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The accuracy of intermittent demand estimates

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Cited by:

  1. Kourentzes, Nikolaos & Trapero, Juan R. & Barrow, Devon K., 2020. "Optimising forecasting models for inventory planning," International Journal of Production Economics, Elsevier, vol. 225(C).
  2. Nenes, George & Panagiotidou, Sofia & Tagaras, George, 2010. "Inventory management of multiple items with irregular demand: A case study," European Journal of Operational Research, Elsevier, vol. 205(2), pages 313-324, September.
  3. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "Predicting/hypothesizing the findings of the M5 competition," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1337-1345.
  4. A A Syntetos & J E Boylan & S M Disney, 2009. "Forecasting for inventory planning: a 50-year review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 149-160, May.
  5. Kamal Sanguri & Kampan Mukherjee, 2021. "Forecasting of intermittent demands under the risk of inventory obsolescence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1054-1069, September.
  6. Teunter, Ruud H. & Syntetos, Aris A. & Zied Babai, M., 2011. "Intermittent demand: Linking forecasting to inventory obsolescence," European Journal of Operational Research, Elsevier, vol. 214(3), pages 606-615, November.
  7. Nikolaos Kourentzes & Dong Li & Arne K. Strauss, 2019. "Unconstraining methods for revenue management systems under small demand," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(1), pages 27-41, February.
  8. Prestwich, S.D. & Tarim, S.A. & Rossi, R. & Hnich, B., 2014. "Forecasting intermittent demand by hyperbolic-exponential smoothing," International Journal of Forecasting, Elsevier, vol. 30(4), pages 928-933.
  9. Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "Forecasting the intermittent demand for slow-moving inventories: A modelling approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 485-496.
  10. Euna Lee & Myungwoo Nam & Hongchul Lee, 2022. "Tab2vox: CNN-Based Multivariate Multilevel Demand Forecasting Framework by Tabular-To-Voxel Image Conversion," Sustainability, MDPI, vol. 14(18), pages 1-20, September.
  11. A A Syntetos & J E Boylan & J D Croston, 2006. "Reply to Kostenko and Hyndman," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(10), pages 1257-1258, October.
  12. 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.
  13. Mariusz Doszyn, 2020. "Accuracy of Intermittent Demand Forecasting Systems in the Enterprise," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 912-930.
  14. Wallström, Peter & Segerstedt, Anders, 2010. "Evaluation of forecasting error measurements and techniques for intermittent demand," International Journal of Production Economics, Elsevier, vol. 128(2), pages 625-636, December.
  15. Hess, Alexander & Spinler, Stefan & Winkenbach, Matthias, 2021. "Real-time demand forecasting for an urban delivery platform," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
  16. Romeijnders, Ward & Teunter, Ruud & van Jaarsveld, Willem, 2012. "A two-step method for forecasting spare parts demand using information on component repairs," European Journal of Operational Research, Elsevier, vol. 220(2), pages 386-393.
  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. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
  19. Tian, Xin & Wang, Haoqing & E, Erjiang, 2021. "Forecasting intermittent demand for inventory management by retailers: A new approach," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
  20. K Nikolopoulos & A A Syntetos & J E Boylan & F Petropoulos & V Assimakopoulos, 2011. "An aggregate–disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 544-554, March.
  21. Murphy Choy & Michelle L. F. Cheong, 2011. "Identification of Demand through Statistical Distribution Modeling for Improved Demand Forecasting," Papers 1110.0062, arXiv.org.
  22. Fotios Petropoulos & Evangelos Spiliotis, 2021. "The Wisdom of the Data: Getting the Most Out of Univariate Time Series Forecasting," Forecasting, MDPI, vol. 3(3), pages 1-20, June.
  23. Kourentzes, Nikolaos, 2013. "Intermittent demand forecasts with neural networks," International Journal of Production Economics, Elsevier, vol. 143(1), pages 198-206.
  24. Kourentzes, Nikolaos & Athanasopoulos, George, 2021. "Elucidate structure in intermittent demand series," European Journal of Operational Research, Elsevier, vol. 288(1), pages 141-152.
