IDEAS home Printed from https://ideas.repec.org/r/pal/jorsoc/v59y2008i4d10.1057_palgrave.jors.2602312.html
   My bibliography  Save this item

Classification for forecasting and stock control: a case study

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Ali Caner Türkmen & Tim Januschowski & Yuyang Wang & Ali Taylan Cemgil, 2021. "Forecasting intermittent and sparse time series: A unified probabilistic framework via deep renewal processes," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-26, November.
  2. Moon, Seongmin & Simpson, Andrew & Hicks, Christian, 2013. "The development of a classification model for predicting the performance of forecasting methods for naval spare parts demand," International Journal of Production Economics, Elsevier, vol. 143(2), pages 449-454.
  3. Petropoulos, Fotios & Wang, Xun & Disney, Stephen M., 2019. "The inventory performance of forecasting methods: Evidence from the M3 competition data," International Journal of Forecasting, Elsevier, vol. 35(1), pages 251-265.
  4. Evangelos Spiliotis & Spyros Makridakis & Artemios-Anargyros Semenoglou & Vassilios Assimakopoulos, 2022. "Comparison of statistical and machine learning methods for daily SKU demand forecasting," Operational Research, Springer, vol. 22(3), pages 3037-3061, July.
  5. Syntetos, A.A. & Teunter, R.H. & Babai, M.Z. & Transchel, S., 2016. "On the benefits of delayed ordering," European Journal of Operational Research, Elsevier, vol. 248(3), pages 963-970.
  6. Ulrich, Matthias & Jahnke, Hermann & Langrock, Roland & Pesch, Robert & Senge, Robin, 2022. "Classification-based model selection in retail demand forecasting," International Journal of Forecasting, Elsevier, vol. 38(1), pages 209-223.
  7. Pierre Dodin & Jingyi Xiao & Yossiri Adulyasak & Neda Etebari Alamdari & Lea Gauthier & Philippe Grangier & Paul Lemaitre & William L. Hamilton, 2023. "Bombardier Aftermarket Demand Forecast with Machine Learning," Interfaces, INFORMS, vol. 53(6), pages 425-445, November.
  8. Hu, Qiwei & Chakhar, Salem & Siraj, Sajid & Labib, Ashraf, 2017. "Spare parts classification in industrial manufacturing using the dominance-based rough set approach," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1136-1163.
  9. Pennings, Clint L.P. & van Dalen, Jan & van der Laan, Erwin A., 2017. "Exploiting elapsed time for managing intermittent demand for spare parts," European Journal of Operational Research, Elsevier, vol. 258(3), pages 958-969.
  10. Aysun Kapucugil Ikiz & Gizem Halil Utma, 2023. "Combined Forecasts of Intermittent Demand for Stock-keeping Units (SKUs)," World Journal of Applied Economics, WERI-World Economic Research Institute, vol. 9(1), pages 1-31, June.
  11. Heinecke, G. & Syntetos, A.A. & Wang, W., 2013. "Forecasting-based SKU classification," International Journal of Production Economics, Elsevier, vol. 143(2), pages 455-462.
  12. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
  13. Kolassa, Stephan, 2016. "Evaluating predictive count data distributions in retail sales forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 788-803.
  14. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
  15. Lindsey, Matthew & Pavur, Robert, 2009. "Prediction intervals for future demand of existing products with an observed demand of zero," International Journal of Production Economics, Elsevier, vol. 119(1), pages 75-89, May.
  16. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
    • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
  17. Hu, Qiwei & Boylan, John E. & Chen, Huijing & Labib, Ashraf, 2018. "OR in spare parts management: A review," European Journal of Operational Research, Elsevier, vol. 266(2), pages 395-414.
  18. Van der Auweraer, Sarah & Boute, Robert N. & Syntetos, Aris A., 2019. "Forecasting spare part demand with installed base information: A review," International Journal of Forecasting, Elsevier, vol. 35(1), pages 181-196.
  19. Turrini, Laura & Meissner, Joern, 2019. "Spare parts inventory management: New evidence from distribution fitting," European Journal of Operational Research, Elsevier, vol. 273(1), pages 118-130.
  20. Bahman Rostami‐Tabar & M. Zied Babai & Aris Syntetos & Yves Ducq, 2013. "Demand forecasting by temporal aggregation," Naval Research Logistics (NRL), John Wiley & Sons, vol. 60(6), pages 479-498, September.
  21. Ernesto Armando Pacheco-Velázquez & Manuel Robles-Cárdenas & Saúl Juárez Ordóñez & Abelardo Ernesto Damy Solís & Leopoldo Eduardo Cárdenas-Barrón, 2023. "A Heuristic Model for Spare Parts Stocking Based on Markov Chains," Mathematics, MDPI, vol. 11(16), pages 1-21, August.
  22. Rego, José Roberto do & Mesquita, Marco Aurélio de, 2015. "Demand forecasting and inventory control: A simulation study on automotive spare parts," International Journal of Production Economics, Elsevier, vol. 161(C), pages 1-16.
  23. Costantino, Francesco & Di Gravio, Giulio & Patriarca, Riccardo & Petrella, Lea, 2018. "Spare parts management for irregular demand items," Omega, Elsevier, vol. 81(C), pages 57-66.
  24. Hekimoğlu, Mustafa & Karlı, Deniz, 2023. "Modeling repair demand in existence of a nonstationary installed base," International Journal of Production Economics, Elsevier, vol. 263(C).
  25. Önkal, Dilek & Zeynep Sayım, K. & Lawrence, Michael, 2012. "Wisdom of group forecasts: Does role-playing play a role?," Omega, Elsevier, vol. 40(6), pages 693-702.
  26. Bacchetti, A. & Plebani, F. & Saccani, N. & Syntetos, A.A., 2013. "Empirically-driven hierarchical classification of stock keeping units," International Journal of Production Economics, Elsevier, vol. 143(2), pages 263-274.
  27. Moon, Seongmin & Hicks, Christian & Simpson, Andrew, 2012. "The development of a hierarchical forecasting method for predicting spare parts demand in the South Korean Navy—A case study," International Journal of Production Economics, Elsevier, vol. 140(2), pages 794-802.
  28. Bacchetti, Andrea & Saccani, Nicola, 2012. "Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice," Omega, Elsevier, vol. 40(6), pages 722-737.
  29. A A Syntetos & M Z Babai & Y Dallery & R Teunter, 2009. "Periodic control of intermittent demand items: theory and empirical analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(5), pages 611-618, May.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.