Abstract

For business operations, determining market demands is necessary for enterprises in establishing appropriate purchase, production and sales plans. However, many enterprises lack this ability, causing them to make risky purchasing decisions. This study combines a back propagation neural network and the Particle Swarm Optimization Algorithm (PSOBPN) to construct a demand prediction model. Using a grey relational analysis, we selected factors that have a high correlation to market demands. These factors were employed to train the prediction model and were used as input factors to predict market demands. The results obtained from the prediction model were compared with those of the experiential estimation model used by health food companies. The comparison showed that the accuracy of PSOBPN predictions was superior to that of the experiential estimation method. Therefore, the prediction model proposed in this study provides reliable and highly efficient analysis data for decision-makers in enterprises.

Keywords:

Artificial neural network, demand prediction, particle swarm optimization algorithm,

References

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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.

Copyright

The authors have no competing interests.