of Arts and Sciences, College of Mathematics and Artificial Intelligence
Lingli Zhang: Chongqing University of Arts and Sciences, College of Mathematics and Artificial Intelligence

A chapter in Proceedings of the 2024 2nd International Conference on Management Innovation and Economy Development (MIED 2024), 2024, pp 276-294 from Springer

Abstract: 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.

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 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.2991/978-94-6463-542-3_36

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