  25. 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.
  26. Prak, Derk & Teunter, Rudolf & Babai, M. Z. & Syntetos, A. A. & Boylan, D, 2018. "Forecasting and Inventory Control with Compound Poisson Demand Using Periodic Demand Data," Research Report 2018010, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
  27. Hasni, M. & Aguir, M.S. & Babai, M.Z. & Jemai, Z., 2019. "On the performance of adjusted bootstrapping methods for intermittent demand forecasting," International Journal of Production Economics, Elsevier, vol. 216(C), pages 145-153.
  28. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
  29. Van der Auweraer, Sarah & Boute, Robert, 2019. "Forecasting spare part demand using service maintenance information," International Journal of Production Economics, Elsevier, vol. 213(C), pages 138-149.
  30. Hekimoğlu, Mustafa & Karlı, Deniz, 2023. "Modeling repair demand in existence of a nonstationary installed base," International Journal of Production Economics, Elsevier, vol. 263(C).
  31. Aiping Jiang & Qiuguo Chi & Junjun Gao & Maoguo Wu, 2019. "An Integrated Approach to Forecasting Intermittent Demand for Electric Power Materials," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1309-1335, April.
  32. Zhang, Bohan & Kang, Yanfei & Panagiotelis, Anastasios & Li, Feng, 2023. "Optimal reconciliation with immutable forecasts," European Journal of Operational Research, Elsevier, vol. 308(2), pages 650-660.
  33. Philippe St-Aubin & Bruno Agard, 2022. "Precision and Reliability of Forecasts Performance Metrics," Forecasting, MDPI, vol. 4(4), pages 1-22, October.
  34. Mohammadipour, Maryam & Boylan, John E., 2012. "Forecast horizon aggregation in integer autoregressive moving average (INARMA) models," Omega, Elsevier, vol. 40(6), pages 703-712.
  35. 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.
  36. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
  37. 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.
  38. Zhu, Sha & Jaarsveld, Willem van & Dekker, Rommert, 2020. "Spare parts inventory control based on maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
  39. van Wingerden, E. & Basten, R.J.I. & Dekker, R. & Rustenburg, W.D., 2014. "More grip on inventory control through improved forecasting: A comparative study at three companies," International Journal of Production Economics, Elsevier, vol. 157(C), pages 220-237.
  40. Van der Auweraer, Sarah & Zhu, Sha & Boute, Robert N., 2021. "The value of installed base information for spare part inventory control," International Journal of Production Economics, Elsevier, vol. 239(C).
  41. 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.
  42. Gutierrez, Rafael S. & Solis, Adriano O. & Mukhopadhyay, Somnath, 2008. "Lumpy demand forecasting using neural networks," International Journal of Production Economics, Elsevier, vol. 111(2), pages 409-420, February.
  43. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
  44. 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.
  45. 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.
  46. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
  47. Anderer, Matthias & Li, Feng, 2022. "Hierarchical forecasting with a top-down alignment of independent-level forecasts," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1405-1414.
  48. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
  49. Sinan Apak, 2015. "A Bayesian Approach Proposal For Inventory Cost and Demand Forecasting," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 3(2), pages 41-48, December.
  50. Svetunkov, Ivan & Boylan, John Edward, 2017. "Multiplicative state-space models for intermittent time series," MPRA Paper 82487, University Library of Munich, Germany.
  51. Kourentzes, Nikolaos, 2014. "On intermittent demand model optimisation and selection," International Journal of Production Economics, Elsevier, vol. 156(C), pages 180-190.
  52. Babai, M. Zied & Ali, Mohammad M. & Nikolopoulos, Konstantinos, 2012. "Impact of temporal aggregation on stock control performance of intermittent demand estimators: Empirical analysis," Omega, Elsevier, vol. 40(6), pages 713-721.
  53. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios & Chen, Zhi & Gaba, Anil & Tsetlin, Ilia & Winkler, Robert L., 2022. "The M5 uncertainty competition: Results, findings and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1365-1385.
