Demand prediction is a crucial activity in managing supply chain. It is going to trigger all operational activities in supply chain. In fashion industry, demand prediction becomes challenging. Previous researchers have identified many factors are affecting demand of fashion product. Short life cycle, trend, season, high product variance are among factors that creating complexity in fashion product demand prediction. This research develops demand prediction model based on artificial neural network for fashion product considering product variance. The model applied feed-forward backpropagation technique as training algorithm with Levenberg-Marquadt training function. Application of the model in real industrial data is showing promising result.

@inproceedings{oscm-2019-491,
  title={FASHION PRODUCT DEMAND PREDICTION MODEL BASED ON ARTIFICIAL NEURAL NETWORK CONSIDERING PRODUCT VARIANCE},
  year={2019},
  author={Andi Cakravastia and Karina Apriana and Adrian Gilrandy},
  booktitle={Proceedings of the 9th International Conference on Operations and Supply Chain Management, Vietnam, 2019},
  doi={10.31387/oscm410221}
}

Andi Cakravastia, Karina Apriana, Adrian Gilrandy (2019). FASHION PRODUCT DEMAND PREDICTION MODEL BASED ON ARTIFICIAL NEURAL NETWORK CONSIDERING PRODUCT VARIANCE. In Proceedings of the 9th International Conference on Operations and Supply Chain Management, Vietnam, 2019. https://doi.org/10.31387/oscm410221