IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v202y2010i2p466-472.html
   My bibliography  Save this article

On the empirical performance of (T, s, S) heuristics

Author

Listed:
  • Zied Babai, M.
  • Syntetos, Aris A.
  • Teunter, Ruud

Abstract

The periodic (T,s,S) policies have received considerable attention from the academic literature. Determination of the optimal parameters is computationally prohibitive, and a number of heuristic procedures have been put forward. However, these heuristics have never been compared in an extensive empirical study. Such an investigation on 3055 SKUs is carried out in this paper. Our study provides insights into the performance of (T,s,S) heuristics, also in relation to demand forecasting. The results show that Naddor's heuristic is best able to minimize the total cost. However, the normal and power approximations achieve more efficient solutions in that backorder volumes are smaller at the same inventory levels, indicating the potentially superior performance of these methods if the balancing of holding and backorder costs can be improved. The results also show that, for all heuristics, the SBA variant of the Croston forecasting method significantly outperforms Croston as well as Single Exponential Smoothing (SES).

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:202:y:2010:i:2:p:466-472
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(09)00384-1
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Blyth C. Archibald, 1981. "Continuous Review (s, S) Policies with Lost Sales," Management Science, INFORMS, vol. 27(10), pages 1171-1177, October.
    2. Evan L. Porteus, 1985. "Numerical Comparisons of Inventory Policies for Periodic Review Systems," Operations Research, INFORMS, vol. 33(1), pages 134-152, February.
    3. 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.
    4. 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.
    5. Blyth C. Archibald & Edward A. Silver, 1978. "(s, S) Policies Under Continuous Review and Discrete Compound Poisson Demand," Management Science, INFORMS, vol. 24(9), pages 899-909, May.
    6. Arthur F. Veinott, Jr. & Harvey M. Wagner, 1965. "Computing Optimal (s, S) Inventory Policies," Management Science, INFORMS, vol. 11(5), pages 525-552, March.
    7. James R. Freeland & Evan L. Porteus, 1980. "Evaluating the Effectiveness of a New Method for Computing Approximately Optimal ( s , S ) Inventory Policies," Operations Research, INFORMS, vol. 28(2), pages 353-364, April.
    8. Tijms, H. C. & Groenevelt, H., 1984. "Simple approximations for the reorder point in periodic and continuous review (s, S) inventory systems with service level constraints," European Journal of Operational Research, Elsevier, vol. 17(2), pages 175-190, August.
    9. Schneider, Helmut & Rinks, Dan B., 1989. "Optimal policy surfaces for a multi-item inventory problem," European Journal of Operational Research, Elsevier, vol. 39(2), pages 180-191, March.
    10. Syntetos, Aris A. & Boylan, John E., 2005. "The accuracy of intermittent demand estimates," International Journal of Forecasting, Elsevier, vol. 21(2), pages 303-314.
    11. Helmut Schneider & Jeffrey L. Ringuest, 1990. "Power Approximation for Computing (s, S) Policies Using Service Level," Management Science, INFORMS, vol. 36(7), pages 822-834, July.
    12. 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.
    13. Porras, Eric & Dekker, Rommert, 2008. "An inventory control system for spare parts at a refinery: An empirical comparison of different re-order point methods," European Journal of Operational Research, Elsevier, vol. 184(1), pages 101-132, January.
    14. Richard Ehrhardt, 1979. "The Power Approximation for Computing (s, S) Inventory Policies," Management Science, INFORMS, vol. 25(8), pages 777-786, August.
    15. Richard Ehrhardt & Charles Mosier, 1984. "A Revision of the Power Approximation for Computing (s, S) Policies," Management Science, INFORMS, vol. 30(5), pages 618-622, May.
    16. Ananth V. Iyer & Linus E. Schrage, 1992. "Analysis of the Deterministic (s, S) Inventory Problem," Management Science, INFORMS, vol. 38(9), pages 1299-1313, September.
    17. Eliezer Naddor, 1975. "Optimal and Heuristic Decisions in Single-and Multi-Item Inventory Systems," Management Science, INFORMS, vol. 21(11), pages 1234-1249, July.
    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. G. P. Kiesmüller & K. Inderfurth, 2018. "Approaches for periodic inventory control under random production yield and fixed setup cost," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(2), pages 449-477, March.
    2. Babai, M.Z. & Jemai, Z. & Dallery, Y., 2011. "Analysis of order-up-to-level inventory systems with compound Poisson demand," European Journal of Operational Research, Elsevier, vol. 210(3), pages 552-558, May.
    3. 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.
    4. 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.
    5. Visentin, Andrea & Prestwich, Steven & Rossi, Roberto & Tarim, S. Armagan, 2021. "Computing optimal (R,s,S) policy parameters by a hybrid of branch-and-bound and stochastic dynamic programming," European Journal of Operational Research, Elsevier, vol. 294(1), pages 91-99.
    6. 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.
    7. 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.
    8. Bacchetti, A. & Plebani, F. & Saccani, N. & Syntetos, A.A., 2013. "Empirically-driven hierarchical classification of stock keeping units," International Journal of Production Economics, Elsevier, vol. 143(2), pages 263-274.
    9. Dettenbach, Marcus & Thonemann, Ulrich W., 2015. "The value of real time yield information in multi-stage inventory systems – Exact and heuristic approaches," European Journal of Operational Research, Elsevier, vol. 240(1), pages 72-83.

    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. 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.
    2. Strijbosch, L.W.G. & Moors, J.J.A., 1999. "Simple Expressions for Safety Factors in Inventory Control," Discussion Paper 1999-112, Tilburg University, Center for Economic Research.
    3. Baker, H. & Ehrhardt, R., 1995. "A dynamic inventory model with random replenishment quantities," Omega, Elsevier, vol. 23(1), pages 109-116, February.
    4. Kleijnen, J.P.C. & Wan, J., 2007. "Optimization of simulated systems : OptQuest and alternatives [also see “Simulation for the optimization of (s, S) inventory system with random lead times and a service level constraint by using Arena," Other publications TiSEM ffaee312-9f6a-4452-9ccc-9, Tilburg University, School of Economics and Management.
    5. Lagodimos, A.G. & Christou, I.T. & Skouri, K., 2012. "Computing globally optimal (s,S,T) inventory policies," Omega, Elsevier, vol. 40(5), pages 660-671.
    6. 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.
    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. 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.
    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. 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.
    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. Bijvank, Marco & Vis, Iris F.A., 2011. "Lost-sales inventory theory: A review," European Journal of Operational Research, Elsevier, vol. 215(1), pages 1-13, November.
    13. 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.
    14. 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.
    15. 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.
    16. van Donselaar, Karel H. & Broekmeulen, Rob A.C.M., 2013. "Determination of safety stocks in a lost sales inventory system with periodic review, positive lead-time, lot-sizing and a target fill rate," International Journal of Production Economics, Elsevier, vol. 143(2), pages 440-448.
    17. 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.
    18. B S Maddah & M Y Jaber & N E Abboud, 2004. "Periodic review (s, S) inventory model with permissible delay in payments," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(2), pages 147-159, February.
    19. 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.
    20. Bijvank, Marco & Bhulai, Sandjai & Tim Huh, Woonghee, 2015. "Parametric replenishment policies for inventory systems with lost sales and fixed order cost," European Journal of Operational Research, Elsevier, vol. 241(2), pages 381-390.

    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:eee:ejores:v:202:y:2010:i:2:p:466-472. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.