Application of machine learning techniques for supply chain demand forecasting
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References listed on IDEAS
- Rüping, Stefan & Morik, Katharina, 2003. "Support vector machines and learning about time," Technical Reports 2003,04, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
- Chandra, Charu & Grabis, Janis, 2005. "Application of multi-steps forecasting for restraining the bullwhip effect and improving inventory performance under autoregressive demand," European Journal of Operational Research, Elsevier, vol. 166(2), pages 337-350, October.
- Dejonckheere, J. & Disney, S. M. & Lambrecht, M. R. & Towill, D. R., 2003. "Measuring and avoiding the bullwhip effect: A control theoretic approach," European Journal of Operational Research, Elsevier, vol. 147(3), pages 567-590, June.
- Hau L. Lee & V. Padmanabhan & Seungjin Whang, 1997. "Information Distortion in a Supply Chain: The Bullwhip Effect," Management Science, INFORMS, vol. 43(4), pages 546-558, April.
- Gunasekaran, A., 2004. "Supply chain management: Theory and applications," European Journal of Operational Research, Elsevier, vol. 159(2), pages 265-268, December.
- Gunasekaran, A. & Ngai, E. W. T., 2004. "Information systems in supply chain integration and management," European Journal of Operational Research, Elsevier, vol. 159(2), pages 269-295, December.
- Yusuf, Y. Y. & Gunasekaran, A. & Adeleye, E. O. & Sivayoganathan, K., 2004. "Agile supply chain capabilities: Determinants of competitive objectives," European Journal of Operational Research, Elsevier, vol. 159(2), pages 379-392, December.
- Thonemann, U. W., 2002. "Improving supply-chain performance by sharing advance demand information," European Journal of Operational Research, Elsevier, vol. 142(1), pages 81-107, October.
CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- Gür Ali, Özden & Yaman, Kübra, 2013. "Selecting rows and columns for training support vector regression models with large retail datasets," European Journal of Operational Research, Elsevier, vol. 226(3), pages 471-480.
- Cang, Shuang & Yu, Hongnian, 2014. "A combination selection algorithm on forecasting," European Journal of Operational Research, Elsevier, vol. 234(1), pages 127-139.
- Trapero, Juan R. & Kourentzes, N. & Fildes, R., 2012. "Impact of information exchange on supplier forecasting performance," Omega, Elsevier, vol. 40(6), pages 738-747.
- repec:eee:appene:v:211:y:2018:i:c:p:492-512 is not listed on IDEAS
- Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(3), pages 341-357, August.
- David Dilts & James Moore, 2009. "Do Arbitrators Use Just Cause Standards in Deciding Discharge and Discipline Cases? A Test," Journal of Labor Research, Springer, vol. 30(3), pages 245-261, September.
- repec:wsi:apjorx:v:34:y:2017:i:01:n:s0217595917400012 is not listed on IDEAS
- Wu, Shaomin & Akbarov, Artur, 2011. "Support vector regression for warranty claim forecasting," European Journal of Operational Research, Elsevier, vol. 213(1), pages 196-204, August.
- Hong, Jungsik & Koo, Hoonyoung & Kim, Taegu, 2016. "Easy, reliable method for mid-term demand forecasting based on the Bass model: A hybrid approach of NLS and OLS," European Journal of Operational Research, Elsevier, vol. 248(2), pages 681-690.
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