References

• 2019Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach387 cited
• 2021Costs of resilience and disruptions in supply chain network design models: A review and future research directions380 cited
• 2020A machine learning based approach for predicting blockchain adoption in supply Chain309 cited
• 2020Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion202 cited
• 2019Sales Demand Forecast in E-commerce Using a Long Short-Term Memory Neural Network Methodology175 cited
• 2022A hybrid deep learning framework with CNN and Bi-directional LSTM for store item demand forecasting105 cited
• 2021Explainable boosted linear regression for time series forecasting84 cited
• 2021On the Application of ARIMA and LSTM to Predict Order Demand Based on Short Lead Time and On-Time Delivery Requirements74 cited
• 2021A Deep Learning‐Based Inventory Management and Demand Prediction Optimization Method for Anomaly Detection69 cited
• 2021Evaluation of deep learning with long short-term memory networks for time series forecasting in supply chain management64 cited
• 2022Cluster-based demand forecasting using Bayesian model averaging: An ensemble learning approach49 cited
• 2020Walmart Sales Forecasting using XGBoost algorithm and Feature engineering48 cited
• 2022Impact of Recycler Information Sharing on Supply Chain Performance of Construction and Demolition Waste Resource Utilization