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Stock keeping unit fill rate specification

Author

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  • Teunter, R.H.
  • Syntetos, A.A.
  • Babai, M.Z.

Abstract

The fill rate is the most widely applied service level measure in industry and yet there is minimal advice available on how it should be differentiated on an individual Stock Keeping Unit (SKU) basis given that there is an overall system target service level. The typical approach utilized in practice, and suggested in academic textbooks, is to set the individual service levels equal to the targeted performance required across an entire stock base or a certain class of SKUs (e.g., in ABC classification). In this paper it is argued that this approach is far from optimal and a simple methodology is proposed that is shown (on real life datasets) to be associated with reductions in stock investments. In addition, the new approach is intuitive, very easy to implement and thus highly likely to be positively received by practitioners and software manufacturers.

Suggested Citation

  • Teunter, R.H. & Syntetos, A.A. & Babai, M.Z., 2017. "Stock keeping unit fill rate specification," European Journal of Operational Research, Elsevier, vol. 259(3), pages 917-925.
    • Handle: RePEc:eee:ejores:v:259:y:2017:i:3:p:917-925
    DOI: 10.1016/j.ejor.2016.11.017
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    References listed on IDEAS

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    1. Silver, Edward A. & Bischak, Diane P., 2011. "The exact fill rate in a periodic review base stock system under normally distributed demand," Omega, Elsevier, vol. 39(3), pages 346-349, June.
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    5. Guijarro, Ester & Cardós, Manuel & Babiloni, Eugenia, 2012. "On the exact calculation of the fill rate in a periodic review inventory policy under discrete demand patterns," European Journal of Operational Research, Elsevier, vol. 218(2), pages 442-447.
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    Cited by:

    1. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    2. van Donselaar, Karel & Broekmeulen, Rob & de Kok, Ton, 2021. "Heuristics for setting reorder levels in periodic review inventory systems with an aggregate service constraint," International Journal of Production Economics, Elsevier, vol. 237(C).
    3. 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.
    4. Bruzda, Joanna, 2020. "Demand forecasting under fill rate constraints—The case of re-order points," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1342-1361.

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