IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/40724.html
   My bibliography  Save this paper

Unbiased estimation of maximum expected profits in the Newsvendor Model: a case study analysis

Author

Listed:
  • Halkos, George
  • Kevork, Ilias

Abstract

In the current paper we study a real life inventory problem whose operating conditions match to the principles of the classical newsvendor model. Applying appropriate tests to the available sample of historical demand data, we get the sufficient statistical evidences to support that daily demand is stationary, uncorrelated, and normally distributed. Given that at the start of each day, the selling price, the purchasing cost per unit, and the salvage value are known, and do not change through the whole period under investigation, we derive exact and asymptotic prediction intervals for the daily maximum expected profit. To evaluate their performance, we derive the analytic form of three accuracy information metrics. The first metric measures the deviation of the estimated probability of no stock-outs during the day from the critical fractile. The other two metrics relate the validity and precision of the two types of prediction interval to the variability of estimates for the ordered quantity. Both theoretical and empirical analysis demonstrates the importance of implications of the loss of goodwill to the adopted inventory policy. Operating the system at the optimal situation, this intangible cost element determines the probability of no stock-outs during the day, and assesses the precision of prediction intervals. The rising of the loss of goodwill leads to smaller estimates for the daily maximum expected profit and to wider prediction intervals. Finally, in the setting of the real life newsvendor problem, we recommend the asymptotic prediction interval since with samples over 25 observations this type of interval has higher precision and probability to include the daily maximum expected profit almost equal to the nominal confidence level.

Suggested Citation

  • Halkos, George & Kevork, Ilias, 2012. "Unbiased estimation of maximum expected profits in the Newsvendor Model: a case study analysis," MPRA Paper 40724, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:40724
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/40724/1/MPRA_paper_40724.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Severini,Thomas A., 2005. "Elements of Distribution Theory," Cambridge Books, Cambridge University Press, number 9780521844727.
    2. Mostard, Julien & Teunter, Ruud, 2006. "The newsboy problem with resalable returns: A single period model and case study," European Journal of Operational Research, Elsevier, vol. 169(1), pages 81-96, February.
    3. U Benzion & Y Cohen & R Peled & T Shavit, 2008. "Decision-making and the newsvendor problem: an experimental study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1281-1287, September.
    4. Halkos, George & Kevork, Ilias, 2012. "Validity and precision of estimates in the classical newsvendor model with exponential and rayleigh demand," MPRA Paper 36460, University Library of Munich, Germany.
    5. Halkos, George & Kevork, Ilias, 2012. "Evaluating alternative frequentist inferential approaches for optimal order quantities in the newsvendor model under exponential demand," MPRA Paper 39650, University Library of Munich, Germany.
    6. Marcelo Olivares & Christian Terwiesch & Lydia Cassorla, 2008. "Structural Estimation of the Newsvendor Model: An Application to Reserving Operating Room Time," Management Science, INFORMS, vol. 54(1), pages 41-55, January.
    7. Mostard, Julien & Teunter, Ruud & de Koster, René, 2011. "Forecasting demand for single-period products: A case study in the apparel industry," European Journal of Operational Research, Elsevier, vol. 211(1), pages 139-147, May.
    8. Beutel, Anna-Lena & Minner, Stefan, 2012. "Safety stock planning under causal demand forecasting," International Journal of Production Economics, Elsevier, vol. 140(2), pages 637-645.
    9. U Benzion & Y Cohen & T Shavit, 2010. "The newsvendor problem with unknown distribution," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(6), pages 1022-1031, June.
    10. Su, Rung Hung & Pearn, Wen Lea, 2011. "Product selection for newsboy-type products with normal demands and unequal costs," International Journal of Production Economics, Elsevier, vol. 132(2), pages 214-222, August.
    11. Kevork, Ilias S., 2010. "Estimating the optimal order quantity and the maximum expected profit for single-period inventory decisions," Omega, Elsevier, vol. 38(3-4), pages 218-227, June.
    12. Syntetos, Aris A. & Nikolopoulos, Konstantinos & Boylan, John E., 2010. "Judging the judges through accuracy-implication metrics: The case of inventory forecasting," International Journal of Forecasting, Elsevier, vol. 26(1), pages 134-143, January.
    13. Halkos, George & Kevork, Ilias, 2012. "The classical newsvendor model under normal demand with large coefficients of variation," MPRA Paper 40414, University Library of Munich, Germany.
    14. George Halkos & Ilias Kevork, 2005. "A comparison of alternative unit root tests," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(1), pages 45-60.
    15. Maurice E. Schweitzer & Gérard P. Cachon, 2000. "Decision Bias in the Newsvendor Problem with a Known Demand Distribution: Experimental Evidence," Management Science, INFORMS, vol. 46(3), pages 404-420, March.
    16. Feng, Tianjun & Keller, L. Robin & Zheng, Xiaona, 2011. "Decision making in the newsvendor problem: A cross-national laboratory study," Omega, Elsevier, vol. 39(1), pages 41-50, January.
    17. Janssen, Elleke & Strijbosch, Leo & Brekelmans, Ruud, 2009. "Assessing the effects of using demand parameters estimates in inventory control and improving the performance using a correction function," International Journal of Production Economics, Elsevier, vol. 118(1), pages 34-42, March.
    18. Silver, Edward A. & Rahnama, Mina Rasty, 1987. "Biased selection of the inventory reorder point when demand parameters are statistically estimated," Engineering Costs and Production Economics, Elsevier, vol. 12(1-4), pages 283-292, July.
    19. R. H. Hayes, 1969. "Statistical Estimation Problems in Inventory Control," Management Science, INFORMS, vol. 15(11), pages 686-701, July.
    20. Alp Akcay & Bahar Biller & Sridhar Tayur, 2011. "Improved Inventory Targets in the Presence of Limited Historical Demand Data," Manufacturing & Service Operations Management, INFORMS, vol. 13(3), pages 297-309, 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. Rung-Hung Su & Dong-Yuh Yang & He-Jhen Lin & Yu-Cheng Yang, 2023. "Estimating conservative profitability of a newsboy-type product with exponentially distributed demand based on multiple samples," Annals of Operations Research, Springer, vol. 322(2), pages 967-989, March.

