Forecasting with Visibility Using Privacy Preserving Federated Learning
Bo Zhang, Wen Jun Tan, Wentong Cai, Allan Neng Sheng Zhang · 2022
In the fluctuating and unstable supply chain environment, accurate demand forecasting is especially important. To improve the prediction accuracy, one possible way is to improve supply chain visibility by sharing information and knowledge among the supply chain entities. However, there is a potential risk that the raw data may be leaked to the competitors, affecting the business opportunities. To avoid information leakage, a secure demand forecasting with supply chain visibility is necessary. This paper proposes a Federated Learning based approach to predict demand for supplier with supply chain visibility while protecting the data privacy of other entities within the supply chain. To evaluate performance of forecasting accuracy, we designed a supply chain simulation model to generate data. From the experimental results, our proposed method outperforms the other demand forecasting methods without visibility and achieves a similar performance to the method with full visibility.