  54. Shuyun Ren & Hau-Ling Chan & Tana Siqin, 2020. "Demand forecasting in retail operations for fashionable products: methods, practices, and real case study," Annals of Operations Research, Springer, vol. 291(1), pages 761-777, August.
  55. 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.
  56. Berry, Lindsay R. & Helman, Paul & West, Mike, 2020. "Probabilistic forecasting of heterogeneous consumer transaction–sales time series," International Journal of Forecasting, Elsevier, vol. 36(2), pages 552-569.
  57. Jože Martin Rožanec & Blaž Fortuna & Dunja Mladenić, 2022. "Reframing Demand Forecasting: A Two-Fold Approach for Lumpy and Intermittent Demand," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
  58. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
  59. 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.
  60. Fildes, Robert & Petropoulos, Fotios, 2015. "Is there a Golden Rule?," Journal of Business Research, Elsevier, vol. 68(8), pages 1742-1745.
  61. Rostami-Tabar, Bahman & Disney, Stephen M., 2023. "On the order-up-to policy with intermittent integer demand and logically consistent forecasts," International Journal of Production Economics, Elsevier, vol. 257(C).
  62. Alfieri, Arianna & Pastore, Erica & Zotteri, Giulio, 2017. "Dynamic inventory rationing: How to allocate stock according to managerial priorities. An empirical study," International Journal of Production Economics, Elsevier, vol. 189(C), pages 14-29.
  63. 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.
  64. Omar, Haytham & Klibi, Walid & Babai, M. Zied & Ducq, Yves, 2023. "Basket data-driven approach for omnichannel demand forecasting," International Journal of Production Economics, Elsevier, vol. 257(C).
  65. Li, Chongshou & Lim, Andrew, 2018. "A greedy aggregation–decomposition method for intermittent demand forecasting in fashion retailing," European Journal of Operational Research, Elsevier, vol. 269(3), pages 860-869.
  66. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
  67. Robert Fildes & Paul Goodwin, 2007. "Against Your Better Judgment? How Organizations Can Improve Their Use of Management Judgment in Forecasting," Interfaces, INFORMS, vol. 37(6), pages 570-576, December.
  68. Sarlo, Rodrigo & Fernandes, Cristiano & Borenstein, Denis, 2023. "Lumpy and intermittent retail demand forecasts with score-driven models," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1146-1160.
  69. Nikolopoulos, Konstantinos I. & Babai, M. Zied & Bozos, Konstantinos, 2016. "Forecasting supply chain sporadic demand with nearest neighbor approaches," International Journal of Production Economics, Elsevier, vol. 177(C), pages 139-148.
  70. Syntetos, Aris A. & Boylan, John E., 2006. "On the stock control performance of intermittent demand estimators," International Journal of Production Economics, Elsevier, vol. 103(1), pages 36-47, September.
  71. Emrouznejad, Ali & Rostami-Tabar, Bahman & Petridis, Konstantinos, 2016. "A novel ranking procedure for forecasting approaches using Data Envelopment Analysis," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 235-243.
  72. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
  73. 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.
  74. Babai, M.Z. & Dallery, Y. & Boubaker, S. & Kalai, R., 2019. "A new method to forecast intermittent demand in the presence of inventory obsolescence," International Journal of Production Economics, Elsevier, vol. 209(C), pages 30-41.
  75. Syntetos, Aris A. & Nikolopoulos, Konstantinos & Boylan, John E. & Fildes, Robert & Goodwin, Paul, 2009. "The effects of integrating management judgement into intermittent demand forecasts," International Journal of Production Economics, Elsevier, vol. 118(1), pages 72-81, March.
  76. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.
  77. Davydenko, Andrey & Fildes, Robert, 2013. "Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts," International Journal of Forecasting, Elsevier, vol. 29(3), pages 510-522.
  78. Hill, Arthur V. & Zhang, Weiyong & Burch, Gerald F., 2015. "Forecasting the forecastability quotient for inventory management," International Journal of Forecasting, Elsevier, vol. 31(3), pages 651-663.
  79. Prak, Dennis & Rogetzer, Patricia, 2022. "Timing intermittent demand with time-varying order-up-to levels," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1126-1136.