    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. Halkos, George & Kevork, Ilias, 2012. "Evaluating alternative frequentist inferential approaches for optimal order quantities in the newsvendor model under exponential demand," MPRA Paper 39650, University Library of Munich, Germany.
    2. Prak, Dennis & Teunter, Ruud & Syntetos, Aris, 2017. "On the calculation of safety stocks when demand is forecasted," European Journal of Operational Research, Elsevier, vol. 256(2), pages 454-461.
    3. Halkos, George & Kevork, Ilias, 2013. "Forecasting the optimal order quantity in the newsvendor model under a correlated demand," MPRA Paper 44189, University Library of Munich, Germany.
    4. Halkos, George & Kevork, Ilias, 2012. "The classical newsvendor model under normal demand with large coefficients of variation," MPRA Paper 40414, University Library of Munich, Germany.
    5. Halkos, George & Kevork, Ilias, 2012. "Validity and precision of estimates in the classical newsvendor model with exponential and rayleigh demand," MPRA Paper 36460, University Library of Munich, Germany.
    6. Villa, Sebastián & Castañeda, Jaime Andrés, 2018. "Transshipments in supply chains: A behavioral investigation," European Journal of Operational Research, Elsevier, vol. 269(2), pages 715-729.
    7. Castañeda, Jaime Andrés & Brennan, Mark & Goentzel, Jarrod, 2019. "A behavioral investigation of supply chain contracts for a newsvendor problem in a developing economy," International Journal of Production Economics, Elsevier, vol. 210(C), pages 72-83.
    8. Halkos, George & Kevork, Ilias, 2012. "Evaluating alternative estimators for optimal order quantities in the newsvendor model with skewed demand," MPRA Paper 36205, University Library of Munich, Germany.
    9. Arshavskiy V. & Okulov V. & Smirnova A., 2014. "Newsvendor Problem Experiments: Riskiness of the Decisions and Learning by Experience," International Journal of Business and Social Research, LAR Center Press, vol. 4(5), pages 137-150, May.
    10. Arshavskiy V. & Okulov V. & Smirnova A., 2014. "Newsvendor Problem Experiments: Riskiness of the Decisions and Learning by Experience," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 4(5), pages 137-150, May.
    11. He, Haonan & Wang, Shanyong, 2019. "Cost-benefit associations in consumer inventory problem with uncertain benefit," Journal of Retailing and Consumer Services, Elsevier, vol. 51(C), pages 271-284.
    12. Yufei Ren & David Croson & Rachel Croson, 2017. "The overconfident newsvendor," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(5), pages 496-506, May.
    13. Schiffels, Sebastian & Fügener, Andreas & Kolisch, Rainer & Jens Brunner, O., 2014. "On the assessment of costs in a newsvendor environment: Insights from an experimental study," Omega, Elsevier, vol. 43(C), pages 1-8.
    14. Strohhecker, Jürgen & Größler, Andreas, 2013. "Do personal traits influence inventory management performance?—The case of intelligence, personality, interest and knowledge," International Journal of Production Economics, Elsevier, vol. 142(1), pages 37-50.
    15. Andreas Fügener & Sebastian Schiffels & Rainer Kolisch, 2017. "Overutilization and underutilization of operating rooms - insights from behavioral health care operations management," Health Care Management Science, Springer, vol. 20(1), pages 115-128, March.
    16. Diego D’Urso & Ferdinando Chiacchio & Evangelia Demerouti, 2021. "Measuring How Decision Support Systems Improve Newsvendors’ Performance: The Subjects’ Version," Sustainability, MDPI, vol. 13(18), pages 1-16, September.
    17. 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.
    18. Foster, Joshua & Deck, Cary & Farmer, Amy, 2019. "Behavioral demand effects when buyers anticipate inventory shortages," European Journal of Operational Research, Elsevier, vol. 276(1), pages 217-234.
    19. Huang, Di & Zhou, Hong & Zhao, Qiu-Hong, 2011. "A competitive multiple-product newsboy problem with partial product substitution," Omega, Elsevier, vol. 39(3), pages 302-312, June.
    20. Rossi, Roberto & Prestwich, Steven & Tarim, S. Armagan & Hnich, Brahim, 2014. "Confidence-based optimisation for the newsvendor problem under binomial, Poisson and exponential demand," European Journal of Operational Research, Elsevier, vol. 239(3), pages 674-684.

    More about this item

    Keywords

    Newsvendor model; Loss of goodwill; Target inventory measures; Prediction interval; Accuracy information metric;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:40724. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.