IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v54y2003i4d10.1057_palgrave.jors.2601530.html
   My bibliography  Save this article

Tuning inventory policy parameters in a small chemical company

Author

Listed:
  • R García-Flores

    (The University of Leeds)

  • X Z Wang

    (The University of Leeds)

  • T F Burgess

    (The University of Leeds)

Abstract

A project to improve inventory management in a small UK chemical company is described. A research group comprising university academics and company managers for logistics and information technology examined current practices and analysed a database of historical records of business operations of the company. Based on the analysis, a scheme to categorise stock and set ordering policies to optimise inventory costs was developed. Some comments are made on process issues and the learning that took place.

Suggested Citation

  • R García-Flores & X Z Wang & T F Burgess, 2003. "Tuning inventory policy parameters in a small chemical company," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(4), pages 350-361, April.
    • Handle: RePEc:pal:jorsoc:v:54:y:2003:i:4:d:10.1057_palgrave.jors.2601530
    DOI: 10.1057/palgrave.jors.2601530
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/palgrave.jors.2601530
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/palgrave.jors.2601530?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Janssen, Fred & Heuts, Ruud & de Kok, Ton, 1998. "On the (R, s, Q) inventory model when demand is modelled as a compound Bernoulli process," European Journal of Operational Research, Elsevier, vol. 104(3), pages 423-436, February.
    2. Lenard, J. D. & Roy, B., 1995. "Multi-item inventory control: A multicriteria view," European Journal of Operational Research, Elsevier, vol. 87(3), pages 685-692, December.
    3. Willemain, Thomas R. & Smart, Charles N. & Shockor, Joseph H. & DeSautels, Philip A., 1994. "Forecasting intermittent demand in manufacturing: a comparative evaluation of Croston's method," International Journal of Forecasting, Elsevier, vol. 10(4), pages 529-538, December.
    4. Altay Guvenir, H. & Erel, Erdal, 1998. "Multicriteria inventory classification using a genetic algorithm," European Journal of Operational Research, Elsevier, vol. 105(1), pages 29-37, February.
    5. Syntetos, A. A. & Boylan, J. E., 2001. "On the bias of intermittent demand estimates," International Journal of Production Economics, Elsevier, vol. 71(1-3), pages 457-466, May.
    6. P Keys & G Midgley, 2002. "Part Special Issue Editorial: The process of OR," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(2), pages 123-125, February.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.

    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. Boylan, John E. & Babai, M. Zied, 2016. "On the performance of overlapping and non-overlapping temporal demand aggregation approaches," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 136-144.
    2. 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.
    3. 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.
    4. 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).
    5. 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.
    6. 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).
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Rob J. Hyndman & Lydia Shenstone, 2005. "Stochastic models underlying Croston's method for intermittent demand forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(6), pages 389-402.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. Kourentzes, Nikolaos, 2013. "Intermittent demand forecasts with neural networks," International Journal of Production Economics, Elsevier, vol. 143(1), pages 198-206.
    19. 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.
    20. 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.

    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:pal:jorsoc:v:54:y:2003:i:4:d:10.1057_palgrave.jors.2601530. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.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.