  80. Teunter, Ruud & Sani, Babangida, 2009. "On the bias of Croston's forecasting method," European Journal of Operational Research, Elsevier, vol. 194(1), pages 177-183, April.
  81. Syntetos, Aris A. & Zied Babai, M. & Gardner, Everette S., 2015. "Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping," Journal of Business Research, Elsevier, vol. 68(8), pages 1746-1752.
  82. Zeynep Hilal Kilimci & A. Okay Akyuz & Mitat Uysal & Selim Akyokus & M. Ozan Uysal & Berna Atak Bulbul & Mehmet Ali Ekmis, 2019. "An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain," Complexity, Hindawi, vol. 2019, pages 1-15, March.
  83. Lolli, F. & Gamberini, R. & Regattieri, A. & Balugani, E. & Gatos, T. & Gucci, S., 2017. "Single-hidden layer neural networks for forecasting intermittent demand," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 116-128.
  84. Aiping Jiang & Kwok Leung Tam & Xiaoyun Guo & Yufeng Zhang, 2020. "A new approach to forecasting intermittent demand based on the mixed zero‐truncated Poisson model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 69-83, January.
  85. Heinecke, G. & Syntetos, A.A. & Wang, W., 2013. "Forecasting-based SKU classification," International Journal of Production Economics, Elsevier, vol. 143(2), pages 455-462.
  86. Murray, Paul W. & Agard, Bruno & Barajas, Marco A., 2018. "ASACT - Data preparation for forecasting: A method to substitute transaction data for unavailable product consumption data," International Journal of Production Economics, Elsevier, vol. 203(C), pages 264-275.
  87. R H Teunter & L Duncan, 2009. "Forecasting intermittent demand: a comparative study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(3), pages 321-329, March.
  88. Jakub Dyntar & Eva Kemrová & Ivan Gros, 2010. "Simulation approach in stock control of products with sporadic demand," Ekonomika a Management, Prague University of Economics and Business, vol. 2010(3).
  89. Zhu, Sha & Dekker, Rommert & van Jaarsveld, Willem & Renjie, Rex Wang & Koning, Alex J., 2017. "An improved method for forecasting spare parts demand using extreme value theory," European Journal of Operational Research, Elsevier, vol. 261(1), pages 169-181.
  90. Mariusz Doszyn, 2020. "Biasedness of Forecasts Errors for Intermittent Demand Data," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 1), pages 1113-1127.
  91. Prestwich, S.D. & Tarim, S.A. & Rossi, R., 2021. "Intermittency and obsolescence: A Croston method with linear decay," International Journal of Forecasting, Elsevier, vol. 37(2), pages 708-715.
  92. F R Johnston & E A Shale & S Kapoor & R True & A Sheth, 2011. "Breadth of range and depth of stock: forecasting and inventory management at Euro Car Parts Ltd," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 433-441, March.
  93. Wang, Wenbin & Syntetos, Aris A., 2011. "Spare parts demand: Linking forecasting to equipment maintenance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 1194-1209.
  94. 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.
  95. Altay, Nezih & Litteral, Lewis A. & Rudisill, Frank, 2012. "Effects of correlation on intermittent demand forecasting and stock control," International Journal of Production Economics, Elsevier, vol. 135(1), pages 275-283.
  96. 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.
  97. Wenhan Fu & Sheng Jing & Qinming Liu & Hao Zhang, 2023. "Resilient Supply Chain Framework for Semiconductor Distribution and an Empirical Study of Demand Risk Inference," Sustainability, MDPI, vol. 15(9), pages 1-14, April.
  98. Teunter, R.H. & Syntetos, A.A. & Babai, M.Z., 2010. "Determining order-up-to levels under periodic review for compound binomial (intermittent) demand," European Journal of Operational Research, Elsevier, vol. 203(3), pages 619-624, June.
  99. 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.
  100. 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.
  101. Petropoulos, Fotios & Kourentzes, Nikolaos & Nikolopoulos, Konstantinos, 2016. "Another look at estimators for intermittent demand," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 154-161.
