Making appropriate decisions is indeed a key factor to help companies facing challenges from supply chains nowadays. In this paper, we propose two data-driven approaches that allow making better decisions in supply chain management. In particular, we suggest a Long Short Term Memory (LSTM) network-based method for forecasting multivariate time series data and an LSTM Autoencoder network-based method combined with a one-class support vector machine algorithm for detecting anomalies in sales. Unlike other approaches, we recommend combining external and internal company data sources for the purpose of enhancing the performance of forecasting algorithms using multivariate LSTM with the optimal hyperparameters. In addition, we also propose a method to optimize hyperparameters for hybrid algorithms for detecting anomalies in time series data. The proposed approaches will be applied to both benchmarking datasets and real data in fashion retail. The obtained results show that the LSTM Autoencoder based method leads to better performance for anomaly detection compared to the LSTM based method suggested in a previous study. The proposed forecasting method for multivariate time series data also performs better than some other methods based on a dataset provided by NASA.

做出正确的决定确实是帮助公司面对当今供应链挑战的关键因素。在本文中，我们提出了两种数据驱动的方法，可以在供应链管理中做出更好的决策。特别是，我们建议使用基于LSTM网络的长期方法来预测多元时间序列数据，并建议将基于LSTM Autoencoder网络的方法与一类支持向量机算法相结合来检测销售中的异常情况。与其他方法不同，我们建议结合使用外部和内部公司数据源，以增强使用具有最佳超参数的多变量LSTM的预测算法的性能。此外，我们还提出了一种优化超参数的方法，用于混合算法来检测时间序列数据中的异常。拟议的方法将应用于时尚零售中的基准数据集和真实数据。获得的结果表明，与先前研究中建议的基于LSTM的方法相比，基于LSTM Autoencoder的方法可导致更好的异常检测性能。基于NASA提供的数据集，针对多元时间序列数据提出的预测方法的性能也优于其他方法。