Supply Chain Inventory Management from the Perspective of “Cloud Supply Chain”—A Data Driven Approach
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- Majd Kharfan & Vicky Wing Kei Chan & Tugba Firdolas Efendigil, 2021. "A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches," Annals of Operations Research, Springer, vol. 303(1), pages 159-174, August.
- Chien-Chih Wang & Hsin-Tzu Chang & Chun-Hua Chien, 2022. "Hybrid LSTM-ARMA Demand-Forecasting Model Based on Error Compensation for Integrated Circuit Tray Manufacturing," Mathematics, MDPI, vol. 10(13), pages 1-16, June.
- Afshin Oroojlooyjadid & Lawrence V. Snyder & Martin Takáč, 2020. "Applying deep learning to the newsvendor problem," IISE Transactions, Taylor & Francis Journals, vol. 52(4), pages 444-463, April.
- Carbonneau, Real & Laframboise, Kevin & Vahidov, Rustam, 2008. "Application of machine learning techniques for supply chain demand forecasting," European Journal of Operational Research, Elsevier, vol. 184(3), pages 1140-1154, February.
- Han Jiang & Yunlong Wu & Qing Zhang, 2020. "Optimization of Ordering and Allocation Scheme for Distributed Material Warehouse Based on IGA-SA Algorithm," Mathematics, MDPI, vol. 8(10), pages 1-17, October.
- Gary D. Eppen & R. Kipp Martin, 1988. "Determining Safety Stock in the Presence of Stochastic Lead Time and Demand," Management Science, INFORMS, vol. 34(11), pages 1380-1390, November.
- Robert C. Carlson & Candace A. Yano, 1986. "Safety Stocks in MRP---Systems with Emergency Setups for Components," Management Science, INFORMS, vol. 32(4), pages 403-412, April.
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Keywords
cloud supply chain; machine learning; inventory optimization;All these keywords.
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