  102. Ducharme, Corey & Agard, Bruno & Trépanier, Martin, 2021. "Forecasting a customer's Next Time Under Safety Stock," International Journal of Production Economics, Elsevier, vol. 234(C).
  103. Prak, Dennis & Teunter, Ruud & Babai, Mohamed Zied & Boylan, John E. & Syntetos, Aris, 2021. "Robust compound Poisson parameter estimation for inventory control," Omega, Elsevier, vol. 104(C).
  104. Ye, Yuan & Lu, Yonggang & Robinson, Powell & Narayanan, Arunachalam, 2022. "An empirical Bayes approach to incorporating demand intermittency and irregularity into inventory control," European Journal of Operational Research, Elsevier, vol. 303(1), pages 255-272.
  105. Syntetos, A.A. & Babai, M.Z. & Davies, J. & Stephenson, D., 2010. "Forecasting and stock control: A study in a wholesaling context," International Journal of Production Economics, Elsevier, vol. 127(1), pages 103-111, September.
  106. 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.
  107. Price, Ilan & Fowkes, Jaroslav & Hopman, Daniel, 2019. "Gaussian processes for unconstraining demand," European Journal of Operational Research, Elsevier, vol. 275(2), pages 621-634.
  108. Mohammad Khajehzadeh & Farhad Pazhuheian & Farima Seifi & Rassoul Noorossana & Ali Asli & Niloufar Saeedi, 2022. "Analysis of Factors Affecting Product Sales with an Outlook toward Sale Forecasting in Cosmetic Industry using Statistical Methods," International Review of Management and Marketing, Econjournals, vol. 12(6), pages 55-63, November.
  109. Zied Babai, M. & Syntetos, Aris A. & Teunter, Ruud, 2010. "On the empirical performance of (T, s, S) heuristics," European Journal of Operational Research, Elsevier, vol. 202(2), pages 466-472, April.
  110. Babai, M.Z. & Chen, H. & Syntetos, A.A. & Lengu, D., 2021. "A compound-Poisson Bayesian approach for spare parts inventory forecasting," International Journal of Production Economics, Elsevier, vol. 232(C).
  111. Syntetos, Aris A., 2007. "A note on managing lumpy demand for aircraft spare parts," Journal of Air Transport Management, Elsevier, vol. 13(3), pages 166-167.
  112. Kolassa, Stephan, 2016. "Evaluating predictive count data distributions in retail sales forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 788-803.
  113. Syntetos, Aris A. & Boylan, John E., 2010. "On the variance of intermittent demand estimates," International Journal of Production Economics, Elsevier, vol. 128(2), pages 546-555, December.
  114. Hasni, M. & Babai, M.Z. & Aguir, M.S. & Jemai, Z., 2019. "An investigation on bootstrapping forecasting methods for intermittent demands," International Journal of Production Economics, Elsevier, vol. 209(C), pages 20-29.
  115. E A Shale & J E Boylan & F R Johnston, 2006. "Forecasting for intermittent demand: the estimation of an unbiased average," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(5), pages 588-592, May.
  116. Mamonov, Nikolay & Golubyatnikov, Evgeny & Kanevskiy, Daniel & Gusakov, Igor, 2022. "GoodsForecast second-place solution in M5 Uncertainty track: Combining heterogeneous models for a quantile estimation task," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1434-1441.
  117. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "M5 accuracy competition: Results, findings, and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1346-1364.
  118. Zhou, Chenxi & Viswanathan, S., 2011. "Comparison of a new bootstrapping method with parametric approaches for safety stock determination in service parts inventory systems," International Journal of Production Economics, Elsevier, vol. 133(1), pages 481-485, September.
  119. Boylan, J.E. & Syntetos, A.A., 2007. "The accuracy of a Modified Croston procedure," International Journal of Production Economics, Elsevier, vol. 107(2), pages 511-517, June.
  120. 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.
  121. Altay, Nezih & Rudisill, Frank & Litteral, Lewis A., 2008. "Adapting Wright's modification of Holt's method to forecasting intermittent demand," International Journal of Production Economics, Elsevier, vol. 111(2), pages 389-408, February.
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