IDEAS home Printed from https://ideas.repec.org/a/anm/alpnmr/v3y2015i2p41-48.html
   My bibliography  Save this article

A Bayesian Approach Proposal For Inventory Cost and Demand Forecasting

Author

Listed:
  • Sinan Apak

Abstract

Technology’s perpetual vicissitude and product models’ distinction in industrial market have a crucial effect on forecasting demand for spare components. In order to set forth the future demand rates for products, inventory managers repetitively update their prognostications. Bayesian model is utilizing a prior probability distribution for the injunctive authorization rate which was habituated in order to get optimum levels of account over a number of periods. However, under sundry demand rates like intermittent demand, Bayesian Model’s performance has not been analyzed. With the help of a research question, the study investigates that circumstance.

Suggested Citation

  • 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.
  • Handle: RePEc:anm:alpnmr:v:3:y:2015:i:2:p:41-48
    DOI: http://dx.doi.org/10.17093/aj.2015.3.2.5000140055
    as

    Download full text from publisher

    File URL: https://www.alphanumericjournal.com/media/Issue/volume-3-issue-2-2015/a-bayesian-approach-proposal-for-inventory-cost-and-demand-f_0NSgJvm.pdf
    Download Restriction: no

    File URL: https://alphanumericjournal.com/article/a-bayesian-approach-proposal-for-inventory-cost-and-demand-forecasting/
    Download Restriction: no

    File URL: https://libkey.io/http://dx.doi.org/10.17093/aj.2015.3.2.5000140055?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. L W G Strijbosch & R M J Heuts & E H M van der Schoot, 2000. "A combined forecast—inventory control procedure for spare parts," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(10), pages 1184-1192, October.
    2. Z S Hua & B Zhang & J Yang & D S Tan, 2007. "A new approach of forecasting intermittent demand for spare parts inventories in the process industries," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(1), pages 52-61, January.
    3. A A Syntetos & J E Boylan & J D Croston, 2005. "On the categorization of demand patterns," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(5), pages 495-503, May.
    4. Popovic, Jovan B., 1987. "Decision making on stock levels in cases of uncertain demand rate," European Journal of Operational Research, Elsevier, vol. 32(2), pages 276-290, November.
    5. Vereecke, Ann & Verstraeten, Peter, 1994. "An inventory management model for an inventory consisting of lumpy items, slow movers and fast movers," International Journal of Production Economics, Elsevier, vol. 35(1-3), pages 379-389, June.
    6. 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.
    7. Willemain, Thomas R. & Smart, Charles N. & Schwarz, Henry F., 2004. "A new approach to forecasting intermittent demand for service parts inventories," International Journal of Forecasting, Elsevier, vol. 20(3), pages 375-387.
    8. Syntetos, Aris A. & Boylan, John E., 2005. "The accuracy of intermittent demand estimates," International Journal of Forecasting, Elsevier, vol. 21(2), pages 303-314.
    9. Leven, Erik & Segerstedt, Anders, 2004. "Inventory control with a modified Croston procedure and Erlang distribution," International Journal of Production Economics, Elsevier, vol. 90(3), pages 361-367, August.
    10. Kennedy, W. J. & Wayne Patterson, J. & Fredendall, Lawrence D., 2002. "An overview of recent literature on spare parts inventories," International Journal of Production Economics, Elsevier, vol. 76(2), pages 201-215, March.
    11. Edward A. Silver, 1965. "Bayesian Determination of the Reorder Point of a Slow Moving Item," Operations Research, INFORMS, vol. 13(6), pages 989-997, December.
    12. Lee, Yun Shin, 2014. "Management of a periodic-review inventory system using Bayesian model averaging when new marketing efforts are made," International Journal of Production Economics, Elsevier, vol. 158(C), pages 278-289.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
    3. 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).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Kourentzes, Nikolaos, 2013. "Intermittent demand forecasts with neural networks," International Journal of Production Economics, Elsevier, vol. 143(1), pages 198-206.
    13. 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.
    14. Kourentzes, Nikolaos, 2014. "On intermittent demand model optimisation and selection," International Journal of Production Economics, Elsevier, vol. 156(C), pages 180-190.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    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.

    More about this item

    Keywords

    Bayesian Model; Forecasting; Inventory; Probability Distribution;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:anm:alpnmr:v:3:y:2015:i:2:p:41-48. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Bahadir Fatih Yildirim (email available below). General contact details of provider: https://www.alphanumericjournal.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.