IDEAS home Printed from https://ideas.repec.org/r/eee/ejores/v214y2011i3p606-615.html
   My bibliography  Save this item

Intermittent demand: Linking forecasting to inventory obsolescence

Citations

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


Cited by:

  1. Md. Iftekharul Alam Efat & Petr Hajek & Mohammad Zoynul Abedin & Rahat Uddin Azad & Md. Al Jaber & Shuvra Aditya & Mohammad Kabir Hassan, 2024. "Deep-learning model using hybrid adaptive trend estimated series for modelling and forecasting sales," Annals of Operations Research, Springer, vol. 339(1), pages 297-328, August.
  2. Zheng, Meimei & Dong, Shuangshuang & Zhou, Yaoming & Choi, Tsan-Ming, 2023. "Sourcing decisions with uncertain time-dependent supply from an unreliable supplier," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1365-1379.
  3. Aleksandr N. Grekov & Elena V. Vyshkvarkova & Aleksandr S. Mavrin, 2024. "Forecasting and Anomaly Detection in BEWS: Comparative Study of Theta, Croston, and Prophet Algorithms," Forecasting, MDPI, vol. 6(2), pages 1-14, May.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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).
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. Wang, Shengjie & Kang, Yanfei & Petropoulos, Fotios, 2024. "Combining probabilistic forecasts of intermittent demand," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1038-1048.
  16. Amniattalab, Ayda & Frenk, J.B.G. & Hekimoğlu, Mustafa, 2023. "On spare parts demand and the installed base concept: A theoretical approach," International Journal of Production Economics, Elsevier, vol. 266(C).
  17. G. Peter Zhang & Yusen Xia & Maohua Xie, 2024. "Intermittent demand forecasting with transformer neural networks," Annals of Operations Research, Springer, vol. 339(1), pages 1051-1072, August.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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).
  23. 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.
  24. Johnson, Andrew & Carnovale, Steven & Song, Ju Myung & Zhao, Yao, 2021. "Drivers of fulfillment performance in mission critical logistics systems: An empirical analysis," International Journal of Production Economics, Elsevier, vol. 237(C).
  25. 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.
  26. Ralph Snyder & Adrian Beaumont & J. Keith Ord, 2012. "Intermittent demand forecasting for inventory control: A multi-series approach," Monash Econometrics and Business Statistics Working Papers 15/12, Monash University, Department of Econometrics and Business Statistics.
  27. 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.
  28. 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.
  29. Kourentzes, Nikolaos & Athanasopoulos, George, 2021. "Elucidate structure in intermittent demand series," European Journal of Operational Research, Elsevier, vol. 288(1), pages 141-152.
  30. 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).
  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. 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.
  33. Dimitrova, Dimitrina S. & Ignatov, Zvetan G. & Kaishev, Vladimir K. & Tan, Senren, 2020. "On double-boundary non-crossing probability for a class of compound processes with applications," European Journal of Operational Research, Elsevier, vol. 282(2), pages 602-613.
  34. María Arquer & Borja Ponte & Raúl Pino, 2022. "Examining the balance between efficiency and resilience in closed-loop supply chains," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(4), pages 1307-1336, December.
  35. 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.
  36. 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.
  37. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
  38. Syntetos, Aris A. & Kholidasari, Inna & Naim, Mohamed M., 2016. "The effects of integrating management judgement into OUT levels: In or out of context?," European Journal of Operational Research, Elsevier, vol. 249(3), pages 853-863.
  39. 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.
  40. 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.
  41. Nikolopoulos, Konstantinos, 2021. "We need to talk about intermittent demand forecasting," European Journal of Operational Research, Elsevier, vol. 291(2), pages 549-559.
  42. De Giovanni, Pietro & Karray, Salma & Martín-Herrán, Guiomar, 2019. "Vendor Management Inventory with consignment contracts and the benefits of cooperative advertising," European Journal of Operational Research, Elsevier, vol. 272(2), pages 465-480.
  43. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
  44. Svetunkov, Ivan & Boylan, John Edward, 2017. "Multiplicative state-space models for intermittent time series," MPRA Paper 82487, University Library of Munich, Germany.
  45. Kim, T.Y. & Dekker, R. & Heij, C., 2016. "Spare part demand forecasting for consumer goods using installed base information," Econometric Institute Research Papers EI2016-11, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  46. Schlaich, Tim & Hoberg, Kai, 2024. "When is the next order? Nowcasting channel inventories with Point-of-Sales data to predict the timing of retail orders," European Journal of Operational Research, Elsevier, vol. 315(1), pages 35-49.
  47. Ponte, Borja & Dominguez, Roberto & Cannella, Salvatore & Framinan, Jose M., 2022. "The implications of batching in the bullwhip effect and customer service of closed-loop supply chains," International Journal of Production Economics, Elsevier, vol. 244(C).
  48. 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.
  49. 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.
  50. Bai, Qingguo & Xu, Jianteng & Gong, Yeming & Chauhan, Satyaveer S., 2022. "Robust decisions for regulated sustainable manufacturing with partial demand information: Mandatory emission capacity versus emission tax," European Journal of Operational Research, Elsevier, vol. 298(3), pages 874-893.
  51. 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.
  52. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
  53. 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).
  54. Mellal, Mohamed Arezki, 2020. "Obsolescence – A review of the literature," Technology in Society, Elsevier, vol. 63(C).
  55. Tianyu Niu & Heng Zhang & Xingyou Yan & Qiang Miao, 2024. "Intricate Supply Chain Demand Forecasting Based on Graph Convolution Network," Sustainability, MDPI, vol. 16(21), pages 1-15, November.
  56. 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.
  57. Zied Babai, Mohamed & Syntetos, Aris & Teunter, Ruud, 2014. "Intermittent demand forecasting: An empirical study on accuracy and the risk of obsolescence," International Journal of Production Economics, Elsevier, vol. 157(C), pages 212-219.
  58. 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.
  59. 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.
  60. Svetunkov, Ivan & Boylan, John E., 2023. "iETS: State space model for intermittent demand forecasting," International Journal of Production Economics, Elsevier, vol. 265(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.