Sales Forecasting in Retail Supply Chain Management

Junkai Zhao

2024

Abstract

In the actual production environment, the forecast of demand or sales volume is extremely important, accurate prediction can not only effectively reduce inventory costs, but also greatly reduce production and manufacturing costs, reduce unnecessary waste, not only that, in management, people find that the oxtail effect will have a great impact on the stability of the supply chain, and the prediction of sales volume can effectively reduce the negative impact of the effect, this study will take Wal-Mart's real sales volume dataset as an example, Comparing the performance of Polynomial Regression and Random Forest (RF) in the face of sales volume datasets, including the accuracy of prediction and generalization ability, and finding the factors that have the greatest impact on sales volume from many objective factors affecting sales volume in the construction process of the model, these experimental results will have important practical significance for inventory management and resource allocation.

Paper Citation

in Harvard Style

Zhao J. (2024). Sales Forecasting in Retail Supply Chain Management. In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI; ISBN 978-989-758-726-9, SciTePress, pages 542-546. DOI: 10.5220/0013270200004568

in Bibtex Style

@conference{ecai24,
author={Junkai Zhao},
title={Sales Forecasting in Retail Supply Chain Management},
booktitle={Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI},
year={2024},
pages={542-546},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013270200004568},
isbn={978-989-758-726-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI
TI - Sales Forecasting in Retail Supply Chain Management
SN - 978-989-758-726-9
AU - Zhao J.
PY - 2024
SP - 542
EP - 546
DO - 10.5220/0013270200004568
PB - SciTePress