Research on E-Commerce Product Inventory Monitoring and Sales Forecasting Based on the Spark Platform: A Case Study of Furniture Products

Chuqi Qiu

doi:10.47297/taposatWSP2633-456911.20250608

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Research on E-Commerce Product Inventory Monitoring and Sales Forecasting Based on the Spark Platform: A Case Study of Furniture Products

6卷第8期

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Research on E-Commerce Product Inventory Monitoring and Sales Forecasting Based on the Spark Platform: A Case Study of Furniture Products
Research on E-Commerce Product Inventory Monitoring and Sales Forecasting Based on the Spark Platform: A Case Study of Furniture Products
School of Management, Hangzhou Dianzi University, Hangzhou, Zhejiang, 31001, China
2025年6卷第8期页码：58-65
纸质出版：2025-08-30
DOI：10.47297/taposatWSP2633-456911.20250608
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(2025). Research on E-Commerce Product Inventory Monitoring and Sales Forecasting Based on the Spark Platform: A Case Study of Furniture Products. 科技理论与实践（英文版）, 6(8), 58-65.

Qiu Chuqi.Research on E-Commerce Product Inventory Monitoring and Sales Forecasting Based on the Spark Platform: A Case Study of Furniture Products[J].Theory and Practice of Science and Technology,2025,06(08):58-65.
(2025). Research on E-Commerce Product Inventory Monitoring and Sales Forecasting Based on the Spark Platform: A Case Study of Furniture Products. 科技理论与实践（英文版）, 6(8), 58-65. DOI： 10.47297/taposatWSP2633-456911.20250608.

Qiu Chuqi.Research on E-Commerce Product Inventory Monitoring and Sales Forecasting Based on the Spark Platform: A Case Study of Furniture Products[J].Theory and Practice of Science and Technology,2025,06(08):58-65. DOI： 10.47297/taposatWSP2633-456911.20250608.

摘要

Abstract

This study presents a forecasting and inventory management framework for e-commerce

integrating Long Short-Term Memory (LSTM) networks and Extreme Gradient Boosting (XGBoost) within a distributed computing environment using Apache Spark. The approach is tested on the UK Online Retail dataset for a specific furniture SKU. Key contributions include integrating deep learning and ensemble learning

implementing a scalable Spark-based pipeline

and linking forecasts directly to inventory replenishment strategies. Experimental results show complementary strengths: LSTM excels at capturing long-term trends

while XGBoost responds quickly to short-term fluctuations. The framework demonstrates practical potential for forecast-driven inventory optimization.

关键词

Keywords

references

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